You are currently browsing the monthly archive for May 2010.

Photo – The devastating Gulf of Mexico oil spill – With criticism of the fuel giant at its peak after 36 days and up to 39 million gallons of crude lost into the sea, engineers attempted a last-ditch clogging method known as a “top kill” (source: Mike Swain, Mirror Journal, UK).

Aside from biblic messianic like “top kill” terms, let us all stop for a moment and think about all this mess. On one hand we have powerful resources directed to just one greedy goal, independently from the rest, on the other, a immense huge fail. Let us recapitulate the whole forest, not the tree: […] So they have the most absolute, extraordinary high-tech science and financial resources to pump oil, but not even a single clue on how to cork it […] @ViRAms , May 30, 2010.

Let me just add this: BP’s logo (here) is rapidly turning onto one of the most ironic logos ever made…

Book – Karl Sigmund, The Calculus of Selfishness, Princeton Series on Theoretical and Computational Biology, Princeton University Press,  ISBN: 978-1-4008-3225-5, 192 pp., 2009.

[…] Cooperation means that a donor pays a cost, c, for a recipient to get a benefit, b. In evolutionary biology, cost and benefit are measured in terms of fitness. While mutation and selection represent the main forces of evolutionary dynamics, cooperation is a fundamental principle that is required for every level of biological organization. Individual cells rely on cooperation among their components. Multicellular organisms exist because of cooperation among their cells. Social insects are masters of cooperation. Most aspects of human society are based on mechanisms that promote cooperation. Whenever evolution constructs something entirely new (such as multicellularity or human language), cooperation is needed. Evolutionary construction is based on cooperation. The five rules for cooperation which we examine in this chapter are: kin selection, direct reciprocity, indirect reciprocity, graph selection, and group selection. Each of these can promote cooperation if specific conditions are fulfilled. […], Martin A. Nowak, Karl Sigmund, How populations cohere: five rules for cooperation, in R. M. May and A. McLean (eds.) Theoretical Ecology: Principles and Applications, Oxford UP, Oxford (2007), 7-16. [PDF]

How does cooperation emerge among selfish individuals? When do people share resources, punish those they consider unfair, and engage in joint enterprises? These questions fascinate philosophers, biologists, and economists alike, for the “invisible hand” that should turn selfish efforts into public benefit is not always at work. The Calculus of Selfishness looks at social dilemmas where cooperative motivations are subverted and self-interest becomes self-defeating. Karl Sigmund, a pioneer in evolutionary game theory, uses simple and well-known game theory models to examine the foundations of collective action and the effects of reciprocity and reputation. Focusing on some of the best-known social and economic experiments, including games such as the Prisoner’s Dilemma, Trust, Ultimatum, Snowdrift, and Public Good, Sigmund explores the conditions leading to cooperative strategies. His approach is based on evolutionary game dynamics, applied to deterministic and probabilistic models of economic interactions. Exploring basic strategic interactions among individuals guided by self-interest and caught in social traps, The Calculus of Selfishness analyses to what extent one key facet of human nature–selfishness–can lead to cooperation. (from Princeton Press). [Karl Sigmund, The Calculus of Selfishness, Princeton Series on Theoretical and Computational Biology, Princeton University Press,  ISBN: 978-1-4008-3225-5, 192 pp., 2009.]

What follows comes partly from chapter 1, available here:

