As somebody said, science is what scientists do. And scientists are human too. Some of these humans have great difficulty in reconciling what science tells them with what they learnt from various sources when they were young and impressionable. So they may unconsciously look for ‘loopholes’ in the scientific premises and reasoning, particularly when it comes to fundamental questions about life, mind, and the universe.

In science there is always a cutting edge, or the frontier line where things are hazy. There is debate among experts as various alternative models are compared and contrasted. The beauty of the scientific method is that it is ruthless and without regard for authority (but see below!). Truth prevails ultimately, sometimes after a prolonged debate about what is the best way of interpreting the available data. When more data come in, science has no difficulty in dumping even its most cherished theories if necessary.

Sometimes, of course, the debate continues endlessly. This is particularly true for the highly counterintuitive quantum theory. To me the most important thing about this theory is that it has been phenomenally successful in explaining a vast multitude of natural phenomena, even though we do not have all the answers. Science accepts it because there is no better theory known to us that can be more successful for explaining what we see in the world around us.

To me it is not important that the quantum theory is counterintuitive. I see no reason why the laws of Nature should be always comprehensible to us. We emerged on the cosmic scene very very recently, but the laws of Nature have been there all the time.

But, as I said, scientists are human too. They do have their failings and weaknesses and gut feelings. We all know about Einstein’s reservations about the quantum theory of his day. His views fell by the wayside. But tomorrow if Einstein turns out to be right, no problem. We would then have an even better theory at hand. That is how science progresses.

2. The Copenhagen interpretation

There was this well-known debate between Einstein and Bohr about the foundations of quantum mechanics. Bohr’s viewpoint prevailed, and this gave him enormous, even undue, authority in scientific circles. If a proof is needed, look at the so-called ‘Copenhagen interpretation’ (CI) of quantum mechanics he gave in 1927, jointly with Heisenberg (another venerated scientist). According to the CI, people and the equipment they use exist in a classical world which is different from the quantum world. A quantum state is a superposition of two or more states, but when it interfaces with the classical world (at the moment of measurement), there is a collapse of the wave function (randomly) to one of the alternatives, and the other alternatives disappear. The CI was put in ‘by hand’ as an additional postulate of quantum mechanics.

I have given some more details of the CI in an article on ‘biocentrism’ I coauthored with Ajita Kamal, published here.

What was done there was to juxtapose the CI with a number of later interpretations. To me it is clear that the CI has been superseded by better interpretations.

So much for science. Now let us look at the scientists part of it.

Among the earliest persons to openly challenge the CI was Hugh Everett III, when he put forward his ‘many worlds’ interpretation. But on the scientific scene at that time he was just a kid (a student at Princeton University in the mid-1950s) compared to stalwarts like Bohr and Heisenberg. [To us in India this is reminiscent of the Chandrasekhar vs. Eddington episode in cosmology.] A. H. Wheeler was the Ph.D. supervisor of Everett. Peter Byrne has written about this story in an article in the December 2007 issue of Scientific American. In 1956 Wheeler took the draft dissertation of Everett to Copenhagen to convince the Royal Danish Academy of Sciences to accept it and publish it. He had ‘three long and strong discussions about it’ with Bohr and Petersen. He also showed the work to many others at the Bohr Institute for Theoretical Physics, including A. S. Stern.

Stern dismissed the work as ‘theology,’ and Wheeler himself was reluctant to challenge Bohr. The thesis had to be whittled down to a quarter of its original length. This abridged version also appeared in Reviews of Modern Physics. Young Everett eagerly looked forward to the reactions of the physics community. All he got was stony silence, such was the awe that the name Bohr inspired (and that continues in some quarters even today). Discouraged, Everett left physics and worked on military and industrial mathematics and computing. As the Editors of Scientific American wrote, ‘He died when he was just 51, not living to see the recent respect accorded to his ideas by physicists.’

3. Entanglement

Bohr, of course, was quite consistent in his views about the basics and limitations of quantum mechanics. This came to the fore again in his reaction to Einstein’s and others’ views on ‘quantum entanglement.’ Now this is another esoteric feature of quantum mechanics that challenges our intuition very seriously. And yet there is no immediate danger to the present edifice and acceptability of quantum theory. Why? The answer comes from experiment, namely the fact that quantum computing is already a reality.

The entanglement feature of quantum mechanics is about the spooky ‘action at a distance’: Two particles behave synchronously without any intermediary, no matter how far apart they are. This nonlocalityfeature bothered Einstein and others, as embodied in the famous EPR (Eistein-Podolsky-Rosen) thought experiment published in 1935 in a paper with title ‘Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?’ EPR argued that the answer to the question is ‘No.’ They took the position that nonlocality is not something real, and therefore quantum mechanics does not provide a complete description of reality.

Bohr did agree with this conclusion, but for his own reasons (see, for example, the article by Albert and Galchen in the March 2009 issue of Scientific American). He argued that we should not even try to read from the equations of quantum mechanics a realistic comprehension of the world. This was in line with what he did in the Copenhagen interpretation mentioned above; namely, introduce one morepostulate or axiom by hand when interfacing the microworld with the macroworld.

Thirty years later John Bell wrote his famous paper in which he established by mathematical proof that the real physical world is indeed nonlocal, no matter what EPR or Bohr believed to be the case. He showed that no local (as opposed to nonlocal) theory can reproduce all the predictions of quantum mechanics because the predictions must always satisfy the now-famous Bell’s inequalities. This meant that the concept of locality was indeed incompatible with quantum theory, so the actual physical world in indeed nonlocal. Both Einstein and Bohr were wrong, though for different reasons.

The influence of Bohr’s line of thinking was so strong and persistent that there was resistance to Bell’s work also. But this situation has changed gradually. I quote from Albert and Galchen (2009): ‘From the early 1980s onward, the grip of Bohr’s conviction — that there could be no old-fashioned, philosophically realistic account of the subatomic world — was everywhere palpably beginning to weaken.’

4. Ockham’s razor

The philosopher Ockham advocated the use of simplest possible explanations for natural phenomena: ‘Plurality should not be posited without necessity‘. The proverbial Ockham’s razor cuts away complicated and long explanations. Ockham declared that simple explanations are the most plausible.

In science, as also in mathematics, we always have some axioms to start with, from which we derive theorems etc. Axioms are something we accept without questioning. If we choose wrong axioms, we get theorems which contradict experiment, so this is not so serious a problem because it is self-correcting. The more serious problem is: How many axioms we should choose?

An extreme situation is wherein we ‘explain’ everything in terms of axioms only, so we have a huge number of axioms, and there is no theory worth the name. Leibniz (1675) was amongst the earliest known investigators of this situation. He argued that a worthwhile theory of anything has to be ’simpler than’ the data it explains. Otherwise, either the theory is useless, or the data are ‘lawless’. The criterion ’simpler than’ is best understood in terms of information theory, particularly its more recently developed offshoot, namely algorithmic information theory (AIT).

Gregory Chaitin is a pioneer of AIT. To understand the essence of the AIT, consider a very simple example. Take the set of all positive integers, and ask the question: How many bits of information are needed to specify all these integers? The answer is an absurdly large number. But the fact is that this set of data has very little information content. It has a structure which we can exploit to write an algorithm which can generate all the integers, and the number of bits of information needed to write the algorithm is indeed not large. So the algorithmic information content in this problem is small.

One can generalize and say that, in terms of computer algorithms, the best theory is that which requires the smallest computer program for calculating (and hence explaining) the observations. The more compact the theory, the smaller is the length of this computer program. Chaitin’s work has shown that the Ockham razor is not just a matter of philosophy; it has deep algorithmic-information underpinnings. If there are competing descriptions or theories of reality, the more compact one has a higher probability of being correct. Ockham’s razor cuts away all the flab. Let us see why.

In AIT, an important concept is that of algorithmic probability (AP). It is the probability that a random program of a given length fed into a computer will give a desired output, say the first million digits of π. Following Bennett and Chaitin’s pioneering work done in the 1970s, let us assume that the random program has been produced by a monkey. The AP in this case is the same as the probability that the monkey would type out the same bit string, i.e. the same computer program as, say, a Java program suitable for generating the first million digits of π. The probability that the monkey would press the first key on the keyboard correctly is 0.5. The probability that the first two keys would be pressed correctly is (0.5)² or 0.25. And so on. Thus the probability gets smaller and smaller very rapidly as the number of correctly sequenced bits increases. The longer the program, the less likely it is that the monkey will crank it out correctly. This means that the AP is the highest for the shortest programs or the most compact theories. The best theory has the smallest number of axioms.

In the present context, suppose we are having a bit string representing a set of data, and we want to understand the mechanism responsible for the creation of that set of data. In other words, we want to discover the computer program (or the best theory), among many we could generate randomly, which is responsible for that set of data. The validation of Ockham’s philosophy comes from the fact that the shortest such program is the most plausible guess because it has the highest AP.

In the Copenhagen interpretation described above, Bohr’s action of adding one more postulate or axiom by hand was unwarranted, as later developments in quantum theory have demonstrated.

5. Pseudoscientists

The narrative so far is enough to illustrate the difficulties we humans face in understanding the nature of reality with our limited collective intellect and other resources. But fortunately we scientists have with us the power of the scientific method of enquiry, which is no respecter of authority. As Bell’s work has shown, both Einstein and Bohr held wrong views about nonlocality. Good science is self-correcting.

There is so much that science cannot answer. But the big question is: Is there ANY other way of getting these answers? No. There is none.

Some questions are indeed very difficult to answer, but scientists keep trying. The incremental progress may be slow, but there is progress nevertheless.

The unfortunate fact of life is that not many humans have the requisite training and mental discipline demanded by the scientific method. They MUST have an answer always. If science cannot provide it at present, they do not have the patience to wait. They just invent answers by introducing more and more axioms (incidentally, this is what is done by most religions). Here is an example.

Deepak Chopra, a medical doctor, who also uses at times the language of quantum mechanics in his discourses and writings, posted an article on the Huffington Post. Here is an excerpt:

‘I consider myself scientific at heart, and so I depend upon a theory as well. Its basic premises are as follows:

1. We live in a universe that exhibits intelligence, self-regulation, and creativity.
2. Consciousness preceded the brain. It created life and went on to create the brain itself.
3. Consciousness is primary in the world; matter is secondary.
4. Evolution is conscious and therefore creative. It isn’t random.
5. At the source of creation one finds a field of pure awareness.
6. Pure awareness is the source of every manifest quality in the universe.’

Anybody is welcome to subscribe to a theory of his/her choice. My response to the above statements is as follows:

1. It is only a belief.
2. What is the basis for making this assertion?
3. Again just a belief.
4. Prove it.
5. Just wishful thinking.
6. That’s what YOU think.

What can any scientist do with the kind of ‘theory’ Chopra subscribes to? I want to invoke Ockham’s razor. If you introduce as many axioms or premises as Chopra wants to, then there is just about nothing left to be derived from those axioms. Practically everything is axiomatic in this ‘theory.’ Ockham’s razor will make mincemeat of Chopra’s set of premises!

Chopra goes on to give a list of questions which science cannot answer at present. So what? Is there ANY better way of getting those answers?

If somebody is a mystic, I have no problem with that. What is not acceptable is peddling mysticism under the garb of science, or rather pseudoscience. And one cannot be ’scientific at heart’ and yet be innocent about the rigours of the scientific method of acquiring knowledge and understanding.

Chopra uses the word ‘consciousness’ again and again. I draw the attention of the reader to my article here.

As I argue there, it is not possible to define consciousness in an unambiguous scientific way. How do we discuss it and investigate it when there is no agreement on what that word really means?

There is nothing sacrosanct about the set of axioms and premises on which modern science is based. Any other set of axioms can be fine if it leads to theorems and conclusions and intellectual progress better than what the existing science has achieved. Deepak Chopra’s set of premises is quite typical of the thinking to which even some scientists subscribe. These people usually are apologists for their religious beliefs. I want to suggest something to them. Why not try to build up the edifice of a self-consistent parallel science based on such axioms? Take the axioms of your choice, and take as many as you want (or rather as few as you can), and see if you can produce something superior to the existing scientific framework. If you succeed, I shall be the first one to proudly walk over to your camp. Why only me? The whole of the existing structure of science will just fade away, because it would have been superseded by something superior.

6. Concluding remarks

• The scientific method is among the greatest achievements of the human mind.
• Science is impersonal, but scientists are not. Scientists come in all shapes, sizes, conditionings, egos, and biases. Their subjectivity does slow down the progress of science, but not for long. Ultimately the best theory prevails.
• Even the best scientific theory holds only till a better one comes along. Scientists have no compunction about dumping their pet theories in favour of better ones. This is true intellectual humility, not commonly seen in non-scientific or unscientific circles.
• All those who love and respect science should try to ensure that it is not hijacked by pseudoscientists to meet their covert or overt agendas.
• Some questions are inherently very difficult to answer. But there is NO method other than the scientific method for getting the answers.

Related posts:

1. Scientists and God: The Indian Scenario
2. Premanand’s “THE METHOD OF SCIENCE” Museum Opens
3. Science Versus Religion: A Report From The World Atheist Conference
4. Why India’s Science Standards Must Improve
5. Science Trust Organizing Two-day Program In Remembrance Of Founder B. Premanand
6. Guns Or Butter: Reflections On How Science And Technology Impact Us
7. The ‘Exact Science’ Of Nadi Jothidam
8. Sacred Reason: Reconciling Science and Emotion
9. Annual Conference: The Science & Rationalists Association Of India- Celebrating 25 Years On March 6-7, 2010
10. Science and Skepticism Interview: Dr. Steven Novella
11. Karen Armstrong’s ‘The Case For God’ (or) Why Science Makes My Head Hurt
12. Basava Premanand Video: Posted on youtube by Sajith C of Science Trust
13. Why ‘Alternative Medicine’ Is Neither Science-Based Nor Medicine

The complete series, Complexity Explained by Dr. Vinod Wadhawan, can be accessed here.

In this concluding part of the series on complexity I recapitulate the basic ideas about complexity, and then revisit the questions about the origin of the universe we live in, the origin of life, and the origin of consciousness. The bottom line is that the word ‘origin’ should be replaced by ‘evolution.’ And what evolves with time is complexity, resulting in the emergence of new properties or phenomena which could not have been anticipated.

17.1 Recapitulation of the Main Ideas in Complexity Science

With reductionism comes the conviction that a court proceeding to try a man for murder is “really” nothing but the movement of atoms, electrons, and other particles in space, quantum and classical events, and ultimately to be explained by, say, string theory.

Stuart Kauffman (2006)

1. Classical microscopic laws of physics are characterized by determinism and time-reversal symmetry. Determinism means that if the position and the momentum of a particle are known at any instant of time, then the laws of classical mechanics determine the position and momentum at all instants of time, both future and past. The success of space missions is an example of the applicability of the deterministic equations of motion to simple (or simplifiable) systems (in contrast to complex systems). Simple systems have the linearity feature: The inevitable imprecision in our knowledge of the physical parameters of such a system does not lead to disastrous or runaway consequences in our predictions about the mechanics of the system.
2. By contrast, chaotic systems, though deterministic, are governed by nonlinear equations of motion, and consequently we cannot predict their behaviour far into the future. Chaos is an example of the fact that determinism does not necessarily imply predictability.
3. The familiar second law of thermodynamics is a striking example of emergence in complex systems. The laws of mechanics (classical or quantum) applicable to any microscopic particle comprising a macroscopic system are time-symmetric; but the macroscopic system has the emergent property of time-asymmetry, embodied in the fact that the entropy of the system cannot decease with the passage of time.
4. In the macroscopic world, we associate the direction of increasing entropy with the direction of increasing time. Entropy is a measure of disorder, and negative entropy or negentropy is a measure of information.
5. The emergence feature of complex systems makes the reductionistic approach to understanding complex natural phenomena quite inapplicable. But that does not mean that we should swing to the other extreme and adopt only a holistic approach. It is important to understand the distinction between chaotic, random, and complex systems. In a chaotic system there is determinism without predictability. Order and disorder coexist in a complex system. And randomness means a complete lack of structure or order (’algorithmic irreducibility’). I shall be addressing these issues in a forthcoming book.
6. Complex systems have a hierarchical structure of complexity. The structure at one level leads to the next level of complexity, and each level of complexity often results in the emergence of new laws.
7. The new laws do not violate any of the laws operating at the lower levels of complexity. There is no question of ‘downward causality’ because, deep down under, everything interacts with everything else and we only have interactions, rather than actions and reactions (or causes and effects).
8. Physical laws, though always valid, are not always convenient or relevant for explaining, say, the chemical behaviour of a system. Similarly, biology is not always conveniently understood in terms of the laws of chemistry or physics alone. Nevertheless, if we consider only neighbouring or contiguous levels of hierarchical complexity, a reductionistic or constructionistic approach can often be useful.
9. Flow of energy through an open thermodynamic system can take the system so far away from equilibrium that there is a bifurcation in phase space, resulting in self-organization. Such bifurcations can occur repeatedly in a complex system, and there is no way to predict as to which branch of a bifurcation will be chosen, because the choice depends on random fluctuations at the moment of the bifurcation. This fact lies at the heart of (unpredictable) emergence of novel features during the time-evolution of a complex system.
10. Simple local rules can lead to the emergence of complex overall patterns, behaviour, or properties. This is how swarm intelligence emerges.
11. The flow of energy through a complex system results in a build up of the information content of the system. A state of complete order, as also a state of complete randomness, has low information content and a low degree of complexity. The more interesting complex systems usually fall in-between these two extremes.
12. Complexity thrives best at the ‘edge’ between order and disorder. Complex adaptive systems tend to self-organize so as to inch towards this so-called ‘edge of chaos.’
13. Per Bak’s notion of self-organized criticality provided important insights into how and why complex systems move to a state at or near the edge of chaos.
14. Positive feedback is an important mechanism of how self-organization can occur. However, it is not the only possible mechanism for this. Often, chain reactions achieve something similar. And negative feedback provides the necessary antidote for maintaining a state of optimal balance and perpetual novelty.

17.2 How did the Universe Emerge out of ‘Nothing’?

Everything existing in the universe is the fruit of chance and necessity.

Diogenes Laertius IX

This is the toughest of the three questions I revisit in this article. I wrote about cosmic evolution in Part 7 of this series, but want to make up here for some important omissions.

What happened immediately before the Big Bang? The answer to this question is important for understanding some observations in astronomy. How can energy be created out of nothing, and how is it continuing to increase as the universe expands? I quoted Seth Lloyd (2006) in Part 7: ‘Quantum mechanics describes energy in terms of quantum fields, a kind of underlying fabric of the universe, whose weave makes up the elementary particles - photons, electrons, quarks. The energy we see around us, then - in the form of Earth, stars, light, heat - was drawn out of the underlying quantum fields by the expansion of our universe. Gravity is an attractive force that pulls things together. . . As the universe expands (which it continues to do), gravity sucks energy out of the quantum fields. The energy in the quantum fields is almost always positive, and this positive energy is exactly balanced by the negative energy of gravitational attraction. As the expansion proceeds, more and more positive energy becomes available, in the form of matter and light - compensated for by the negative energy in the attractive force of the gravitational field.’

Apart from quantum-mechanical effects and the gravitational interaction, other dominant factors in the early stages were the immensely high temperatures and pressures. In the beginning it was all radiation, and no matter. And the energy content and the information content were very small. The energy content and the information content built up as the universe expanded and extracted more and more energy out of the underlying quantum fabric of space and time.

According to the current theories, the energy grew very rapidly in the beginning (by a process called inflation), and the amount of information grew less rapidly. Immediately after the Big Bang there was a hot plasma of elementary particles, which expanded and cooled very quickly. In fact, the first structures got formed within a fraction of a second after the explosion. Protons and neutrons were formed from quarks.

One minute after the Big Bang, helium nuclei were formed. Soon, a full 24% of all matter was in the form of helium nuclei. Radiation interacts primarily with ions (rather than atoms).A few tens of thousand of years after the Big Bang, the first electrically neutral matter was formed, when protons and electrons combined to form atoms of hydrogen. This marked the separation of electrically neutral matter from radiation. On further cooling, gravitational effects became more and more important, as electrically neutral atoms could now clump together because of gravitational attraction. This clumping went on to produce galaxies ultimately.

There are gaps in our understanding of how structure arose out of what was a structureless field of radiation in the beginning. In particular, we do not yet know whether there are forms of matter other than what we already know. Even as early as in the 1930s, it was known that gravitational effects in large galactic clusters are much higher than what can be expected from the known amount of matter there. Apparently, there is another, unknown, form of matter that is a full 90% of all matter, as indicated indirectly by the gravitational effects. It is called dark matter because we are unable to observe it; we infer its existence only through its gravitational effects.

Perhaps neutrinos have something to do with this dark matter. Or perhaps some still undiscovered elementary particles, including some very heavy (but unobserved) ones, may be involved. These particles might have got formed in the very hot conditions soon after the Big Bang.

The reasons for the occurrence of the Big Bang are still a puzzle. Another puzzle in modern cosmology is the fact that matter and the cosmic background radiation are distributed quite homogeneously throughout the observable universe. Consider a galaxy that is 5000 million light years away today from our galaxy, namely the Milky Way. When the universe was, say, just one million years old, it (the universe) was only a thousandth of its present size. Therefore at that time the two galaxies must have been 5 million years apart. But since the age of the universe at that time was only one million years, not enough time was available for the two galaxies to have exchanged signals of any kind (assuming that nothing travels faster than the speed of light). There could not have been any kind of communication between the contents of one galaxy and the other. So how did the homogenization of the shock waves associated with the Big Bang occur?

There is general agreement that the emergence of matter from the early radiation field was a kind of symmetry-breaking phase transition. This can be likened to the phase transition from liquid water (which is homogeneous, or translation-invariant) to ice (which is not translation-invariant). The radiation field was translation-invariant, and the appearance of matter broke this translational symmetry. A hypothetical field called the Higgs field has been introduced in cosmology to understand these phenomena. This field breaks the symmetries of the interactions among the elementary particles, and gives the particles their mass.

The Higgs-field theory predicts the existence of a cosmological constant. Such a constant was indeed introduced much earlier by Einstein, and then withdrawn because it amounted to introducing into his theory of gravitation a parameter ‘by hand,’ with no theoretical justification. Einstein’s cosmological constant was intended to provide the repulsive force needed to compensate for the attractive force of long-distance gravity. In other words, if gravity could be switched off, Einstein’s cosmological constant would result in a rapid inflation of the universe. But once it was known that the universe is expanding, it became unnecessary to try to counterbalance the attractive gravitational force.

The Higgs field results in the existence of a new cosmological constant, which turns ‘empty’ space into a space that has an energy content. The problem at present is that the predicted cosmological constant has too large a value for a correct understanding of the observed cosmic evolution. It is believed that perhaps the Higgs cosmological constant had a large value right after the Big Bang, resulting in a violent and very rapid expansion (or inflation) of the universe. At a certain stage of this inflation, a cosmic phase transition occurred, which freed enormous amounts of energy (rather like the release of latent heat when steam condenses to liquid water). In a way, this energy flash or Big Bang marked the actual birth of our cosmos. After this prelude of inflation and cosmic phase transition, the normal (much slower) expansion of the universe set in, and has continued ever since.

During the inflation prelude, the universe grew extremely rapidly from a volume smaller than that of the nucleus of an atom to the size of a tennis ball. If we associate the Big Bang with the moment at the end of the (very quick) inflation episode, certain cosmological mysteries get resolved. When the universe was just the size of a tennis ball, regions that are far apart today could have been in contact then, thus resulting in the observed homogenization of the universe.

This new model of the Big Bang (i.e. a phase transition after the inflation prelude) answers a few additional perplexing questions as well. The model implies that the observable cosmos is a part of a much bigger system. Our Big Bang occurred in a certain region of the cosmos, leaving other regions untouched. More Big Bangs can keep occurring in other regions of the cosmos, opening up the possibility of parallel universes. There is thus a multiverse, rather than a universe.

In a multiverse, Big Bangs occur repeatedly, and each resulting universe has values of fundamental constants that just happen to be what they are. The universe we live in happens to have values of fundamental constants that make our emergence and existence possible. Otherwise we would not have emerged and evolved. This brings us to the much-maligned anthropic principle. The principle states that: The parameters and the laws of physics in our universe can be taken as fixed; it is simply that we humans have appeared in the universe to ask such questions at a time when the conditions were just right for our life. I have not included a discussion of this principle in the present series because it is covered in another article (on biocentrism) on this website, which I coauthored with Ajita Kamal.

Although there is no law saying that the degree of complexity of the universe must always increase, an empirical observation is that it is increasing, and increasing at an exponential rate. There can be some local decreases in complexity (there is even an anthropocentric angle to this issue), but the overall complexity of our universe is increasing. This has been explained in terms of the fact that our universe is expanding, and thus getting a continuous supply of free energy or negentropy (cf. Part 7).

But how long will the universe continue to expand? Did time begin? Will time end? Here are three likely answers given by the noted cosmologist Paul Frampton in a recent (2010) book:

Most likely: The present expansion will end after a finite amount of time, the universe will contract, bounce and repeat the cycle. In this cyclic universe, time had no beginning, and will have no end.

Next most likely: The present expansion will end after a finite time in a Big Rip. Time began in the Big Bang some 13.7 billion years ago, and will end some trillion years in the future.

Least likely: The present expansion will continue for an infinite time. Time began 13.7 billion years ago, and will never end. In his book Prof. Frampton challenges this prevailing ‘conventional wisdom.’

17.3 How did Life Emerge out of No-Life?

It was discovered that RNA molecules can not only carry genetic information, but act as enzymes, speeding chemical reactions. Work is underway to create an RNA enzyme, or ribozyme, that can copy any RNA molecule including itself. The probability that an RNA molecule can catalyze a given reaction is roughly 10 divided by 10 raised to the 15th power. It is conceivable that such a molecule can arise by chance, but it faces the difficulty that were it to copy itself and make errors, those error copies would be more error prone than the initial copy, and a run away error catastrophe might ensue.

Stuart Kauffman (2006)

As discussed in Part 10, it is not easy to define life. One consequence of this situation is that life must have emerged very very gradually. Thus it is meaningless to try to identify a point of time which marked the ‘origin’ of life on Earth. As discussed in Parts 8, 9, and 12, a whole lot of chemical evolution of complexity preceded the emergence of what we intuitively understand as life.

I discussed only two models of the likely origins of life in Part 12. For a more comprehensive description, please see the 2006 online article by Stuart Kauffman.

I described Kauffman’s work on autocatalytic sets of molecules in Part 9, and his RBNs (random Boolean networks) in Part 12. He has been emphasizing the importance of the self-organization feature of complex systems in the evolution of biological complexity. He uses the phrase ‘order for free‘ for this non-Darwinian evolution of complexity:

“While it may sound as if ‘order for free’ is a serious challenge to Darwinian evolution, it’s not so much that I want to challenge Darwinism and say that Darwin was wrong. I don’t think he was wrong at all. I have no doubt that natural selection is an overriding, brilliant idea and a major force in evolution, but there are parts of it that Darwin couldn’t have gotten right. One is that if there is order for free - if you have complex systems with powerfully ordered properties - you have to ask a question that evolutionary theories have never asked: Granting that selection is operating all the time, how do we build a theory that combines self-organization of complex systems - that is, this order for free - and natural selection? There’s no body of theory in science that does this. There’s nothing in physics that does this, because there’s no natural selection in physics - there’s self organization. Biology hasn’t done it, because although we have a theory of selection, we’ve never married it to ideas of self-organization. One thing we have to do is broaden evolutionary theory to describe what happens when selection acts on systems that already have robust self-organizing properties. This body of theory simply does not exist.” (Chapter 20, “Order for Free”, The Third Culture, 1995).

Kauffman’s work brings out the inevitability of the emergence of life. The prevailing conditions were such that life just had to appear because of the relentless evolution of complexity. A knowledgeable alien would be very surprised if life had not emerged here. Thus, the ‘origin’ of life is the easiest of the three questions I am revisiting in this article. There is nothing miraculous or supernatural about the origin of life.

17.4 How does Consciousness Arise?

Meanwhile, my approximate theory is that mind is acausal, quantum mechanics is acausal on the familiar Born interpretation of the Schrödinger equation, (to the grief of Einstein), that consciousness is due to a special state where a system is persistently poised between quantum and classical behaviour, that the emergence of classical behaviour in the mind-brain system, perhaps by decoherence, is the “mind making something actual” happen in the physical world, and - big jump - that consciousness itself consists in this quantum coherent state as lived by the organism. This is a long jump, but not impossible. I don’t even think it is stupider than other theories of consciousness, and may be true. Whatever the case, consciousness is ontologically emergent in this universe.

Stuart Kauffman (2006)

The problem with the word ‘consciousness’ is that it is what Marvin Minsky calls a ’suitcase word.’ It stands for a whole set of processes. Naturally, it is difficult to discuss it in a scientific manner. From the complexity perspective, consciousness arises from swarm intelligence, the swarm here being that of neurons. In a large swarm, local rules can lead to astonishingly complex behaviour and novel phenomena and sensations.

The self-referential nature of consciousness is what makes it look so puzzling. But the fact is that, long ago (in 1931), Kurt Gödel shook the foundations of mathematics by proving that even such an innocuous thing as the formal system of positive integers can have self-referential properties. Self-reference and formal rules can make systems acquire meaning, despite the fact that each constituent of the system in without meaning.

