Archive for the ‘Darwinism’ Category

Darwinian Evolution is a form of Computational Learning

The punchline of this book is perhaps: “Changing or increasing functionality of circuits in biological evolution is a form of computational learning“; although it also speaks of topics other than evolution, the underlying framework is of the Probably Approximately Correct model [1] from the theory of Machine Learning, from which the book gets its name.

"Probably Approximately Correct" by Leslie Valiant

“Probably Approximately Correct” by Leslie Valiant

[Clicking on the image above will direct you to the amazon page for the book]

I had first heard of this explicit connection between Machine Learning and Evolution in 2010 and have been quite fascinated by it since. It must be noted, however, that similar ideas have appeared in the past. It won’t be incorrect to say that usually they have been in the form of metaphor. It is another matter that this metaphor has generally been avoided for reasons I underline towards the end of this review. When I first heard about the idea it immediately made sense and like all great ideas, in hindsight looks completely obvious. Ergo, I was quite excited to see this book and preordered it months in advance.

This book is basically a popular science version and expansion of the ideas on the matter that appeared in a J-ACM article titled “Evolvability” in 2007 [2]. I have to say that I was expecting a bit more from the book than what it already had, in this sense I was somewhat disappointed. But at the same time, it can perhaps be a useful ‘starting’ book for those interested in the broad idea but without much background. We all have examples of books like this. Here’s one from me: about a couple of years ago I read a book on Knots by Alexei Sossinsky, the book got bad reviews from many serious mathematicians for not really being about mathematics, besides having many mistakes. But for a complete beginner, I thought it was an absolutely wonderful book, introducing the reader to the basic ideas very informally besides sharing his infectious enthusiasm for problems in Knot Theory. Coming back, in short if you have enough background of the basic notions of PAC Learning then the book might feel quite redundant in some chapters, but depending on how you read, it might still turn out to be a good read any way. Judging from some other (rude) reviews: If you are a professional nitpicker then this book is probably not worth your time since this book deals with ideas and not with a formal treatment of them. Given what it is aiming at, I think it is a good book.

Before attempting to sketch a skiagram of the main content of the book: One of the main subthemes of the book, constantly emphasized is to look at computer science as a kind of an enabling tool to study natural science. This is oft ignored, perhaps because of the reason that CS curricula are rarely designed with any natural science component in them and hence there is no impetus for aspiring computer scientists to view them from the computational lens. On the other hand,  the relation of computer science with mathematics has become quite well established. As a technology the impact of Computer Science has been tremendous. All this is quite remarkable given the fact that just about a century ago the notion of a computation was not even well defined. Unrelated to the book: More recently people have started taking the idea of digital physics (i.e. physics from a solely computable/digital perspective) seriously. But in the other natural sciences its usage is still woeful. Valiant draws upon the example of Alan Turing as a natural scientist and not just as a computer scientist to make this point. Alan Turing was more interested in natural phenomenon (intelligence, limits of mechanical calculation, pattern formation etc) and used tools from Computer Science to study them, a fact that is evident from his publications. That Turing was trying to understand natural phenomenon was remarked in his obituary by Max Neumann by summarizing the body of his work as: “the extent and the limitations of mechanistic explanations of nature”.


The book begins with a delightful and quite famous quote by John von Neumann (through this paragraph I also discovered the context to the quote). This paragraph also adequately summarizes the motivation for the book very well:

“In 1947 John von Neumann, the famously gifted mathematician, was keynote speaker at the first annual meeting of the Association for Computng Machinery. In his address he said that future computers would get along with just a dozen instruction types, a number known to be adequate for expressing all of mathematics. He went on to say that one need not be surprised at this small number, since 1000 words were known to be adequate for most situations in real life, and mathematics was only a small part of life, and a very simple part at that. The audience responded with hilarity. This provoked von Neumann to respond: “If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is”

Though counterintuitive, von Neumann’s quip contains an obvious truth. Einstein’s theory of general relativity is simple in the sense that one can write the essential content on one line as a single equation. Understanding its meaning, derivation, and consequences requires more extensive study and effort. However, this formal simplicity is striking and powerful. The power comes from the implied generality, that knowledge of one equation alone will allow one to make accurate predictions about a host of situations not even connected when the equation was first written down.

Most aspects of life are not so simple. If you want to succeed in a job interview, or in making an investment, or in choosing a life partner, you can be quite sure that there is no equation that will guarantee you success. In these endeavors it will not be possible to limit the pieces of knowledge that might be relevant to any one definable source. And even if you had all the relevant knowledge, there may be no surefire way of combining it to yield the best decision.

This book is predicated on taking this distinction seriously […]”

In a way, aspects of life as mentioned above appear theoryless, in the sense that there seems to be no mathematical or scientific theory like relativity for them. Something which is quantitative, definitive and short. Note that these are generally not “theoryless” in the sense that there exists no theory at all since obviously people can do all the tasks mentioned in a complex world quite effectively. A specific example is of how organisms adapt and species evolve without having a theory of the environment as such. How can such coping mechanisms come into being in the first place is the main question asked in the book.