THE SOCIAL ANIMAL: Aristotle classified humans as social animals, along with other species, such as ants and bees. Since then, countless authors have compared cities or states with bee hives and ant hills: for instance, Bernard de Mandeville, who published his The Fable of the Bees more than three hundred years ago. Today, we know that the parallels between human communities and insect states do not reach very far. The amazing degree of cooperation found among social insects is essentially due to the strong family ties within ant hills or bee hives. Humans, by contrast, often collaborate with non-related partners. Cooperation among close relatives is explained by kin selection. Genes for helping offspring are obviously favouring their own transmission. Genes for helping brothers and sisters can also favour their own transmission, not through direct descendants, but indirectly, through the siblings’ descendants: indeed, close relatives are highly likely to also carry these genes. In a bee hive, all workers are sisters and the queen is their mother. It may happen that the queen had several mates, and then the average relatedness is reduced; the theory of kin selection has its share of complex and controversial issues. But family ties go a long way to explain collaboration. The bee-hive can be viewed as a watered-down version of a multicellular organism. All the body cells of such an organism carry the same genes, but the body cells do not reproduce directly, any more than the sterile worker-bees do. The body cells collaborate to transmit copies of their genes through the germ cells – the eggs and sperm of their organism. Viewing human societies as multi-cellular organisms working to one purpose is misleading. Most humans tend to reproduce themselves. Plenty of collaboration takes place between non-relatives. And while we certainly have been selected for living in groups (our ancestors may have done so for thirty million years), our actions are not as coordinated as those of liver cells, nor as hard-wired as those of social insects. Human cooperation is frequently based on individual decisions guided by personal interests. Our communities are no super-organisms. Former Prime Minister Margaret Thatcher pithily claimed that “there is no such thing as society“. This can serve as the rallying cry of methodological individualism – a research program aiming to explain collective phenomena bottom-up, by the interactions of the individuals involved. The mathematical tool for this program is game theory. All “players” have their own aims. The resulting outcome can be vastly different from any of these aims, of course.

THE INVISIBLE HAND: If the end result depends on the decisions of several, possibly many individuals having distinct, possibly opposite interests, then all seems set to produce a cacophony of conflicts. In his Leviathan from 1651, Hobbes claimed that selfish urgings lead to “such a war as is every man against every man“. In the absence of a central authority suppressing these conflicts, human life is “solitary, poor, nasty, brutish, and short“. His French contemporary Pascal held an equally pessimistic view: : “We are born unfair; for everyone inclines towards himself…. The tendency towards oneself is the origin of every disorder in war, polity, economy etc“. Selfishness was depicted as the root of all evil. But one century later, Adam Smith offered another view.An invisible hand harmonizes the selfish efforts of individuals: by striving to maximize their own revenue, they maximize the total good. The selfish person works inadvertently for the public benefit. “By pursuing his own interest he frequently promotes that of the society more effectually than when he really intends to promote it“. Greed promotes behaviour beneficial to others. “It is not from the benevolence of the butcher, the brewer, or the baker, that we expect our dinner, but from their regard to their own self-interest. We address ourselves, not to their humanity but to their self-love, and never talk to them of our own necessities but of their advantages“. A similar view had been expressed, well before Adam Smith, by Voltaire in his Lettres philosophiques: “Assuredly, God could have created beings uniquely interested in the welfare of others. In that case, traders would have been to India by charity, and the mason would saw stones to please his neighbour. But God designed things otherwise….It is through our mutual needs that we are useful to the human species; this is the grounding of every trade; it is the eternal link between men“. Adam Smith (who knew Voltaire well) was not blind to the fact that the invisible hand is not always at work. He merely claimed that it frequently promotes the interest of the society, not that it always does. Today, we know that there are many situations – so-called social dilemmas – where the invisible hand fails to turn self-interest to everyone’s advantage.

Book – Erwin Schrödinger (1944), “What Is Life?” Cambridge: Cambridge University Press, (my edition is from 2002 – book cover above).

…living matter, while not eluding the “laws of physics” as established up to date, is likely to involve “other laws of physics” hitherto unknown, which however, once they have been revealed, will form just as integral a part of science as the former.“, Erwin Schrödinger (1944), Chapter VI, [1].

[…] The structure of DNA and the genetic code may have alluded us for some time more if Crick had not read Erwin Schrödinger‘s What Is Life? [1,2]. The research lead that Crick got by doing so was how a small set of repeating elements could give rise to a large number of combinatorial products, a mathematical relationship that Schrödinger illustrated using the Morse Code, based on an idea that he had actually got from the visionary work of Max Delbrück. Delbrück, Schrödinger and Crick were physicists with an enthusiasm for tackling the unknown for the natural world. Crick‘s own motivation came directly from reading What Is Life? [3]. It seemed reasonable to make the cross-over as the infant field of biochemistry was bound to be governed by the same chemical and physical laws revealed in other, non-biological, disciplines. This was especially true given the progressive focus of biology on the increasingly small, until an effective convergence of scales in the studies of the biologically relevant on the biologically irrelevant. Hence the justification for Schrödinger‘s unspecific book title. Although some of the notions in the book have been superseded by modern science, this remains a classic, written with great insight and modesty (Schrödinger downplays his potential as a biologist), and is worth the read if only as a portal in to the minds of those luminary workers. By the time Watson and Crick were piecing together the jigsaw that would lead to their grand discovery, the far-reaching potential of Schrödinger‘s code script had been aligned with Chargaff‘s finding of a variable sequence of nucleotide bases, and the stage was set for that immortal terminal sentence, “It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material.” […], Derry, J. F. (2004). Review of What Is Life? By Erwin Schrödinger. Human Nature Review. 4: 124-125.