Nevertheless, there are difficulties galore:

‘All the limitative theorems of metamathematics and the theory of computation suggest that once the ability to represent your own structure has reached a certain critical point, that is the kiss of death: it guarantees that you can never represent yourself totally. Gödel’s Incompleteness Theorem, Church’s Undecidability Theorem, Turing’s Halting Theorem, Tarski’s Truth Theorem — all have the flavour of some ancient fairy tale which warns you that “To seek self-knowledge is to embark on a journey which … will always be incomplete, cannot be charted on any map, will never halt, cannot be described.”‘ (Douglas Hofstadter 1979)

The debate on consciousness is not likely to end anytime soon.

17.5 Acknowledgements

The idea of writing this series of articles was suggested by Mr. Ajita Kamal, Editor of Nirmukta. Ajita has been of great help throughout, and made several useful suggestions.

My Ph. D. student Indranil Bhaumik was immensely helpful by sending me several important books in pdf format.

Ms. Malgorzata Koraszewska took the trouble of translating these articles into Polish and publishing them at www.racjonalista.pl. She has done a thorough job indeed, consulting experts when in doubt about the exact Polish equivalent of a technical word in English. The Polish versions of these articles were discussed in a much more lively way than the originals in English. Unfortunately I could not take part there because of the language barrier, but was happy to answer some questions forwarded to me by Malgorzata.

I not only enjoyed writing these articles, it was also a great learning experience for me because of the comments and questions posted on nirmukta.com, as also on richarddawkins.net and some other websites which picked up some of these articles. I also received a lot of feedback from scientists-friends through private emails.

I shall feel amply rewarded for the time and effort I have put into the writing of these articles if I have succeeded in inducing even a few of the readers to shun all kinds of irrational belief systems.

Science is rational. Science is fun. Science has both a humbling and a liberating influence on those who have imbibed the spirit of the scientific method. The skepticism inherent in the scientific method, and its emphasis on making only falsifiable statements, are essential tools for acquiring knowledge we can trust with a high degree of confidence.

Nature is highly creative, and this creativity comes from the relentless evolution of complexity. A flower is a piece of art, and complexity science tells us how this ‘natural art’ can arise (emerge) without the need for the existence of the artist or the creator.

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS. All parts of Dr. Wadhawan’s series on Complexity Explained can be found here.

Related posts:

1. COMPLEXITY EXPLAINED: 6. Emergence of Complexity in Far-from-Equilibrium Systems
2. COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity
3. COMPLEXITY EXPLAINED: 3. Thermodynamic Explanation for the Increasing Complexity of our Ecosphere
4. COMPLEXITY EXPLAINED: 1. What is Complexity?
5. COMPLEXITY EXPLAINED: 14. Biological Complexity at the Edge of Chaos
6. COMPLEXITY EXPLAINED: 5. Defining Different Types of Complexity
7. COMPLEXITY EXPLAINED: 13. Evolution of Biological Complexity
8. COMPLEXITY EXPLAINED: 8. Evolution of Chemical Complexity
9. COMPLEXITY EXPLAINED: 15. Evolution of Cultural Complexity
10. COMPLEXITY EXPLAINED: 10. What is Life?
11. COMPLEXITY EXPLAINED: 11. Cellular Automata
12. COMPLEXITY EXPLAINED: 4. The Nature of Information
13. COMPLEXITY EXPLAINED: 2. Swarm Intelligence

The human brain is a physical organ, governed by the laws of physics. The mind is ‘brain power,’ or the capacity of the brain to feel, think, and reason. The brain carries the mind, as well as what we often call consciousness (although we cannot tell where exactly in the brain is the so-called consciousness located). Our intelligence may be no different from ’swarm intelligence,’ the swarm here being that of neurons. There is a belief that the transition from intelligence to consciousness needs the acquisition of a human language. The ’society of mind’ (comprising of ‘communities’ of large numbers of interacting neurons) emerged as a hierarchical structure, so typical of any complex adaptive system. Consciousness is an emergent phenomenon.

16.1 Evolution of the Mammalian Brain

Any living entity exploits the existing structure and order of its surroundings to ensure its survival and reproduction. Consider a single-celled organism in a pond. On its surface are molecules which can ‘detect’ (are influenced by) the presence of nutrients. There is usually a gradient of the nutrient concentration, so that it is higher on one side of the organism than on the other. The single-celled organism has chemical sensors which can detect this gradient. Biological evolution has programmed it to propel itself in the direction of increasing concentration of nutrient. An attribute of intelligence is the problem-solving capacity of the system; other important attributes are prediction and memory capabilities. As Hawkins (2004) points out, both prediction and memory are involved here. The prediction is that, by moving in the direction of increasing concentration of nutrient, more nutrient will be found. This is not something the organism has ‘learnt’ and ‘remembered’ in its lifetime. The memory, evolved over many generations of evolution, is in its DNA.

To cut a long evolutionary story short, let us jump from bacteria to plants. Plants also exploit the existing order and structure (constancy or sameness over reasonably long time scales) by employing memory and prediction. The memory in the genes of a tree tells it that it will find greater sunshine by sending its branches and leaves towards the sky. And that it will find water and minerals by sending its roots down into the soil. These actions are automatic, and there is no ‘thinking’ involved, just as there is no thinking involved in the actions of a bacterium.

At a certain stage in the evolutionary history of plants, more complex behaviour emerged in the form of communication systems among the various parts of a plant, based mainly on chemical signals. Suppose an insect damaged some part of a tree, and this led to the slow transmittal of a chemical through the vascular system to its other parts. This triggered a defence mechanism; e.g. the making of a toxin for the insect.

It is conceivable that neurons evolved in due course, as a faster way of communicating information to different parts of an organism. The electrochemical spikes in a neuron travel much faster than the diffusion of chemicals. In due course, the ’synaptic’ connections between neurons became modifiable. A neuron may or may not send a signal, depending on what happened in the past. This rudimentary nervous system had elements of both memory and learning.

The evolutionary advantage of this to the animal was qualitatively different. Instead of depending on just ‘genetic memory’ and instinct coded in DNA, the animal could now learn from experience during its own lifetime, and modify its behaviour for achieving better survival and propagation rates. In particular, if the environmental structure and order changed rather suddenly, the animal could still make a generally adequate response, instead of having to depend only on the somewhat outdated (and therefore inadequate) genetic memory and instinct. Such plastic nervous systems entailed a huge evolutionary advantage, and there was a burst of new species from fish to snails to mammals, including humans.

Why is it that intelligence evolved mainly in the animal kingdom, but not in the plant kingdom? As explained by the noted robotist Hans Moravec, the difference has arisen because animals are mobile and plants are generally not. The mobility of animals presents to them an ever-changing environment, and therefore intelligence is an important prerequisite for survival and propagation: An animal can survive only if it has a large repertoire of solutions to the continuous stream of problems it faces in a changing environment.

The human brain, like the brain of any other mammal, has something distinctly additional compared to the brain of reptiles from which it evolved, namely the neocortex. Thus the human brain has two main parts: the ‘old brain’ or the reptilian brain or the R-brain or the ‘primitive’ brain, and the neocortex.

Practically everything we associate with conscious memory and intelligence occurs in the neocortex, although the thalamus and the hippocampus also play important roles. In the evolutionary history of life on Earth, sophisticated sensory and actuation organs had evolved in reptiles, and their behaviour was controlled by the old brain, with no cortex. The evolution of the cortex in one of the offshoots of the reptiles, along with the availability of a stream of sensory inputs into it which it could remember and analyse much better than reptiles could, gave the mammals an evolutionary advantage: When they found themselves in situations they remembered to have faced earlier, their much-improved memory and analysis power told them what to expect next, and how to respond effectively.

16.2 The Human Brain

The human brain, along with the spinal chord, comprises the central nervous system. The top outer portion of the brain, just under the scalp, is the neocortex (or cortex for short). It covers most of the R-brain, and has a crumpled appearance, with many ridges and valleys. The R-brain is rather similar in reptiles and mammals, and has a number of parts, including the thalamus and the hippocampus.

Humans are special compared to other mammals because of their very prominent prefrontal cortex (or frontal lobe).The prefrontal cortex (particularly the upper two-thirds of it, including the dorsolateral prefrontal cortex) can be regarded as the rational centre of the brain; or the rational brain. The rest of the human brain is the emotional brain.

The human cortex, if stretched flat, is the size of a large napkin, and about 2 mm thick. It has six layers, each roughly the thickness of a playing card. There is a branching hierarchy among the layers. Layer 6 is at the bottom of the hierarchy, and Layer1 is at the top. The inputs from the various sensory organs are received in Layer 6, and then interpreted and correlated. Then more and more abstract and generalized versions of the information are sent up the hierarchical layers. There is a very high degree of feedback and feedforward among the layers, as also cross-correlations.

Image credit: K. Svoboda, as reproduced in the book Biological Physics: Energy, Information, Life, by Philip Nelson (2008). The image shows the dendritic tree of a neuron which receives more than 100,000 synaptic inputs, which are integrated by the neuron to create a single output signal.

There are ~10¹¹ nerve cells or neurons in the human cortex. Most of them have a pyramidal shaped central body or nucleus, as well as an axon, and a number of branching structures called dendrites. We can think of the axon as a signal emitter, and the dendrites as signal receivers. When a strand of an axon of one neuron (the presynaptic neuron) ‘touches’ a dendrite of another neuron (the postsynaptic neuron), a connection called a synapse is established. A typical axon is involved in several thousand synapses.

Portions of the cortex can be identified as different functional areas or regions. For example, a portion of the frontal lobe (see illustration) is the motor cortex. It controls movement and other actuator functions of the body.

The cortical tissue can be functionally divided into vertical units or columns. Neurons within a column respond in a similar manner to external signals with a particular attribute.

When a sensory or other pulse (’spike’) involving a particular synapse arrives at the axon, it causes the synaptic vesicles in the presynaptic neuron to release chemicals called neurotransmitters into the gap or synaptic cleft between the axon of the first neuron and the dendrite of the second. These chemicals bind to the receptors on the dendrite, triggering a brief local depolarization of the membrane of the postsynaptic cell. This is described as a firing of the synapse by the presynaptic neuron.

If a synapse is made to fire repeatedly at high frequency, it becomes more sensitive; i.e. subsequent signals make it undergo greater voltage swings or spikes. Building up of memories amounts to formation and strengthening of synapses.

The firing of neurons follows two general rules: (1) Neurons which fire together wire together. Connections between neurons firing together in response to the same signal get strengthened. (2) Winner-takes-all inhibition. When several neighbouring neurons respond to the same input signal, the strongest or the ‘winner’ neuron will inhibit the neighbours from responding to the same signal in future. This makes these neighbouring neurons free to respond to other types of input signals.

The functionality of the cortex is arranged in a branching hierarchy. The primary sensory regions constitute the lowest rung of the hierarchy (Layer 6). The sensory region for, say, vision (called V1) is different from that for hearing etc. V1 feeds information to higher layers called V2, V4 and IT, and to some other regions. The higher they are in the hierarchy, the more abstract they become. V2, V4 etc. are concerned with more specialized or abstract aspects of vision. The higher echelons of the functional region responsible for vision have the visual memories of all sorts of objects. Similarly for other sensory perceptions.

In the higher echelons are areas called association areas. They receive inputs from several functional regions. For example, signals from both vision and audition reach one such association area.

Although the primary sensor mechanism for, for example, vision is not the same as for hearing, what reaches the brain at higher levels of the hierarchy is qualitatively the same. The axons carry neural signals or spikes which are partly chemical and partly electrical, but their nature is independent of whether the primary input signal was visual or auditory or tactile. Finally, they are just patterns.

16.3 Creation of Short-Term and Long-Term Memories

Creation of short-term memory in the brain amounts to a stimulation of the relevant synapses, which is enough to temporarily strengthen or sensitize them to subsequent signals.

This strengthening of the synapses becomes permanent in the case of long-term memory. This involves the activation of genes in the nuclei of postsynaptic neurons, initiating the production of proteins in them. Thus learning requires the synthesis of proteins in the brain within minutes of the training. Otherwise the memory is lost.

Information meant to become the higher-level or generalized memory, called declarative memory, passes through the hippocampus, before reaching the cortex. The hippocampus is like the principal server on a computer network. It plays a crucial role in consolidating long-term memories and emotions by integrating information coming from sensory inputs with information already stored in the brain.

16.4 The Prefrontal Cortex and its ‘Working Memory’

What sorts of ‘rules’ could possibly capture all of what we think of as intelligent behaviour however? Certainly there must be rules on all sorts of different levels. There must be many ‘just plain’ rules. There must be ‘metarules’ to modify the ‘just plain’ rules; then ‘metametarules’ to modify the metarules, and so on. The flexibility of intelligence comes from the enormous number of different rules, and levels of rules. The reason that so many rules on so many different levels must exist is that in life, a creature is faced with millions of situations of completely different types. In some situations, there are stereotyped responses which require ‘just plain’ rules. Some situations are mixtures of stereotyped situations - thus they require rules for deciding which of the ‘just plain’ rules to apply. Some situations cannot be classified - thus there must exist rules for inventing new rules … and on and on. Without doubt, Strange Loops involving rules that change themselves, directly or indirectly, are at the core of intelligence. Sometimes the complexity of our minds seems so overwhelming that one feels that there can be no solution to the problem of understanding intelligence - that it is wrong to think that rules of any sort govern a creature’s behaviour, even if one takes ‘rule’ in the multilevel sense described above.

Douglas Hofstadter, Gödel, Escher, Bach

We cannot make decisions without involving emotions. This conclusion of modern psychology goes against the grain of what was believed to be the case about the nature of rational behaviour for most of the 20^th century. The conventional picture has been that at the bottom of the hierarchical complexity of the human brain is the brain stem, which controls bodily functions like heartbeat, breathing, and body temperature. At the next higher level is the diencephalon, which regulates hunger pangs and sleep cycles etc. Then comes the limbic region, which generates and controls emotions (violence, lust, impulsive behaviour, etc.). These three levels of brain complexity are common to all mammals, including humans. Lastly there is the prefrontal cortex, predominantly responsible for our reasoning power and intelligence etc. Although it enables us to suppress emotions to a small or large extent, it is wrong to think that this ‘rationality’ portion of our brain can completely overpower or overrule what the three hierarchically lower parts of the brain tend to do. In other words, it is impossible for us to make decisions which are completely dispassionate or ‘reasoned.’

It is also true that a substantial portion of the prefrontal cortex is involved in our emotional behaviour. How do we ‘manage’ our emotions? We do so by thinking about them, and the thinking is done mainly by the prefrontal cortex. The term metacognition is used for the capacity of our prefrontal cortex to contemplate about our own mind. The frontal cortex knows when we are, say, angry. In fact, every emotional state comes with self-awareness attached to it. This enables us to figure out or ‘think’ why we are feeling the way we are feeling. Thus we humans are able to exercise a certain degree of control over our emotions by what is commonly called ‘rational thinking.’ This is also how we make decisions. The emotional brain is constantly sending out signals about its likes and dislikes. The prefrontal cortex monitors these emotional outputs and tries to decide which signals to take seriously and which ones to overrule. Although the rational brain cannot silence emotions, it can help figure out which ones should be followed. A highly readable account of the role of intuition and emotions in our decision-making process has been given in a recent (2009) book How We Decide by Jonah Lehrer.

Unlike other regions (columns) of the cortex, which specialize in processing specific types of stimuli, the cells of the prefrontal cortex can process whatever kind of data they need to process. This enables our brain to look at a given problem from a variety of vantage points, and even come out with creative solutions. How does the prefrontal cortex accomplish this? The answer has to do with its special kind of memory called the working memory. It is a short-term memory, but it has a persistence feature. It is a meeting ground, and also a melting pot, of information from various sources. Neurons in this part of the brain fire in response to a stimulus, and then keep on firing for several seconds after the stimulus has disappeared. This allows the brain to make creative associations. This is the so-called restructuring phase of problem-solving: Here information is mixed together in new ways and overlapping of ideas occurs, leading to new insights. The resultant novel neural wiring enables you to identify the answers you were looking for. This is an important feature of human intelligence.

The emotional brain is very important too

Excessively rational thinking can backfire, because it often amounts to suppressing what the primitive brain is trying to tell us. This problem arises because the rational brain is not an infinitely powerful supercomputer, meaning that rational analysis cannot always provide the best solution to a complicated problem. The cumulative wisdom buried in the (much larger) primitive brain must also be used.

The psychologist George Miller demonstrated in his essay ‘The Magical Number Seven, Plus or Minus Two’ that the conscious brain can only handle about seven pieces of data at any one moment. The computational circuitry of the rational part of our brain is only a tiny fraction of the total capacity of the brain, ‘just a few microchips within the vast mainframe of the mind.’ As a result, too many choices, or too much data, can overwhelm the prefrontal cortex, leading to bad decisions. The trick lies in learning when to trust your intuitions more than your reasoning power. ‘Because working memory and rationality share a common cortical source — the prefrontal cortex — a mind trying to remember lots of information is less able to exert control over its impulses. The substrate of reason is so limited that a few extra digits can become an extreme handicap’ (Lehrer 2009). The fact of life is that the rational part of our brain (which is really a very recent novelty on the evolutionary time scale) has a rather slow and small, even erratic, CPU. Too much information can interfere with understanding. When the prefrontal cortex is overwhelmed, correlation is confused with causation, and people tend to make theories out of coincidences.

Excessive dependence on the emotional brain can be risky too. The ideal situation is that exemplified by, say, a champion chess player. Through an unhurried analysis of the games he won or lost, he builds up experience (turning mistakes into educational events) which gets ‘internalised’ into his emotional brain. In due course, it becomes ’second nature’ for him to make the right moves, not having to consciously analyse the consequences of too large a number of prospective moves. The emotional brain is a huge supercomputer, with massive parallel-processing capabilities.

16.5 Marvin Minsky’s ‘Society of Mind’

Our minds did not evolve to serve as instruments for observing themselves, but for solving such practical problems as nutrition, defence, and reproduction

Marvin Minsky (2006)

Marvin Minsky is a pioneer of the field of machine intelligence. Efforts at developing machine intelligence have resulted in deep insights into how the human brain functions.

In 1986 Minsky published his book The Society of Mind, in which he formulated his ideas about human cognition. His next book, The Emotion Machine, published in 2006, reflects the progress made in gaining insights into the workings of the human mind via the machine-intelligence approach.

Minsky’s ’society’ of mind comprises of ‘agents’ or ‘resources,’ which are the simplest individuals that populate the brain. Each agent or resource can be visualized as a typical component of a computer program, like a simple subroutine or data structure. The agents can get connected and composed into larger systems called agencies or societies of agents. The agencies self-organize into still larger conglomerates that can perform still more complex functions, and so on into still higher and higher levels of self-organization and complexity, ultimately leading to the emergence of abilities we attribute to minds. There is a hierarchical structure and organization, like in any complex adaptive system.

The idea of hierarchical levels of organization was well documented in an earlier publication of Minsky (1980): One could say but little about “mental states” if one imagined the Mind to be a single, unitary thing. But if we envision a mind (or brain) as composed of many partially autonomous “agents”-a “Society” of smaller minds-then we can interpret “mental state” and “partial mental state” in terms of subsets of the states of the parts of the mind. To develop this idea, we will imagine first that this Mental Society works much like any human administrative organization. On the largest scale are gross “Divisions” that specialize in such areas as sensory processing, language, long-range planning, and so forth. Within each Division are multitudes of subspecialists-call them “agents”-that embody smaller elements of an individual’s knowledge, skills, and methods. No single one of these little agents knows very much by itself, but each recognizes certain configurations of a few associates and responds by altering its state.

As is the case with any complex adaptive system, we cannot predict with certainty the properties of the mind-system in terms of the laws of physics applied to the constituent agents, nor can we start from the observed complexity of the brain and work our way downwards all the way to understand why the increasing complexity took a particular route in phase space. To quote Minsky (1990): ‘The functions performed by the brain are the products of the work of thousands of different, specialized sub-systems, the intricate product of hundreds of millions of years of biological evolution. We cannot hope to understand such an organization by emulating the techniques of those particle physicists who search for the simplest possible unifying conceptions. Constructing a mind is simply a different kind of problem-of how to synthesize organizational systems that can support a large enough diversity of different schemes, yet enable them to work together to exploit one another’s abilities.’

Here is Minsky’s (1986) take on consciousness: ‘In this book, the word (consciousness) is used mainly for the myth that human minds are “self aware” in the sense of perceiving what happens inside themselves. I maintain that human consciousness can never represent what is occurring at the present moment, but only a little of the recent past - partly because each agency has a limited capacity to represent what happened recently and partly because it takes time for agencies to communicate with one another. Consciousness is peculiarly hard to describe because each attempt to examine temporary memories distorts the very records it is trying to inspect.’

Minsky describes ‘free will’ as a myth, the myth that human volition is based upon some third alternative to either causality or chance.

The ‘Single-Self’ concept

Some of us subscribe to the concept that there is creature (or a set of creatures) inside us that does all the feeling or thinking for us, and makes all the important decisions for us. It is our ‘identity’ or ’self.’ Even our legal system distinguishes between deliberate wilful murder, and murder that was not pre-planned. This Single-Self concept may be useful, but has no scientific basis.

Why do humans entertain such fiction? It may be partly because it makes life look pleasant, ‘by hiding from us how much we’re controlled by all sorts of conflicting, unconscious goals.’ According to Minsky, ‘That image makes us efficient, whereas better ideas might slow us down. It would take too long for our hardworking minds to understand everything all the time. However, although the Single-Self concept has practical uses, it does not help us to understand ourselves-because it does not provide us with smaller parts we could use to build theories of what we are. When you think of yourself as a single thing, this gives you no clues about issues like these: What determines the subjects I think about? How do I choose what next to do? How can I solve this difficult problem? Instead, the Single-Self concept offers only useless answers like these: My Self selects what to think about. My Self decides what I should do next. I should try to make my Self get to work.’ He goes on to say that: ‘Whenever you think about your “Self” you are switching among a huge network of models, each of which tries to represent some particular aspects of your mind-to answer some questions about yourself.’

16.6 Daniel Dennett’s Model of Consciousness

Dennett’s 1994 book Consciousness Explained has been hailed as a major milestone in understanding the nature of consciousness. Both he and Minsky give due respect to what people say about their feelings and emotions and other internal subjective experiences, but only as evidence of how things appear to them to be, rather than as direct evidence of ‘things as they actually are.’ Dennett calls this the heterophenomenological approach.

Dennett has formulated his so-called multiple drafts model of consciousness. A point emphasized by both Dennett and Minsky is that mental processes are spread over both space and time. Consider the analogy of the preparation and publication of a book. The manuscript undergoes a number of draftings and distributions among the author, the referees, and the editor, and is thus spread over both space and time before it is ultimately finalized. The multiple drafts of the book are also a reality. Ditto with what we perceive as consciousness: There are multiple drafts, and only one may get chosen in a given situation.

Dennett emphasizes that it is only an illusion that a person is conscious of what is perceived as ‘now.’ Processes in the brain occur at millisecond (and not infinite) speeds, and many of them occur simultaneously. Therefore it is impossible to carry out a sequential timing or ordering of events in the brain at and below the millisecond time scale. There is no objective ‘now’ for a person’s brain; there can be only a subjective ‘now’ which depends on the choice made by the brain from among the recent events and processes occurring in it.

In other words, there is no central or single place in the brain (the so-called Cartesian Theatre) where everything is presented together (to Minsky’s ’single agent’), and decisions are made. Dennett presents evidence for this model from a vast range of experiments in cognitive psychology and neuroscience, as well as from ideas from evolutionary biology.

He not only rejects the notion of a Cartesian Theatre in the brain, but also those of qualia and homunculus. The term ‘qualia’ refers to the mistaken notion that feelings associated with sensation are somehow independent of sensory input. And homunculus is the name used for the now-discredited unproductive and paradoxical idea of a small agent or intelligent thing or experiencing subject, located deep inside a person’s head, determining or controlling his behaviour.

Dennett also rejects the philosophy of Cartesian Dualism, according to which consciousness (a subjective experience) belongs to a different plane of reality than the one on which the material universe is constructed. Consciousness arises from the processes of information exchange in the brain. Multiple sets of sensory information, memories and emotional cues are competing with each other at all times in the brain, but at any particular instant only one set of these factors dominates the brain. At the next instant, another set of slightly different factors are dominant. At all instants, multiple sets of information are competing with each other for dominance. This creates the illusion of a continuous stream of thoughts, leading to the impression that consciousness is the entirety of the mental functions of the individual.

Dennett (2006) believes that acquisition of a human language is a necessary prerequisite for consciousness to emerge:

I believe, but cannot yet prove, that acquiring human language (an oral or sign language) is a necessary precondition for consciousness - in the strong sense of there being a subject, an I, a ’something it is like something to be.’ It would follow that nonhuman animals and prelinguistic children - although they can be sensitive, alert, responsive to pain and suffering, and cognitively competent in many remarkable ways (including ways that exceed normal adult human competence) - are not really conscious, in a strong sense: There is no organized subject (yet) to be the enjoyer or sufferer, no owner of the experience as contrasted with a mere cerebral locus of effects.

16.7 Hawkins’ Model for Intelligence and Consciousness

Jeff Hawkins, in his 2004 book On Intelligence, proposed the so-called memory and prediction theory of how human intelligence arises. The basic idea of Hawkins’ theory of intelligence, in his own words, is as follows: The brain uses vast amounts of memory to create a model of the world. Everything we know and have learnt is stored in this model. The brain uses this memory-based model to make continuous predictions of future events. It is the ability to make predictions about the future that is the crux of intelligence.

Hawkins points out that the neocortical memory differs from that of a conventional computer in four ways:

1. The cortex stores sequences of patterns. For example, our memory of the alphabet is a sequence of patterns. It is not something stored or recalled in an instant, or all together. That is why we have difficulty saying it backwards. Similarly our memory of songs is an example of temporal sequences in memory.
2. The cortex recalls patterns auto-associatively. The patterns are associated with themselves. One can recall complete patterns when given only partial or distorted inputs. During each waking moment, each functional region is essentially waiting for familiar patterns or pattern-fragments to come in. Inputs to the brain link to themselves auto-associatively, filling in the present, and auto-associatively linking to what normally flows next. We call this chain of memories, thought.
3. The cortex stores patterns in an invariant form. Our brain does not remember exactly what it sees, hears, or feels; the brain remembers the important relationships in the world, independent of details.
4. The cortex stores patterns in a hierarchy.

Storing sequences, auto-associative recall, and invariant representation are the necessary ingredients for predicting the future based on memories of the past. How this happens is the subject matter of Hawkins’ book. According to him, making such predictions is the essence of intelligence.

Hawkins takes the view that perhaps consciousness is simply what it feels like to have a neocortex. He suggests that the self-awareness aspect of consciousness is synonymous with the formation of declarative memories. These are memories we can recall and talk about.

Hawkins, while formulating his theory of intelligence, was enamoured of the so-called Mountcastle’s hypothesis. Since the same types of layers, cell types and connections exist in the entire cortex, Mountcastle (1978) put forward the following hypothesis: There is a common function, a common algorithm, that is performed by all the cortical regions. What makes the various functional areas different is the way they are connected. He went further to suggest that the reason why the different functional regions look different when imaged is because of these different connections only. Hawkins suggests that, although hearing, touch, vision etc. are processed by the same algorithm in the neocortex, they are handled differently in the R-brain: ‘Hearing relies on a set of audition-specific subcortical structures that process auditory patterns before they reach the cortex. Somatosensory patterns also travel through a set of subcortical areas that are unique to somatic senses. Perhaps qualia, like emotions, are not mediated purely by the neocortex. If they are somehow bound up with subcortical parts of the brain that have unique wiring, perhaps tied to emotion centres, this might explain why we perceive them differently, even if it doesn’t explain why there is any sort of qualia sensation in the first place.’

The structure of the inputs (i.e. the spatio-temporal information pattern) is qualitatively different for, say, the auditory nerve and the optic nerve. The optic nerve has a million fibres, and the auditory nerve has only thirty thousand. The optic nerve caries information that is more spatial than temporal, and the auditory nerve carries information that is more temporal than spatial. This may have bearing on why is red red and green green. No matter how consciousness is defined, memory and prediction play crucial roles in creating it.

Here is how Hawkins answers why our thoughts appear to be independent of our bodies: ‘To the cortex our bodies are just part of the external world. Remember, the brain is in a quiet and dark box. It knows about the world only via the patterns on the sensory nerve fibres. From the brain’s perspective as a pattern device, it doesn’t know about your body any differently than it knows about the rest of the world. There isn’t a special distinction between where the body ends and the rest of the world begins. But the cortex has no ability to model the brain itself because there are no senses in the brain. Thus we can see why our thoughts appear independent of our bodies, . .’

16.8 Concluding Remarks

Consciousness is subjective and internal; perhaps a ‘virtual reality.’ In this article I have briefly discussed a few models of consciousness. The clear message is that there is nothing mystical or supernatural about consciousness. In fact, conscious superintelligent machines (robots) are likely to be a reality in the present century itself.

How did consciousness arise out of no-consciousness? It did so via the complexity-evolution route as an emergent property. Through self-organization and through cumulative natural selection, neurons emerged as a means of more efficient communication among the various parts of the brain. Interactions among neurons led to a further increase in complexity in the form of memory and prediction, and thence intelligence. From intelligence to consciousness is a difficult conceptual step because in science we have place only for testable or falsifiable statements, made in terms of symbols or words with a preassigned unambiguous meaning. But there is no agreement on what exactly we mean by the word ‘consciousness.’ There is a whole spectrum of definitions of this word.

Richard Dawkins takes the stand that, if you take a set of statements made about consciousness, and replace this word by some meaningless word like hkzisrkjd everywhere, you would have lost or gained nothing in understanding the meaning of that set of statements!