Let’s stick to the specific example of biological evolution. Clearly, it is one of the central ideas in biology and perhaps one of the most important theories in science that changed the way we look at the world. But inspite of its importance, Valiant claims (and correctly in my opinion) that evolution is not understood well in a quantitative sense. Evidence that convinces us of its correctness is of the following sort: Biologists usually show a sequence of evolving objects; stages, where the one coming later is more complex than the previous. Since this is studied mostly via the fossil record there is always a lookout for missing links between successive stages (As a side note: Animal eyes is an extremely fascinating and very readable book that delves with this question but specifically dealing with the evolution of the eye. This is particularly interesting given that due to the very nature of the eye, there can be no fossil records for them). Darwin had remarked that numerous successive paths are necessary – that is, if it was not possible to trace a path from an earlier form to a more complicated form then it was hard to explain how it came about. But again, as Valiant stresses, this is not really an explanation of evolution. It is more of an “existence proof” and not a quantitative explanation. That is, even if there is evidence for the existence of a path, one can’t really say that a certain path is likely to be followed just because it exists. As another side note: Watch this video on evolution by Carl Sagan from the beautiful COSMOS

Related to this, one of the first questions that one might ask, and indeed was asked by Darwin himself: Why has evolution taken as much time as it has? How much time would have sufficed for all the complexity that we see around us to evolve? This question infact bothered Darwin a lot in his day. In On the Origins of Species he originally gave an estimate of the Earth’s age to be at least 300 million years, implying indirectly, that there was enough time. This estimate was immediately thrashed by Lord Kelvin, one of the pre-eminent British physicists of the time, who estimated the age of the Earth to be only about 24 million years. This caused Darwin to withdraw the estimate from his book. However, this estimate greatly worried Darwin as he thought 24 million years just wasn’t enough time. To motivate on the same theme Valiant writes the following:

“[…] William Paley, in a highly influential book, Natural Theology (1802) , argued that life, as complex as it is, could not have come into being without the help of a designer. Numerous lines of evidence have become available in the two centuries since, through genetics and the fossil record, that persuade professional biologists that existing life forms on Earth are indeed related and have indeed evolved. This evidence contradicts Paley’s conclusion, but it does not directly address his argument. A convincing direct counterargument to Paley’s would need a specific evolution mechanism to be demonstrated capable of giving rise to the quantity and quality of the complexity now found in biology, within the time and resources believed to have been available. […]”

A specific, albeit more limited version of this question might be: Consider the human genome, which has about 3 billion base pairs. Now, if evolution is a search problem, as it naturally appears to be, then why did the evolution of this genome not take exponential time? If it would have taken exponential time then clearly such evolution could not have happened in any time scale since the origin of the earth. Thus, a more pointed question to ask would be: What circuits could evolve in sub-exponential time (and on a related note, what circuits are evolvable only in exponential time?). Given the context, the idea of thinking about this in circuits might seem a little foreign. But on some more thought it is quite clear that the idea of a circuit is natural when it comes to modeling such systems (at least in principle). For example: One might think of the genome as a circuit, just as how one might think of the complex protein interaction networks and networks of neurons in the brain as circuits that update themselves in response to the environment.

The last line is essentially the key idea of adaptation, that entities interact with the environment and update themselves (hopefully to cope better) in response. But the catch is that the world/environment is far too complex for simple entities (relatively speaking), with limited computational power, to have a theory for. Hence, somehow the entity will have to cope without really “understanding” the environment (it can only be partially modeled) and improve their functionality. The key thing to pay attention here is the interaction or the interface between the limited computational entity in question and the environment. The central idea in Valiant’s thesis is to think of and model this interaction as a form of computational learning. The entity will absorb information about the world and “learn” so that in the future it “can do better”. A lot of Biology can be thought of as the above: Complex behaviours in environments. Wherein by complex behaviours we mean that in different circumstances, we (or alternately our limited computational entity) might behave completely differently. Complicated in the sense that there can’t possibly be a look up table for modeling such complex interactions. Such interactions-responses can just be thought of as complicated functions e.g. how would a deer react could be seen as a function of some sensory inputs. Or for another example: Protein circuits. The human body has about 25000 proteins. How much of a certain protein is expressed is a function depending on the quantities of other proteins in the body.  This function is quite complicated, certainly not something like a simple linear regression. Thus there are two main questions to be asked: One, how did these circuits come into being without there being a designer to actually design them? Two, How do such circuits maintain themselves? That is, each “node” in protein circuits is a function and as circumstances change it might be best to change the way they work. How could have such a thing evolved?

Given the above, one might ask another question: At what rate can functionality increase or adapt under the Darwinian process? Valiant (like many others, such as Chaitin. See this older blog post for a short discussion) comes to the conclusion that the Darwinian evolution is a very elegant computational process. And since it is so, with the change in the environment there has to be a quantitative way of saying how much rate of change can be kept up with and what environments are unevolvable for the entity. It is not hard to see that this is essentially a question in computer science and no other discipline has the tools to deal with it.