This is the first time any synthetic DNA has been in complete control of a cell“, Craig Venter, May 2010 (video below).

In 1953, when the structure of DNA was determined, there were 53 kilobytes of high-speed electronic storage on planet earth. Two entirely separate forms of code were set on a collision course. Primitive as it may be, we now have one of the long-awaited results.”, George Dyson, May 2010.

On April 9, 2010, 41 days ago, Science Journal receives a manuscript for revision signed by no least than 24 scientists. Then, 7 days ago it was accepted for publication. It was released today, May 20, 2010. And what we are now assisting today, is no less than a pivotal moment in Human history, in fact, a turning-point for the entire planet and it’s life. Entitled “Creation of a Bacterial Cell controlled by a Chemical Synthesized Genome” [4], the paper describes how these 24 scientists have succeeded in developing the first synthetic living cell. Being the ability to design and create new forms of life so extraordinary, that a truly scientific landmark was indeed today realized.  That’s  -indeed- one small step for synthetic biology, one giant leap for mankind.

The new cell, is in some-ways a code within a code. As science historian George Dyson points out, “from the point of view of technology, a code generated within a digital computer is now self-replicating as the genome of a line of living cells. From the point of view of biology, a code generated by a living organism has been translated into a digital representation for replication, editing, and transmission to other cells.”

First step was to previously made a synthetic bacterial genome, and transplanted the genome of one bacterium into another. Then, both methods were put together in order to create the present synthetic cell, even if only its genome is truly synthetic. By sequencing its genetic code and then using synthesis machines to chemically construct a copy, a different organism could then be form, taking the synthetic chromosome, and transplant it into a recipient cell. As Venter and his team point out, “As soon as this new software goes into the cell, that cell reads that software and converts the new cell into the species specified in that genetic code.”

We code it, and the new cell reads it. It’s anyhow of full interest to follow with caution their final words on the paper [4] (the entire work could be accessed here from where both pictures were depicted):

[…] If the methods described here can be generalized, design, synthesis, assembly, and transplantation of synthetic chromosomes will no longer be a barrier to the progress of synthetic biology. We expect that the cost of DNA synthesis will follow what has happened with DNA sequencing and continue to exponentially decrease. Lower synthesis costs combined with automation will enable broad applications for synthetic genomics. We have been driving the ethical discussion concerning synthetic life from the earliest stages of this work. Assynthetic genomic applications expand, we anticipate that this work will continue to raise philosophical issues that have broad societal and ethical implications. We encourage the continued discourse . […]

Watermarked on the new synthetic cell DNA (embedded) there is a quote from Richard Feynman:What I can not build I can not understand“. No matter what, from this point on, we should really re-question what Life is?

TED in the field video – Craig Venter unveils synthetic life, May 2010.

Ref. notes: [1] Erwin Schrödinger (1944), “What Is Life?” Cambridge: Cambridge University Press, (novel edition 2002). | [2] Francis Crick (1989) What Mad Pursuit. Penguin. | [3] James Watson (1981) The Double Helix. Weidenfeld and Nicholson. | [4] Daniel G. Gibson, John I. Glass, … Craig Venter et al., (2010), “Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome“, Science Journal, released and visited on-line on May 20, 2010.

Picture – Albert Einstein standing on a rock stepping-stone, enjoying grabbing some sun at the sea shore (1945). Oh! … the sea shore. By the way, Mr. Einstein, what lovely sexy shoes you have!

[…] Einstein always appeared to have a clear view of the problems of physics and the determination to solve them. He had a strategy of his own and was able to visualize the main stages on the way to his goal. He regarded his major achievements as mere stepping-stones for the next advance. […] In his early days in Berlin, Einstein postulated that the correct interpretation of the special theory of relativity must also furnish a theory of gravitation and in 1916 he published his paper on the general theory of relativity. During this time he also contributed to the problems of the theory of radiation and statistical mechanics. […] After his retirement he continued to work towards the unification of the basic concepts of physics, taking the opposite approach, geometrisation, to the majority of physicists. […] (source Nobel prize org.)