The philosopher Daniel Dennett takes consciousness very seriously. And he ends up saying that nonhuman animals and prelinguistic children are not really conscious (in the ’strong’ sense of the word). He admits that this assertion will shock many people, but also says that ‘. . . of course, the truth of the empirical hypothesis is in any case strictly independent of its ethical implications, whatever they are.’

Marvin Minsky uses the word ‘myth’ for describing consciousness. Like any complex adaptive system, the human brain functions in a way that cannot always be understood in terms of a few simple fundamental rules or laws. To quote Marvin Minsky (2006): ‘… every brain has hundreds of parts, each of which evolved to do certain particular kinds of jobs; some of them recognize situations, others tell muscles to execute actions, others formulate goals and plans, and yet others accumulate and use enormous bodies of knowledge. And though we don’t yet know enough about how each of those brain-centres works, we do know their construction is based on information that is contained in tens of thousands of inherited genes, so that each brain-part works in a way that depends on a somewhat different set of laws.’ According to him, none of the popular psychology words like ‘feelings,’ ‘emotions,’ and ‘consciousness’ is about any single and definite process. Each such ‘suitcase word‘ vaguely refers to the effects of a large network of processes in the brain. Minsky argues that feelings are not basic at all, but are processes made of many parts. Similarly he demonstrates that ‘consciousness’ refers to more than 20 different processes (e.g. the process of reasoning and making decisions; the process of how the brain represents ‘our’ intentions; the process of how the brain knows what it has done recently; and so on).

Jeff Hawkins takes the view that ‘reality’ is largely a matter of how accurately our cortical model of the world reflects the true nature of the world.

As Douglas Hofstadter has explained in detail, consciousness emerges in a system that is powerful enough to have a sort of self-referential, self-modelling capability (’strange loops’ is the term he uses in this context). The stage for this conclusion of his was set by Kurt Gödel’s discovery in 1931 that even things as simple as integers are powerful enough to be used for representing (at a different level) statements about themselves. Hofstadter builds on this fact to argue how conscious beings can think about and represent themselves.

. . . our intelligence is not disembodied, but is instantiated in physical objects: our brains. Their structure is due to the long process of evolution, and their operations are governed by the laws of physics. Since they are physical entities, our brains run without being told how to run.

Douglas Hofstadter, Gödel, Escher, Bach

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS

Related posts:

1. COMPLEXITY EXPLAINED: 2. Swarm Intelligence
2. COMPLEXITY EXPLAINED: 15. Evolution of Cultural Complexity
3. COMPLEXITY EXPLAINED: 8. Evolution of Chemical Complexity
4. COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity
5. COMPLEXITY EXPLAINED: 13. Evolution of Biological Complexity
6. COMPLEXITY EXPLAINED: 3. Thermodynamic Explanation for the Increasing Complexity of our Ecosphere
7. COMPLEXITY EXPLAINED: 14. Biological Complexity at the Edge of Chaos
8. COMPLEXITY EXPLAINED: 6. Emergence of Complexity in Far-from-Equilibrium Systems
9. COMPLEXITY EXPLAINED: 1. What is Complexity?
10. COMPLEXITY EXPLAINED: 5. Defining Different Types of Complexity
11. COMPLEXITY EXPLAINED: 4. The Nature of Information
12. COMPLEXITY EXPLAINED: 11. Cellular Automata
13. COMPLEXITY EXPLAINED: 17. Epilogue

(Note: All previous parts in the Complexity Explained series by Dr. Vinod Wadhawan can be accessed through the ‘Related Posts’ listed below the article.)

Man invented language to satisfy his deep need to complain, opined Lily Tomlin. On a more serious note, the evolution of language, speech, and culture are believed to be some of the causative factors for the rapid evolution of the size and capacity of the human brain. The emergence of human language has been a major milestone in the relentless evolution of complexity on our planet, and has also played a role in the evolution of human consciousness. Apart from the emergence and evolution of language, I also discuss memetics and econophysics in this article.

15.1 Introduction

A mostly Lamarckian process whereby evolution of a transformational nature proceeds via the passage of acquired characters, cultural evolution, like the stellar evolution before it, involves no DNA chemistry and perhaps less selectivity than biological evolution. Culture enables animals to transmit survival kits to their offspring by nongenetic routes; the information gets passed on behaviourally, from brain to brain, from generation to generation, the upshot being that cultural evolution acts much faster than biological evolution.

Eric Chaisson, Cosmic Evolution

According to Richard Dawkins (1989), ‘most of what is unusual about man can be summed up in one word: “culture”.’ Of course, one must make a distinction between ‘culture’ and ’society.’ ‘A society refers to an actual group of people and how they order their social relations. A culture . . . refers to a body of socially transmitted information’ (Barkhow 1989). The term ‘culture’ encompasses ‘all ideas, concepts and skills that are available to us in society. It includes science and mathematics, carpentry and engineering designs, literature and viticulture, systems of musical notation, advertisements and philosophical theories - in short, the collective product of human activities and thought’ (Distin 2005).

15. 2 Evolution of Language

If there had been no speech, then right and wrong, truth and falsehood, good and bad, attractive and unattractive would not have been made known. Speech makes known all this. Worship speech.

Chandogya Upanishad VII-2-1

As far as humans are concerned, language has got to be the ultimate evolutionary innovation. It is central to most of what makes us special, from consciousness, empathy and mental time travel to symbolism, spirituality to morality.

Kate Douglas

Somewhere in the last 100,000 years or so, human beings hit upon language. Human language must have seemed an odd-sounding innovation to the other animals around. But by allowing the expression of arbitrarily complicated concepts, human language allowed people to process information in a highly distributed fashion. The distributed nature of human information processing in turn allowed people to cooperate in new ways, forming groups, associations, societies, companies, and so on. Some of these new forms of cooperation proved strikingly effective, as various forms of distributed information processing, such as democracy, communism, capitalism, religion, and science, took on a life of their own, propagating themselves and evolving over time. It is the richness and complexity of our shared information processing that has brought us this far.

Seth Lloyd, Programming the Universe

It is notable that, on an evolutionary time scale, there has been an exceptionally rapid expansion of brain capacity in the course of evolution of one of the ape forms (chimpanzees?) to Homo sapiens, i.e. ourselves. This has happened in spite of the fact that the genome of humans is incredibly close to that of chimpanzees. The evolution of language, speech, and culture are believed to be some of the causative factors for this rapid evolution of the human brain. Let us see how.

Homo sapiens was preceded by Homo heidelbergensis, which also had a fairly large brain, but was not very effective as a hunter. He was not able to establish ecological dominance over other animals, even after two million years of evolution. The human advantage is believed to have arisen from the emergence of language. ‘No topic is more intriguing and more difficult to address concretely than the evolution of language, but … [it] is almost a kind of sixth sense, since it allows people to supplement their five primary senses with information drawn from the primary senses of others. Seen in this light, language becomes a kind of “knowledge sense” that promotes the construction of extraordinarily complex mental models, and language alone may have provided sufficient benefit to override the cost of brain expansion’ (Klein and Edgar 2002).

The reference to ‘the cost of brain expansion’ here is to the fact that in humans the brain takes up ~20% of the metabolic resources of the body, and the brain tissue requires 22 times more energy than a comparable piece of muscle at rest.

Deacon (1997) emphasizes the big difference between human language (talking) on one hand and the various modes of communication among other live entities: ‘Although other animals communicate with one another, at least within the same species, this communication resembles language only in a very superficial way - for example, using sounds - but none that I know of has the equivalents of such things as words, much less nouns, verbs, and sentences. Not even simple ones.’

Deacon (1997) continues: ‘Though we share the same earth with millions of living creatures, we also live in a world that no other species has access to. We inhabit a world full of abstractions , impossibilities, and paradoxes … We tell stories about our real experiences and invent stories about imagined ones, and we even make use of these stories to organize our lives. In a real sense, we live our lives in this shared virtual world. … The doorway into this virtual world was opened to us alone by the evolution of language, because language is not merely a mode of communication, it is also the outward expression of an unusual mode of thought - symbolic representation. Without symbolization the entire virtual world is … out of reach: inconceivable … symbolic thought does not come innately built in, but develops by internalising the symbolic process that underlies language.’

Homo heidelbergensis had a big brain. But was he also a great symbolic thinker? Probably not. Deacon argues that probably a single symbolic innovation triggered a coevolution of language and brain-size. Greater brain power resulted in a greater capacity to symbolise, speak, think. The cascading effect led to more complex languages and more complex brains. But all this required social interaction and support: ‘Language is a social phenomenon. … [and] … The relationship between language and people is symbiotic.’

Deacon traces the evolution of social complexity by assuming that the early humans were dual-parenting. Since their sense of smell was not very acute (thus ruling out a role for chemical signalling through pheromones), some other type of sexual signalling evolved between the male and the female. This is how social communication originated and evolved as a kind of social hormone.

Other than sex, availability of food is the major factor determining the survival of a species. Males had to cooperate with one another for hunting. Deacon again: ‘Males must hunt cooperatively; females cannot hunt because of their ongoing reproductive burdens; and yet hunted meat must get to those females least able to gain access to it directly (those with young), if it is to be a critical subsistence food. It must come from males … [who] … must maintain constant pair-bonding relationships.’

This need for hunting in groups resulted in the evolution of a social structure implying a symbolisational solution to the problem of survival. This is because symbolic reference, as also speaking and thinking, are basically of a social nature. There was naturally a concomitant evolution of the speech organ (voice box).

Grooming

According to Robin Dunbar, ‘One of the most important ways that primate allies show their affection to each other is by grooming. Grooming not only gets rid of lice and other skin parasites, but it also is soothing. Primates turn grooming into a social currency that they can use to buy the favour of other primates. But grooming takes a lot of time, and the larger the group size, the more time primates spend grooming one another. Gelada baboons, for example, live on the savannas of Ethiopia in groups that average 110, and they have to spend twenty percent of their day grooming one another. … If we had to bond our groups of 150 the way primates do, by grooming alone, we would have to spend about 40 to 45 percent of our total daytime in grooming.’

The primates in the savannas also had to find food, and therefore such a large investment of time in grooming would have caused a non-sustainable work vs. life balance. Language emerged as a better way of bonding.

Evolution of word-speaking species

Humans began with sound language, gradually increasing the vocabulary. But there is a severe limit to how many sound calls you can have which still sound distinct. The next step in the evolution of language was a stringing together of sounds into specific sequences, namely words. Word-speaking species naturally had an evolutionary advantage.

Sentences syntaxing words were the next level of evolving complexity. Brain size increased concomitantly to understand and remember words, syntax, grammar, and sentences (Zimmer 2001): ‘A syntax-free language beats out syntax when there are only a few events that have to be described. But above a certain threshold of complexity, syntax became more successful. When a lot of things are happening, and a lot of people or animals are involved, speaking in sentences wins … Something about the life of our ancestors became complex and created a demand for a complex way in which they could express themselves … A strong candidate for that complexity, as Dunbar and others have shown, was the evolving social life of hominids.’

This social evolution of complexity is the advantage humans have over other animals. They have the capacity to introduce and expand complexity in social life, and development of language is both a cause and an effect of this capacity. As Kate Douglas (2005) said, ‘In a sense, language is the last word in biological evolution. That’s because this particular evolutionary innovation allows those who possess it to move beyond the realms of the purely biological. With language, our ancestors were able to create their own environment - we now call it culture - and adapt to it without the need for genetic changes.’

Whereas humans and chimpanzees have many genes in common, the expression of certain genes is more common in the human brain. Moreover, the brains of newborn humans are far less developed than those of newborn chimpanzees, and the neural networks of human babies are developed over many years of exposure to a linguistic environment. Through a continuous process of unsupervised learning (experimentation), supervised learning (from parents, teachers, etc.), and reinforced learning (the hard-knocks of life, and rewards for certain kinds of action), the child’s brain performs evolutionary computation.

With language came the possibility of emergence of ‘memes.’ Language coevolved with memes.

15.3 Memes and Their Evolution

We are different from all other animals because we alone, at some time in our far past, became capable of widespread generalized imitation. This let loose new replicators - memes - which then began to propagate, using us as their copying machinery much as genes use the copying machinery inside cells. From then on, this one species has been designed by two replicators, not one. This is why we are different from the millions of other species on the planet. This is how we got our big brains, our language and all our other peculiar ’surplus’ abilities.

Susan Blackmore (2000)

It is information that evolves in any type of evolution. The most basic aspects of evolution are replication of information (which involves preservation of the replicated information), and the mode of transmittal of information. Genes preserve biological information, and they use DNA for this. What about culture?

Similar to the gene, which is the unit of biological inheritance, Richard Dawkins (1989, 1998) introduced the notion of the meme, which is the unit of cultural inheritance. A meme may be some good idea, a soul-stirring tune, a logical piece of reasoning, or a great philosophical concept. Dawkins visualized that two different evolutionary processes must have operated in tandem: the classical Darwinian evolution, and another one centred round intelligence, language, and culture. Memes are, roughly speaking, the cultural analogues of genes.

The genes that exist in many copies in a population are those that are good at surviving and replicating. Through a reinforcement effect, genes in the population that are good at cooperating with one another stand a better chance of surviving. Similarly, the fittest set of cooperating memes has a better chance of surviving to form the meme pool of the population. They replicate themselves by imitation or copying (Blackmore 1999, 2000), and also by a variety of other mechanisms (Distin 2005). Cultural evolution and progress occurs through a selective propagation of the fittest set of cooperating memes.

Meme-gene coevolution

Memes evolve, just as genes evolve. In fact, any entities that can replicate, and that have a variation both in their specific features and in their reproductive success, are candidates for Darwinian selection. The coevolution of gene pools and meme pools (through language etc.) resulted in a rapid enlargement of the brain size of Homo sapiens. A large brain size, once attained, resulted in several other capabilities as well.

An important difference between memes and genes is that the speed of cultural evolution (development of ideas, customs, etc.) is far higher than the speed of genetic evolution. Nevertheless, there are several proposed analogies between the two. How far can we carry the gene analogy for understanding the nature of memes? This continues to be a subject of debate. Following Distin (2005), I list here some characteristics of memes.

The essential particulate nature of memes

The most efficient methods of replicating complexity are hierarchical (or modular or particulate). If variation were permitted in every element of a complex structure, then copying processes would lose much of their stability. In genetics, Mendel’s work established the particulateness of genes, namely the clear presence or absence of the effects of these replicators on the world. Something similar is necessary for memes in their role in cultural evolution of complexity. This means that memes must be able to fit into established cultural assemblies without their own informational content being lost or blended in the process. That is, memes must have a certain degree of particulateness, so that the results that they produce are generally of a fixed nature. Their identity should be such that they are discernible packets of information (like the genotype). But, whereas the genotype is distinct and clearly definable, the phenotype (which is a manifestation of the genotype) in biological systems possesses a certain degree of flexibility and variability. Likewise, the manifestations of memes have a certain degree of flexibility that enables their effects to be produced in a variety of cultural contexts. Copied in these ways, information is given the stability to grow and develop in complexity. The breadth and depth of human culture is thus explained by the cumulative replication of particulate information.

In both genetics and memetics, the replicators carry information about the effects that they control. In the case of genes, their independence is maintained via the medium of DNA, which preserves biological information in a form that is replicable and can produce its effects in a variety of contexts. In the case of memes, this role is performed by ‘representational content,’ which is thus the memetic or cultural equivalent of DNA.

Representational content of memes

Memes are specified by their representational content. As representations of a portion of information, memes can be regarded as having a certain content. A representation in the human mind is some piece of our ‘mental furniture’ that carries information about the world. For example, a thought that ‘the object on my desk is a book’ is a mental representation of a bit of the world (i.e. that book). Therefore ‘representational content’ refers to the information that is included in the content of our representations.

It is representational content which accounts for the mechanisms of memetic heredity and for the influence of the memes over their phenotypic effects. Distin (2005) uses the term memetic DNA for the representational content. It provides the mechanism for memetic evolution, just as DNA provides the mechanism for genetic evolution.

How is the representational content fixed in our brains?

Replicators preserve and copy specific portions of information. For memes, we should be able to identify precisely which bits are carried in each replicator. This means pinpointing the exact content of any representation, and this is something determined partly by the various properties of the object or situation being represented. Yet representational content is determined by other factors as well, e.g. by the capabilities and history of the organism doing the representing.

Some organisms are capable of forming representations the content of which is determined by a combination of the relevant properties of that which is represented, and the organism’s own individual and social learning capacities. Such organisms are able, in other words, both to preserve information and to transmit it among themselves.

In the case of complex representations, which have links not only externally to perceptions and behaviour but also internally to other representations, the resultant behavioural flexibility can enable us to track down their content more completely. It should be possible to test all of the links, by altering the associations that the organism encounters, and observing the effects on its behaviour. Only representations with this determinacy of content can count as memes, since a crucial aspect of any replicator is the preservation of given information.

Thus memes are representations which preserve their content in a way that can be copied between generations. As representations, they are specifically those bits of our mental furniture which control our behaviour in response to the information that they carry. In other words, the basis of memes in representational content is precisely what accounts for their ability to exert executive effects on the world.

Multiple representational systems

Representations gain meaning from their context within a representational system (RS), and the uniquely human capacity that lies at the heart of culture is our ability to copy and develop RSs, as well as adding individual representations to our repertoire: the ability, in other words, to meta-represent. Natural languages, as also systems of mathematical and musical notation, are some examples of cultural RSs, and each is peculiarly appropriate to its particular cultural area. Human minds acquire replicators on an ongoing basis throughout their lives, and this means that they can acquire novel RSs as well as novel representations. Amongst these various RSs, the natural languages have primacy: they alone benefit from an innate device for their acquisition. Yet they benefit, too, from the innate ability to meta-represent - and it is this which allows us also to develop nonlinguistic RSs, whose diverse rules and structures are realized in media other than speech. Once these sorts of RS have been taken into account, it becomes clear that there are many concepts that are not available to us until the RS that supports them has been developed.

Human minds and culture

According to Distin (2005), humans are born with a degree of mindedness that includes, for example, the ‘representation instinct’: an ability and tendency to learn and manipulate vast numbers of representations, as well as the various systems in which they are embedded. And this innate mental potential of an infant is realized as a result of exposure to the cultural environment.

Genes preserve and replicate biological information by building vehicles for their own propagation and protection. The effects of the genes are found in the machines that they build for their survival, and their replication also depends ultimately on this same machinery. Memes depend for their replication on a faculty of the human mind that is ultimately of a genetic nature, namely the representation instinct. Organisms, as well as minds, develop via interaction between the innate potential and the environment, and in the case of the mind a crucial part of that environment comprises of the memes. A human mind is thus partly a product of the memes, but only because it has the innate potential to interact with and develop in response to these memes. And culture is the product of human minds, although the preservation of information in representational content ensures that the culture we see today is mostly the result of memes produced by human minds of long ago. The development of human minds depends on a combination of two types of processes: their innate potential is the result of an interaction between genes and the physical environment, and that potential is fulfilled as a result of interaction with memes.

The selfish meme?

Memes are best thought about not by analogy with genes but as new replicators, with their own ways of surviving and getting copied.

Susan Blackmore

Dawkins (1989) described the essence of his ’selfish gene’ theory as the insight ‘that there are two ways of looking at natural selection, the gene’s angle and that of the individual.’ The essence of his selfish meme hypothesis is the insight that there are two ways of looking at cultural change, the meme’s angle and that of the human individual.

One of the most significant implications of the theory of Dawkins’ selfish gene is that the individual organism was not an inevitable outcome of genetic evolution: it so happens that genes have banded together to build survival machines, but the only crucial feature of any form of evolution is the replicator - the unit of selection. Although organisms clearly exist, and have a perspective from which the world of genes is irrelevant to their everyday lives, fundamentally their lives and evolution are determined by that world. According to Distin (2005), no analogous insight arises from the theory of the selfish meme, because memes do not build survival machines. Their replicative mechanisms, and the means of their variation and selection, lie in genetically determined human faculties, and not in vehicles that they themselves build.

Dennett (1991) and Blackmore (1999), however, take the view that we are meme machines, just as we are gene machines. Consequently, they argue that ‘there is no conscious self inside’ those machines; and that ‘a complex interplay of replicators and environment’ is all there is to life. Our sense of self may not be illusory, but our sense of control over the collective products of our minds may well be. Although our minds provide the mechanisms of memetic evolution, there is a very real sense in which the directions of that evolution are independent of us.

15.4 Econophysics

A financial market is a complex system in which a large number of traders interact with one another, and also react to external information, and determine the ‘best’ price for a given item. The time evolution of the price and the number of transactions of a traded item is generally unpredictable. The time series indicating the price variation of an item is found to be essentially indistinguishable from a stochastic or random process. Like other complex systems, financial markets are open systems with many interacting subunits, and the subunits interact nonlinearly.

The efficient-market hypothesis

An efficient market is defined as one in which all the available information is processed instantly when it reaches the market, and in which this fact is immediately reflected in new values of prices of the traded assets. The efficient-market hypothesis (EMH) says that any market is highly efficient in determining the most rational price of a traded item or asset. It was originally formulated in the 1960s. There are two assumptions involved here: (i) the market is efficient; and (ii) the behaviour of traders is strictly rational.

Why does the time series of returns appear to be random? This is because it carries so much information that there are no readily discernible regularities in it. It is, by and large, a nonredundant time series. The information it carries is almost irreducible, or algorithmically incompressible for most practical purposes (cf. Part 4). The EMH requires that the concerned time series for market prices has a dense amount of nonredundant information. Since there are limits on the speed and capacity of our computers, a time series carrying this information is almost indistinguishable from a totally random time series. Of course, analysis of the deviation from a totally random time series is a good way of testing the degree of validity of the EMH in a given situation.

The law of diminishing returns

Suppose there is good demand for a commodity because of its attractive existing price. Naturally, the price will increase. This will then reduce the demand. And a reduced demand will entail a lowering of the price, and so on, till the demand and the price have reached a state of equilibrium. Thus negative feedbacks tend to stabilize an economy, as per conventional economic theory. This law of diminishing returns implies a single equilibrium point for an economy, and such situations are amenable to analytical control.

By and large, resource-based economic activities (e.g. agriculture and mining) tend to follow the law of diminishing returns. By contrast, knowledge-based parts of an economy are generally governed by the law of increasing returns or positive feedback.

The law of increasing returns

As demonstrated by the pioneering studies of Brian Arthur during the 1990s, positive feedbacks often occur in an economy, with the resultant multiple equilibrium points. Small shifts in the economy can get amplified, rather than smothered out. The economy evolves like any open, nonlinear complex system. There can be multiple bifurcations in phase space, and it is difficult to predict which bifurcation branch will be chosen by the market forces. What is more, once the random events select a particular branch or path in phase space, the choice tends to get locked-in, regardless of the advantages of the alternatives. An example is the history of the VCR industry. The market started out with two competing formats, VHS and Beta, selling at about the same price. It appears, in hindsight, that Beta was technically superior. In the beginning there were increasing returns for each format, as their market shares increased. For example, a large number of VHS recorders in the hands of consumers motivated the vendors to stock more prerecorded tapes in the VHS format. This encouraged more people to buy VHS recorders. The same law of increasing returns operated for the Beta format also. In the beginning there were fluctuations in the fortunes of the two competing brands, attributable to factors such as external circumstances, ‘luck’, and corporate manoeuvring. Then, perhaps by chance, increasing returns on early gains by VHS (reduced production costs per unit on increased volumes of production) tilted the game in favour of VHS, driving the other technology out of the market. This is something which could not have been predicted in the beginning.

The law of increasing returns can go beyond the product with which a company started (Arthur 1990): ‘Not only do the costs of producing high-technology products fall as a company makes more of them, but the benefits of using them increase. Many items such as computers or telecommunications equipment work in networks that require compatibility. When one brand gains a significant market share, people have a strong incentive to buy more of the same product so as to be able to exchange information with those using it already.’

Path dependence

In a positive-feedback economy, although the individual transactions are small and essentially random events, they can accumulate by the positive (nonlinear) feedbacks. A number of characteristics or historical antecedents of positive-feedback economies can be listed:

1. In a particular industry, there is often a clustering of firms in a specific geographical location. A different location would have been better, but there is a kind of freezing of historical accidents in what has actually happened. Why? The first firm chooses a location for some logical (or even illogical) reason. The choice of the second firm depends not only on the (real or perceived) merits of that region, but also on the fact that it is profitable to be near the first firm. There is cascading effect because the third firm may be influenced more by the presence of the first two firms in that region, than by the absolute merit of that region; and so on.

2. Railroad gauges are what they are at present because, once a particular choice was made (even arbitrarily), it was economical to stick to that choice everywhere in that region. There is a self-enforcement effect operating here.

3. The initial advantage possessed by a country or a multinational corporation can snowball into total dominance at the global level, until a better or cheaper product overcomes the monopoly. This highlights the importance of industrial research in any knowledge-based economy. Anther important factor is the timing of release of a product.

In the language of evolution of the phase-space trajectory, what we are seeing here are random bifurcations in phase space. Once a branch of a bifurcation gets selected for further time-evolution, there is no going back; there is only a locked-in trajectory along a particular path in phase space. Thus the evolution of a positive-feedback economy has a strong path dependence. This path dependence can cause even hitherto successful economies to become locked into inferior paths of development. There is always a danger that a sound technology, with good long-term potential, may get rejected just because it has a long gestation period and slow initial growth. Similarly, when two new technologies compete, the one with a better initial acceptance by people may oust the other from the market, even when the other technology is inherently better (as shown by later events). Early superiority or ’selectional advantage’ is no guarantee of long-term fitness. Arthur (1990) cites the example of how the U.S. nuclear-power programme got ‘phase-locked’ into the light-water-cooled reactors option, even though the high-temperature, gas-cooled, reactor designs may be inherently superior.

The bottom line is that, unlike negative-feedback economies, positive feedback economies do not head for a unique equilibrium; their phase-space trajectory is not path-independent. Like in a chaotic system, even identical-looking initial conditions can lead to divergence in trajectories, simply because even small events or errors may get hugely amplified as time passes. Long-term accurate forecasting then becomes difficult, if not impossible.

15.5 Concluding Remarks

The emergence of humans has sharply accelerated the rise of overall complexity of our Earth. This has happened, and is still happening at an ever-increasing pace, because of the evolution of cultural complexity. A major reason for this is the very high level of intelligence possessed by humans. I shall discuss intelligence and consciousness in the next article in this series.

Human beings and their institutions process more energy per unit mass than do stars or galaxies.

Eric Chaisson, Cosmic Evolution

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS

Related posts:

1. COMPLEXITY EXPLAINED: 13. Evolution of Biological Complexity
2. COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity
3. COMPLEXITY EXPLAINED: 8. Evolution of Chemical Complexity
4. COMPLEXITY EXPLAINED: 16. Evolution of Intelligence and Consciousness
5. COMPLEXITY EXPLAINED: 1. What is Complexity?
6. COMPLEXITY EXPLAINED: 6. Emergence of Complexity in Far-from-Equilibrium Systems
7. COMPLEXITY EXPLAINED: 14. Biological Complexity at the Edge of Chaos
8. COMPLEXITY EXPLAINED: 5. Defining Different Types of Complexity
9. COMPLEXITY EXPLAINED: 3. Thermodynamic Explanation for the Increasing Complexity of our Ecosphere
10. COMPLEXITY EXPLAINED: 2. Swarm Intelligence
11. COMPLEXITY EXPLAINED: 10. What is Life?
12. COMPLEXITY EXPLAINED: 11. Cellular Automata
13. COMPLEXITY EXPLAINED: 4. The Nature of Information

Living entities have evolved to possess enormous amounts of order and complexity. Can Darwinian natural selection alone explain this order? Probably not. We must also take note of the inherent tendency of all complex adaptive systems to move towards self-organized states of optimum order. Biological and other kinds of complexity thrive best at the ‘edge of chaos,’ and this is where evolutionary forces usually operate. The dynamics of complexity around the edge of chaos is ideally suited for evolution that does not destroy self-organization.

14.1 Introduction

Self-organization is a characteristic feature of any open, far-from-equilibrium complex system. As emphasized by Stuart Kauffman, it is on this existing order that Darwinian natural selection operates and further adapts it to the environment. In other words, natural selection is not the sole source of order in biology. Being complex systems, biological entities tend to self-organize anyway. As Kauffman said in At Home in the Universe (1995):

I suspect that the fate of all complex adaptive systems in the biosphere — from single cells to economies — is to evolve to a natural state between order and chaos, a grand compromise between structure and surprise. Here, at this poised state, small and large avalanches of coevolutionary change propagate through the system as a consequence of the small, best choices of the actors themselves, competing and cooperating to survive.

He mentions ‘chaos.’ Let us begin by getting familiar with some elementary concepts in chaos theory.

14.2 Elements of Chaos Theory

A chaotic system is characterized by unpredictable evolution in space or time, even though the differential equations or difference equations describing it are deterministic (if we can neglect noise). The motions in a chaotic system are unstable, and this instability leads to a sensitive dependence on initial conditions. In the language of algorithmic information theory (cf. Part 4), chaos has the largest (but finite) degree of complexity.

Several basic features of chaos can be illustrated by recourse to the so-called logistic equation. It embodies a 1-dimensional feedback system, and was formulated as early as in 1845 to model the population dynamics of a species. The question posed was: How will the population of a species, confined to a certain geographic area, vary from year to year? Obviously, the population in a year t+1 will depend on the population in the previous year t: x_t+1 = kx_t; here k is some suitable constant of proportionality (or a control parameter). This equation simply models the fact that the larger the population is, the more is the number of offspring it would produce. But this cannot be the full story. There are other factors to consider. For example, if the population in the year t becomes too large, there can be an extra decimation of the numbers, either by predators, or due to shortage of food, or due to the altered competition in the reproduction dynamics. Therefore a more realistic logistic equation is as follows:

_{xt+1 = k xt (1-xt)}

Even this is only a simplistic model of population dynamics, and more sophisticated models have been proposed and investigated. But it is sufficient for providing a good insight into how chaos sets in under certain conditions.