In so far that (biological) interactions-responses might be thought of as complicated functions and that the limited computational entity that is trying to cope has to do better in the future, this is just machine learning! This idea, that changing or increasing functionality of circuits in biological evoution is a form of computational learning, is perhaps very obvious in hindsight. This (changing functionality) is done in Machine Learning in the following sense: We want to acquire complicated functions without explicitly programming for them, from examples and labels (or “correct” answers). This looks at exactly at the question at how complex mechanisms can evolve without someone designing it (consider a simple perceptron learning algorithm for a toy example to illustrate this). In short: We generate a hypothesis and if it doesn’t match our expectations (in performance) then we update the hypothesis by a computational procedure. Just based on a single example one might be able to change the hypothesis. One could draw an analogy to evolution where “examples” could be experiences, and the genome is the circuit that is modified over a period of time. But note that this is not how it looks like in evolution because the above (especially drawing to the perceptron example) sounds more Lamarckian. What the Darwinian process says is that we don’t change the genome directly based on experiences. What instead happens is that we make a lot of copies of the genome which are then tested in the environment with the better one having a higher fitness. Supervised Machine Learning as drawn above is very lamarckian and not exactly Darwinian.

Thus, there is something unsatisfying in the analogy to supervised learning. There is a clear notion of a target in the same. Then one might ask, what is the target of evolution? Evolution is thought of as an undirected process. Without a goal. This is true in a very important sense however this is incorrect. Evolution does not have a goal in the sense that it wants to evolve humans or elephants etc. But it certainly does have a target. This target is “good behaviour” (where behaviour is used very loosely) that would aid in the survival of the entity in an environment. Thus, this target is the “ideal” function (which might be quite complicated) that tells us how to behave in what situation. This is already incorporated in the study of evolution by notions such as fitness that encode that various functions are more beneficial. Thus evolution can be framed as a kind of machine learning problem based on the notion of Darwinian feedback. That is, make many copies of the “current genome” and let the one with good performance in the real world win. More specifically, this is a limited kind of PAC Learning.  If you call your current hypothesis your genome, then your genome does not depend on your experiences. Variants to this genome are generated by a polynomial time randomized Turing Machine. To illuminate the difference with supervised learning, we come back to a point made earlier that PAC Learning is essentially Lamarckian i.e. we have a hidden function that we want to learn. One however has examples and labels corresponding to this hidden function, these could be considered “queries” to this function. We then process these example/label pairs and learn a “good estimate” of this hidden function in polynomial time. It is slightly different in Evolvability. Again, we have a hidden “ideal” function f(x). The examples are genomes. However, how we can find out more about the target is very limited, since one can empirically only observe the aggregate goodness of the genome (a performance function). The task then is to mutate the genome so that it’s functionality improves. So the main difference with the usual supervised learning is that one could query the hidden function in a very limited way: That is we can’t act on a single example and have to take aggregate statistics into account.

Then one might ask what can one prove using this model? Valiant demonstrates some examples. For example, Parity functions are not evolvable for uniform distribution while monotone disjunctions are actually evolvable. This function is ofcourse very non biological but it does illustrate the main idea: That the ideal function together with the real world distribution imposes a fitness landscape and that in some settings we can show that some functions can evolve and some can not. This in turn illustrates that evolution is a computational process independent of substrate.

In the same sense as above it can be shown that Boolean Conjunctions are not evolvable for all distributions. There has also been similar work on real valued functions off late which is not reported in detail in the book. Another recent work that is only mentioned in passing towards the end is the study of multidimensional space of actions that deal with sets of functions (not just one function) that might be evolvable together. This is an interesting line of work as it is pretty clear that biological evolution deals with the evolution of a set of functions together and not functions in isolation.


Overall I think the book is quite good. Although I would rate it 3/5. Let me explain. Clearly this book is aimed at the non expert. But this might be disappointing to those who bought the book because of the fact that this recent area of work, of studying evolution through the lens of computational learning, is very exciting and intellectually interesting. The book is also aimed at biologists, and considering this, the learning sections of the book are quite dumbed down. But at the same time, I think the book might fail to impress most of them any way. I think this is because generally biologists (barring a small subset) have a very different way of thinking (say as compared to the mathematicians or computer scientists) especially through the computational lens. I have had some arguments about the main ideas in the book over the past couple of years with some biologist friends who take the usage of “learning” to mean that what is implied is that evolution is a directed process. It would have been great if the book would have spent more time on this particular aspect. Also, the book explicitly states that it is about quantitative aspects of evolution and has nothing to do with speciation, population dynamics and other rich areas of study. However, I have already seen some criticism of the book by biologists on this premise.

As far as I am concerned, as an admirer of Prof. Leslie Valiant’s range and quality of contributions, I would have preferred if the book went into more depth. Just to have a semi-formal monograph on the study of evolution using the tools of PAC Learning right from the person who initiated this area of study. However this is just a selfish consideration.