Einstein on the Beach : Philip Glass / Robert Wilson, 1976.

[…] Einstein on the Beach (1976) is a pivotal work in the oeuvre of Philip Glass. It is the first, longest, and most famous of the composer’s operas, yet it is in almost every way unrepresentative of them. Einstein was, by design, a glorious “one-shot” – a work that invented its context, form and language, and then explored them so exhaustively that further development would have been redundant. But, by its own radical example, Einstein prepared the way – it gave permission – for much of what has happened in music theater since its premiere. Einstein broke all the rules of opera. It was in four interconnected acts and five hours long, with no intermissions (the audience was invited to wander in and out at liberty during performances). The acts were intersticed by what Glass and Wilson called “knee plays” – brief interludes that also provided time for scenery changes. The text consisted of numbers, solfege syllables and some cryptic poems by Christopher Knowles, a young, neurologically-impaired man with whom Wilson had worked as an instructor of disturbed children for the New York public schools. To this were added short texts by choreographer Lucinda Childs and Samuel M. Johnson, an actor who played the Judge in the “Trial” scenes and the bus driver in the finale. There were references to the trial of Patricia Hearst (which was underway during the creation of the opera); to the mid-’70s radio lineup on New York’s WABC; to the popular song “Mr. Bojangles”; to the Beatles and to teen idol David Cassidy. Einstein sometimes seemed a study in sensory overload, meaning everything and nothing…  […] (continues) [source ]

KNEE 5 | KNEE PLAY CHARACTER 1 : Numbers and Mr Bojangles /  KNEE PLAY CHARACTER 2 : Text from Knee Play 1 / BUS DRIVER : Lovers on a Park Bench

1,2,3,4… 1,2,3,4,5,6, …,1,2,3,4,5,6,7,8,… 1,2,3,4… 1,2,3,4,5,6, …,1,2,3,4,5,6,7,8,… 1,2,3,4… 1,2,3,4,5,6, … 2,3,4, … 1,2,3,4, … 1,6 …

Two lovers sat on a park bench with their bodies touching each other, holding hands in the moonlight. There was silence between them. So profound was their love for each other, they needed no words to express it. And so they sat in silence, on a park bench, with their bodies touching, holding hands in the moonlight. Finally she spoke. “Do you love me, John ?” she asked. “You know I love you. darling,” he replied. “I love you more than tongue can tell. You are the light of my life. my sun. moon and stars. You are my everything. Without you I have no reason for being.” Again there was silence as the two lovers sat on a park bench, their bodies touching, holding hands in the moonlight. Once more she spoke. “How much do you love me, John ?” she asked. He answered : “How’ much do I love you ? Count the stars in the sky. Measure the waters of the oceans with a teaspoon. Number the grains of sand on the sea shore. Impossible, you say? “, (text by Samuel Johnson).

[…] Cuenta la leyenda que segundos antes de llegar a una curva, Juan Manuel Fangio dirigía una fugaz mirada a las hojas de los árboles. Si se movían, levantaba el pie del acelerador; si, por el contrario, no soplaba el viento, pisaba a fondo. […], in Ángel Luis Menéndez, “Los abuelos de Alonso”, Pú (link)

If you want to be incrementally better: Be competitive. If you want to be exponentially better: Be cooperative“. ~ Anonymous

Two hunters decide to spent their week-end together. But soon, a dilemma emerges between them. They could choose for hunting a deer stag together or either -individually- hunt a rabbit on their own. Chasing a deer, as we know, is something quite demanding, requiring absolute cooperation between them. In fact, both friends need to be focused for a long time and in position, while not being distracted and tempted by some arbitrary passing rabbits. On the other hand, stag hunt is increasingly more beneficiary for both, but that benefice comes with a cost: it requires a high level of trust between them. Somehow at some point, each hunter concerns that his partner may diverts while facing a tempting jumping rabbit, thus jeopardizing the possibility of hunting together the biggest prey.