It is convenient to describe the population x as a fraction of the largest value, N, that it can attain. Then x varies between 0 and 1. For any fixed value of k, a plot of x_t+1 against x_t gives an inverted parabola, and x_t = 0.5 corresponds to the top point on the parabola. Putting x_t = 0.5 in the above equation gives x_t+1 = k/4. Since x cannot be larger than 1, the largest value that the control parameter k can have is 4.

A fascinating variety of dynamics, including chaos, is observed as k takes various values in the range 0 to 4. Suppose we imagine a situation for the population in which k is less than 1. Suppose x₀ is the value of the population in a particular year t = 0. Then we can use the logistic equation to calculate the expected population x₁ in the next year. Then x₁ can be put back into the logistic equation to obtain the population value x₂ for the following year. We can carry out such iteration repeatedly.

Figure (a) shows how the population will change in successive years if we take k = 0.95. We find that the population eventually becomes zero. This eventual or final value of x, denoted by x*, is an attractor; attractors were introduced in Section 12.4 (Part 12). There is a basin of attraction such that every starting value x₀ is eventually drawn towards the attractor x* = 0. Thus if k < 1, we have a fixed-point attractor at the zero value of the population; the conditions are too inimical for the population to survive.

A different population dynamics is predicted by the logistic equation for 1 < k < 3. Now x* is not zero; rather it increases from a near-zero value to ~0.667 as the control parameter k is increased from 1 to 3. Figure (b) shows the results for k = 2.8.

A fundamentally different kind of dynamics emerges for 3 < k < 4: The population trajectory no longer converges to a single fixed point or attractor in phase space. Further, the trajectory becomes increasingly sensitive to the value of k. For k = 3.4 (results shown in Figure (c)), the trajectory has not one but two fixed points: one at x₁* ≈ 0.452 and the other at x₂* ≈ 0.842. This means that, from year to year, the population oscillates between ~45% and ~84% of the maximum possible value. We now have a two-point attractor (Figure (c)). We describe such an oscillating system as having a period 2.

This periodic attractor is actually just the beginning of still more complex dynamics as the value of k is increased. There is a critical value k ≈ 3.4495 beyond which we get a four-point attractor. For example, for k = 3.5 we get x₁* ≈ 0.875, x₂* ≈ 0.383, x₃* ≈ 0.827, x₄* ≈ 0.501. Such successive bifurcations of each attractor into two, such that there are 4, 8, 16, 32, .. etc. fixed points, occur with smaller and smaller increases in k.

We move into the chaotic regime of complexity for values of k above ~3.57. The periods now double every time k is increased by even an infinitesimally small amount. The number of points that comprise the attractor is now extremely large, and the trajectory looks quite erratic (Figure (d)), although there are some ranges of k values for which there is apparent stability. I have taken these numbers and the figures from the book Chaos Theory Tamed by G. P. Williams (1997), which should be consulted for many other fascinating details.

If the periods are going to double even for infinitesimally small increases in the value of k, there are two situations to face, one practical and the other computational. The practical aspect is that the logistic equation, or any mathematical model for explaining reality, has to finally interface with (or explain) experimental data, and such data can never be obtained with infinite accuracy. The computational aspect is that, even if we have access to infinitely accurate data, no computer can perform calculations based on the modelling equation(s) with infinite precision. It is irrelevant whether or not the computer program written for modelling a complex system is a simple one. This is why one makes the statement that a chaotic system, or rather a system in a chaotic regime, has the largest (though finite) degree of complexity.

Having achieved a nodding acquaintance with chaos theory, let us now hark back to the work of a stalwart in the field of complexity, namely Stephen Wolfram.

14.3 Wolfram’s Four Universal Classes of Cellular Automata

I introduced Wolfram’s work on cellular automata (CA) in Section 11.4 (Part 11). An extensive empirical analysis by him of all 1-dimensional CA showed that the patterns generated by them (even when we start from random or disordered initial conditions) can be generally divided into four distinct classes:

In Class 1, evolution from almost any initial state leads finally to a unique homogeneous state. This is like the occurrence of a ‘limit point’ or attractor in the phase space of a nonlinear dynamical system.

In Class 2, there is ultimately a sequence of simple stable or periodic structures. This corresponds to the occurrence of ‘limit cycles’ in phase space.

Class 1 patterns are repetitive, and Class 2 patterns are nested. Both are predictable after their repetitive or nested nature has been discerned, and are therefore computationally reducible. The black and white figure I showed in Section 11.4 is an example of a class 2 CA, and is reproduced here again.

Image Source : wolframscience.com

Class 3 CA exhibit chaotic or aperiodic long-time behaviour. Such CA grow indefinitely at a fixed speed. Their patterns are often self-similar or scale-invariant. They are characterized by a fractal dimension, with log₂3 or ~1.59 as the most common value for the dimension.

Class 4 CA are the most interesting from the point of view of complex behaviour. For them the pattern grows and contracts irregularly. There are complicated localized structures, some of which propagate with time. Therefore their long-time behaviour is undecidable.

14.4 Langton’s ‘Edge of Chaos’ Idea

Evolution thrives in systems with a bottom-up organization, which gives rise to flexibility. But at the same time, evolution has to channel the bottom-up approach in a way that doesn’t destroy the organization. There has to be a hierarchy of control - with information flowing from the bottom up as well as from the top down. The dynamics of complexity at the edge of chaos seems to be ideal for this kind of behaviour.

Doyne Farmer

I introduced Neumann’s self-reproducing CA in Section 11.6. Christopher Langton (1989) (and also Norman Packard) extended the CA approach to the field of artificial life (AL) (cf. Section 10.4 in Part 10) by introducing evolution into the Neumann universe. In the self-reproducing CA created by Langton, a set of rules (the GTYPE) specified how each cell interacted with its neighbours, and the overall pattern that resulted was the PTYPE. The local rules could evolve with time, rather than remaining fixed. This pioneering work was a fine example of adaptive computation.

Langton also correlated his work on AL with Wolfram’s four universal classes of CA. We have seen above that small values of the control parameter k in the logistic equation give rise to nonchaotic behaviour. This is similar to the dynamics described by Wolfram’s Class 1 and Class 2 CA. And sufficiently large values of k result in totally chaotic dynamics, which corresponds to Class 3 CA. Langton investigated the introduction of a parameter similar to k into the rules controlling CA behaviour to check this analogy more clearly, and particularly to investigate the connection between Class 4 CA on one hand, and partially chaotic systems on the other.

After a number of trials, he came upon a parameter λ for the CA rules which corresponded to the control parameter k of the logistic equation. This λ was defined as the probability that any cell in the CA will be ‘alive’ after the next time step. In the nested CA figure above, we have chosen the colours black and white, which we can now relate to ‘alive’ and ‘dead.’ For example, if λ = 0 in the rule governing the evolution of a particular set of CA, all cells would be white or dead after one time step. The same would be true if λ = 1.

In his computer experiments, Langton found that, as expected, λ = 0 corresponds to Class 1 rules. The same was true for very small nonzero vales of λ.

As this control parameter was increased gradually, Class 2 features started appearing at some stage, with characteristic oscillating behaviour. With increasing values of the control parameter, the oscillating pattern took longer and longer to settle down.

Taking λ = 0.5 resulted in totally chaotic behaviour, typical of the Wolfram Class 3.

Langton found that clustered around the critical value λ ≈ 0.273 were Class 4 CA.

Thus, as the control parameter increased from zero onwards, he saw a transition from ‘order’ to ‘complexity’ to ‘chaos.’

The next conceptual jump Langton made was to equate this qualitative change of behaviour of CA with a phase transition. Recall that a phase transition can occur in a material when some control parameter like temperature is varied (e.g. water changes to ice as it is cooled through its freezing point). Langton realized that his control parameter λ plays a role in determining the dynamics of CA that is similar to the role played by temperature in a phase transition. At low temperatures a material is solid, say in a crystalline state, which is an ordered state. At high temperatures we have a fluid state (liquid or vapour), which signifies chaos or disorder. Langton drew the analogy with such phase transitions for describing the Class 4 behaviour in CA which sets in for values of λ around 0.273.

Phase transitions in a material are represented in phase diagrams, in which there are lines or boundaries which separate one phase from another. Langton gave the corresponding phase boundary in the Neumann universe (a kind of phase space) the name edge of chaos. We should remember, however, that this ‘edge’ or boundary is not a sharp one. It is more like a thin or thick membrane in phase space, with chaotic behaviour on one side, and ordered behaviour on the other side of the membrane. There is a gradation from chaos to complexity to order across the membrane in phase space. And complex behaviour is at its most versatile within the membrane.

Many instances can be cited for the gradation from order to complexity to chaos. Even a simple computational algorithm like the Game of Life (mentioned in Section 11.3) is a universal computing device. The Game of Life is independent of the computer used for running it, and exists in the Neumann universe, just as other Class 4 CA do. As explained by Wolfram, such CA are capable of information processing and data storage etc. They are a mixture of coherence and chaos. They have enough stability to store information, and enough fluidity to transmit signals over arbitrary distances in the Neumann universe.

There are analogies of this, not only with computation, but with life, economies, and social systems also. After all, they are all just a series of computations. Life, for example, is nothing if it cannot process information. And life strikes a right balance between too static a behaviour and excessively chaotic or noisy behaviour.

14.5 Biological Complexity at the Edge of Chaos

The occurrence of complex phase-transition-like behaviour in the edge-of-chaos domain is something very common in practically all branches of human knowledge. Kauffman (1969), for example, recognized it in genetic regulatory networks (cf. Section 12.5 in Part 12). In his work on such networks in the 1960s, he discovered that if the connections were too sparse, the network would just settle down to a ‘dead’ configuration and stay there. If the connections were too dense, there was a chaotic churning around. Only for an optimum density of connections did the stable state cycles arise.

Similarly, in the mid-1980s, Farmer, Packard and Kauffman found in their autocatalytic-set model (Section 9.4) that when parameters such as the supply of ‘food’ molecules, and the catalytic strength of the chemical reactions etc., were chosen arbitrarily, nothing much happened. Only for an optimum range of these parameters did a ‘phase transition’ to autocatalytic behaviour set in quickly.

Many more examples can be given: Coevolutionary systems; economies; social systems; etc. A right balance of defence and combat in the coevolution of two species ensures the survival and propagation of both. Similarly, the health of economies and social systems can be ensured only by a right mix of feedbacks and regulation on the one hand, and plenty of flexibility and scope for creativity, innovation, and response to new conditions on the other. The dynamics of complexity around the edge of chaos is ideally suited for evolution that does not destroy self-organization.

But why and how do complex systems move towards the edge-of-chaos regime, and then manage to stay there? Per Bak supplied a clear and profound answer in terms of his important notion of self-organized criticality.

14.6 Self-Organized Criticality

Per Bak and coworkers (1996) argued that a particularly important consequence of self-organization (in a complex adaptive system) is the occurrence of self-organized criticality (SOC).

Let us consider a tabletop on which grains of sand are drizzling down steadily. To start with, the flat sandpile just grows thicker with time, and the sand grains remain close to where they land. A stage comes when the sand starts cascading down the sides of the table. The pile gets steeper and steeper with time, and there are more and more sandslides. With time the sandslides (avalanches or catastrophes) become bigger and bigger, and eventually some of the sandslides may span all or most of the pile. The average slope now becomes constant with time, and we speak of a stationary state.

This is a system far removed from equilibrium. Its behaviour has become collective. Falling of just one more grain on the pile may cause a huge avalanche (or it may not). The sandpile is then said to have reached a self-organized critical state. The edges and surfaces of the grains are interlocked in a very intricate pattern, and are just on the verge of giving way. Even the smallest perturbation can lead to a chain reaction (avalanche), which has no relationship to the smallness of the perturbation; the response is unpredictable, except in a statistical-average sense. The period between two avalanches is called a period of tranquillity (stasis) or ‘punctuated equilibrium.’

In a system in an SOC, big avalanches are rare, and small ones frequent. And all sizes are possible. There is power law behaviour: the average frequency of occurrence, N(s), of any particular size, s, of an avalanche is inversely proportional to some power τ of its size: N(s) = s^-τ. A log-log plot of this power-law equation gives a straight line, with a negative slope determined by the value of the exponent τ. The system is scale-invariant: Usually the same straight line holds for all values of s. Large catastrophic events (corresponding to large values of s) are consequences of the same dynamics which causes small events.

This is complex behaviour, and according to Bak, large avalanches, not gradual variation, can lead to qualitative changes of behaviour, and may form the basis for emergent phenomena and complexity. Bak (1996) gave several examples to make the point that Nature operates at the SOC state (or equivalently at the edge-of-chaos state). According to him, even biological evolution is an SOC phenomenon.

How do systems reach the SOC state, and then tend to stay there? The sandpile experiment provides an answer. Just like the constant input drizzle of sand in that system, a steady input of energy, or water, or electrons, can drive systems towards criticality, and then they self-organize into criticality by repeated spontaneous pullbacks from super-criticality, so that they are always poised at or near the edge between chaos and order.

We discussed beehives and ant colonies in Part 2. They are again nothing but examples of self-organization in open mutually-interacting systems. Many more examples of such ‘out of control’ complex adaptive systems exist.

14.7 Further Evolution of Complexity at the Edge of Chaos

The next question is: What do complex systems do when they have reached the edge of chaos? In the phase space of the dynamical system, the edge of chaos is a thin membrane, a region of complexity separating the ordered regime from the chaotic regime.

I introduced complex adaptive systems (CASs) formally in Part 5 of this series. John Holland (1998) pointed out the occurrence of ‘perpetual novelty‘ in a CAS, and said that this essentially amounts to saying that the system moves around in the edge-of-chaos membrane. But that is not all. The moving around can actually take the system to states of higher and higher sophistication of structure and complexity. Learning and evolution not only take a CAS towards the edge-of-chaos membrane in phase space, they also make it move within this membrane towards states of higher and higher complexity. The ultimate reason for this, of course, is that the universe is ever expanding, and there is therefore a perpetual input of free energy or negative entropy into it.

Farmer (1986) gave the example of the autocatalytic-set model (which he proposed along with Packard and Kauffman) to further illustrate the point about perpetual novelty and the ever-increasing degree of complexity of a CAS. When certain chemicals can collectively catalyze the formation of one another, their concentrations increase by a large factor spontaneously, far above the equilibrium values. This implies that the set of chemicals as a whole emerges as a new ‘individual’ in a far-from-equilibrium configuration. Such sets of chemicals can maintain and propagate themselves, in spite of the fact that there is no genetic code involved. In a set of experiments, Farmer and colleagues tested the autocatalytic model further by allowing occasionally for novel chemical reactions. Mostly such reactions caused the autocatalytic set to crash or fall apart, but the ones that crashed made way for a further evolutionary leap. New reaction pathways were triggered, and some variations got amplified and stabilized. Of course, the stability lasted only till the next crash. Thus a succession of autocatalytic metabolisms emerged. Apparently, each level of emergence through evolution and adaptation sets the stage for the next level of emergence and organization.

14.8 Concluding Remarks

It is often not realized by Darwinists and neo-Darwinists that natural selection alone cannot lead to such high levels of order and complexity as seen in living organisms. A high degree of order already exits in complex adaptive systems because of their self-organization and perpetual-novelty tendencies. Natural selection only hones this order to still higher levels of complexity.

The self-organization feature of complex adaptive systems may worry the Creationists some more. They have been busy attacking Darwin and his followers for the ‘blasphemies,’ and have been trying to argue that the fascinating degree of order observed in living creatures cannot possibly be the result of a series of ‘accidents’ in the form of mutations etc. The fact is that, as emphasized by Stuart Kauffman and others, Darwin or no Darwin, complex adaptive systems have the fundamental property that they self-organize into states of high (and ever-increasing) degree of order, so long as they are able to exchange matter and energy with the surroundings. Darwinian natural selection does lead to some increase of complexity and order but, by and large, it only hones the already available order and complexity to help a population adapt itself to the prevailing conditions.

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS

Related posts:

1. COMPLEXITY EXPLAINED: 13. Evolution of Biological Complexity
2. COMPLEXITY EXPLAINED: 3. Thermodynamic Explanation for the Increasing Complexity of our Ecosphere
3. COMPLEXITY EXPLAINED: 6. Emergence of Complexity in Far-from-Equilibrium Systems
4. COMPLEXITY EXPLAINED: 15. Evolution of Cultural Complexity
5. COMPLEXITY EXPLAINED: 8. Evolution of Chemical Complexity
6. COMPLEXITY EXPLAINED: 5. Defining Different Types of Complexity
7. COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity
8. COMPLEXITY EXPLAINED: 1. What is Complexity?
9. COMPLEXITY EXPLAINED: 11. Cellular Automata
10. COMPLEXITY EXPLAINED: 17. Epilogue
11. COMPLEXITY EXPLAINED: 12. The Likely Origins of Life
12. COMPLEXITY EXPLAINED: 2. Swarm Intelligence
13. COMPLEXITY EXPLAINED: 10. What is Life?

In any evolutionary process, what evolves is complexity. Chemical complexity evolved till some of it became indistinguishable from biological complexity. Evolution of biological complexity is determined by two main factors: natural selection (made famous by Charles Darwin), and self-organization. I focus on the natural-selection aspect of biological evolution in this article.

13.1 Darwinian Evolution

The greatest single contribution to the subject of complexity was made (unwittingly, perhaps) by Charles Darwin. The year 2009 marked the second birth centenary of Darwin, as also 150 years of the publication of his celebrated book On the Origin of Species by Means of Natural Selection.

Living organisms are open systems, i.e. they are constantly exchanging matter and energy with the environment. There is a fair amount of dynamic equilibrium between a living organism and its surroundings. The organism cannot survive if this equilibrium is disturbed too much, or for too long. The fact that an organism survives implies that, in its present form, it has been able to adapt itself to the environment. If the environment changes slowly enough, living entities can evolve (over a long enough time period) a new set of capabilities or features which enable them to survive even under the changed conditions. Over long periods of such evolutionary change, creatures may even develop into new species. This was the message of Charles Darwin’s (1859) bold theory of evolution through cumulative natural selection. He demonstrated that adaptation to the environment was a necessary outcome of the exchange processes going on between organisms and their surroundings. A consequence of his theory was that all living organisms are the descendants of one or a few simple ancestral forms.

Darwin started with the observation that, given enough time, food, space, and safety from predators and disease etc., the size of the population of any species can increase in each generation. But this indefinite (exponential) increase does not actually occur, i.e. the so-called ‘biotic potential’ of a species is actually never realized. In fact, usually only a very tiny fraction of the biotic potential is realized, meaning that only a small minority of the offspring reach maturity to produce the next generation of offspring; the rest die prematurely.

Thus, there must be limiting factors in operation. Influenced by Malthusian ideas, Darwin imagined that if, for example, available food is limited, only a fraction of the population can survive and propagate itself. [Of course, it is now known that limited resources are seldom the primary factor influencing the course of evolution.] What decides who will survive and who will not?

Darwin’s answer was that, since not all individuals in a species are exactly alike (i.e. there is variation in the population), those which are better suited to cope with the prevailing conditions will stand a better chance of survival (survival of the fittest). The fittest individuals not only have a better chance of survival, they are also more likely to procreate. Thus, attributes conducive to survival get ‘naturally selected’ at the expense of less conducive attributes. And the effects of this natural selection accumulate over time. This is the process of cumulative natural selection recognized by Darwin.

It is also observed that children tend to resemble their parents to a substantial extent. The progeny of better-adapted individuals in each generation, which survive and leave behind more offspring than others, acquire more and more of those features which are suitable for good adaptation to the existing or changing environment. A species perfects itself, or adjusts itself, for the environment in which it must survive, through the processes of both cumulative natural selection and inheritance.

Thus there are four basic features of Darwinian evolution:

1. Variability and variety in members of a population in the matter of coping with a given environment.
2. Inheritance of this variation by the next generation, with random modifications.
3. Differential survival and reproductive success of individual members of this new generation in the given environment.
4. Establishment of a new population more adapted to the environment, possessing new variations to pass onto the next generation.

13.2 Neo-Darwinism

Darwin’s main postulate was that a species evolves because natural selection acts on small inheritable variations in the members of the species. But it was argued by his opponents that, since a species is also characterized by interbreeding among its members, such small variations should get averaged away. Darwin had no answer to counter this because the actual mechanism of inheritance was not known at that time. The answer in fact had been provided in 1865 (i.e. during the lifetime of Darwin, but apparently unknown to him) by the work of Gregor Mendel, the founder of the subject of genetics. We now know that the genotype or genome of an organism is its genetic blueprint. It is present in every cell of the body of the organism. The phenotype, on the other hand, is the end-product (the organism) which emerges through execution of the instructions carried by the genotype. It is the phenotype that is subjected to the battle for survival, but it is the genotype which carries the accumulated evolutionary benefits to succeeding generations. The phenotypes compete, and the fittest among them have a higher chance of exchanging genes among themselves.

Mendel’s laws of genetics were rediscovered independently by quite a few workers. One of them was the Dutch botanist Hugo de Vries, who not only rediscovered Mendel’s laws for the inheritance of dominant and recessive characteristics, but also genetic mutations. These were sudden (unexplained) changes of form which could be inherited by the offspring.

The present, post-Darwinian picture is that the inherited characteristics of the progeny are caused by genes. In sexually reproducing organisms, each parent provides one complete set of genes to the offspring. Genes are portions of molecules of DNA, and their specificity is governed by the sequences in which their four bases (adenine (A), thymine (T), guanine (G), and cytosine (C)) are arranged. The double-helix structure of DNA, together with the restriction on the pairing of bases comprising the DNA molecule to only A-T and G-C, provides a mechanism for the exact replication of DNA molecules. The DNA sequence on a gene determines the sequence of amino acids in the specific proteins created by the live organism.

Genes programme embryos to develop into adults with certain characteristics, and these characteristics are not entirely identical among the individuals in the population. Genes of individuals with characteristics that enable them to reproduce successfully tend to survive in the gene pool, at the expense of genes that tend to fail. This feature of natural selection at the gene level has consequences which become manifest at the organism or phenotype level. Cumulative natural selection is not a random process.

If like begets like (through inheritance of characteristics), by what mechanism do slight differences arise in the gene pool of successive generations so that the species evolves towards evolutionary novelty? Apart from crossover (where applicable), one mechanism is that of mutations. Mutations, brought about by radiation or by chemicals in the environment, or by any other agents causing replication errors, change the sequence of bases in the DNA molecules comprising the genes.

In organisms in which sexual reproduction has been adopted as the means for procreation, since the genetic material has two sources instead of one (namely, the two parents), the occurrence of variations in the offspring is higher. The genes from the parents are reshuffled in each new generation. This increases the evolutionary plasticity of the species. However, not all differences in individuals in a population are due to the genetic makeup. Factors such as nutrition also play a role. The observable characteristics of an organism, i.e. its phenotype, are determined both by the genetic potentiality and by the environment.

If all living beings have the same or only a few ancestors, how have the various species arisen? The Darwinistic answer lies in isolation and branching, aided by evolution. Migrations of populations also play a role in the evolutionary development of species. If there are barriers to interbreeding, geographical or otherwise, single populations can branch and evolve into distinct species over long enough periods of time. Each such branching event is a speciation: A population accidentally separates into two, and they evolve independently. When separate evolution has reached a stage that no interbreeding is possible even when there is no longer any geographical or other barrier, a new species is said to have originated.

The term neo-Darwinism essentially connotes a modification of the original ideas of Darwin in the light of later knowledge about the mechanism of transmittal of genetic information from one generation to the next. Margulis and Sagan (2003), who disagree with this neo-Darwinistic view of the origin of species, have summed up neo-Darwinism as follows (in their book Acquiring Genomes: A Theory of the Origins of Species): ‘All organisms derive from common ancestors by natural selection. Random mutations (heritable changes) appear in the genes, the DNA of organisms, and the best “mutants” (individuals bearing the mutations) in competition with the others, are naturally selected to survive and persist. The unsuited offspring die - they tend to be called “unfit” - with fitness, a technical term, referring to the relative numbers of offspring left by an individual to the next generation. The most fit, by definition, produce the largest number of offspring. The mutant variations then leave more offspring, and populations evolve; that is, they change through time. When the number of changes in the offspring accumulates to recognizable proportions, in geographically isolated populations, new species gradually emerge. When sufficient numbers of changes in offspring populations accumulate, higher (more inclusive) taxa gradually appear. Over geological periods of time new species and higher taxa (genera, families, orders, classes, phyla, and so on) are easily distinguished from their ancestors.’

13.3 Lamarckism

When Darwin published his theory of biological evolution, the field of genetics had not yet taken shape. Naturally, Lamarck, whose work on biological evolution preceded that of Darwin, was also unaware of the crucial role of the genetic mechanism in the evolution of species. We now have the familiar concepts of genotype and phenotype. The term genotype (or genome) refers to the genetic constitution of an organism. It is the genetic blueprint encoded in its strings of DNA chains. Phenotype, on the other hand, signifies the characteristics manifested by an organism; it is the structure created by the organism from the instructions in its genotype. The phenotype is the organism itself. Genotypes correspond to the ’search space,’ and phenotypes to the ’solution space.’

Biological evolution is generally believed to be all Darwinian. Namely, variety in a population means that some individuals have a slight evolutionary advantage with respect to a particular characteristic, which is therefore more likely to be passed on to the next generation if it helps in the survival and propagation of the species. Over time, this characteristic gets strengthened and is easily noticeable in the phenotype. Lamarck’s theory of evolution, or Lamarckism, on the other hand, was based on two premises: the principle of use and disuse; and the principle of inheritance of acquired characteristics (without involving the genotype).

But can the environmental conditions in which the parents live indeed affect the genetic characteristics of the offspring? ‘No’ according to the neo-Darwinian theory of evolution, and ‘Yes’ according to the theory of Lamarck. The Lamarckian viewpoint of inheritance of acquired characteristics is not acceptable in modern biology because it runs counter to the central dogma of modern molecular genetics, according to which information can flow from DNA to proteins (or from genotype to phenotype), but not from proteins to DNA (or from phenotype to genotype).

Although Lamarckism is unacceptable for explaining biological evolution, nothing prevents us from using it in artificial evolution (i.e. inside a computer) and exploiting the much higher speed it may offer for reaching an end-goal.

13.4 Epigenetics

The new (or rather currently hotting up) field of research called epigenetics has brought us dangerously close to Lamarckism, without violating the central dogma of molecular biology. It is now clear that changes other than those in the sequence of nucleic acids in DNA, acquired during the lifetime of parents or grand-parents, can indeed be inherited. Gene expression or interpretation can be influenced by molecules hitchhiking on genes. This heritable non-genetic hitchhiking is called epigenetic inheritance.

The DNA in the cell of an organism carries the genetic blueprint for the synthesis of various proteins needed by the organism. Some of these proteins even help in the synthesis of other proteins via the instructions embodied in the nucleotide sequence along the DNA chain. But there is no way proteins by themselves can engineer the synthesis of the nucleic acids comprising the DNA. Thence the central dogma of molecular biology that information can flow from nucleic acids to proteins, but not from proteins to nucleic acids. Lamarckism in its original form is unacceptable in the theory of biological evolution because it implies that the information acquired by an organism during its life time, and therefore incorporated in its phenotype only (e.g. as proteins), can be somehow transmitted to the offspring via the genotype.

But there is a ‘loophole’ in this argument! Genes, which are nothing but portions of the long sequence of nucleic acids in the DNA chain, contain the coded information for synthesizing various proteins. And we have known since the days of Mendel that not all genes are active all the time. Some genes are dominant, while others are recessive. One of the Mendelian laws of genetics is that it is the combination of dominant and recessive genes inherited from the parents that dictates the characteristics (phenotype) of an offspring. The dominant genes are the active or ’switched on’ genes, and recessive genes are the ’switched off’ genes. We saw in Section 12.3 (Part 12) how the presence of certain hormones can influence gene expression, and once a gene has been switched on by the presence of a hormone, it acts as a switch which can alter the ‘on’ or ‘off’ states of other genes. So hormones are one example of what can influence gene activity.

But hormones, after all, are nothing but chemical compounds. Can other chemicals, for example those in the diet of an organism, also influence gene expression? The answer is ‘Yes’. And not just food, but even the mental state of an animal can be responsible for the secretion of chemicals which can influence gene expression. From the point of view of genetics and transmittal of acquired characteristics, the influence on the genotype of a parent should be of an irreversible nature; only then can it affect the progeny in a permanent manner; then only can we have an inheritance of acquired characteristics by the progeny. This is the subject matter of the field of epigenetics.

Epigenetics is the study of changes in gene function that do not depend on changes in the primary DNA sequence, and depend on stable, heritable marking of DNA. Epigenetic effects influence the phenotype, without changing the genotype.

One particular heritable marking of DNA that has been investigated substantially is that of methylation, i.e. attachment of the -CH₃ group to one or more nucleic acids along the DNA chain. It has been found, for example, that methylation is quite frequent in cancer cells and it is difficult to distinguish from mutations. Methylation affects gene expression, and, as a feedforward mechanism, can have serious transgenerational effects. Epigenetic changes can be passed through the germ line for many generations. And epigenetic changes can occur throughout the life time of an individual: Methylation can turn some genes off, and demethylation can turn other genes on.