[1] A Theory of the Learnable, Leslie Valiant, Communications of the ACM, 1984 (PDF).

[2] Evolvability, Leslie Valiant, Journal of the ACM, 2007 (PDF).



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A second post in a series of posts about Information Theory/Learning based perspectives in Evolution, that started off from the last post.

Although the last post was mostly about a historical perspective, it had a section where the main motivation for some work in metabiology due to Chaitin (now published as a book) was reviewed. The starting point about that work was to view evolution solely through an information processing lens (and hence the use of Algorithmic Information Theory). Ofcourse this lens by itself is not a recent acquisition and goes back a few decades (although in hindsight the fact that it goes back just a few decades is very surprising to me at least). To illustrate this I wanted to share some analogies by John Maynard Smith (perhaps one of my favourite scientists), which I had found to be particularly incisive and clear. To avoid clutter, they are shared here instead (note that most of the stuff he talks about is something we study in high school, however the talk is quite good, especially because it tends to emphasize on the centrality of information throughout). I also want this post to act as a reference for some upcoming posts.


Molecular Biology is all about Information. I want to be a little more general than that; the last century, the 19th century was a century in which Science discovered how energy could be transformed from one form to another […] This century will be seen […] where it became clear that information could be translated from one from to another.

[Other parts: Part 2, Part 3, Part 4, Part 5, Part 6]

Throughout this talk he gives wonderful analogies on how information translation underlies the so called Central Dogma of Molecular Biology, and how if the translation was one-way in some stages it could have implications (i.e how August Weismann noted that acquired characters are not inherited by giving a “Chinese telegram translation analogy”; since there was no mechanism to translate acquired traits (acquired information) into the organism so that it could be propagated).

However, the most important point from the talk: One could see evolution as being punctuated by about 6 or so major changes or shifts. Each of these events was marked by the way information was stored and processed in a different way. Some that he talks about are:

1. The origin of replicating molecules.

2. The Evolution of chromosomes: Chromosomes are just strings of the above replicating molecules. The property that they have is that when one of these molecules is replicated, the others have to be as well. The utility of this is the following: Since they are all separate genes, they might have different rates of replication and the gene that replicates fastest will soon outnumber all the others and all the information would be lost. Thus this transition underlies a kind of evolution of cooperation between replicating molecules or in other other words chromosomes are a way for forced cooperation between genes.

3. The Evolution of the Code: That information in the nucleic could be translated to sequences of amino acids i.e. proteins.

4. The Origin of Sex: The evolution of sex is considered an open question. However one argument goes that (details in next or next to next post) the fact that sexual reproduction hastens the acquisition from the environment (as compared to asexual reproduction) explains why it should evolve.

5. The Evolution of multicellular organisms: A large, complex signalling system had to evolve for these different kind of cells to function in an organism properly (like muscle cells or neurons to name some in Humans).

6. Transition from solitary individuals to societies: What made these societies of individuals (ants, humans) possible at all? Say if we stick to humans, this could have only happened only if there was a new way to transmit information from generation to generation – one such possible information transducing machine could be language! Thus giving an additional mechanism to transmit information from one generation to another other than the genetic mechanisms (he compares the genetic code and replication of nucleic acids and the passage of information by language). This momentous event (evolution of language ) itself dependent on genetics. With the evolution of language, other things came by:  Writing, Memes etc. Which might reproduce and self-replicate, mutate and pass on and accelerate the process of evolution. He ends by saying this stage of evolution could perhaps be as profound as the evolution of language itself.


As a side comment: I highly recommend the following interview of John Maynard Smith as well. I rate it higher than the above lecture, although it is sort of unrelated to the topic.


Interesting books to perhaps explore:

1. The Major Transitions in EvolutionJohn Maynard Smith and Eörs Szathmáry.

2. The Evolution of Sex: John Maynard Smith (more on this theme in later blog posts, mostly related to learning and information theory).


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Changing or increasing functionality of circuits in biological evolution is a form of computational learning. – Leslie Valiant

The title of this post comes from Prof. Leslie Valiant‘s The ACM Alan M. Turing award lecture titled “The Extent and Limitations of Mechanistic Explanations of Nature”.

Prof. Leslie G. Valiant

Click on the image above to watch the lecture

[Image Source: CACM “Beauty and Elegance”]

Short blurb: Though the lecture came out sometime in June-July 2011, and I have shared it (and a paper that it quotes) on every online social network I have presence on, I have no idea why I never blogged about it.

The fact that I have zero training (and epsilon knowledge of) in biology that has not stopped me from being completely fascinated by the contents of the talk and a few papers that he cites in it. I have tried to see the lecture a few times and have also started to read and understand some of the papers he mentions. Infact, the talk has inspired me enough to know more about PAC Learning than the usual Machine Learning graduate course might cover. Knowing more about it is now my “full time side-project” and it is a very exciting side-project to say the least!