The original story comes from Jean Jacques Rousseau, French philosopher (above). While, the dilemma is known in game theory has the “Stag Hunt Game” (Stag = adult deer). The dilemma could then take different quantifiable variations, assuming different values for R (Reward for cooperation), T (Temptation to defect), S (Sucker’s payoff) and P (Punishment for defection). However, in order to be at the right strategic Stag Hunt Game scenario we should assume R>T>P>S. A possible pay-off table matrix taking in account two choices C or D (C = Cooperation; D = Defection), would be:

Choice — C ——- D ——
C (R=3, R=3) (S=0, T=2)
D (T=2, S=0) (P=1, P=1)

Depending on how fitness is calculated, stag hunt games could also be part of a real Prisoner’s dilemma, or even Ultimatum games. As clear from above, highest pay-off comes from when both hunters decide to cooperate (CC). Over this case (first column – first row), both receive a reward of 3 points, that is, they both really focused on hunting a big deer while forgetting everything else, namely rabbits. However – and here is where exactly the dilemma appears -, both CC or DD are Nash equilibrium! That is, at this strategic landscape point no player has anything to gain by changing only his own strategy unilaterally. The dilemma appears recurrently in biology, animal-animal interaction, human behaviour, social cooperation, over Co-Evolution, in society in general, and so on. Philosopher David Hume provided also a series of examples that are stag hunts, from two individuals who must row a boat together up to two neighbours who wish to drain a meadow. Other stories exist with very interesting variations and outcomes. Who does not knows them?!

The day before last school classes, two kids decided to do something “cool”, while conjuring on appearing before their friends on the last school day period, both with mad and strange haircuts. Although, despite their team purpose, a long, anguish and stressful night full of indecisiveness followed for both of them…

Figure – A swarm cognitive map (pheromone spatial distribution map) in 3D, at a specific time t. The artificial ant colony was evolved within 2 digital grey images based on the following work. The real physical “thing” can be seen here.

[] Vitorino Ramos, The MC2 Project [Machines of Collective Conscience]: A possible walk, up to Life-like Complexity and Behaviour, from bottom, basic and simple bio-inspired heuristics – a walk, up into the morphogenesis of information, UTOPIA Biennial Art Exposition, Cascais, Portugal, July 12-22, 2001.

Synergy (from the Greek word synergos), broadly defined, refers to combined or co-operative effects produced by two or more elements (parts or individuals). The definition is often associated with the holistic conviction quote that “the whole is greater than the sum of its parts” (Aristotle, in Metaphysics), or the whole cannot exceed the sum of the energies invested in each of its parts (e.g. first law of thermodynamics) even if it is more accurate to say that the functional effects produced by wholes are different from what the parts can produce alone. Synergy is a ubiquitous phenomena in nature and human societies alike. One well know example is provided by the emergence of self-organization in social insects, via direct (mandibular, antennation, chemical or visual contact, etc) or indirect interactions. The latter types are more subtle and defined as stigmergy to explain task coordination and regulation in the context of nest reconstruction in Macrotermes termites. An example, could be provided by two individuals, who interact indirectly when one of them modifies the environment and the other responds to the new environment at a later time. In other words, stigmergy could be defined as a particular case of environmental or spatial synergy. Synergy can be viewed as the “quantity” with respect to which the whole differs from the mere aggregate. Typically these systems form a structure, configuration, or pattern of physical, biological, sociological, or psychological phenomena, so integrated as to constitute a functional unit with properties not derivable from its parts in summation (i.e. non-linear) – Gestalt in one word (the English word more similar is perhaps system, configuration or whole). The system is purely holistic, and their properties are intrinsically emergent and auto-catalytic.

A typical example could be found in some social insect societies, namely in ant colonies. Coordination and regulation of building activities on these societies do not depend on the workers themselves but are mainly achieved by the nest structure: a stimulating configuration triggers the response of a termite worker, transforming the configuration into another configuration that may trigger in turn another (possibly different) action performed by the same termite or any other worker in the colony. Recruitment of social insects for particular tasks is another case of stigmergy. Self-organized trail laying by individual ants is a way of modifying the environment to communicate with nest mates that follow such trails. It appears that task performance by some workers decreases the need for more task performance: for instance, nest cleaning by some workers reduces the need for nest cleaning. Therefore, nest mates communicate to other nest mates by modifying the environment (cleaning the nest), and nest mates respond to the modified environment (by not engaging in nest cleaning).