The Human Genome Project was completed a few years ago. It has mapped out the sequence of all the three billion nucleotide pairs comprising the human DNA. And the Human Epigenome Project has been already started. Its goal is to add an indicator to every spot on the DNA where methyl markers can attach and change the gene expression there.

13.5 Theories of the Origins of Species

Ironically the popular evolutionist’s view that organisms evolve by the accumulation of random mutation best describes the evolutionary process in bacteria. All of the larger, more familiar organisms originated by symbiont integration that led to permanent associations.

Margulis and Sagan (2002)

Indeed, as Wallin wrote in 1927, ‘It is a rather startling proposal that bacteria, the organisms which are popularly associated with disease, may represent the fundamental causative factor in the origins of species.’ We agree.

Margulis and Sagan (2002)

The classical viewpoint that speciation occurs, i.e. new species arise, as a result of the cumulative effect of mutations, has been strongly contested by Lynn Margulis. According to Margulis and Sagan (2002), ‘No evidence in the vast literature of heredity change shows unambiguous evidence that random mutation itself, even with geographical isolation of populations, leads to speciation. Then how do new species come into being? How do cauliflowers descend from tiny, Mediterranean cabbagelike plants, or pigs from wild boars?’ Their answer is that species arise largely by the acquisition of entire genomes through symbiogenesis.

Margulis’s stance has raised debate. Ernst Mayr wrote an appreciative foreword to the book by Margulis and Sagan (2002). But the foreword also said this: ‘Speciation - the multiplication of species - and symbiogenesis are two independent, superimposed processes. There is no indication that any of the 10,000 species of birds or the 4,500 species of mammals originated by symbiogenesis.’ Contrast this with the statement of Rachel Nowak (2005): ‘Symbiosis has popped up so frequently during evolution that it is safe to say that it’s the rule, not the exception.’

Life appeared on Earth during what F. Niele (2005) calls the thermophilic regime of energy in the history of the Earth. This form of life comprised of microorganisms that thrived in hot conditions. Chemical evolution and diversification of molecular structure had occurred, and closed-loop autocatalytic reactions had led to the creation of life-like molecules of increasing complexity. Things progressed to a point where the forebears of DNA started appearing, which had potential for replication. The biological prokaryotic cell emerged in due course. This energy regime also saw the emergence and establishment of a metabolism mechanism for the supply of energy, with ATP as the principal ‘cellular energy currency.’ The living organisms of this period used nucleotides for synthesising DNA, and amino acids for synthesising proteins. There was practically no free oxygen in the atmosphere of the Earth.

The next energy regime, namely the phototrophic regime, was dominated by the exploitation of solar energy. It came about because some of the living organisms of the thermophilic regime reached the surface of the sea, where they encountered sunlight. In due course they developed a new metabolism which used solar energy through photosynthesis. Fixing of carbon dioxide, as also the stripping of hydrogen from water (resulting in the creation of free oxygen), originated in this energy regime. The new microorganisms which achieved this were cyanobacteria or blue-greens. They stripped electrons from water molecules, thus releasing hydrogen for use, along with carbon dioxide, in the production of carbohydrates. This gradually built up the molecular-oxygen content of the Earth. Within a few hundred thousand years the atmospheric oxygen levels rose from less than 1% to ~15% of present-day levels.

Abundant availability of solar light made the population of the blue-greens to grow, producing more and more oxygen. But oxygen itself was poison to them. Therefore evolutionary adaptation led to the development of a new kind of cell, namely the eukaryotic cell, which had ‘organelles’ limited by membranes. Let us see how this resolved the crisis.

The atmospheric oxygen in the phototrophic regime was conducive to the aerobes, but poison for the anaerobic blue-greens. However, the blue-greens did not simply fade away in such a situation, as they were instrumental, not only in the production of molecular oxygen, but also in the production of food for the respiring aerobes. Therefore the build-up of oxygen in the atmosphere was really a threat to both these types of organisms. They responded by evolving a symbiogenesis of the two, resulting in the emergence of the eukaryotic cell. Such a cell has an outer membrane which protects its contents from the harsh conditions outside. It also has internal membranes housing the organelles. Lynn Margulis pointed out that the organelles called mitochondria of a green plant cell are descended from an oxygen-respiring bacterium. Similarly, the chloroplasts are descended from another free-living bacterium, namely a cyanobacterium. The first aerobic eukaryotes had the enzymatic tools to detoxify reactive oxygen products. This is how they could ensure the survival of the symbiont blue-greens. In a photosynthesizing eukaryotic cell, the chloroplasts store solar energy in sugars and supply it to the host. The host, in turn, supplies the sugar to the mitochondria, which then supply the host with ATP, the cell fuel.

This symbiogenesis between oxygenic photosynthesis and aerobic respiration was at the heart of the oxo-energy revolution (Niele 2005), resulting in the emergence of the aerobic energy regime. The emergence of the eukaryotic cell embodied sunlight-harvesting photosynthesis, and protection against oxygen toxicity. Its highly efficient metabolic combustion via aerobic respiration triggered the appearance of multicellular life forms which, in turn, led to the emergence of still more complex life forms and ecosystems. Humans appeared on the scene in due course.

Of course, the eukaryotic organisms have continued to coexist with the prokaryotic organisms (namely the bacteria and the archaea) in several schemes. In fact, as Knoll said, the prokaryotes ‘maintain the foundation of all functioning ecosystems on this planet.’ An example is the nitrogen that bacteria make available for biological processes.

Emergence of new species

Life originated with bacteria. Bacteria do not speciate the way eukaryotic organisms do. The idea of a species does not apply to them. Bacteria can pass genes back and forth. There is no fixed genome to define the species of any bacteria. Bacteria are prokaryotes.

The first eukaryote emerged by the symbiogenesis of two prokaryotes. The concept of a species can apply only to eukaryotes. It follows that the origin of species occurred long after the origin of life in the form of bacteria. Therefore, the species of all the larger organisms (protoctists, fungi, animals, plants) originated symbiogenetically in the beginning. Nucleated organisms emerged on Earth some 1200 million years ago.

However, there is no reason to believe that symbiogenesis is the only way in which new species can arise. It is characteristic of complex systems that, often, small changes can have unexpectedly large consequences, including the emergence of new species. Effects of mutations can gradually build up to a stage wherein a sudden bifurcation occurs in phase space, and a new species arises. Speciation may well be an emergent phenomenon. This is in disagreement with the statement of Margulis and Sagan (2002) that ‘intraspecific variation never seems to lead, by itself, to new species.’

13.6 Concluding Remarks

Any entities that can replicate, and that have a variation both in their specific features and in their reproductive success, are candidates for Darwinian selection and evolution.

Darwin changed the way we humans look at ourselves and our place in the world. The basic idea of evolution by natural selection has gone far beyond the precincts of biology, and has permeated the human psyche in all sorts of ways. Apart from biological Darwinism, we speak of chemical Darwinism, quantum Darwinism, neural Darwinism, and what not. Deep down under, what evolves in any system is complexity.

Evolution of biological complexity is determined by two factors: natural selection, and self-organization. Self-organization creates order in any complex system. Darwinian natural selection acts on this existing order and hones it further. I shall dwell on the self-organization aspect of biological complexity in the next article in this series. Symbiogenesis is not the only way in which new species can arise. Often, small changes in complex adaptive systems can have unexpectedly large consequences, including the emergence of new species. Effects of mutations can gradually build up to a stage wherein a sudden bifurcation occurs and a new species arises. Speciation may well be an emergent phenomenon in a complex adaptive system.

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS

Related posts:

1. COMPLEXITY EXPLAINED: 14. Biological Complexity at the Edge of Chaos
2. COMPLEXITY EXPLAINED: 15. Evolution of Cultural Complexity
3. COMPLEXITY EXPLAINED: 8. Evolution of Chemical Complexity
4. COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity
5. COMPLEXITY EXPLAINED: 16. Evolution of Intelligence and Consciousness
6. COMPLEXITY EXPLAINED: 10. What is Life?
7. COMPLEXITY EXPLAINED: 3. Thermodynamic Explanation for the Increasing Complexity of our Ecosphere
8. COMPLEXITY EXPLAINED: 6. Emergence of Complexity in Far-from-Equilibrium Systems
9. COMPLEXITY EXPLAINED: 5. Defining Different Types of Complexity
10. COMPLEXITY EXPLAINED: 9. How Did Complex Molecules Like Proteins and DNA Emerge Spontaneously?
11. COMPLEXITY EXPLAINED: 12. The Likely Origins of Life
12. COMPLEXITY EXPLAINED: 1. What is Complexity?
13. COMPLEXITY EXPLAINED: 11. Cellular Automata

According to one model of the origins of life, it is likely that life originated twice, with two separate kinds of organisms, one capable of metabolism without exact replication, and the other capable of replication without metabolism; at some stage the two features came together. Another model is that life originated with the emergence of RNA molecules which could act as both enzymes and self-replicators. In either case, the emergence of self-replicators also marked the first step towards the evolution of consciousness.

12.1 Freeman Dyson’s Dual-Origin Model for Life

Freeman John Dyson is a theoretical physicist and mathematician, well known for his work in quantum field theory, solid-state physics, and nuclear engineering. In 1949 he demonstrated the equivalence of the two formulations of quantum electrodynamics, one by Richard Feynman and the other by Julian Schwinger and Sin-Itiro Tomonaga. In 1985 he wrote a little book Origins of Life, in which he argued that metabolic reproduction and replication are logically separable propositions, and that natural selection does not require replication, at least for simple creatures. In higher-level life as seen today, reproduction of cells and replication of molecules occur together. But there is no reason to presume that this was always the case. According to Dyson, it is more likely that life originated twice, with two separate kinds of organisms, one capable of metabolism without exact replication, and the other capable of replication without metabolism. At some stage the two features came together. When replication and metabolism occurred in the same creature, natural selection as an agent for novelty became more vigorous.

Dyson acknowledged the influence of Erwin Schrödinger and John von Neumann on his work. Two other scientists whose work he used for proposing his dual-origin hypothesis for life were the chemists Manfred Eigen and Leslie Orgel. They had demonstrated that a solution of nucleotide monomers will, under suitable conditions in the laboratory, give rise to a nucleic-acid polymer molecule (RNA) which replicates and mutates and competes with its progeny for survival. For achieving this, Eigen used a polymerase enzyme, which was a protein catalyst extracted from a bacteriophage (the synthesis and replication of the RNA depends on the structural guidance provided by the enzyme). Orgel did something complementary to the experiment of Eigen. He made RNA grow out of nucleotide monomers by adding a template for the monomers to copy, but did not add a polymerase enzyme. Thus Eigen made RNA using an enzyme but no template, and Orgel made RNA using a template but no enzyme. Living cells use both templates and enzymes for making RNA. This pointed to a possible parasitic development of RNA-based life in an environment created by a pre-existing protein-based life.

Dyson also drew support and inspiration from the work of Lynn Margulis, who has been a major proponent of the idea that parasitism and symbiosis were the driving forces in the evolution of cellular complexity. [Symbiosis means a prolonged living arrangement or physical association among members of two or more different species. Levels of partner integration in symbiosis may vary in intimacy; and integration may be behavioural, metabolic, of gene products, or 'genic.']

Margulis has been hammering home the point that the main components of eukaryotic cells have descended from independent living creatures which ‘attacked’ the cells from outside. In due course, the attackers and the host evolved a relationship of mutual dependence and benefit. In stages, the erstwhile invading organisms became first chronic parasites, then symbiotic partners, and finally an indispensable part of the host. The evidence for this is that the molecular structures of mitochondria and chloroplasts are indeed very close to certain bacteria.

Margulis has marshalled evidence to argue that most of the big steps in cellular evolution were caused by parasites. And the nucleic acids were the oldest and the most successful cellular parasites. According to Dyson’s model, the original living creatures were cells with a metabolic apparatus directed by certain proteins (enzymes), which had no genetic appurtenances to start with. Such cells lacked the ability for exact replication, but could still grow and divide and reproduce themselves in an average statistical manner.

12.2 ATP and RNA

During millions of years of chemical (and now also biological) evolution, the initial primitive but living cells diversified and refined their metabolic reaction pathways. In particular, they evolved the synthesis of ATP (adenosine triphosphate) through some autocatalytic reaction mechanisms (cf. Part 9). ATP is the main energy-carrying molecule in all present-day cells. ATP-carrying primitive cells had an evolutionary advantage over other, less efficient, cells. In time, other molecules like AMP (adenosine monophosphate) emerged; or perhaps AMP came first, and then ATP.

The molecular structures of Adenosine triphosphate (ATP) and Adenosine 5'-monophosphate (AMP), otherwise known as adenine nucleotide.

Now, although ATP and AMP have similar chemical structures (see figure), they play totally different roles in present-day cells. ATP is the universal biological currency for energy. AMP, on the other hand, is one of thenucleotides in the structure of the RNA molecule. RNA functions as the carrier of information, and it can replicate exactly. [RNA is like DNA, except that thymine (T) is replaced by uracil (U). In RNA, A bonds to U only, and G bonds to C only.] AMP is the A (i.e. the nucleotide adenine) in the RNA structure.

If ATP loses two of its three phosphate groups, it becomes AMP. Dyson argued that, although the primitive cells had no genetic apparatus to begin with, they were loaded with ATP molecules which could easily convert to AMP molecules. Accidentally, in one such cell which happened to be carrying AMP and other nucleotides (the ‘chemical cousins’ of AMP), the Eigen experiment for synthesizing RNA happened spontaneously. With some help from pre-existing enzymes, an RNA molecule got produced. Once created, it went on replicating itself because of the proclivity of base A to hydrogen-bond with base U, and of G to hydrogen-bond with C.

Thus, RNA first appeared as a parasitic disease in the cell. Although most such cells died of disease, some evolved to survive the infection, à la Lynn Margulis. In such cells, the parasite gradually became a symbiont. Further evolution resulted in a situation in which the protein-based life learnt to make use of the ability for exact replication provided by the chemical structure of RNA. This is how the modern genetic mechanism came into being. Hardware came before software, and that makes sense.

Is it really true that proteins emerged before RNA? The early evidence came from laboratory experiments done during the 1950s. The well-known experiments by Miller and others (done from 1953 onwards) demonstrated that amino acids form easily in a reducing atmosphere from the still simpler molecules, in the presence of ultraviolet radiation. What about nucleotides?

They are more difficult to synthesize from their constituents in a Miller-style experiment. A nucleotide has three parts: an organic base, a sugar, and a phosphate ion. The phosphate ion occurs naturally as a constituent of rocks and sea water.  The sugar part can be synthesized with substantial efficiency from formaldehyde. And the synthesis of an organic base was demonstrated by Oró in 1960. He prepared a concentrated solution of ammonium cyanide in water, and just let it stand. Adenine was self-created, with a 0.5% yield. Guanine also got synthesized in a similar way. But the catch here is that it is difficult to imagine how such high degrees of concentration of ammonium cyanide could occur in Nature, although some possible scenarios have been suggested. In any case, the nucleotide molecules, even if formed, are unstable in solution, and tend to get hydrolysed back into their components. Another major difficulty is to get the three components of a nucleotide into a correct configuration for bonding.

All told, whereas it is easy to simulate a pre-biotic synthesis of amino acids in the laboratory, the same is not the case for nucleotides (but see below). Dyson argued that this lends credence to his model that proteins appeared on the scene before RNA etc. Of course, he was also quick to point out that perhaps we have not been clever enough to create proper simulation conditions in the laboratory. I shall return to this point in Section 12.6.

12.3 How the Mystery of Cell Differentiation was Solved

For introducing certain concepts and terminology, I make a small digression here and discuss cell differentiation. Each cell of our body carries the same genome. What tells some cells to become kidney cells, and others to become liver cells, and still others to become neurons? The term ‘cell differentiation’ is used for this phenomenon. How does cell differentiation occur, and with such high precision?

French scientists François Jacob and Jacques Monod were awarded (along with Andre Lwoff) the Nobel Prize for physiology or medicine for 1965 for their work on ‘genetic circuits.’ There are thousands of genes arrayed along a DNA molecule. Jacob and Monod discovered that a small fraction of these are ‘regulatory’ genes which can function as switches. Such activity is triggered by, say, the availability of a particular hormone in the surroundings of a cell. This hormone may switch-on a particular gene. The newly activated gene sends out chemical signals to fellow genes, that can switch them on or off, depending on the states they are already in. The altered state of each of these genes then releases, or stops releasing, other chemical signals, which are received by the genetic switches in the network, altering their states in turn, in a cascading manner. This continues till the network of genetic switches settles down to a stable, self-consistent pattern.

From left to right: François Jacob, Jacques Monod and André Lwoff, who were awarded the Nobel Prize for Physiology of Medicine in 1965. Jacques Monod was director of the Pasteur Institute in Paris from 1971 to 1976.

This work had several implications. For example, it established DNA as not just a repository of the blueprint for the cell, telling it how to manufacture the various proteins, but also as an engineer in charge of construction. The DNA was established to be a molecular-scale computer that computed how the cell was to build and repair itself, and how it was to interact with the surrounding world.

The work of Jacob and Monod also solved the mystery of cell differentiation. It was concluded from this work that each type of cell corresponds to a different pattern of the genetic network, influenced by the presence of specific hormones etc. Although there is only a single genome involved, the genome can have many stable patterns of activation, each corresponding to a different cell type (liver, kidney, brain, etc.). Thus the genome was viewed as a complex network of interacting components, which control homeostasis and differentiation through very specific control circuits among the genes. [Homeostasis is the ability of higher animals to maintain an internal consistency.]

Back to Dyson. Further support for his dual-origin model for life has come from the work of Stuart Kauffman who carried forward the regulatory-genetic-networks idea. Before describing this, I must introduce the important idea of attractors in phase space. Sorry about the digression; I had vowed not to use any unexplained jargon in this series of articles.

12.4 Attractors in Phase Space

The concept of phase space or state space was introduced in Section 6.2 (Part 6) of this series. Imagine a loosely wound spring, oriented vertically (i.e. along the z-axis), and fixed securely at its top end to some heavy object. At its bottom end I attach a small particle. I am interested in the dynamics of this particle after I pull the lower end of the spring by a small distance, and then release the spring. The spring will be set into vibration, and the attached particle will execute an oscillatory, up-and-down motion. At any instant of time, the particle has position coordinates (x, y, z), and momentum coordinates (p_x, p_y, p_z). What is the phase-space trajectory for this system? The answer is that it is a closed loop in a plane defined by the z-axis and the p_z-axis. Let us see how.

To start with (i.e. at time t = 0), the particle attached to the spring is at rest, and its representative point in phase space has the ‘coordinates’ (0, 0, 0, 0, 0, 0). At the moment I release the spring after pulling it by a small distance z, the phase-space coordinates are (0, 0, -z, 0, 0, 0).

When I was pulling the spring, I was doing work against its restorative force, and this work got stored as the potential energy of the spring. When I release the spring, this stored potential energy is available for doing work, making the spring (and the particle attached to it) move towards the initial position (0, 0, 0) of the particle in real space. By the time the particle reaches this point, all the potential energy has got converted to kinetic energy, and the representative point in phase space now has the coordinates (0, 0, 0, 0, 0, p_z). Nothing much is happening along the x-axis and the y-axis, as also along the p_x-axis and the p_y-axis. All the action is along the z-axis and the p_z-axis, so we can use a more compact notation, and say that at the moment when all the potential energy has got converted to kinetic energy, the representative point in phase space has the coordinates (0, p_z).

The kinetic energy of the spring will make the particle overshoot the origin point of the z-axis till the particle reaches the representative point (z, 0); this is when the particle will be at rest again, as all the kinetic energy has been converted back to potential energy. This potential energy will again make the particle move in the opposite direction. And so on. Thus the particle will successively and repeatedly pass through a whole continuum of points in phase space, including the following points: (-z, 0), (0, p_z), (z, 0), (0, -p_z).

If there is no dissipation of energy, the phase-space trajectory in this experiment is a closed loop, as the particle repeatedly passes through all the allowed (i.e. energy-conserving) position-momentum combinations again and again. Since the trajectory is fixed or constant, the area enclosed by it is also constant.

But in reality, dissipative forces like friction are always present, and in due course all the energy I expended in stretching the spring will be dissipated as heat. What happens to the phase-space trajectory of the particle as the total energy (potential energy plus kinetic energy) is lost gradually? As the total energy decreases, the maximum value of the z-coordinate during the trajectory cycle, as also the maximum value of p_z, will also decrease, implying that the area enclosed by the trajectory in phase space will decrease, till the particle finally comes to a state of rest or zero momentum.

This final configuration corresponds to an attractor in phase space: It is as if the dissipative dynamics of the system is ‘attracted’ by the point (0, 0, 0, 0, 0, 0) as its energy gets dissipated. Thus, because of the gradual dissipation of energy, the closed-loop phase-space trajectory spirals towards a state of zero area. This is like a particle set rolling in a bowl, spiralling towards the bottom of the bowl; the bowl thus acts as a basin of attraction. Similarly, the phase-space region around the attractor (0, 0, 0, 0, 0, 0) is the basin of attraction for the oscillator problem we have considered here. I had pulled the spring by an arbitrary small amount. The exact magnitude of this small amount of pulling is not important. In each such experiment (with different starting values of z), the dissipative system always gets attracted towards the same attractor. We say that there is a unique basin of attraction around the unique attractor.

Nonlinear  Dynamical  Systems

In the above experiment, if we pull the spring only by a small amount, its restorative force is linearly proportional to the displacement of the tip of the spring to which we have attached the small particle: If we plot this force f_z as a function of z, we get a straight line (which is a linear curve). Incidentally, this is the defining feature of what is called a simple-harmonic oscillator.

But if the displacement is too large, the restorative force is no longer linearly proportional to the displacement z, and we are then dealing with a nonlinear dynamical system: The plot of f_z against z is no longer a straight line. Most real-life phenomena involve nonlinear dynamics. In particular, all evolution of complexity in Nature concerns systems which receive a persistent and therefore cumulatively large amount of energy from the surroundings, and are thus pushed into the nonlinear regimes of dynamic behaviour. All complex systems are nonlinear, although not all nonlinear systems may exhibit complex behaviour.

12.5 Kauffman’s Work on the Origins of Life

In 1993 Kauffman had established by his cellular-automata approach that regulatory genetic networks can indeed arise spontaneously in complex systems by self-organization. But he still had to tackle the question of how extremely large molecules like RNA and DNA came into existence in the first place. In any case, as stated earlier in this series of articles, even DNA requires the availability of certain protein molecules for its genetic role. Therefore, there must have been a mechanism which resulted in the spontaneous creation of protein molecules without the intervention of DNA.

In other words, there must have been a non-random origin of life. There must have been another way, independent of the need to involve DNA molecules, for self-reproducing molecular systems to have got started. Kauffman carried Melvin Calvin’s (1969) idea of autocatalytic reactions (cf. Part 9) much further to explain how this could happen: In Kauffman’s model, like in Dyson’s, life originated before the advent of RNA or DNA. And Kauffman’s network model could incorporate features like reproduction, as also competition and cooperation for survival and evolution (including coevolution). Kauffman had introduced in 1969 his ‘random Boolean networks’ (RBNs) as a part of his pioneering work on the functioning of genetic regulatory networks. He went a step further than Jacob and Monod and demonstrated that even randomly constructed networks of high molecular specificity can undergo homeostasis and differentiation:

In the absence of knowledge regarding the parameters describing real cells, Kauffman investigated (on a computer) a variety of genetic control networks to see if any of them simulated biological activity reasonably well. In his binary network model (or the RBN model), a gene (represented as a node of the network) was modelled as a binary device, the whole network having N such nodes. Thus, each node or gene had two possible states: ‘on’ or 1, and ‘off’ or 0. The ‘on’ state meant that the gene was being transcribed, and the ‘off’ state meant that it was not being transcribed. Each gene or node was modelled as receiving exactly K (K less than or equal toN) inputs from randomly chosen ‘controlling’ genes or nodes, and also receiving one random ‘update’ function for its K inputs. The update function prescribes the state of the gene or the automaton in the next time step, given its state in the current time step, and is chosen according to some probability-distribution function. By varying N and K for these RBNs, the behaviour of a variety of such finite sequential switching automata could be investigated. At any time step, each gene or node had a value 1 or 0, and the network was a collection of these 1s and 0s, representing the ’state’ of the network or the biological cell. This pattern of 1s and 0s served as the input, determining the pattern for the next time step of the automaton. Shown in the adjoining figure is the activity pattern for an RBN with 16 nodes for 50 time steps. The initial state is the column furthest to the left with nodes represented vertically and time moving to the right.

The RBN has 2^N possible states; i.e. it has a finite number of states. This finiteness, coupled with the fact that the dynamics is det  erministic, implies that, as the RBN proceeds through a sequence of states, it must eventually return to a pattern it had at some earlier time step, and from then on it must repeat the same pattern-sequence periodically. That is, it must be trapped in a re-entrant cycle of states, or an attractor in phase space. Each such state cycle or attractor represents a distinct temporal mode of behaviour of the net, and was equated by Kauffman with a distinct cell type (kidney, liver, etc.). Cell types differ only in the pattern of gene activity; they all carry the same genome. Shown in the adjoining figure is a periodic attractor (yellow) and its basin of attraction (cyan). Each point in the state space represents a network state.

Kauffman focussed his attention on ‘critical‘ RBNs. These lie at the ‘edge of chaos,’ i.e. at the boundary between frozen networks and chaotic networks. Frozen networks have very short attractors or cycle lengths. And chaotic networks have large-sized attractors that may include a substantial portion of the phase space. To quote Kauffman:

Let’s talk about networks as a model of the genetic regulatory system. My claim is that sparsely connected networks in the ordered regime, but not too far from the edge (of chaos) do a pretty good job of fitting lots of features about real embryonic development, and real cell types, and real cell differentiation. And insofar as that’s true, then it is a good guess that a billion years of evolution has in fact tuned real cell types to be near the edge of chaos. So that’s very powerful evidence that there must be something good about the edge of chaos. So let’s say the phase transition is the place to be for complex computation. Then the second assertion is something like ‘Mutation and selection will get you there.’

[The edge-of-chaos idea is very important for understanding complexity and the origin and sustenance of life, and I shall discuss it in some detail in a separate article.]

Jacob and Monod’s cell types, distinguished from one another by the distinct and stable network patterns of gene activity, were interpreted by Kauffman as represented by different attractors in phase space. For K = 1and for K = N the length of the attractor cycles is very large. But for K = 2, i.e. when there are two inputs per gene, the lengths of the cycles are very small, roughly scaling as ~√N for critical networks. For example, for N = 1000, i.e. for 2¹⁰⁰⁰ possible states of the network, the modelled genome was found to cycle typically among just 30 time steps, a remarkable result indeed. Kauffman also found that the number of cell types scales as √N, in line with the biological information available at that time.

Thus Kauffman demonstrated that highly ordered dynamical behaviour is typical even for randomly constructed genetic networks getting just a few inputs per component. This implied that homeostasis in living complex systems is a direct consequence of the high molecular specificity among the macromolecules involved. Similarly, cell differentiation reflects the capacity of complex adaptive systems to behave in several distinct, highly localized ways. Kauffman’s work established that complex genetic networks could come into being by spontaneous self-organization, without the need for slow evolution by trial and error. After all, the whole thing had to be there together, and not partially, to function at all. He also established that genetic regulatory networks are no different from neural networks.

Kauffman’s work, though extremely important and path-breaking, was handicapped by the limited computational power available at that time, as also the limited nature of biological data. We now know that the number of genes (N) is not proportional to the mass of DNA, contrary to what was assumed by biologists at that time; it is much smaller for higher organisms. And that, for larger N, the increase in the number of genes with N is much faster than √N. In fact, the attractor number, as also the attractor length of K = 2 networks, both increase with the size of the network faster than any power law.

12.6 Freeman Dyson Revisited

I summarize here an updated version of Dyson’s ideas, as given in the recent (2008) book Life: What a Concept! In his model, there are six stages in the evolution of chemical complexity, leading to the emergence of life as we see it today.

Stage 1. The early cells were just little bags of some kind of cell membrane; this is the ‘garbage bag model’ for Stage 1. And inside the bag there was a more or less random collection of organic molecules, with the characteristic that small molecules could diffuse in through the membrane, but big molecules could not diffuse out. The ‘garbage bag’ situation was conducive to the conversion of small molecules into large molecules. And the higher concentration of organic material in the bag led to a higher efficiency of the chemical processes involved. This was conducive to fairly rapid evolution of chemical complexity.

And this evolution did not involve any replication processes. ‘When a cell became so big that it got cut in half, or shaken in half, by some rainstorm or environmental disturbance, it would then produce two cells which would be its daughters, which would inherit, more or less, but only statistically, the chemical machinery inside. Evolution could work under those conditions. In Stage 1, evolution was happening, but only on a statistical basis. This was pre-Darwinian evolution.’

Stage 2. Parasitic RNA appeared in some of the cells in Stage 2. ATP had appeared in one of the garbage bags by a random process in Stage 1, and the cell hosting it had a metabolic advantage over other cells. Therefore many cells with large amounts of ATP got created. Then, again by chance, ATP changed to AMP in one of the cells, and AMP is nothing but the adenine nucleotide. In due course, AMP and its chemical cousins polymerized into a primitive form of RNA. Thus there was parasitic RNA inside these cells, forming a separate form of life, which was pure replication without metabolism. To quote Dyson: ‘Then the RNA invented viruses. RNA found a way to package itself in a little piece of cell membrane, and travel around freely and independently. Stage two of life has the garbage bags still unorganized and chemically random, but with RNA zooming around in little packages we call viruses carrying genetic information from one cell to another. That is my version of the RNA world. It corresponds to what Manfred Eigen considered to be the beginning of life, which I regard as stage two. You have RNA living independently, replicating, travelling around, sharing genetic information between all kinds of cells.’