Getting back to the title: One of the motivating questions about this work is the following:

It is widely accepted that Darwinian Evolution has been the driving force for the immense complexity observed in life or how life evolved. In this beautiful 10 minute video Carl Sagan sums up the timeline and the progression:

There is however one problem: While evolution is considered the driving force for such complexity, there isn’t a satisfactory explanation of how 13.75 billion years of it could have been enough. Many have often complained that this reduces it to a little more than an intuitive explanation. Can we understand the underlying mechanism of Evolution (that can in turn give reasonable time bounds)? Valiant makes the case that this underlying mechanism is of computational learning.

There have been a number of computational models that have been based on the general intuitive idea of Darwinian Evolution. Some of these include: Genetic Algorithms/Programming etc. However, people like Valiant amongst others find such methods useful in an engineering sense but unsatisfying w.r.t the question.

In the talk Valiant mentions that this question was asked in Darwin’s day as well. To which Darwin proposed a bound of 300 million years for such evolution to occur. This immediately fell into a problem as Lord Kelvin, one of the leading physicists of the time put the figure of the age of Earth to be 24 million years. Now obviously this was a problem as evolution could not have happened for more than 24 million years according to Kelvin’s estimate. The estimate of the age of the Earth is now much higher. ;-)

The question can be rehashed as: How much time is enough? Can biological circuits evolve in sub-exponential time?

For more I would point out to his paper:

Evolvability: Leslie Valiant (Journal of the ACM – PDF)

Towards the end of the talk he shows a Venn diagram of the type usually seen in complexity theory text books for classes P, NP, BQP etc but with one major difference: These subsets are fact and not unproven:

Fact: Evolvability \subseteq SQ Learnable \subseteq PAC Learnable

*SQ or Statistical Query Learning is due to Michael Kearns (1993)

Coda: Valiant claims that the problem of evolution is no more mysterious than the problem of learning. The mechanism that underlies biological evolution is “evolvable target pursuit”, which in turn is the same as “learnable target pursuit”.


Onionesque Reality Home >>

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Disclaimer: This is not a post contrary to the nature of this blog. This is not a political post as some might think. My blog is basically about science and I mention I have an interest in people and minds, and this is what this particular post is about. No politics or political opinions in this post at all.

I think my job and aim (one of them) in life is to do good science and I think politics should be kept out of science. Scientists though entitled to have strong opinions on these matters should focus on their primary work and not take their opinions (unless very necessary) beyond general coffee break discussions.

Edit(1 Dec 2008): I realize that there could be a confusion by what I meant by the above paragraph, a clarification is issued in the comments here.


Terrorism invokes a wide range of responses depending on the crowd one is looking at. One that is common from most societies goes along these lines – “Outrageous”, “Cowards”, “Retards”, “Beasts”, and  one could add to that a lot of censored words also. Which is almost immediately followed by diatribes on societies and religions either out in the open or in hushed tones.

There has been a major terrorist strike in the Indian city of Mumbai today morning, killing scores and more importantly aimed to destabilize a country growing in clout rapidly (eyewitness account on the attacks). Ofcourse the terrorists would never succeed in that for a multiplicity of reasons, however this is not the objective of my post. India has been under attack by terror for more than one and a half decade now, more than what any other country has faced in the world.

After such strikes there are a number of animated discussions with people thumping their desks and asking angrily: Where is security for the citizen? Where are the security forces? Where are the Intelligence and spy agencies? Where is the government? Is it sleeping? And a number of other totally understandable questions which are expected from and SHOULD come from any citizen who would get angry or upset after such incidences. A lot of people say: destroy terrorist hideouts, destroy terror networks, kill all terrorists etc. Fair enough. But the basic question that most people duck is what actually makes terrorists? What prompts a young man to hold a gun and indulge in suicidal behavior and kill innocent people indiscriminately, almost heartlessly?

I had been reading on this for some time now, for almost three four years, and also I have some gifts in terms of sound observational powers to do some people watching to understand and make sense of things. Over the past couple of years, books that have resonated with my own understanding of the situation which i don’t claim to be above that of a novice, but nonetheless that of a concerned world citizen have been:

1. A review by Freeman Dyson of Dan Dennett’s book “Breaking The Spell”. Dyson has become one  writer on science and human nature whose opinions I greatly respect. Though I don’t agree with a considerable chunk of his ideas, they are most thought provoking anyway. And in my opinion thought provoking ideas are the most important.

2. Daniel Dennett: Breaking the Spell;

3. Marc Sageman: Understanding Terror Networks;

4. Emiko Ohnuki-Tierney : Kamikaze Diaries – Reflections of Japanese Student Soldiers;

A Memetics Based Social Prespective:


Daniel Dennett’s thesis is interesting, thought provoking and to a large extent true. I have written on this on a previous post. Since then there has been considerable refinement in my thoughts on it. And though I lost my patience in the post that time, I would largely still agree with Dennett’s idea. Taking some parts from that post with considerable editing (the quote below has also been taken from my previous post, this is a part of a presentation that Dan Dennett made at TED):

So you are out in the woods or this pasture, and you see this Ant crawling up this blade of grass. It climbs up to the top, and it climbs and it falls and it climbs and it falls and it climbs, trying to stay at the very top of this blade of grass. What is this Ant doing? What is it in aid of? What goals is this ant trying to achieve by climbing this blade of grass? What’s in it for the ant?