Swarms of social insects construct trails and networks of regular traffic via a process of pheromone (a chemical substance) laying and following. These patterns constitute what is known in brain science as a cognitive map. The main differences lies in the fact that insects write their spatial memories in the environment, while the mammalian cognitive map lies inside the brain, further justified by many researchers via a direct comparison with the neural processes associated with the construction of cognitive maps in the hippocampus.

But by far more crucial to the present project, is how ants form piles of items such as dead bodies (corpses), larvae, or grains of sand. There again, stigmergy is at work: ants deposit items at initially random locations. When other ants perceive deposited items, they are stimulated to deposit items next to them, being this type of cemetery clustering organization and brood sorting a type of self-organization and adaptive behaviour, being the final pattern of object sptial distribution a reflection of what the colony feels and thinks about that objects, as if they were another organism (a meta- global organism).

As forecasted by Wilson [E.O. Wilson. The Insect Societies, Belknam Press, Cambridge, 1971], our understanding of individual insect behaviour together with the sophistication with which we will able to analyse their collective interaction would advance to the point were we would one day posses a detailed, even quantitative, understanding of how individual “probability matrices” (their tendencies, feelings and inner thoughts) would lead to mass action at the level of the colony (society), that is a truly “stochastic theory of mass behaviour” where the reconstruction of mass behaviours is possible from the behaviours of single colony members, and mainly from the analysis of relationships found at the basic level of interactions.

The idea behind the MC2 Machine is simple to transpose for the first time, the mammalian cognitive map, to a environmental (spatial) one, allowing the recognition of what happens when a group of individuals (humans) try to organize different abstract concepts (words) in one habitat (via internet). Even if each of them is working alone in a particular sub-space of that “concept” habitat, simply rearranging notions at their own will, mapping “Sameness” into “Neighborness“, not recognizing the whole process occurring simultaneously on their society, a global collective-conscience emerges. Clusters of abstract notions emerge, exposing groups of similarity among the different concepts. The MC2 machine is then like a mirror of what happens inside the brain of multiple individuals trying to impose their own conscience onto the group.

Through a Internet site reflecting the “words habitat”, the users (humans) choose, gather and reorganize some types of words and concepts. The overall movements of these word-objects are then mapped into a public space. Along this process, two shifts emerge: the virtual becomes the reality, and the personal subjective and disperse beliefs become onto a social and politically significant element. That is, perception and action only by themselves can evolve adaptive and flexible problem-solving mechanisms, or emerge communication among many parts. The whole and their behaviours (i.e., the next layer in complexity – our social significant element) emerges from the relationship of many parts, even if these later are acting strictly within and according to any sub-level of basic and simple strategies, ad-infinitum repeated.

The MC2 machine will reveal then what happens in many real world situations; cooperation among individuals, altruism, egoism, radicalism, and also the resistance to that radicalism, memory of that society on some extreme positions on time, but the inevitable disappearance of that positions, to give rise to the convergence to the group majority thought (Common-sense?), eliminating good or bad relations found so far, among in our case, words and abstract notions. Even though the machine composed of many human-parts will “work” within this restrict context, she will reveal how some relationships among notions in our society (ideas) are only possible to be found, when and only when simple ones are found first (the minimum layer of complexity), neglecting possible big steps of a minority group of visionary individuals. Is there (in our society) any need for a critical mass of knowledge, in order to achieve other layers of complexity? Roughly, she will reveal for instance how democracies can evolve and die on time, as many things in our impermanent world.

It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change“. Charles Darwin (On the Origin of Species, Nov. 1859)

During the Victorian era where high prudery and morality were constant, it would be hard to imagine seeing Charles Darwin wearing a Scottish-kilt. In fact, men’s formal clothing was less colourful than it was in the previous century, while women’s tight-fitting jersey dresses of the 1880s covered the body, leaving little to the imagination (source). There is however, one beautiful – as in strict sense of delighting the senses for exciting intellectual or emotional admiration – reason, I think he should have done it (!), regardless the  obvious bearing consequences of a severe Victorian society. Surprisingly, some how, that reason is linked to cheetahs chasing gazelles, among many other things…

As the image of Charles Darwin wearing a kilt, you will probably find these awkward too, but when a cheetah chases a gazelle, banded tartan Scottish-kilt woven textile like patterns soon start to pop-up everywhere. Not at the ground terrain level, of course. Instead, they appear as a phenotype-like map between your present and the past. You may think that this banded tartans will have no significance for your life, but do mind this: crying babies do it all the time with their mommy’s and fathers, companies do it with other companies in their regular business, people commuting in large cities do it over large highways, human language, literature and culture does it, friends do it, PC virus and anti-virus software do it, birds singing do it also, … and even full countries at war do it.