Stage 3. This stage started when the protein and the RNA systems started to collaborate. This happened after the emergence of the ribosome. Although this arrangement had the rudiments of the modern cell, the genetic information was shared mostly via viruses travelling from cell to cell. This was some kind of open-source heredity. The chemical inventions made by one cell could be shared with others. Evolution went on in parallel in many different cells. The best chemical devices could be shared between different cells and combined, so the chemical evolution was very rapid, as it occurred in parallel by many pathways. This is when most of the basic biochemical inventions must have been made.

The emergence of the ribosome is still a scientific mystery. This is one reason why I did not dwell on it when I discussed in Part 9 the role of autocatalytic sets of molecules for explaining the emergence of complex molecules. The ribosome plays a crucial role in the production of proteins in the cell. This production involves the transcription of a stretch of DNA into a portable form, namely the mRNA. The mRNA travels to the cytoplasm of the cell, where the information is conveyed to the ribosome. This is where the code is read, and the corresponding amino acid is brought into the ribosome. Each amino acid comes connected to a specific tRNA molecule. There is a three-letter recognition site on the tRNA that is complementary to, and pairs with, the three-letter code sequence for that amino acid on the mRNA.

Stage 4. Speciation and sex appeared in Stage 4, and that marked the beginning of the Darwinian era, when species appeared. ‘Some cells decided it was advantageous to keep their intellectual property private, to have sex only with themselves or with the members of their own species, thereby defining species. That was then the state of life for the next two billion years, the Archeozoic and Proterozoic eras. It was a rather stagnant phase of life, continued for two billion years without evolving fast.’

Stage 5. Multicellular organisms appeared in Stage 5, which also involved death.

Stage 6. This is the stage when we humans appeared.

12.7 The RNA-World Hypothesis

At present there is a strong section of opinion, embodied in the so-called RNA-World hypothesis, according to which RNA acted both as an information-storage molecule and as an enzyme at an early stage in theappearance of life. In other words, life started as nude replicating RNA molecules. This view of the origin of life had its genesis in the discovery, made in the mid-1980s by Thomas Cech and coworkers, that certain RNA sequences called ribozymes can themselves act as enzymes and catalyze reactions. The dual functionality of RNA might have allowed for the existence of an RNA species that could replicate itself and thus seed the beginning of molecular evolution. RNA is indeed known to be involved in a number of fundamental cell biological processes. Moreover, the ribosome is made up largely of RNA sequences, along with some proteins, and the ribosome machinery is almost identical throughout the living world; perhaps it existed almost from the beginning of life on Earth.

Several scientists have expressed reservations about this model. I shall quote the objections of one of them, namely Stuart Kauffman, author of the 1995 book At Home in the Universe: The Search for the Laws of Self-Organization and Complexity, and the 2000 book Investigations.

• It is difficult to get RNA strands to reproduce in a test tube. ‘No one has succeeded in achieving experimental conditions in which a single-stranded DNA or RNA could line up free nucleotides, one by one, as complements to a single strand, catalyze the ligation of the free nucleotides into a second strand, melt the two strands apart, then enter another replication cycle. It just has not worked’ (Kauffman 2000).

• Even if life did tend to originate and evolve by the RNA route, naked RNA molecules must have suffered an ‘error catastrophe’ during the replication processes, thus corrupting the genetic message from generation to generation. In present-day cells, such errors (mutations) are kept to a minimum by ‘proofreading’ and ‘editing’ enzymes.

• RNA-based life, even if it did emerge, was not complex enough to sustain itself. In other words, it was too far from the edge of chaos where complexity thrives best. Why viruses do not have life? Why is it that the simplest free-living cells are the so-called pleuromona, and nothing less complex than them? Pleuromona are the simplest known bacteria, and they are complete with cell membrane, genes, RNA, protein-synthesizing machinery, proteins. All free-living cells have at least the minimal molecular diversity of pleuromona. Why nothing simpler exists that is alive on its own? The nude RNA or the nude ribozyme polymerase idea for the origin of life offers no decent explanation for the observed minimum necessary complexity of any life form.

The explanation, discussed in detail by Kauffman in his trilogy of books (culminating in the 2000 book Investigations), has to do with the all-important self-organization feature of complex adaptive systems which makes them gradually but inexorably climb the complexity ladder till they reach the ‘phase transition’ region (or the edge of chaos) in state space. Once there, they tend to stay there. Nude RNA was probably not complex enough to have self-propagated and survived as a life form.

I wish to say that life is an expected, emergent property of complex chemical reaction networks. Under rather general conditions, as the diversity of molecular species in a reaction system increases, a phase transition is crossed beyond which the formation of collectively autocatalytic sets of molecules suddenly becomes almost inevitable. If so, we are birthed by molecular diversity, children of second-generation stars.

Stuart Kauffman, Investigations (2000)

12.8 The First Step towards the Evolution of Consciousness

The emergence of self-replicators like RNA and DNA marked the first step towards the evolution of consciousness. Of course, nobody equates a self-replicator with a conscious entity. But a self-replicator which has repeatedly survived the depredations of the second law of thermodynamics, namely its own decay into a state of disorder and destruction, must have entailed the existence of a reason for surviving.

In the beginning, there were no reasons, only causes and effects. No self-interests, no purpose, no function, no teleology. The emergence of replicators changed all that. The fact that some of them have survived means (in anthropomorphic terms) that they had a kind of ‘interest’ in self-replication.

The blind forces of Nature did not distinguish between a piece of rock and a replicator. Nobody cared (there was nobody to care) whether or not a rock or a replicator survived for any length of time. But the fact is that we can see that a certain kind of replicator has indeed survived by repeated self-replication. And this has happened in spite of the fact that nobody did anything deliberately to ensure the survival of the replicator. Survival by self-replication requires the existence of a suitably conducive environment. There were all kinds of replicators (chemical entities) to start with, but those which could avoid the ‘bad’ conditions and seek ‘good’ conditions had a better chance of survival. This was just natural selection at the molecular level.

Such a successfully self-replicating entity thus ‘creates’ for itself a ‘point of view’, according to which it partitions the environment into ‘favourable’, ‘unfavourable’, and ‘neutral’. If this chemical entity is such that there is a better chance that it would ’seek’ favourable environments and ‘avoid’ unfavourable ones, it has the equivalent of what we humans recognize as ’self interest’. The chemical entity is not doing anything ‘consciously’, but the end result is the same. As Daniel Dennett (1984) pointed out, once an entity comes to have ‘interests’ and is a ‘problem-solver,’ the world and its events begin creating reasons for it. The first problem faced by such primitive problem-solvers was to ‘learn’ how to recognize and act on the reasons that their very existence brought into existence.

What is more, boundaries become important for any self-preserving entity. The entity must ‘know’ what to preserve; the boundaries limit and determine what needs to be preserved by self-replication. This primordial form of ’selfishness’ is a characteristic of life. The distinction between everything on the inside of a closed boundary and everything outside is a central feature of all biological processes.

Thus the emergence of self-replicating entities in Nature led to:

• reasons to recognize;
• points of view from which to recognize or evaluate; and
• the need to distinguish between ‘here inside’ and ‘the external world.’

The point of view of a modern-day conscious observer is, of course, not identical to, but is a sophisticated descendant of, the primordial points of view of the first self-replicators which divided their worlds into good and bad.

12.9 Concluding Remarks

First an updated (2008) summary of Dyson’s model, and in his own words: ‘The essential idea (regarding the origin of life) is that you separate metabolism from replication. We know modern life has both metabolism and replication, but they’re carried out by separate groups of molecules. Metabolism is carried out by proteins and all kinds of other molecules, and replication is carried out by DNA and RNA. That maybe is a clue to the fact that they started out separate rather than together. So my version of the origin of life is that it started with metabolism only.’

I mentioned the RNA-world hypothesis in Section 12.6, which is at variance with what Dyson and Kauffman have been emphasizing. I am inclined to agree with Kauffman that the RNA-world hypothesis is probably not a good one because it ignores the minimum-necessary-complexity requirement for a live system to sustain and propagate itself. The tendency for the edge-of-chaos existence of complex adaptive systems (which I shall discuss in Part 14 of this series) is another argument in favour of Dyson’s model, which involves the existence of proteins before RNA emerged.

Following Dennett (1984), I have made an important point in this article that the emergence of self-replicators like RNA and DNA provided the first reason for the evolution of consciousness.

In 1944, Oswald Avery successfully converted one strain or species (the so-called R-strain) of pneumococci bacteria into another (the S-strain) by exposing the R-strain to an extract of the heat-killed S-strain (this extract was shown to consist of pure DNA). In June 2007, Craig Venter announced the results of the work done in his laboratory on genome transplantation. He reported the successful transformation of one type of bacteria into another; the new bacterium was dictated entirely by the transplanted chromosome. In other words, one species became another. We can say that he created life in the form of a new species (without any ‘divine’ intervention). This was an event of enormous significance. Life had been created in the laboratory, even though there was a concomitant annihilation of a different form of life. The next target is to create life starting from ’scratch’, i.e. by not using any precursors derived from living organisms. I have no doubt that this will happen in the near future. Such is the power of the scientific method we humans have invented and nurtured.

Related posts:

1. COMPLEXITY EXPLAINED: 10. What is Life?
2. COMPLEXITY EXPLAINED: 6. Emergence of Complexity in Far-from-Equilibrium Systems
3. COMPLEXITY EXPLAINED: 14. Biological Complexity at the Edge of Chaos
4. COMPLEXITY EXPLAINED: 13. Evolution of Biological Complexity
5. COMPLEXITY EXPLAINED: 9. How Did Complex Molecules Like Proteins and DNA Emerge Spontaneously?
6. COMPLEXITY EXPLAINED: 3. Thermodynamic Explanation for the Increasing Complexity of our Ecosphere
7. COMPLEXITY EXPLAINED: 8. Evolution of Chemical Complexity
8. COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity
9. COMPLEXITY EXPLAINED: 15. Evolution of Cultural Complexity
10. COMPLEXITY EXPLAINED: 5. Defining Different Types of Complexity
11. COMPLEXITY EXPLAINED: 11. Cellular Automata
12. COMPLEXITY EXPLAINED: 17. Epilogue
13. COMPLEXITY EXPLAINED: 1. What is Complexity?

“It is almost irresistible for humans to believe that we have some special relation to the universe, that human life is not just a more-or-less farcical outcome of a chain of accidents reaching back to the first three minutes, but that we were somehow built in from the beginning.”

-Steven Weinberg

“You are here to enable the divine purpose of the universe to unfold. That is how important you are.”

-Eckhart Tolle

1. Introduction

The impulse to see human life as central to the existence of the universe is manifested in the mystical traditions of practically all cultures. It is so fundamental to the way pre-scientific people viewed reality that it may be, to a certain extent, ingrained in the way our psyche has evolved, like the need for meaning and the idea of a supernatural God. As science and reason dismantle the idea of the centrality of human life in the functioning of the objective universe, the emotional impulse has been to resort to finer and finer misinterpretations of the science involved. Mystical thinkers use these misrepresentations of science to paint over the gaps in our scientific understanding of the universe, belittling, in the process, science and its greatest heroes.

In their recent article in The Huffington Post, biologist Robert Lanza and mystic Deepak Chopra put forward their idea that the universe is itself a product of our consciousness, and not the other way around as scientists have been telling us. In essence, these authors are re-inventing idealism, an ancient philosophical concept that fell out of favour with the advent of the scientific revolution. According to the idealists, the mind creates all of reality. Many ancient Eastern and Western philosophical schools subscribe to this idealistic notion of the nature of reality. In the modern context, idealism has been supplemented with a brand of quantum mysticism and relabeled as biocentrism. According to Chopra and Lanza, this idea makes Darwin’s theory of the biological evolution and diversification of life insignificant. Both these men, although they come from different backgrounds, have independently expressed these ideas before with some popular success. In the article under discussion their different styles converge to present a uniquely mystical and bizarre worldview, which we wish to debunk here.

2. Biocentrism Misinterprets Several Scientifically Testable Truths

The scientific background to the biocentrism idea is described in Robert Lanza’s book Biocentrism: How Life and Consciousness Are the Keys to Understanding the True Nature of the Universe, in which Lanza proposes that biology and not physics is the key to understanding the universe. Vital to his proposal is the idea that the universe does not really exist unless it is being observed by a conscious observer. To support this idea, Lanza makes a series of claims:

(a) Lanza questions the conventional idea that space and time exist as objective properties of the universe. In doing this, he argues that space and time are products of human consciousness and do not exist outside of the observer. Indeed, Lanza concludes that everything we perceive is created by the act of perception.

The intent behind this argument is to help consolidate the view that subjective experience is all there is. However, if you dig into what Lanza says it becomes clear that he is positioning the relativistic nature of reality to make it seem incongruous with its objective existence. His reasoning relies on a subtle muddling of the concepts of subjectivity and objectivity. Take, for example, his argument here:

“Consider the color and brightness of everything you see ‘out there.’ On its own, light doesn’t have any color or brightness at all. The unquestionable reality is that nothing remotely resembling what you see could be present without your consciousness. Consider the weather: We step outside and see a blue sky - but the cells in our brain could easily be changed so we ’see’ red or green instead. We think it feels hot and humid, but to a tropical frog it would feel cold and dry. In any case, you get the point. This logic applies to virtually everything.“

There is only some partial truth to Lanza’s claims. Color is an experiential truth - that is, it is a descriptive phenomenon that lies outside of objective reality. No physicist will deny this. However, the physical properties of light that are responsible for color are characteristics of the natural universe. Therefore, the sensory experience of color is subjective, but the properties of light responsible for that sensory experience are objectively true. The mind does not create the natural phenomenon itself; it creates a subjective experience or a representation of the phenomenon.

Similarly, temperature perception may vary from species to species, since it is a subjective experience, but the property of matter that causes this subjective experience is objectively real; temperature is determined by the average kinetic energy of the molecules of matter, and there is nothing subjective about that. Give a thermometer to a human and to an ass: they would both record the same value for the temperature at a chosen spot of measurement.

The idea that ‘color’ is a fact of the natural universe has been described by G. E. Moore as a naturalistic fallacy. Also, the idea that color is created by an intelligent creator is a supernaturalistic fallacy. It can be said that the idea that color is created objectively in the universe by the subjective consciousness of the observer is an anthropic fallacy. The correct view is that ‘color’ is the subjective sensory perception by the observer of a certain property of the universe that the observer is a part of.

Time and space receive similar treatment as color and heat in Lanza’s biocentrism. Lanza reaches the conclusion that time does not exist outside the observer by conflating absolute time (which does not exist) with objective time (which does). In 2007 Lanza made his argument using an ancient mathematical riddle known as Zeno’s Arrow paradox. In essence, Zeno’s Arrow paradox involves motion in space-time. Lanza says:

“Even time itself is not exempted from biocentrism. Our sense of the forward motion of time is really the result of an infinite number of decisions that only seem to be a smooth continuous path. At each moment we are at the edge of a paradox known as The Arrow, first described 2,500 years ago by the philosopher Zeno of Elea. Starting logically with the premise that nothing can be in two places at once, he reasoned that an arrow is only in one place during any given instance of its flight. But if it is in only one place, it must be at rest. The arrow must then be at rest at every moment of its flight. Logically, motion is impossible. But is motion impossible? Or rather, is this analogy proof that the forward motion of time is not a feature of the external world but a projection of something within us? Time is not an absolute reality but an aspect of our consciousness.”

In a more recent article Lanza brings up the implications of special relativity on Zeno’s Arrow paradox. He writes:

“Consider a film of an archery tournament. An archer shoots an arrow and the camera follows its trajectory. Suddenly the projector stops on a single frame — you stare at the image of an arrow in mid-flight. The pause enables you to know the position of the arrow with great accuracy, but it’s going nowhere; its velocity is no longer known. This is the fuzziness described by in the uncertainty principle: sharpness in one parameter induces blurriness in the other. All of this makes perfect sense from a biocentric perspective. Everything we perceive is actively being reconstructed inside our heads. Time is simply the summation of the ‘frames’ occurring inside the mind. But change doesn’t mean there is an actual invisible matrix called “time” in which changes occur. That is just our own way of making sense of things.”

In the first case Lanza seems to state that motion is logically impossible (which is a pre-relativistic view of the paradox) and in the next case he mentions that uncertainty is present in the system (a post-relativistic model of motion). In both cases, however, Lanza’s conclusion is the same - biocentrism is true for time. No matter what the facts about the nature of time, Lanza concludes that time is not real. His model is unfalsifiable and therefore cannot be a part of science. What Lanza doesn’t let on is that Einstein’s special-relativity theory removes the possibility of absolute time, not of time itself. Zeno’s Arrow paradox is resolved by replacing the idea of absolute time with Einstein’s relativistic coupling of space and time. Space-time has an uncertainty in quantum mechanics, but it is not nonexistent. The idea of time as a series of sequential events that we perceive and put together in our heads is an experiential version of time. This is the way we have evolved to perceive time. This experiential version of time seems absolute, because we evolved to perceive it that way. However, in reality time is relative. This is a fundamental fact of modern physics. Time does exist outside of the observer, but allows us only a narrow perception of its true nature.

Space is the other property of the universe that Lanza attempts to describe as purely a product of consciousness. He says “Wave your hand through the air. If you take everything away, what’s left? The answer is nothing. So why do we pretend space is a thing”. Again, Einstein’s theory of special relativity provides us with objective predictions that we can look for, such as the bending of space-time. Such events have been observed and verified multiple times. Space is a ‘thing’ as far as the objective universe is concerned.

Lanza says “Space and time are simply the mind’s tools for putting everything together.” This is true , but there is a difference between being the ‘mind’s tools’ and being created by the mind itself. In the first instance the conscious perception of space and time is an experiential trick that the mind uses to make sense of the objective universe, and in the other space and time are actual physical manifestations of the mind. The former is tested and true while the latter is an idealistic notion that is not supported by science. The experiential conception of space and time is different from objective space and time that comprise the universe. This difference is similar to how color is different from photon frequency. The former is subjective while the latter is objective.

Can Lanza deny all the evidence that, whereas we humans emerged on the scene very recently, our Earth and the solar system and the universe at large have been there all along? What about all the objective evidence that life forms have emerged and evolved to greater and greater complexity, resulting in the emergence of humans at a certain stage in the evolutionary history of the Earth? What about all the fossil evidence for how biological and other forms of complexity have been evolving? How can humans arrogate to themselves the power to create objective reality?

Much of Lanza’s idealism arises from a distrust/incomprehension of mathematics. He writes:

“In order to account for why space and time were relative to the observer, Einstein assigned tortuous mathematical properties to an invisible, intangible entity that cannot be seen or touched. This folly continues with the advent of quantum mechanics.”

Why should the laws of Nature ‘bother’ about whether you can touch something or not? The laws of Nature have been there long before Lanza appeared on the scene. Since he cannot visualize how the mathematics describes an objective universe outside of experience, Lanza announces that reality itself does not exist unless created by the act of observation. Some cheek!

(b) Lanza claims that without an external observer, objects remain in a quantum probabilistic state. He conflates this observer with consciousness (which he admits to being “subjective experience”). Therefore, he claims, without consciousness any possible universe will only exist as probabilities. The misunderstanding of quantum theory that Lanza is promoting is addressed further in the article in the section on quantum theory (Section 4.).

(c) The central argument from Lanza is a hard version of the anthropic principle. Lanza says:

“Why, for instance, are the laws of nature exactly balanced for life to exist? There are over 200 physical parameters within the solar system and universe so exact that it strains credulity to propose that they are random — even if that is exactly what contemporary physics baldly suggests. These fundamental constants (like the strength of gravity) are not predicted by any theory — all seem to be carefully chosen, often with great precision, to allow for existence of life. Tweak any of them and you never existed. “

This reveals a total lack of understanding of what the anthropic principle really says. So let us take a good, detailed, look at this principle.

3. The Planetary Anthropic Principle

And the beauty of the anthropic principle is that it tells us, against all intuition, that a chemical model need only predict that life will arise on one planet in a billion billion to give us a good and entirely satisfying explanation for the presence of life here.

Richard Dawkins, The God Delusion (2007)

The anthropic principle was first enunciated by the mathematician Brandon Carter in 1974. Further elaboration and consolidation came in 1986 in the form of a book The Anthropic Cosmological Principle by Barrow and Tipler. There are quite a few versions of the principle doing the rounds. The scientifically acceptable version, also called the ‘weak’ (or planetary) version, states that: The particular universe in which we find ourselves possesses the characteristics necessary for our planet to exist and for life, including human life, to flourish here.

In particle physics and cosmology, we humans have had to introduce ‘best fit’ parameters (fundamental constants) to explain the universe as we see it. Slightly different values for some of the critical parameters would have led to entirely different histories of the cosmos. Why do these parameters have the values they have? According to a differently worded form of the weak version of the anthropic principle stated above: the parameters and the laws of physics can be taken as fixed; it is simply that we humans have appeared in the universe to ask such questions at a time when the conditions were just right for our life.

This version suffices to explain quite a few ‘coincidences’ related to the fact that the conditions for our evolution and existence on the planet Earth happen to be ‘just right’ for that purpose. Life as we know it exists only on planet Earth. Here is a list of favourable necessary conditions for its existence, courtesy Dawkins (2007):

• Availability of liquid water is one of the preconditions for our kind of life. Around a typical star like our Sun, there is an optimum zone (popularly called the ‘Goldilocks zone’), neither so hot that water would evaporate, nor so cold that water would freeze, such that planets orbiting in that zone can sustain liquid water. Our Earth is one such planet.
• This optimum orbital zone should be circular or nearly circular. Once again, our Earth fulfils that requirement. A highly elliptical orbit would take the planet sometimes too close to the Sun, and sometimes too far, during its cycle. That would result in periods when water either evaporates or freezes. Life needs liquid water all the time.
• The location of the planet Jupiter in our Solar system is such that it acts like a ‘massive gravitational vacuum cleaner,’ intercepting asteroids that would have been otherwise lethal to our survival.
• Planet Earth has a single relatively large Moon, which serves to stabilize its axis of rotation.
• Our Sun is not a binary star. Binary stars can have planets, but their orbits can get messed up in all sorts of ways, entailing unstable or varying conditions, inimical for life to evolve and survive.

Most of the planets of stars in our universe are not in the Goldilocks zones of their parent stars. This is understandable because, as the above list of favorable conditions shows, the probability for this to happen must be very low indeed. But howsoever low this probability is, it is not zero: The proof is that life does indeed exist on Earth.

What we have listed above are just some necessary conditions. They are by no means sufficient conditions as well. With all the above conditions available on Earth, another highly improbable set of phenomena occurred, namely the actual origin of life. This origin was a set of highly improbable (but not impossible) set of chemical events, leading to the emergence of a mechanism for heredity. This mechanism came in the form of emergence of some kind of genetic molecules like RNA. This was a highly improbable thing to happen, but our existence implies that such an event, or a sequence of events, did indeed take place. Once life had originated, Darwinian evolution of complexity through natural selection (which is not a highly improbable set of events) did the rest and here we are, discussing such questions.

Like the origin of life, another extremely improbable event (or a set of events) was the emergence of the sophisticated eukaryotic cell (on which the life of we humans is based). We invoke the anthropic principle again to say that, no matter how improbable such an event was statistically, it did indeed happen; otherwise we humans would not be here. The occurrence of all such one-off highly improbable events can be explained by the anthropic principle.

Before we discuss the cosmological or ’strong’ version of the anthropic principle, it is helpful to recapitulate the basics of quantum theory.

4. Quantum Theory

In conventional quantum mechanics we use wave functions, ψ, to represent quantum states. The wave function plays a role somewhat similar to that of trajectories in classical mechanics. The Schrödinger equation describes how the wave function of a quantum system evolves with time. This equation predicts a smooth and deterministic time-evolution of the wave function, with no discontinuities or randomness. Just as trajectories in classical mechanics describe the evolution of a system in phase space from one time step to the next, the Schrödinger equation transforms the wave function at time t₀ (corresponding to a specific point in phase space) to its value ψ(t) at another time t. The physical interpretation of the wave function is that |ψ|² is the probability of occurrence of the state of the system at a given point in phase space.

An elementary particle can exist as a superposition of two or more alternative quantum states. Suppose its energy can take two values, E₁ and E₂. Let u₁ and u₂ denote the corresponding wave functions. The quantum interpretation is that the system exists in both the states, with u₁²and u₂² as the respective probabilities. Thus we move from a pure state to a mixture or ensemble of states. What is more, something striking happens when we humans observe such a system, say an electron, with an instrument. At the moment of observation, the wave function appears to collapse into only one of the possible alternative states, the superposition of which was described by the wave function before the event of measurement. That is, a quantum state becomes decoherent when measured or monitored by the environment. This amounts to the introduction of a discontinuity in the smooth evolution of the wave function with time.

This apparent collapse of the wave function does not follow from the mathematics of the Schrödinger equation, and was, in the early stages of the history of quantum mechanics, introduced ‘by hand’ as an additional postulate. That is, one chose to introduce the interpretation that there is a collapse of the wave function to the state actually detected by the measurement in the ‘real’ world, to the exclusion of other states represented in the original wave function. This (unsatisfactory) dualistic interpretation of quantum mechanics for dealing with the measurement problem was suggested by Bohr and Heisenberg at a conference in Copenhagen in 1927, and is known as the Copenhagen interpretation.

Another basic notion in standard quantum mechanics is that of time asymmetry. In classical mechanics we make the reasonable-looking assumption that, once we have formulated the Newtonian (or equivalent) equations of motion for a system, the future states are determined by the initial conditions. In fact, we can not only calculate the future conditions from the initial conditions, we can even calculate the initial conditions if the future conditions or states are known. This is time symmetry. In quantum mechanics, the uncertainty principle destroys the time symmetry. There can be now a one-to-many relationship between initial and final conditions. Two identical particles, in identical initial conditions, need not be observed to be in the same final conditions at a later time.

Multiple universes

Hugh Everett, during the mid-1950s, expressed total dissatisfaction with the Copenhagen interpretation: ‘The Copenhagen Interpretation is hopelessly incomplete because of its a priori reliance on classical physics … as well as a philosophic monstrosity with a “reality” concept for the macroscopic world and denial of the same for the microcosm.’ The Copenhagen interpretation implied that equations of quantum mechanics apply only to the microscopic world, and cease to be relevant in the macroscopic or ‘real’ world.

Everett offered a new interpretation, which presaged the modern ideas of quantum decoherence. Everett’s ‘many worlds’ interpretation of quantum mechanics is now taken more seriously, although not entirely in its original form. He simply let the mathematics of the quantum theory show the way for understanding logically the interface between the microscopic world and the macroscopic world. He made the observer an integral part of the system being observed, and introduced a universal wave function that applies comprehensively to the totality of the system being observed and the observer. This means that even macroscopic objects exist as quantum superpositions of all allowed quantum states. There is thus no need for the discontinuity of a wave-function collapse when a measurement is made on the microscopic quantum system in a macroscopic world.

Wave function bifurcation

Everett examined the question: What would things be like if no contributing quantum states to a superposition of states are banished artificially after seeing the results of an observation? He proved that the wave function of the observer would then bifurcate at each interaction of the observer with the system being observed. Suppose an electron can have two possible quantum states A and B, and its wave function is a linear superposition of these two. The evolution of the composite or universal wave function describing the electron and the observer would then contain two branches corresponding to each of the states A and B. Each branch has a copy of the observer, one which sees state A as a result of the measurement, and the other which sees state B. In accordance with the all-important principle of linear superposition in quantum mechanics, the branches do not influence each other, and each embarks on a different future (or a different ‘universe’), independent of the other. The copy of the observer in each universe is oblivious to the existence of other copies of itself and other universes, although the ‘full reality’ is that each possibility has actually happened. This reasoning can be made more abstract and general by removing the distinction between the observer and the observed, and stating that, at each interaction among the components of the composite system, the total or universal wave function would bifurcate as described above, giving rise to multiple universes or many worlds.

A modern and somewhat different version of this interpretation of quantum mechanics introduces the term quantum decoherence to rationalise how the branches become independent, and how each turns out to represent our classical or macroscopic reality. Quantum computing is now a reality, and it is based on such understanding of quantum mechanics.

Parallel histories

Richard Feynman formulated a different version of the many-worlds idea, and spoke in terms of multiple or parallel histories of the universe (rather than multiple worlds or universes). This work, done after World War II, fetched him the Nobel Prize in 1965. Feynman, whose path integrals are well known in quantum mechanics, suggested that, when a particle goes from a point P to a point Q in phase space, it does not have just a single unique trajectory or history. [It should be noted that, although we normally associate the word 'history' only with past events, history in the present context can refer to both the past and the future. A history is merely a narrative of a time sequence of event - past, present, or future.] Feynman proposed that every possible path or trajectory from P to Q in space-time is a candidate history, with an associated probability. The wave function for every such trajectory has an amplitude and a phase. The path integral for going from P to Q is obtained as the weighted vector sum, or integration over all such individual paths or histories. Feynman’s rules for assigning the amplitudes and phases for computing the sum over histories happen to be such that the effects of all except the one actually measured for a macroscopic object get cancelled out. For sub-microscopic particles, of course, the cancellation is far from complete, and there are indeed competing histories or parallel universes.

Quantum Darwinism

A different resolution to the problem of interfacing the microscopic quantum description of reality with macroscopic classical reality is offered by what has been called ‘quantum Darwinism.’ This formalism does not require the existence of an observer as a witness of what occurs in the universe. Instead, the environment is the witness. A selective witness at that, rather like natural selection in Darwin’s theory of evolution. The environment determines which quantum properties are the fittest to survive (and be observed, for example, by humans). Many copies of the fitter quantum property get created in the entire environment (’redundancy’). When humans make a measurement, there is a much greater chance that they would all observe and measure the fittest solution of the Schrödinger equation, to the exclusion (or near exclusion) of other possible outcomes of the measurement experiment.