And the answer is NOTHING! There is nothing in it for the Ant. Well then, why is it doing this? Is it just a fluke? Yeah it is just a fluke.

It is a Lancet Fluke, it’s a little parasitic brain worm that has to get into the stomach of a sheep or a cow in order to continue its life cycle. So salmons, you know swim upstream to get to their spawning grounds and Lancet Flukes commandeer this passing Ant, crawl into its brain and drive it up a blade of grass like an all terrain vehicle. So there is nothing in it for the Ant, the Ant’s brain has been hijacked by a parasite that infects the brain inducing suicidal behavior.

An analogy to the above is seen in humans, For example terrorists can be seen in parallel to the “Ant” I mentioned above with their brain been hijacked by “virulent ideas” (parallel to the lancet fluke) inducing suicidal behavior. Such “ideas” are more or less embedded in their brains and removing these toxic ideas is rather difficult if not impossible. This “embedding” of “virulent” ideas is caused by a number of socio-economic factors like anger towards other cultures, trauma, ignorance, anger over repression, social injustice and probably also hate. Such embedding takes place culturally over a long period of time, after which it becomes a part and parcel of the vector (human) carrying it. If one hears stories of how the world has been cruel and unjust and how the world is out to destroy your own world from childhood, that person  will definitely be filled of hate. There are many other “ideas to die for”, like a lot of people have laid down their lives for Communism, Capitalism, Love can be said to be another brain parasite that can induce “abnormal” behavior. Others may be freedom, religion, etc.

Please Note: “Parasite” and “abnormal” are used in a neutral context. Let us say that an idea that alters behavior considerably is a “parasite” (treat it just as a word than a harmful word) and the resulting altered behavior is “abnormal”. Please do not take the literal meanings of these words used. I don’t mean to say that love is a “parasite” in the literal sense. ;-) . (Note ends).

Basically “Ideas” are like lancet flukes, entering the brains of their hosts and encouraging them to work for the continuance of the idea rather than the host or his/her progeny. On the other hand, some ideas (say like love) doubtless make their hosts more fit to survive and propagate, at least through this one mechanism, in a way they are similar to genes (that is why i mentioned that the basic scheme here is to apply evolutionary principles to how we think and behave).  And ofcourse ideas mutate – this leads to what is called the misinterpretation of the original idea by the masses.

This memetics based synthesis explains to some extent why terrorists are generally from poor, uneducated and sometimes extremely orthodox and fundamentalist backgrounds and societies. But Dennett’s treatment which speaks of virus like ideas propagating, getting mutated and propagating further, though nice and reasonable has some problems. It could be one part of the various reasons to what makes terrorists and terror networks, it (social unrest, brainwashing etc what I have covered above) though a necessary condition might not be a sufficient condition. One more reason could be what is explored and reviewed in the next part.

I was reading the review of Dennett’s book by Dyson which introduced me to two books, both extremely interesting. In most of the west and elsewhere too, the idea of looking at terrorists is looking at them as mad zombies, who are totally dehumanized, with their thought process driven by hate alone.

This view is challenged by the two books Dyson’s review introduced me to.

Kinship Amongst Cell Members:


Marc Sageman is a professor of psychiatry and ethnopolitical conflict at the University of Pennsylvania. Sageman was a foreign service and CIA officer and was posted in Pakistan in the late 80s at the time of the Soviet conflict in Afghanistan and had worked closely with the Mujahideen which made him intimately familiar with the working and structure of such networks. He in the book writes that as contrary to popular belief the bonds holding the people together in terror groups are more personal than political. Citing good evidence Sageman asserts that economic backwardness, ignorance, religious zealotry and the likes are not enough to attract the youth to terror organizations (as I mentioned at the end of the previous part), one of the prime reasons is to escape alienation. Quoting him:

Despite popular accounts of the 9/11 perpetrators in the press, in-group love rather than out group hate seems to be a better explanation for their behavior.

Such kinship gives rise to semi-independent cells and dispells the notion that recruitment in terror organizations is top-down as believed. Such comradeship also makes it difficult for intelligence agencies to track or find out information about secret operations. I will now talk about another interesting piece and then will return to Sageman’s work.