One extreme example is the Cold War, where for the first time on our Human history, co-evolutionary arms-race raised to unprecedented levels. Co-Evolution is indeed the right common key-word for all these phenomena, while large white banded strips punctuated by tiny black ones (bottom-left woven kilt above), would be the perfect correspondent tartan pattern for the case of the Cold War example mentioned. But among these, there is of course, much more Scottish-kilt like patterns we could find. Ideas, like over this TV ad above, co-evolve too. Here, the marketeer decided to co-evolve with a previous popular famous meme image: Sharon Stone crossing his legs at the 1992 ‘Basic Instinctmovie. In fact, there is an authentic plethora of different possible behavioural patterns. Like a fingerprint (associated with different Gaelic clans), each of these patterns correspond to a lineage of current versus ancestral strategies, trying to solve a specific problem, or achieving one precise goal. But as the strategic landscape is dynamically changing all the time, a good question is, how can we visualize it. And, above all, what vital information and knowledge could we retrieve from this evolutionary Scottish-kilts maps.

Fig. – The frontispiece drawing to the English edition of Ernst Haeckel‘s Evolution of Man (trans. 1903) presents a skull labelled “Australian Negro” as an intervening evolutionary stage between the “Mediterranean” skull and those of the lower primates (from the 1891 ed. of the Anthropogenie).

In nature, organisms and species coexist in an ecosystem, where each species has its own place or niche in the system. The environment contains a limited number and amount of resources, and the various species must compete for access to those resources, where successive adaptations in one group put pressure on another group to catch up (e.g., the coupled phenomena of speed in the cheetah and evasive agility in the gazelle). Through these interactions, species grow and change, each influencing the others evolutionary development [7]. This process of bi-adaptive relationship (in some cases can also assume a form of cooperation and mutualism) or reciprocal adaptation is know as Co-evolution, i.e. the evolution of two or more competing populations with coupled fitness.

The phenomena has several interesting features that may potentially enhance the adaptive power of artificial evolution [7], or  other types of bio-inspired learning systems. In particular, competing populations may reciprocally drive one another to increasing levels of complexity by producing an evolutionary “arms race”, where each group may become bigger, faster, more lethal, more intelligent, etc. Co-Evolution can then happen either between a learner (e.g., single population) and its environment (i.e. based on competitions among individuals in the population) or between learning species (two populations evolving), where the fitness of individuals is based on their behaviour in the context of the individuals of the other population [7]. This latter type of co-evolutionary search is often described as “host-parasite”, or “predator-prey” co-evolution. A good example and application of co-evolutionary learning include the pioneering work by Hillis in 1990 [1] on sorting networks.

It can occur at multiple levels of biology: it can be as microscopic as correlated mutations between amino acids in a protein, or as macroscopic as co-varying traits between different species in an environment. Being biological Co-Evolution, in a broad sense, “the change of a biological object triggered by the change of a related object” [2], his visualization however, could be profoundly hard. In fact, attempting to define and monitor “progress” in the context of Co-Evolutionary systems can be a somewhat nightmarish experience , as stated in [4]. It’s exactly here where Scottish-kilts come into play.

In 1995 [3], two researchers had a simple, yet powerful idea. In order to monitor the dynamics of artificial competitive co-evolutionary systems between two populations, Dave Cliff and Geoffrey Miller [3,4,5] proposed evaluating the performance of an individual from the current population in a series of trials against opponents from all previous generations. while visualizing the results as 2D grids of shaded cells or pixels: qualitative patterns in the shading can thus indicate different classes of co-evolutionary dynamic. Since their technique involves pitting a Current Individual (CI) against Ancestral Opponents (AO), they referred to the visualizations as CIAO plots (fig. above [3]).