In a computer experiment, Blume-Kohout and Zurek (2007) demonstrated quantum Darwinism (http://www.arxiv.org/abs/0704.3615) in zero-temperature quantum Brownian motion (QBM). A harmonic oscillator system (S) is made to evolve in contact with a bath (ε) of harmonic oscillators. The question asked is: How much information about S can an observer extract from the bath ε? ε consists of subenvironments ε_i; i = 1, 2, 3, … Each observer has exclusive access to a fragment F consisting of m subenvironments. The so-called ‘mutual information entropy’ is calculated from the quantum mutual information between S and F.

An important result of this approach is that substantial redundancy appears in the QBM model; i.e., multiple redundant records get made in the environment. As the authors state, this redundancy accounts for the objectivity and the classicality; the environment is a witness, holding many copies of the evidence. When humans make a measurement, it is most likely that they would all interact with one of the stable recorded copies, rather than directly with the actual quantum system, and thus observe and measure the classical value, to the exclusion of other possible outcomes of the measurement experiments.

Gell-Mann’s coarse-graining interpretation of quantum mechanics

For this interpretation, let us first understand the difference between fine-grained and coarse-grained histories of the universe. Completely

Murray Gel-Mann

fine-grained histories of the universe are histories that give as complete a description as possible of the entire universe at every moment of time. Consider a simplified universe in which elementary particles have no attributes other than positions and momenta, and in which the indistinguishability among particles of a given type is ignored. Then, one kind of fine-grained history of the simplified universe would be one in which the positions of all the particles are known at all times. Unlike classical mechanics which is deterministic, quantum mechanics is probabilistic. One might think that we can write down the probability for each possible fine-grained history. But this is not so. It turns out that the ‘interference’ terms between fine-grained histories do not usually cancel out, and we cannot assign probabilities to the fine-grained histories. One has to resort to coarse-graining to be able to assign probabilities to the histories. Murray Gell-Mann and coworkers applied this approach to a description of the quantum-mechanical histories of the universe. It was shown that the interference terms get cancelled out on coarse-graining. Thus we can work directly with wave functions, rather than having to work with wave-function amplitudes, and then there is no problem interfacing the microscopic description with the macroscopic world of measurements etc.

Gell-Mann also emphasized the point that the term ‘many worlds or universes’ should be substituted by ‘many alternative histories of the universe’, with the further proviso that the many histories are not ‘equally real’; rather they have different probabilities of occurrence.

5. The Cosmological Anthropic Principle

Some quantum cosmologists like to talk about a so-called anthropic principle that requires conditions in the universe to be compatible with the existence of human beings. A weak form of the principle states merely that the particular branch history on which we find ourselves possesses the characteristics necessary for our planet to exist and for life, including human life, to flourish here. In that form, the anthropic principle is obvious. In its strongest form, however, such a principle will supposedly apply to the dynamics of the elementary particles and the initial conditions of the universe, somehow shaping those fundamental laws so as to produce human beings. That idea seems to me so ridiculous as to merit no further discussion.

Murray Gell-Mann, The Quark and the Jaguar

Much confusion and uncalled-for debate has been engendered by the (scientifically unsound) ’strong’ or cosmological version of the anthropic principle, which is sometimes stated as follows: Since the universe is compatible with the existence of human beings, the dynamics of the elementary particles and the initial conditions of the universe must have been such that they shaped the fundamental laws so as to produce human beings. This is clearly untenable. There are no grounds for the existence of a ‘principle’ like this. A scientifically untenable principle is no principle at all. No wonder, the Nobel laureate Gell-Mann, as quoted above, described it as ‘so ridiculous as to merit no further discussion.’

The chemical elements needed for life were forged in stars, and then flung far into space through supernova explosions. This required a certain amount of time. Therefore the universe cannot be younger than the lifetime of stars. The universe cannot be too old either, because then all the stars would be ‘dead’. Thus, life can exist only when the universe has just the age that we humans measure it to be, and has just the physical constants that we measure them to be.

It has been calculated that if the laws and fundamental constants of our universe had been even slightly different from what they are, life as we know it would not have been possible. Rees (1999), in the book Just Six Numbers, listed six fundamental constants which together determine the universe as we see it. Their fine-tuned mutual values are such that even a slightly different set of these six numbers would have been inimical to our emergence and existence. Consideration of just one of these constants, namely the strength of the strong interaction (which determines the binding energies of nuclei), is enough to make the point. It is defined as that fraction of the mass of an atom of hydrogen which is released as energy when hydrogen atoms fuse to form an atom of helium. Its value is 0.007, which is just right (give or take a small acceptable range) for any known chemistry to exist, and no chemistry means no life. Our chemistry is based on reactions among the 90-odd elements. Hydrogen is the simplest among them, and the first to occur in the periodic table. All the other elements in our universe got synthesised by fusion of hydrogen atoms. This nuclear fusion depends on the strength of the strong or nuclear interaction, and also on the ability of a system to overcome the intense Coulomb repulsion between the fusing nuclei. The creation of intense temperatures is one way of overcoming the Coulomb repulsion. A small star like our Sun has a temperature high enough for the production of only helium from hydrogen. The other elements in the periodic table must have been made in the much hotter interiors of stars larger than our Sun. These big stars may explode as supernovas, sending their contents as stellar dust clouds, which eventually condense, creating new stars and planets, including our own Earth. That is how our Earth came to have the 90-odd elements so crucial to the chemistry of our life. The value 0.007 for the strong interaction determined the upper limit on the mass number of the elements we have here on Earth and elsewhere in our universe. A value of, say, 0.006, would mean that the universe would contain nothing but hydrogen, making impossible any chemistry whatsoever. And if it were too large, say 0.008, all the hydrogen would have disappeared by fusing into heavier elements. No hydrogen would mean no life as we know it; in particular there would be no water without hydrogen.

Similarly for the other finely-tuned fundamental constants of our universe. Existence of humans has become possible because the values of the fundamental constants are what they are; had they been different, we would not exist; that is how the anthropic principle (planetary or cosmological, weak or strong) should be stated. The weak version is the only valid version of the principle.

But why does the universe have these values for the fundamental constants, and not some other set of values? Different physicists and cosmologists have tried to answer this question in different ways, and the investigations go on. One possibility is that there are multiple universes, and we are in one just right for our existence. Another idea is based on string theory.

6. String Theory and the Anthropic Principle

A ’string’ is a fundamental 1-dimensional object, postulated to replace the concept of structureless elementary particles. Different vibrational modes of a string give rise to the various elementary particles (including the graviton). String theory aims to unite quantum mechanics and the general theory of relativity, and is thus expected to be a unified ‘theory of everything.’ When this theory makes sufficient headway, the six fundamental constants identified by Rees will turn out to be inter-related, and not free to have any arbitrary values. But this still begs the question asked above: Why this particular set of fundamental constants, and not another? Hawking (1988) asked an even deeper question: ‘Even if there is only one possible unified theory, it is just a set of rules and equations. What is it that breathes fire into the equations and makes a universe for them to describe? The usual approach of science of constructing a mathematical model cannot answer the questions of why there should be a universe for the model to describe. Why does the universe go to all the bother of existing?’

Our universe is believed to have started at the big bang, shown by Hawking and Penrose in the 1970s to be a singularity point is space-time (some physicists disagree with the singularity idea). The evidence for this seems to be that the universe has been expanding (’inflating’) ever since then. It so happens that we have no knowledge of the set of initial boundary conditions at the moment of the big bang. Moreover, as Hawking and Hertog said in 2006, things could be a little simpler ‘if one knew that the universe was set going in a particular way in either the finite or infinite past.’ Therefore Hawking and coworkers argued that it is not possible to adopt the bottom up approach to cosmology wherein one starts at the beginning of time, applies the laws of physics, calculates how the universe would evolve with time, and then just hopes that it would turn out to be something like the universe we live in. Consequently a top down approach has been advocated by them (remember, this is just a model), wherein we start with the present and work our way backwards into the past. According to Hawking and Hertog (2006), there are many possible histories (corresponding to successive unpredictable bifurcations in phase space), and the universe has lived them all. Not only that, there is also an anthropic angle to this scenario:

As mentioned above, Stephen Hawking and Roger Penrose had proved that the moment of the big bang was a singularity, i.e. a point where gravity must have been so strong as to curve space and time in an unimaginably strong way. Under such extreme conditions our present formulation of general relativity would be inadequate. A proper quantum theory of gravity is still an elusive proposition. But, as suggested by Hawking and Hertog in 2006, because of the small size of the universe at and just after the big bang, quantum effects must have been very important. The origin of the universe must have been a quantum event. This statement has several weird-looking consequences. The basic idea is to incorporate the consequences of Heisenberg’s uncertainty principle when considering the evolution of the (very small) early universe, and combine it with Feynman’s sum-over-histories approach. This means that, starting from configuration A, the early universe could go not only to B, but also to other configurations B’, B”, etc. (as permitted by the quantum-mechanical uncertainty principle), and one has to do a sum-over-histories for each of the possibilities AB, AB’, AB”, … And each such branch corresponds to a different evolution of the universe (with different cosmological and other fundamental constants), only one or a few of them corresponding to a universe in which we humans could evolve and survive. This provides a satisfactory answer to the question: ‘why does the universe have these values for the fundamental constants, and not some other set of values?’.

The statement ‘humans exist in a universe in which their existence is possible’ is practically a tautology. How can humans exist in a universe which has values of fundamental constants which are not compatible with their existence?! Stop joking, Dr. Lanza.

The other possible universes (or histories) also exist, each with a specific probability. Our observations of the world are determining the history that we see. The fact that we are there and making observations assigns to ourselves a particular history.

Let A denote the beginning of time (if there is any), and B denote now. The state of the universe at point B can be broadly specified by recognizing the important aspects of the world around us: There are three large dimensions in space, the geometry of space is almost flat, the universe is expanding, etc. The problem is that we have no way of specifying point A. So how do we perform the various sums over histories? An interesting point of the quantum mechanical sums-over-histories theory is that the answers come out right when we work with imaginary (or complex) time, rather than real time. The work of Hawking and Hertog (2006) has shown that the imaginary-time approach is crucial for understanding the origin of the universe. When the histories of the universe are added up in imaginary time, time gets transformed into space. It follows from this work that when the universe was very small, it had four spatial dimensions, and none for time. In terms of the history of the universe, it means that there is no point A, and that the universe has no definable starting point or initial boundary conditions. In this no-boundary scheme of things, we can only start from point B and work our way backwards (the top-down approach).

This approach also solves the fine-tuning problem of cosmology. Why has the universe a particular inflation history? Why does the cosmological constant (which determines the rate of inflation) have the value it has? Why did the early universe have a particular ‘fine-tuned’ initial configuration and a specific (fast) initial rate of inflation? In the no-boundary scenario there is no need to define an initial state. And there is no need for any fine tuning. What is more, the very fact of inflation, as against no inflation, follows from the theory as the most probable scenario.

Artistic Rendition of the Multiverse. Source: Nature

String theory defines a near-infinity of multiple universes. This goes well with the anthropic-principle idea that, out of the multiple choices for the fundamental constants (including the cosmological constant) for each such universe, we live in the universe that makes our existence possible. In the language of string theory, there are multiple ‘pocket’ universes that branch off from one another, each branch having a different set of fundamental constants. Naturally, we are living in one with just the right fundamental constants for our existence.

While many physicists feel uncomfortable with this unconfirmed world view, Hawking and Hertog (2006) have pointed out that the picture of a never-ending proliferation of pocket universes is meaningful only from the point of view of an observer outside a universe, and that situation (observer outside a universe) is impossible. This means that parallel pocket universes can have no effect on an actual observer inside a particular pocket.

Hawking’s work has several other implications as well. For example, in his scheme of things the string theory ‘landscape’ is populated by the set of all possible histories. All possible versions of a universe exist in a state of quantum superposition. When we humans choose to make a measurement, a subset of histories that share the specific property measured gets selected. Our version of the history of the universe is determined by that subset of histories. No wonder the cosmological anthropic principle holds. How can any rational person use the anthropic principle to justify biocentrism?

Hawking and Hertog’s theory can be tested by experiment, although that is not going to be easy. Its invocation of Heisenberg’s uncertainty principle during the early moments of the universe, and the consequent quantum fluctuations, leads to a prediction of specific fluctuations in the cosmic microwave background, and in the early spectrum of gravitational waves. These predicted fluctuations arise because there is an uncertainty in the exact shape of the early universe, which is influenced, among other things, by other histories with similar geometries. Unprecedented precision will be required for testing these predictions. In any case, gravitation waves have not even been detected yet.

In any case, good scientists are having a serious debate about the correct interpretation of the data available about life and the universe. While this goes on, non-scientists and charlatans cannot be permitted to twist facts to satisfy the hunger of humans for the feel-good or feel-important factor. The scientific method is such that scientists feel good when they are doing good science.

7. Wolfram’s Universe

Stephen Wolfram has emphasized the role of computational irreducibility when it comes to trying to understand our universe. The notion of probability (as opposed to certainty) is inherent in our worldview if quantum theory is a valid theory. Wolfram argues that this may not be a correct worldview. He does not rule out the possibility that there really is just a single, definite, rule for our universe which, in a sense, deterministically specifies how everything in our universe happens. Things only look probabilistic because of the high degree of complexity involved, particularly regarding the very structure and connectivity of space and time. It is computational irreducibility that sometimes makes certain things look incomprehensible or probabilistic, rather than deterministic. Since we are restricted to doing the computational work within the universe, we cannot expect to ‘outrun’ the universe, and derive knowledge any faster than just by watching what the universe actually does.

Wolfram points out that there is relief from this tyranny of computational irreducibility only in the patches or islands of computational reducibility. It is in those patches that essentially all of our current physics lies. In natural science we usually have to be content with making models that are approximations. Of course, we have to try to make sure that we have managed to capture all the features that are essential for some particular purpose. But when it comes to finding an ultimate model for the universe, we must find a precise and exact representation of the universe, with no approximations. This would amount to reducing all physics to mathematics. But even if we could do that and know the ultimate rule, we are still going to be confronted with the problem of computational irreducibility. So, at some level, to know what will happen, we just have to watch and see history unfold.

8. The Nature of Consciousness

One criticism of biocentrism comes from the philosopher Daniel Dennett, who says “It looks like an opposite of a theory, because he doesn’t explain how consciousness happens at all. He’s stopping where the fun begins.”

The logic behind this criticism is obvious. Without a descriptive explanation for consciousness and how it ‘creates’ the universe, biocentrism is not useful. In essence, Lanza calls for the abandonment of modern theoretical physics and its replacement with a magical solution. Here are a few questions that one might ask of the idea:

1. What is this consciousness?
2. Why does this consciousness exist?
3. What is the nature of the interaction between this consciousness and the universe?
4. Is the problem of infinite regression applicable to consciousness itself?
5. Even if Lanza’s interpretation of the anthropic principle is a valid argument against modern theoretical physics, does the biocentric model of consciousness create a bigger ontological problem than the one it attempts to solve?

Consider this statement by Lanza:

“Consciousness cannot exist without a living, biological creature to embody its perceptive powers of creation.“

How can consciousness create the universe if it doesn’t exist? How can the “living, biological creature” exist if the universe has not been created yet? It becomes apparent that Lanza is muddling the meaning of the word ‘consciousness.’ In one sense he equates it to subjective experience that is tied to a physical brain. In another, he assigns to consciousness a spatio-temporal logic that exists outside of physical manifestation. In this case, the above questions become: 1. What is this spatio-temporal logic?; 2. Why does this spatio-temporal logic exist? and so on…

Daniel Dennett’s criticism of biocentrism centres on Lanza’s non-explanation of the nature of consciousness. In fact, even from a biological

The Cartesian Theater

perspective Lanza’s conception of consciousness is unclear. For example, he consistently equates consciousness with subjective experience while stressing its independence from the objective universe (see Lanza’s quote below). This is an appeal to the widespread but erroneous intuition towards Cartesian Dualism. In this view, consciousness (subjective experience) belongs to a different plane of reality than the one on which the material universe is constructed. Lanza requires this general definition of consciousness to construct his theory of biocentrism. He uses it in the same way that Descartes used it - as a semantic tool to deconstruct reality. In fact, Lanza’s theory of biocentrism is a sophisticated non-explanation for the ‘brain in a vat’ problem that plagued philosophers for centuries. However, instead of subscribing to Cartesian Dualism, he attempts a Cartesian Monism by invoking quantum mechanics. To be exact, his view is Monistic Idealism - the idea that consciousness is everything- but the Cartesian bias is an essential element in his arguments.

In a dualistic or idealistic context, Lanza’s definition of consciousness as subjective experience may be acceptable. However, Lanza’s definition is incomplete from a scientific perspective. The truth is that there are difficulties in analysing consciousness empirically. In scientific terms, consciousness is a ‘hard problem’, meaning that its complete subjective nature places it beyond direct objective study. Lanza exploits this difficulty to deny science any understanding of consciousness.

Lanza trivializes the current debate in the scientific community about the nature of consciousness when he says:

“Neuroscientists have developed theories that might help to explain how separate pieces of information are integrated in the brain and thus succeed in elucidating how different attributes of a single perceived object-such as the shape, colour, and smell of a flower-are merged into a coherent whole. These theories reflect some of the important work that is occurring in the fields of neuroscience and psychology, but they are theories of structure and function. They tell us nothing about how the performance of these functions is accompanied by a conscious experience; and yet the difficulty in understanding consciousness lies precisely here, in this gap in our understanding of how a subjective experience emerges from a physical process.”

This criticism of the lack of a scientific consensus on the nature of consciousness is empty, considering that Lanza himself proposes no actual mechanism for consciousness, but still places it at the centre of his theory of the universe.

There is no need to view consciousness as such a mystery. There are some contemporary models of consciousness that are quite explanatory, presenting promising avenues for studying how the brain works. Daniel Dennett’s Multiple Drafts Model is one. According to Dennett, there is nothing mystical about consciousness. It is an illusion created by tricks in the brain. The biological machinery behind the tricks that create the illusion of consciousness is the product of successive evolutionary processes, beginning with the development of primitive physiological reactions to external stimuli. In the context of modern humans, consciousness consists of a highly dynamic process of information exchange in the brain. Multiple sets of sensory information, memories and emotional cues are competing with each other at all times in the brain, but at any one instant only one set of these factors dominates the brain. At the next instant, another set of slightly different factors are dominant. At all instants, multiple sets of information are competing with each other for dominance. This creates the illusion of a continuous stream of thoughts and experiences, leading to the intuition that consciousness comprises the entirety of the voluntary mental function of the individual. There are other materialist models, such as Marvin Minsky’s view of the brain as an emotional machine, that provide us with ways of approaching the problem from a scientific perspective without resorting to mysticism.

Consciousness is not something that requires a restructuring of objective reality. It is a subjective illusion on one level, and the mechanistic outcome of evolutionary processes on another.

“A human being is a part of a whole, called by us ‘universe’, a part limited in time and space. He experiences himself, his thoughts and feelings as something separated from the rest… a kind of optical delusion of his consciousness.”

Albert Einstein

9. Deepak Chopra Finds an Ally for Hijacking and Distorting Scientific Truths

Deepak Chopra, Lanza’s coauthor in the article, is known for making bold claims about the nature of the universe. He peddles a form of new-age Hinduism. Chopra’s ideas about a conscious universe are derived from an interpretation of Vedic teachings. He supplements this new-age Hinduism with ideas from a minority view among physicists that the Copenhagen Interpretation implies a conscious universe. This view is expounded by Amit Goswami in his book The Self-Aware Universe. In turn, Goswami and his peers were influenced by Fritjof Capra’s book The Tao of Physics in which the author attempts to reconcile reductionist science with Eastern mystical philosophies. Much of modern quantum mysticism in the popular culture can be traced back to Capra. Chopra’s philosophy is essentially a distillation of Capra’s work combined with a popular marketing strategy to sell all kinds of pseudoscientific garbage.

Considering Chopra’s reputation in the scientific community for making absurd quack claims about every subject under the sun, one must wonder about the strange pairing between the two writers. With Lanza’s experience in biomedical research, he could not possibly be in agreement with Chopra’s brand of holistic healing and quantum mysticism. Rather, it seems likely that this is an arrangement of convenience. If you look at what drives the two men, a mutually reinforced disenchantment with Darwin’s ideas emerges as a strong motive behind the pairing. Both Chopra and Lanza are disillusioned with a certain perceived implication of Darwinian evolution on human existence - that the meaning of life is inconsequential to the universe. Evolutionary biology upholds the materialist view of modern science that consciousness is a product of purely inanimate matter assembling in highly complex states. Such a view is disillusioning to anyone who craves a more central role for the human ego in determining one’s reality. The view that human life is central to existence is found in most philosophical and religious traditions. This view is so fundamental to our nature that we can say it is an intuitive reaction to the very condition of being conscious. It has traditionally been the powerful driving force behind philosophers, poets, priests, mystics and scholars of history. Darwin dismantled the idea in one clean stroke. Therefore, Darwin became the enemy. The entire theory of biocentrism is an attempt to ingrain the idea of human destiny into popular science.

The title of Chopra and Lanza’s article is “Evolution Reigns, but Darwin Outmoded”. This may mislead you to think that the article is about new discoveries in biological evolution. On reading the article, however, it becomes apparent that the authors are not talking about biological evolution at all. It is relevant to note that not once in their article do they say how Darwin has been outmoded.

Towards the end of their article, Chopra and Lanza say:

“Darwin’s theory of evolution is an enormous over-simplification. It’s helpful if you want to connect the dots and understand the interrelatedness of life on the planet — and it’s simple enough to teach to children between recess and lunch. But it fails to capture the driving force and what’s really going on.”

There is irony in dismissing the most brilliant and explanatory scientific theory in all of biology as an ‘over-simplification’, by over-simplifying it as a way to “connect the dots and understand the interrelatedness of life on the planet”. Contrast this with what Richard Dawkins said: “In 1859, Charles Darwin announced one of the greatest ideas ever to occur to a human mind: cumulative evolution by natural selection.” The irony of Chopra and Lanza’s statement is compounded by the fact that biocentrism does not address biological evolution at all! The authors are simply interested in belittling the uncomfortable implications of evolutionary theory, while not actually saying anything about the theory itself! We can safely assume that Lanza and Chopra are more concerned with the implications of Darwinian evolution on the nature of the human ego, and not on the theory of evolution by natural selection.

Interestingly, Chopra has demonstrated his dislike and ignorance of biological evolution multiple times. Here are some prize quotations from the woo-master himself (skip these if you feel an aneurysm coming):

“To say the DNA happened randomly is like saying that a hurricane could blow through a junk yard and produce a jet plane. “

“How does nature take creative leaps? In the fossil record there are repeated gaps that no “missing link” can fill. The most glaring is the leap by which inorganic molecules turned into DNA. For billions of years after the Big Bang, no other molecule replicated itself. No other molecule was remotely as complicated. No other molecule has the capacity to string billions of pieces of information that remain self-sustaining despite countless transformations into all the life forms that DNA has produced. “

“If mutations are random, why does the fossil record demonstrate so many positive mutations–those that lead to new species–and so few negative ones? Random chance should produce useless mutations thousands of times more often than positive ones. “

“Evolutionary biology is stuck with regard to simultaneous mutations. One kind of primordial skin cell, for example, mutated into scales, fur, and feathers. These are hugely different adaptations, and each is tremendously complex. How could one kind of cell take three different routs purely at random? “

“If design doesn’t imply intelligence, why are we so intelligent? The human body is composed of cells that evolved from one-celled blue-green algae, yet that algae is still around. Why did DNA pursue the path of greater and greater intelligence when it could have perfectly survived in one-celled plants and animals, as in fact it did? “

“Why do forms replicate themselves without apparent need? The helix or spiral shape found in the shell of the chambered nautilus, the centre of sunflowers, spiral galaxies, and DNA itself seems to be such a replication. It is mathematically elegant and appears to be a design that was suited for hundreds of totally unrelated functions in nature. “

“What happens when simple molecules come into contact with life? Oxygen is a simple molecule in the atmosphere, but once it enters our lungs, it becomes part of the cellular machinery, and far from wandering about randomly, it precisely joins itself with other simple molecules, and together they perform cellular tasks, such as protein-building, whose precision is millions of times greater than anything else seen in nature. If the oxygen doesn’t change physically–and it doesn’t–what invisible change causes it to acquire intelligence the instant it contacts life? “

“How can whole systems appear all at once? The leap from reptile to bird is proven by the fossil record. Yet this apparent step in evolution has many simultaneous parts. It would seem that Nature, to our embarrassment, simply struck upon a good idea, not a simple mutation. If you look at how a bird is constructed, with hollow bones, toes elongated into wing bones, feet adapted to clutching branches instead of running, etc., none of the mutations by themselves give an advantage to survival, but taken altogether, they are a brilliant creative leap. Nature takes such leaps all the time, and our attempt to reduce them to bits of a jigsaw puzzle that just happened to fall into place to form a beautifully designed picture seems faulty on the face of it. Why do we insist that we are allowed to have brilliant ideas while Nature isn’t? “

“Darwin’s iron law was that evolution is linked to survival, but it was long ago pointed out that “survival of the fittest” is a tautology. Some mutations survive, and therefore we call them fittest. Yet there is no obvious reason why the dodo, kiwi, and other flightless birds are more fit; they just survived for a while. DNA itself isn’t fit at all; unlike a molecule of iron or hydrogen, DNA will blow away into dust if left outside on a sunny day or if attacked by pathogens, x-rays, solar radiation, and mutations like cancer. The key to survival is more than fighting to see which organism is fittest. “

“Competition itself is suspect, for we see just as many examples in Nature of cooperation. Bees cooperate, obviously, to the point that when a honey bee stings an enemy, it acts to save the whole hive. At the moment of stinging, a honeybee dies. In what way is this a survival mechanism, given that the bee doesn’t survive at all? For that matter, since a mutation can only survive by breeding–”survival” is basically a simplified term for passing along gene mutations from one generation to the next-how did bees develop drones in the hive, that is, bees who cannot and never do have sex? “

“How did symbiotic cooperation develop? Certain flowers, for example, require exactly one kind of insect to pollinate them. A flower might have a very deep calyx, or throat, for example than only an insect with a tremendously long tongue can reach. Both these adaptations are very complex, and they serve no outside use. Nature was getting along very well without this symbiosis, as evident in the thousands of flowers and insects that persist without it. So how did numerous generations pass this symbiosis along if it is so specialized? “

“Finally, why are life forms beautiful? Beauty is everywhere in Nature, yet it serves no obvious purpose. Once a bird of paradise has evolved its incredibly gorgeous plumage, we can say that it is useful to attract mates. But doesn’t it also attract predators, for we simultaneously say that camouflaged creatures like the chameleon survive by not being conspicuous. In other words, exact opposites are rationalized by the same logic. This is no logic at all. Non-beautiful creatures have survived for millions of years, so have gorgeous ones. The notion that this is random seems weak on the face of it. “

Now comes the kicker. All these quotes that demonstrate a complete lack of understanding of biology, let alone the theory of evolution by natural selection, are from one single article as compiled by P. Z. Myers in his blog post in 2005. Since then, Chopra has continued to spout his ignorance of evolution over and over.

Chopra’s brand of mysticism gets its claimed legitimacy from science and its virulence from discrediting science’s core principles. He continues this practice through his association with Robert Lanza. Both Chopra and Lanza seem to be disillusioned by the perceived emptiness of a non-directional evolutionary reality. Chopra has invested much time and effort in promoting the idea that consciousness in a property of the universe itself. He finds in Lanza a keen mind with an inclination towards a similar dislike for a perceived lack of anthropocentric meaning in the nature of biological life as described by Darwin’s theory of evolution by natural selection.

10. Conclusions

Let us recapitulate the main points:

(a) Space and time exist, even though they are relative and not absolute.

(b) Modern quantum theory, long after the now-discredited Copenhagen interpretation, is consistent with the idea of an objective universe that exists without a conscious observer.

(c) Lanza and Chopra misunderstand and misuse the anthropic principle.

(d) The biocentrism approach does not provide any new information about the nature of consciousness, and relies on ignoring recent advances in understanding consciousness from a scientific perspective.

(e) Both authors show thinly-veiled disdain for Darwin, while not actually addressing his science in the article. Chopra has demonstrated his utter ignorance of evolution multiple times.

Modern physics is a vast and multi-layered web that stretches over the entire deck of cards. All other natural sciences - all truths that exist in the material world- are interrelated, held together by the mathematical reality of physics. Fundamental theories in physics are supported by multiple lines of evidence from many different scientific disciplines, developed and tested over decades. Clearly, those who propose new theories that purport to redefine fundamental assumptions or paradigms in physics have their work cut out for them. Our contention is that the theory of biocentrism, if analysed properly, does not hold up to scrutiny. It is not the paradigm change that it claims to be. It is also our view that one can find much meaning, beauty and purpose in a naturalistic view of the universe, without having to resort to mystical notions of reality.

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS.