The image of the Kamikaze pilots at the end of the second world war in America was similar to what terrorists have today. The Kamikaze pilots were Japanese aviators involved in suicide missions against the allied shipping towards the end of the war, their aim being to destroy as many ships as possible.

uss_columbia_attacked_by_kamikaze[USS Columbia Attacked by a Kamikaze Suicide Mission: Wikipedia]

The book by Emiko Ohnuki-Tierney, Kamikaze Diaries:Reflections of Japanese Student Soldiers, contains extracts from diaries of Kamikaze pilots who knew they were going to die in suicide missions. As opposed to western ideas about the Kamikaze pilots, their diaries were absolutely clear in thought, free from illusions and astonishingly lucid. Some of the pilots who had had western education wrote down their tragic views of life in clear poetry. These were simple young men, neither brainwashed nor nationalistic bigots. Their diaries give a poignant point of view of the war from their frame of reference.


Now the connection that Dyson drew from the two books was extremely interesting and made perfect sense to me once it was mentioned, something of the sort: oh! why didn’t this occur to me! He goes on to elaborate, that though we don’t have first hand testimonies from many terrorists involved in suicide missions, and most probably these terrorists were not even hardly educated as well as the Kamikaze pilots, and were probably more influenced by religion and hate. However it can’t be said that they are zombies, but are fighters in a secret brotherhood that gives meaning and purpose to their lives. they are like good soldiers enlisted for an evil cause. Like the Kamikaze pilots they are motivated mostly by kinship to their comrades than by hate towards the enemy. Once the operation has been decided on by the ideologues (Dennett applies to these people very well. Not so much to the common person), it would have been unthinkable not to carry it out.

Though there are considerable differences between 1945 and 2001-08, both Sageman and Dyson write and I almost totally agree that there are a lot of similarities. The minds of the Kamikaze pilots could give clues to what goes on in the minds of terrorists on suicide missions. Thus to really prevent youth from being lured to such organizations we need to understand first what our enemies stand for and how they work.

I think the three probably unrelated references make a good case on what drives a young lad to become a terrorist. How can we prevent this from happening? This I think readers would have a better view. Also I am not trying to suggest that intelligence and other policing is to be reduced in any way.

Recommended Reads and References:

1. Review of “Breaking the Spell (link below)” By Freeman Dyson on NYRB.

2. Breaking the Spell: Religion as  Natural Phenomenon – Daniel Dennett.

3. Understanding Terror Networks – Marc Sageman.

4. Kamikaze Diaries: Reflections of Japanese Student Soldiers – Emiko Ohnuki-Tierney.

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Many science fiction movies have featured a “good guy” whose mind has been hijacked by an evil mad scientist and he is induced into bizarre behavior beyond his control. A closer look at this “idea” reveals that such brain hijacking is relatively commonplace in nature. I have tried to look for such instances for a month or so and I intend to compile my list here.

I must admit that I actually started wondering of such “real” parasites after a FANTASTIC talk by Daniel Dennett at TED and not after some creepy movie, I saw the talk about a year ago and was especially fascinated by Dennett’s start to the talk. Later i read a book by him, Breaking the Spell: Religion as a Natural Phenomenon that started on a similar note. This book is highly recommended, you must try to read it!  Ofcourse the idea that Dennett talks about is old and was originated by Richard Dawkins, however i liked how he talks about the same.

Here basically Dennett talks on terrorism and gives a memetics based perspective on it, which can be said to be IMHO an extension of the famous Richard Dawkins essay “Viruses of the Mind”. The basic objective of the talk is applying evolutionary thinking to thinking, ideas and their flow and propagation. Wonderful talk, however i might write about the same in later posts.

Dennett opens the talk with a deceptively simple example of an ant.The start goes as:

So you are out in the woods or this pasture, and you see this Ant crawling up this blade of grass. It climbs up to the top, and it climbs and it falls and it climbs and it falls and it climbs, trying to stay at the very top of this blade of grass. What is this Ant doing? What is it in aid of? What goals is this ant trying to achieve by climbing this blade of grass? What’s in it for the ant?

And the answer is NOTHING! There is nothing in it for the Ant. Well then, why is it doing this? Is it just a fluke? Yeah it is just a fluke.

It is a Lancet Fluke, it’s a little parasitic brain worm that has to get into the stomach of a sheep or a cow in order to continue its life cycle. So salmons, you know swim upstream to get to their spawning grounds and Lancet Flukes commandeer this passing Ant, crawl into its brain and drive it up a blade of grass like an all terrain vehicle. So there is nothing in it for the Ant, the Ant’s brain has been hijacked by a parasite that infects the brain inducing suicidal behavior.

An analogy to the above is seen in humans, For example terrorists can be seen in parallel to the “Ant” i mentioned above with their brain been hijacked by “virulent ideas” (parallel to the lancet fluke) inducing suicidal behavior. It is more or less embedded in their brains and removing these toxic ideas is rather difficult. There are many other “ideas to die for”, like a lot of people have laid down their lives for Communism, Capitalism, Love can be said to be another brain parasite that can induce “abnormal” behavior. Others may be freedom, religion, etc.