Related posts:

1. Deepak Chopra: A New Age Shaman (Watch Video)
2. Deepak Chopra And His New-Age Claptrap
3. Are You A Freethinker? Naturalism, Life and Meaning in a Causal Universe
4. Victor Stenger on The Future of Naturalism
5. COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity
6. COMPLEXITY EXPLAINED: 6. Emergence of Complexity in Far-from-Equilibrium Systems
7. Darwin’s Triumph
8. Philosophy With Selvi - What Is Knowledge? (Epistemology For Beginners)
9. COMPLEXITY EXPLAINED: 13. Evolution of Biological Complexity
10. COMPLEXITY EXPLAINED: 17. Epilogue
11. Sacred Reason: Reconciling Science and Emotion
12. Nirmukta Exclusive: Interview with Daniel Dennett.
13. Naturalism: Scientific, Philosophical and Socio-Political.

There is a distinction between replication and reproduction. Probably, the earliest living entities were able to reproduce but not to replicate. Cells can reproduce, but only molecules can replicate. Reproduction in the case of such primitive cells means to divide into two cells with the daughter cells inheriting approximately equal shares of the constituents of the cell. By contrast, replication for a molecule means the creation of an exact copy of itself by suitable chemical processes. Here I describe John von Neumann’s computer-simulation studies on self-reproduction, after introducing the notion of cellular automata. Studies on cellular automata help us understand life processes.

11.1 Introduction

Present-day life processes involve metabolic reproduction and replication. Freeman Dyson (1985) argued that metabolic reproduction and replication are logically separable propositions. He pointed out that Darwinian natural selection does not require replication, at least for simple creatures. According to Dyson, it is likely that life originated twice, with two separate kinds of organisms, one capable of metabolism without exact replication, and the other capable of replication without metabolism. At some stage the two features came together. He suggested, probably, that the earliest living creatures were able to reproduce but not to replicate. I shall discuss Dyson’s dual-origin-hypothesis for life in the next article in this series. Here I focus on some computer-simulation aspects of replication and reproduction.

An automaton has two components, which are now known by the names hardware and software. Roughly speaking, software embodies information, and hardware processes information. And the rough analogy to biology is: nucleic acid is software, and protein is hardware. Usually, protein is the essential component for metabolism, and nucleic acid is the essential component for replication. An automaton that has only hardware but no software can exist independently and maintain its metabolism so long as it finds food to eat or numbers to crunch. By contrast, an automaton that has only software but no hardware can lead only a parasitic existence (e.g. viruses).

11.2 Cellular Automata

John von Neumann

The notion of cellular automata was put forward in the 1940s by Stanislas Ulam, a colleague of John von Neumann. What Ulam suggested to Neumann was to consider a digital programmable universe in which time is imagined as defined by the ticking of a cosmic clock, and space is a discrete lattice of cells, eachcell occupied by an abstractly defined very simple computer called a finite automaton. Very simple local rules determine the state of any cell at any discrete point of time. There are only a finite number of states available to a cell or automaton. These states could be, say, a few colours, or a few integers, or just ‘dead’ or ‘alive’, etc. At each tick of the digital clock, every automaton changes over to a new state determined by its present state and the present states of the neighbouring cellular automata (CA). The rules by which the state of each automaton changes at a given instant of digital time are the equivalent of the physical laws of the universe. There is thus a state-transition table, which describes how each automaton changes for each of the possible configurations of the states of the neighbouring cells.

11.3 John Conway’s ‘Game of Life’

A particularly popular example of CA is the Game of Life invented (around 1970) by John Conway. It provides a graphic demonstration of ‘artificial evolution’ because the fascinating evolving structures can be seen on a computer screen. One starts with a 2-dimensional lattice of square cells, each cell randomly taken to be either black or white. Let black mean that the ‘creature’ denoted by a cell is ‘alive’, and let white mean that the corresponding creature is ‘dead.’ Very simple local rules are introduced for how the cells will change from one time step to the next. For example, if a cell has two or three neighbours which are alive (i.e. black), then the cell becomes alive (black) if it was dead to start with, or remains alive if it was already so. If the number of live neighbours is less than two, the cell dies of ‘loneliness.’ And if the number of neighbours is more than three, again the cell dies, this time due to ‘overcrowding.’ Remarkable patterns emerge on the computer screen when this program is run. Every run is different, and it is not possible to predict or exhaust all possibilities, in keeping with what one would expect from a complex system. The live cells organize themselves into coherent and ever-changing patterns, like real creatures in Nature.

11.4 Wolfram’s ‘New Kind of Science’

What secret it is that allows nature seemingly so effortlessly to produce so much that appears to us so complex (?)

Stephen Wolfram

Studies on cellular automata got a big boost through the work of Stephen Wolfram. He introduced a whole new dimension to the study of complex systems. His monumental book on complexity (A New Kind of Science (NKS)), published in 2002, has been widely read, and continues to raise debate. I mentioned in Part 4 the notion of algorithmic irreducibility analyzed by Chaitin. That was information irreducibility. An information-irreducible digit stream is that for which there is no theory or model more compact than simply writing the string of bits directly. There is no program for calculating the string of bits that is substantially smaller than the string of bits itself. A somewhat different but related kind of irreducibility, namely computation-irreducibility was analyzed on a computer by Wolfram. (It is also called time-irreducibility because the longer a computation is, the more time it takes to perform it.) It pertains to physical and other systems for which there are no computational shortcuts, and for which the quickest way to see what a system will be like at a distant time is just to run the computer program (or the automaton) that is modelling the system. By contrast, a computationally reducible system is one which can be described by exact mathematical formulas that give the outcome at any chosen instant of time without working through all the time steps.

Wolfram is of the view that the complex phenomena we see in the world around us can be usually thought of as the running of simple computer programs. And the best and often the only way to understand these phenomena is by modelling them on a computer, rather than by working out the consequences of idealized and approximate mathematical models based on a set of equations. Wolfram’s idea of a ’simple program’ typically has the following ingredients: a set of transformation or operation rules (usually local rules); data to operate on; and an engine that applies the rules to the data. In a cellular automaton, the data enter only at the beginning of the computation (’initial conditions’), and the engine keeps applying the same deterministic rules to the outputs of its previous application of the rules. Extremely complex-looking patterns can be generated by any of a large number of simple programs investigated by Wolfram.

Shown here is a very simple 1-dimensional cellular automaton, which generates a complex-looking (nested) pattern. It consists of a row of squares, and each square can be either black or white. Starting from just one such row of squares (the top row in the figure), each time the system is updated, a new row of squares is created just below the previous row, following a simple rule. The simple rule operative in this figure says that a square in the new row should be black only if one or the other, but not both, of its vertically-above predecessor’s neighbours is black. Shown in a separate figure at the bottom is a graphical depiction of this rule, in which 8 blocks are drawn, corresponding to the 8 conceivable configurations of three neighbouring cells, each configuration determining the colour of the cell in the next row. Starting with a single black square in the top row of squares, this rule produces a complex pattern of nested triangles.

Here is another, beautiful, example of what a simple cellular automaton can generate:

Any process that follows definite rules can be regarded as a computation. Thus the CA can carry out computation, as can Turing machines, and many other systems in Nature. In computations carried out by humans on computers, the computer programs define the rules of computation. In Nature, the rules of computation are nothing but the laws of Nature.

The notion of a universal computer emerged from the work of Alan Turing in the 1950s, and this launched the computer revolution. It was demonstrated that it is possible to build universal machines with a fixed underlying construction, but which can be made to perform different computations by being programmed in different ways. With suitable programming, any computer system and computer language can be ultimately made to perform exactly the same set of tasks. Can at least some of the CA be universal computers? ‘Yes’ according to Wolfram. He describes the construction of a CA that can be a universal cellular automaton (UCA). The rule for this UCA is extremely simple. In fact it is a somewhat complex version of the so-called ‘rule 90′, which we have illustrated in the black-and-white figure above. My recent article should be consulted for more details on this.

Alan Turing

The immense number of CA examined by Wolfram has led to the formulation of his Principle of Computational Equivalence (PCE). According to this principle, almost all processes that are not ‘obviously simple’ correspond to computations that are of equivalent complexity. In other words, irrespective of the simple or complicated nature of the rules or the initial conditions of a process, any such process will always correspond to a computation of equivalent difficulty or sophistication.

The genesis of the PCE lies in the idea of computational universality: It is possible to construct universal systems that can perform essentially any computation, and which must therefore all be capable of exhibiting the highest level of computational sophistication. It does not matter how simple or complicated either the rules or the initial conditions for a process are; so long as the process itself does not look obviously simple (e.g. purely repetitive or purely ‘nested’), it will almost always correspond to a computation of equivalent sophistication. The PCE, though still under some debate, may have far-reaching consequences for science, and for much else.

Let us now see how the PCE rationalizes the rampant occurrence of computational irreducibility (or complexity) in Nature. For this we have to address the question of comparing the computational sophistication of the systems that we study with the computational sophistication of the systems that we use for studying them. The PCE implies that, once a threshold has been crossed, any real system must exhibit essentially the same level of computational sophistication. And this applies to our own perception and analysis capabilities also; according to the PCE they are not computationally superior to the complex systems we seek to observe and understand. Beyond a certain threshold, all systems are computationally equivalent in terms of complexity.

If predictions about the behaviour of a system are to be possible, it must be the case that the system making the predictions is able to outrun the system it is trying to make predictions about. But this is possible only if the predicting system is able to perform more sophisticated computations than the system under investigation. And the PCE does not allow that. Therefore, except for simple systems, no systematic predictions can be made about their behaviour at a chosen time in the future. Thus there is no general way to shortcut their process of evolution. In other words, most such systems are computationally irreducible.

As Wolfram emphasizes, the whole idea of doing science with mathematical formulas makes sense only for computationally reducible systems. For others there are no computational shortcuts; practically the only way of knowing a future configuration is to actually run through all the evolutionary time steps. And Wolfram’s NKS is ideally suited for that purpose. Exploiting the immense power of modern computers, one can generate a huge repertoire of the consequences of all sorts of simple programs as embodied in the corresponding CA. For understanding the basics of a given complex system observed in Nature, one can try to see if the observed behaviour pattern can be matched with any of the archived CA. If yes, then the simple program used for generating that particular CA pattern is the ‘explanation’ of the time or space evolution of the complex behaviour observed in the actual physical system under study (entailing a collapse in the degree of complexity of the system; cf. Part 5). Thus, rather than using CA to mimic or model the observed behaviour of complex systems, Wolfram advocates their use to reveal unknown aspects of the systems that they model. It remains to be seen how far this will turn out to be a useful way of doing science.

11.5 Does Randomness Rule Our Universe?

Is there a fundamental deterministic rule from which all else follows? Conventional wisdom says that randomness is at the heart of quantum mechanics, and because of this randomness the universe has infinite complexity. Wolfram suggests that this may or may not be so. According to him, there may be no real randomness; only pseudo-randomness, like the randomness produced by random-number generators in a computer. The computer generates these numbers by using mathematical equations, and what we get are actually deterministic sequences of numbers.

Wolfram gives the analogy of π = 3.1415926. . . Suppose you are given, not the whole equation, but only a string of digits coming from far inside the decimal expansion. It would look random, in the absence of complete knowledge. In reality it is only pseudo-random. Wolfram puts forward the viewpoint that, similarly, the randomness we see in the physical world may really be pseudo-randomness, and the physical world may actually be deterministic. It is simply that we do not know the underlying law, which may well be a simple CA for all we know. But there is also an important computational-irreducibility aspect to this scenario, as described above.

According to Wolfram, complexity in Nature follows from the existence of computational equivalence: ‘We tend to consider (a certain) behaviour complex when we cannot readily reduce it to a simple summary. If all processes are viewed as computations, then doing such reduction in effect requires us as observers to be capable of computations that are more sophisticated than the ones going on in the systems we are observing. But the PCE implies that usually the computations will be of nearly the same sophistication - providing a fundamental explanation of why the behaviour we observe must seem to us complex’ (cf. the website www.wolframscience.com).

There is a human or anthropic angle to the meaning of complexity. Let us go back to the equation π = 3.1415926… . It has only a small information content or degree of complexity: A small algorithm using the fact that π is given by the ratio of the circumference of a circle to its diameter can generate the entire information contained in this equation. But if we humans do not have knowledge about the entire equation, but are given only a string of digits coming from far inside the decimal expansion, then the degree of complexity is just about as large as the length of the string and can, in principle, be infinite. For us humans, the degree of complexity of a system depends on our knowledge about the system. As more knowledge is acquired by us, the degree of complexity may keep collapsing. Of course, this happens only for systems which are not irreducibly complex. If the complexity of a system is irreducibly or intrinsically large, our increasing knowledge about the system can have little effect on its degree of complexity.

11.6 Self-Reproducing Automata

In the late 1940s, Neumann got interested in the question: Can a machine be programmed to make a copy of itself? Can there be self-reproducing machines? To bring out the essence of self-reproduction, Neumann imagined a thought experiment. Consider a machine moving around on the surface of a pond. The pond contains all sorts of machine parts. Our machine is a ‘universal constructor‘ (rather like a universal computer); i.e., given a recipe for constructing any machine, it can search the pond for the right parts and construct the desired machine. In particular, it can construct a copy of itself if the requisite description is known to it.

But it is still not a self-reproducing machine, because the copy it has constructed of itself has no information about its own description for constructing another copy of itself. Neumann argued that for this to be possible, the original machine must have a ‘description copier’; i.e. a mechanism for duplicating the original description and for attaching this duplicate description to the new copy it is constructing of itself. The offspring will then have the wherewithal for a sustainable self-reproduction in this so-called Neumann universe.

For testing his thought experiment, Neumann used the CA suggested by Ulam. He proved that there exists at least one cellular-automaton pattern which can reproduce itself. The pattern he found involved a large lattice of cells, with 29 possible states for each cell. This was an important result because it meant that self-reproduction was possible in machines, and was not confined to living beings only.

Thus any self-reproducing system (living or nonliving) must play two roles: It should serve as an algorithm that can be executed during the copying and constructing process; and it should serve as a data bank that can be duplicated and attached to the offspring. These two predictions were confirmed for real-life systems in 1953 when Watson and Crick determined the structure of the DNA molecule. The DNA molecule not only serves as a data base for synthesizing various proteins etc., but it also unwinds and makes a copy of itself when the biological cell divides into two. Studies on CA help us understand life processes.

11.7 Concluding Remarks

As demonstrated by the work on CA, very simple local rules can lead to enormous amounts of complexity. This is why, although the laws of Nature are simple, we see so much complexity around us, including biological complexity.

The emergence of RNA and DNA molecules resulted in the replication aspect of self-reproduction in Nature. The next article in this series will elaborate on this when I discuss the origin(s) of life on Earth.

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS.

Related posts:

1. COMPLEXITY EXPLAINED: 14. Biological Complexity at the Edge of Chaos
2. COMPLEXITY EXPLAINED: 5. Defining Different Types of Complexity
3. COMPLEXITY EXPLAINED: 6. Emergence of Complexity in Far-from-Equilibrium Systems
4. COMPLEXITY EXPLAINED: 13. Evolution of Biological Complexity
5. COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity
6. COMPLEXITY EXPLAINED: 8. Evolution of Chemical Complexity
7. COMPLEXITY EXPLAINED: 10. What is Life?
8. COMPLEXITY EXPLAINED: 3. Thermodynamic Explanation for the Increasing Complexity of our Ecosphere
9. COMPLEXITY EXPLAINED: 1. What is Complexity?
10. COMPLEXITY EXPLAINED: 4. The Nature of Information
11. COMPLEXITY EXPLAINED: 15. Evolution of Cultural Complexity
12. COMPLEXITY EXPLAINED: 12. The Likely Origins of Life
13. COMPLEXITY EXPLAINED: 9. How Did Complex Molecules Like Proteins and DNA Emerge Spontaneously?

We live only to discover beauty. All else is a form of waiting. Khalil Gibran was happy describing life like this, but scientists have a lot of trouble defining it succinctly and comprehensively. It is not easy to give a crisp definition of life, just as it is not easy to define complexity in a context-independent and unique way. Perhaps there is no clear dividing line between life and nonlife. Nevertheless, the emergence of what many of us intuitively understand to be life marked a major milestone in the evolution of complexity in our world. I survey some of the scientific attempts at defining life, as a prelude to discussing the likely mechanisms for the origin of life in a future article in this series.

10.1 What is Life?

Here are a couple of descriptions of life. Eric Chaisson (2001) first:

But what is life? Like time, life is obvious to discern yet elusive to define. Although most biologists generally skirt the issue, we suggest that our very essence can be defined as follows: Life is an open, coherent, spacetime structure maintained far from thermodynamic equilibrium by a flow of energy through it - a carbon-based system operating in a water-based medium, with higher forms metabolizing oxygen.

Margulis and Sagan (2002) next:

Life does not exist in a vacuum but dwells in the very real difference between 5800 Kelvin incoming solar radiation and 2.7 Kelvin temperature of outer space. It is the gradient upon which life’s complexity feeds.

The origin of life (as also consciousness) is the most dramatic of all emergent phenomena in nonlinear open systems. But it has not been easy to define life the way we define so many other things in science. For every characteristic believed to define life, people have come up with an example from the world of the nonliving which also possesses that characteristic. In fact, as we make further progress in the development of sophisticated ‘artificial’ smart structures, including truly smart or intelligent robots, the distinction between the living and the nonliving will get more and more blurred. I have discussed these things in my book on smart structures, and also in an article on robots of the future.

Daniel Koshland is an ex-Editor-in-Chief of the prestigious magazine Science. He attended a conference focused on the vexing question of defining life. He recounts that, after considerable discussion, somebody formulated the essential characteristic of life as ‘the ability to reproduce.’ There seemed to be general consensus on this, till somebody said: ‘Then one rabbit is dead. Two rabbits  -  a male and female  -  are alive but either one alone is dead.’ Similarly, a mule must be a dead entity by this definition. Nevertheless, many of us still have, at least intuitively, an idea of what is living and what is nonliving. Koshland has come up, reluctantly, with the following short definition of life: A living organism is an organized unit, which can carry out metabolic reactions, defend itself against injury, respond to stimuli, and has the capacity to be at least a partner in reproduction. But he is happier giving a large set of criteria for deciding what constitutes life. He calls them the seven pillars of life, like the pillars of a Greek temple.

10.2 The Seven Pillars of Life

I asked the rose how long
was its life,
The bud heard and
softly smiled.

Mir Taqi Mir

Koshland’s (2002) seven ‘pillars’ of life are the essential thermodynamic and kinetic principles which enable a living system to operate and propagate. The mechanisms listed by him are with reference to life as we know it on our Earth. The same seven principles may involve other mechanisms for other forms of life, or for life elsewhere. The acronym PICERAS introduced by Koshland has seven letters, corresponding to the seven principles or pillars of life: program; improvisation; compartmentalization; energy; regeneration; adaptability; seclusion.

The first pillar of life is a program that describes the ingredients themselves, as well as the kinetics of the interactions among the ingredients. For life on Earth, this program resides in the DNA molecules that encode the genes.

Improvisation is the second pillar of life, and is to be distinguished from another pillar, namely adaptability, discussed below. Both involve a response to change. The difference is in time scales. Adaptability is about direct response to quick changes, and does not entail a change of the genetic program of the organism. Improvisation is about gradual change (evolution) in response to long-term changes in the environment.

A living organism depends on the reaction kinetics of its ingredients. The kinetics requires certain concentrations of the chemicals involved, and that requires confinement in a ‘container’. Therefore the third pillar of life is compartmentalization, namely the presence of a membrane or skin that confines the organism to a certain volume. In fact, large organisms have several compartments (organs), because the concentration requirements of different organs are different.

Energy is the fourth pillar of life on Earth. Without a steady input of energy, a living system would soon approach a state of equilibrium and death.

Regeneration (including reproduction) is the fifth pillar of life. There are always some thermodynamic losses and wear and tear as a living organism is sustained by the myriad chemical reactions going on inside it. Food and other intakes are one means of ensuring that the organism does not degenerate or degrade inexorably with time. Several organs of the human body (e.g. the heart) have a mechanism for tissue regeneration. However, over time, aging effects become too strong and the organism dies. To ensure that the species can still survive, Nature has evolved replication (cell division) and reproduction (which amounts to regeneration by starting all over again through the progeny) as an essential pillar of life.

A living organism may face a variety of sudden hazards. Adaptability is therefore the sixth pillar of life. For example, a living system must quickly move away from an environment that is too hot for its well-being and survival.

The seventh pillar of life listed by Koshland is what he calls seclusion. In a living cell there exist a large number of chemical reaction pathways, all simultaneously active. Natural evolutionary processes have ensured that the enzymes catalyzing the various reactions have specific shapes and reactivities, so that the different reactions do not interfere with one another. The specificity of an enzyme provides a high degree of seclusion to the relevant chemical reaction occurring in the living cell. Various feedback and feedforward mechanisms also ensure that the specificity is not completely unchangeable, but changes only under certain special signals.

The existence of many of Koshland’s pillars of life can be ultimately traced back to the DNA molecule, portions of which constitute the genes. DNA is a large molecule with very high information content. Life is information. Therefore there is a direct link between life and free energy (cf. Part 4). Without an input of free energy or negative entropy, all processes would tend to take a system towards a state of entropic death (cf. Part 6). Intake of food keeps an organism alive by providing negative entropy. As Szent-Györgyi (1957) said, ‘We need energy to fight against entropy’. The complex molecules constituting food are full of free energy or negative entropy, which is derived ultimately from the Sun.

10.3 Schrödinger and Life

Life is a partial, continuous, progressive and conditionally interactive self-realization of the potentialities of atomic electron states.

J. D. Bernal

The Nobel laureate Erwin Schrödinger made a profound discovery in 1927 by showing that the discrete energy states of matter are determined by wave equations. He became one of the founders of modern science, best known for the famous wave equation in quantum mechanics, named after him:

Here is an equivalent formulation of the Schrödinger equation:

Here Ĥ is the so-called Hamiltonian operator. The Hamiltonian H is defined as the sum of the kinetic and potential energies of the system. This equation determines all phenomena in our world, subject to the constraints of the first and the second laws of thermodynamics. How did Schrödinger arrive at this very basic equation? Here is Richard Feynman’s answer: ‘Where did we get that [Schrödinger's equation] from? It’s not possible to derive it from anything you know. It came out of the mind of Schrödinger.’

In 1943-1944 Schrödinger wrote a little book What is Life: The Physical Aspect of the Living Cell. This is how Roger Penrose described this book (in 1991): ‘… which, as I now realize, must surely rank among the most influential of scientific writings in this century. It represents a powerful attempt to comprehend some of the genuine mysteries of life, made by a physicist whose own deep insights had done so much to change the way in which we understand what the world is made of. … Indeed, many scientists who have made fundamental contributions in biology, such as J. B. S. Haldane and Francis Crick, have admitted to being strongly influenced by (although not always in complete agreement with) the broad-ranging ideas put forward here by this highly original and profoundly thoughtful physicist.’

It is important to realize that when Schrödinger wrote his book, the atomic structure of DNA was not known (it was determined later by Watson and Crick). I quote Freeman Dyson (1985): ‘Schrödinger’s book was seminal because he knew how to ask the right questions. The basic questions which Schrödinger asked were the following: What is the physical structure of the molecules which are duplicated when chromosomes divide? How is the process of duplication to be understood? How do these molecules retain their individuality from generation to generation? How do they succeed in controlling the metabolism of cells? How do they create the organization that is visible in the structure and function of higher organisms? He did not answer these questions, but by asking them he set biology moving along the path which led to the epoch-making discoveries of the subsequent forty years: to the discovery of the double helix and the triplet code, to the precise analysis and wholesale synthesis of genes, and to the quantitative measurement of the evolutionary divergence of species.’

How did Schrödinger define life? He avoided giving a direct definition of life, but highlighted an important property of it by invoking the idea of negative entropy, which I have outlined in Part 4 of this series of articles (also see Part 3 for a fuller description). He characterized living matter as that which stays alive (’evades the decay to equilibrium’) by feeding on negative entropy or negentropy. Karl Popper did not agree: ‘Now admittedly organisms do all this. But I denied, and I still deny, Schrödinger’s thesis that it is this which is characteristic of life, or of organisms; for it holds for every steam engine. In fact, every oil-fired boiler and every self-winding watch may be said to be “continually sucking orderliness from its environment”. Thus Schrödinger’s answer to his question cannot be right.’

10.4 Artificial Life

Life and artificial? Is that a contradiction in terms? Not at all. Christopher Langton is the originator of this subject. The term artificial life (AL) was coined by him around 1970. AL is ‘. . an inclusive paradigm that attempts to realize lifelike behaviour by imitating the processes that occur in the development or mechanics of life.’

In the field of AL, one uses computers to model the basic biological mechanisms of evolution and life. In abstracting the basic life processes, the AL approach emphasizes the fact that life is not a property of matter per se, but the organization of that matter. The laws of life must be laws of dynamical form, independent of the details of a particular carbon-based chemistry that happened to arise here on Earth. It attempts to explore other possible biologies in new media, namely computers and robots. The idea is to view life-as-we-know-it in the context of life-as-it-could-be.

In conventional biology one tries to understand life phenomena by a process of analysis: We take a living community or organism, and try to make sense of it by subdividing it into its building blocks. By contrast, AL takes the synthesis or bottom-up route. We start with an assembly of very simple interacting units, and see how they evolve under a given set of conditions, and how they change when the environmental conditions are changed. One of the most striking characteristics of a living organism is the distinction between its genotype and phenotype. The genotype can be thought of as a collection of little computer programs, running in parallel, one program per gene. When activated, each of these programs enters into the logical fray by competing and/or cooperating with the other active programs. And collectively, these interacting programs carry out an overall computation that is the phenotype. The system evolves towards the best solution of a posed problem. By analogy, the term GTYPE is introduced in the field of AL to refer to any collection of low-level rules. Similarly, PTYPE means the structure and/or behaviour that results (emerges) when these rules are activated in a specific environment.

What makes life and brain and mind possible is a certain kind of balance between the forces of order and the forces of disorder. In other words, there should be an edge-of-chaos existence. Only such systems are both stable enough to store information, and yet evanescent enough to transmit it. I shall return to this theme in the article on the origins of life.

Life is not just like a computation, in the sense of being a property of the organization rather than the molecules: Life literally is computation. And once we have made a link between life and computation, an immense amount of theory can be brought in. For example, the question ‘Why is life full of surprises?’ is answered in terms of the undecidability theorem of computer science, according to which, unless a computer program is utterly trivial, the fastest way to find out what it would do (does it have bugs or not) is to run it and see. This explains why, although a biochemical machine or an AL machine is completely under the control of a program (the GTYPE), it still has surprising, spontaneous behaviour in the PTYPE. It never reaches equilibrium.

The computational aspect of the AL approach invokes the theory of complex dynamical systems. Such systems can be described at various levels of complexity, the global properties at one level emerging from the interactions among a large number of simple elements at the next lower level of complexity. The exact nature of the emergence is, of course, unpredictable because of the nonlinearities involved.

Here are some websites devoted to artificial life and virtual worlds:

• http://www.biota.org/nervegarden
• http://www.digitalspace.com/avatars
• http://www.2nd-world.com
• http://www.fl.aec.at/~watson
• http://www.digitalworks.org/rd/p5
• http://www.fraclr.org/howareyou.htm

With the advent of artificial life, we may be the first creatures to create our own successors. . . If we fail in our task as creators, they may indeed be cold and malevolent. However, if we succeed, they may be glorious, enlightened creatures that far surpass us in their intelligence and wisdom. It is quite possible that, when conscious beings of the future look back on this era, we will be most noteworthy not in and of ourselves but rather for what we gave rise to. Artificial life is potentially the most beautiful creation of humanity.

Doyne Farmer and Alletta Belin

10.5 Concluding Remarks

Definition of life is problematic, particularly at the level of bacteria. This is partly linked to the question of what exactly we mean when we use the word ’species’ for a bacterium.

Fresh challenges to what we understand by the term ‘life’ will also arise when the fields of artificial life and super-intelligent robots come of age. I shall discuss artificial evolution in a separate article.

Until 1944 most scientists were of the view that genetic information was carried by the proteins of the chromosome. Schrödinger’s 1944 book What is Life?, apart from invoking negative entropy for the sustenance of life, introduced new concepts for the genetic code. It inspired Watson and Crick to investigate the gene, which led to their discovery of the double-helix structure of DNA. In their 1953 paper they wrote: ‘It has not escaped our notice that the specific pairing [of the two strands of DNA] we have postulated suggests a possible copying mechanism for genetic material.’ This sudden blaze of understanding laid bare the inside story of heredity, and of present-day life itself.

What is even more relevant for the stated objective of the present series of articles, Schrödinger argued that life is not a mysterious or inexplicable phenomenon, as some people believe, but a scientifically comprehensible process like any other, ultimately explainable by the laws of physics and chemistry.

The good life is one inspired by love and guided by knowledge.

Bertrand Russell

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS.

Related posts:

1. COMPLEXITY EXPLAINED: 12. The Likely Origins of Life
2. COMPLEXITY EXPLAINED: 5. Defining Different Types of Complexity
3. COMPLEXITY EXPLAINED: 3. Thermodynamic Explanation for the Increasing Complexity of our Ecosphere
4. COMPLEXITY EXPLAINED: 6. Emergence of Complexity in Far-from-Equilibrium Systems
5. COMPLEXITY EXPLAINED: 13. Evolution of Biological Complexity
6. COMPLEXITY EXPLAINED: 8. Evolution of Chemical Complexity
7. COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity
8. COMPLEXITY EXPLAINED: 14. Biological Complexity at the Edge of Chaos
9. COMPLEXITY EXPLAINED: 4. The Nature of Information
10. COMPLEXITY EXPLAINED: 15. Evolution of Cultural Complexity
11. COMPLEXITY EXPLAINED: 1. What is Complexity?
12. COMPLEXITY EXPLAINED: 11. Cellular Automata
13. COMPLEXITY EXPLAINED: 17. Epilogue