Edit ( .4 July 2008. ) – Please Note a Clarification before continuing to read further: The usage of “to die for” is not in a negative light at all. It is only in terms of an outcome. Not if the result would be negative or positive. Just in the sense that they would influence behavior significantly. Please also note that i am NOT trying to suggest that something like freedom, or communism, love or religion is a parasite in the literal (and hence negative) sense. I am only saying that somebody bound by an idea will work for its propagation and hence what is interesting is how his/her behavior is influenced if bound by such an idea ( and yes ideas also mutate, but let us for the time being not consider that). Please note again that it is not in a negative sense. Also if there are some who still think that it is still in a negative light then i would suggest please have a look at the video, it is a brief talk by Dan Dennett and he talks about it beautifully, I think he would clarify the misconception in terms of what i actually mean and what might be perceived by a certain class of readers. I am NOT making a statement on what communism is about or what religion is about BUT about how such ideas influence our behavior ONLY. And it is a neutral perspective that way.

Basically “Ideas” are like lancet flukes, entering the brains of their hosts and encouraging them to work for the continuance of the idea rather than the host or his/her progeny. On the other hand, some ideas doubtless make their hosts more fit to survive and propagate, at least through this one mechanism, in a way they are similar to genes (that is why i mentioned that the basic scheme here is to apply evolutionary principles to how we think and behave). Such ideas as we are aware of are called as memes. The term meme was coined by Richard Dawkins in the now famous The Selfish Gene. Again a must read for those who have not read it.

Anyway, I had been thinking of more such examples. Not of “ideas”, but of real parasites that can infect the brain and induce “abnormal” behavior. And i tried to make a short list.

1. Toxoplasma gondii: This is by far the most interesting of the lot as it can “infect” humans too. This is a single celled parasite that lives in the guts of Cats, and sheds eggs which can be picked up by rats or other animals that can be eaten by a cat. T. gondii forms cysts in the hosts body, including the brain. The host otherwise remains completely healthy! It can look as fit as the non infected ones, can compete for mates and forage for food. However the most interesting thing came up after some research on T. gondii infected rats and normal rats. And it was found that T.gondii infected rats had no fear at all of cat odour, something by which they are normally terrified. Scientists continue that it is most likely that this happens so that the parasite in the rat can reach the intestines of the cat! If that is not weird enough then these can also infect humans too.

[Life Cycle of T. gondii – Photo Source]

Click to Enlarge

Humans can be hosts to T.gondii as well. People can get infected by handling soil or kitty litter. For most people it causes no harm. It can only be dangerous in cases wherein the immune system of the person under consideration is very weak. Thus people with immunity deficiency related conditions such as AIDS, and also pregnant ladies are advised not to handle kitty litter. Anyhow, in most cases the parasite lives peacefully in the body (and the brain). Since human brains have a lot of similarities with rat brains. It must induce some change in behavior. It is obvious that humans are too big to be eaten by cats, however there can still be some alteration. Studies showed that it had different effects on women and men. It can make woman more outgoing and kind hearted and men more jealous and suspicious. For more read this wonderful article: The Return of the Puppet Masters by Carl Zimmer.

2. Lancet Fluke (Dicrocoelium lanceolatum): I already spoke about this parasite earlier. However this time i hope to look at it in more detail. The Terminal Hosts of Lancet Flukes are cows and sheeps and intermediate ones are snails. These reach the inside of a snail through the excreta of cows and sheeps, the snail leaves them in mucus trails. The ants are attracted to these because of the scent and in doing so they ingest the fluke. They grow in the abdomen of the ant and then reach the “brain”, okay there is nothing like a brain in an Ant, but something like a ganglion. This induces suicidal behavior in the ant and clamps it to a blade of grass so that the fluke can complete its life cycle in the intestine of a cow or a sheep.

[Photo Source]

3. Rabies: Rabies is different from the above two yet similar in many ways. Rabies induces violent  self destructive behavior in the host. Leading to biting and scratching. And obviously nothing can be more direct than that in spreading. Rabies is relatively well known than the above two, and rather different as it does not affect the brain directly so I would not write much in details about it.

4. Ampulex compressa: This one is macabre! Carl Zimmer again gives a graphic description of how it operates. But this one is like from the Science Fiction – Horror movies.

The wasp sits on the cockroach and slips its stinger through the exoskeleton into the brain. The wasp uses sensors to guide its way through the brain. It probes the brain till it reaches an area that controls the escape reflex, it then injects a venom there and the escape reflex disappears. The result from the outside is rather dramatic. The roach is not paralyzed, but it lifts its front legs and walks again albiet controlled by the wasp that uses its antennae to guides its movement. The roach crawls where the wasp leads it. Which is generally its burrow. The wasp then lays its eggs on the roach, the roach does not resist at all. The larvae then grows in the roach. Creepy!

5. Spinochordodes tellinii: This is another macabre example. Its larvae develop in insects such as grasshoppers and crickets. This parasite is able to influence its host’s behavior: once the parasite is grown, it causes its grasshopper host to seek out and jump into water, where the grasshopper will likely drown. The parasite then leaves its host; the adult worm lives and reproduces in water. Source

I will keep editing this post as and when i come across more such examples.

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