Important Co-Evolutionary dynamics such as limited evolutionary memory, “Red Queen” effects or intransitive dominance cycling, will then be revealed like a fingerprint as certain qualitative patterns. Dominance cycling, for instance, it’s a major factor on Co-Evolution, wish could appear or not, during the entire co-evolutionary process. Imagine, for instance, 3 individuals (A,B,C) or strategies. Like over the well known “Rock, Paper, Scissors” game, strategy B could beat strategy A, strategy C could beat B, and strategy A could beat C, over and over in an eternal cycling, where only “arms race” specialized learning will emerge, at the cost of a limited learning generalization against a possible fourth individual-strategy D. If you play poker, you certainly know what I am talking about, since 2 poker players are constantly trying to broke this behavioural cycle, or entering it, depending on their so-far success.

Above (left and right figures – [3]), two idealised typical CIAO plot patterns can be observed, where darker shading denotes higher scores. On the left figure, however, co-evolutionary intransitive dominance cycling is a constant, where current elites (population A elites) score highly against population B opponents from 3, 8 and 13 generations ago, but not so well against generations in between. On the other hand (right figure), the behavioural pattern is completely different: over here we do observe limited evolutionary memory, where the current elites do well against opponents from 3,4 and 5 generations ago, but much less well against more distant ancestral opponents.

For to win one hundred victories in one hundred battles is not the acme of skill. To subdue the enemy without fighting is the acme of skill.” ~ Sun Tzu

Of course, in increasingly complex real-world situations Scottish-kilt like CIAO plots are much noisy than this (fig. above -[7]) where banded tartans could be less prominent, while the same could happen in irregular dominance cycling as elegantly showed by Cartlidge and Bullock in 2004 [6]. Above, some of my own experiences can be observed (submitted work). Over here I decided to co-evolve a AI agent strategy to play against a pool of 15 different strategies (6 of those confronts are presented above), and as a result, 6 different behavioural patterns emerged between them. All in all, the full spectrum of co-evolving dynamics could be observed, from the “Red Queen” effect, till alternate dominant cycles, and limited or long evolutionary memory. Even if some dynamics seem counter-productive in one-by-one confronts, in fact, all of these dynamics are useful in some way, as when you play Poker or the “Rock, Paper, Scissors” game. A typical confront between game memory (exploitation) and the ability to generalize (exploration). Where against precise opponents limited evolutionary memory was found, the same effect produced dominant cycles or long evolutionary memory against other strategies. The idea of course, is not to co-evolve a super-strategy to win all one-by-one battles (something that would be rather impossible; e.g. No free Lunch Theorem) but instead to win the whole round-robin tournament, by being highly adaptive and/or exaptive.

So next time you see someone wearing a banded tartan Scottish-kilt do remind yourself that, while getting trapped in traffic, that precise pattern could be the result of your long year co-evolved strategies to find the quickest way home, while confronting other commuters doing the same. And that, somewhere, somehow, Charles Darwin is envying your observations…


[1] W. Daniel Hillis (1990), “Co-Evolving Parasites improve Simulated Evolution as an Optimization Procedure”, Physica D, Vol. 42, pp. 228-234 (later in, C. Langton et al. (Eds.) (1992), Procs. Artificial Life II, Addison-Welsey, pp. 313-324).

[2] Yip et al.; Patel, P; Kim, PM; Engelman, DM; McDermott, D; Gerstein, M (2008). “An integrated system for studying residue Coevolution in Proteins“. Bioinformatics 24 (2): 290-292. doi:10.1093/bioinformatics/btm584. PMID 18056067.

[3] Dave Cliff, Geoffrey F. Miller, (1995), “Tracking the Red Queen: Methods for measuring co-evolutionary progress in open-ended simulations“. In F. Moran, A. Moreno, J. J. Merelo, & P. Cachon (Eds.), Advances in artificial life: Proceedings of the Third European Conference on Artificial Life (pp. 200-218). Berlin: Springer-Verlag.

[4] Dave Cliff, Geoffrey F. Miller, (2006), “Visualizing Co-Evolution with CIAO plots“, Artificial Life, 12(2), 199-202

[5] Dave Cliff, Geoffrey F. Miller (1996). “Co-evolution of pursuit and evasion II: Simulation methods and results“. In P. Maes, M. J. Mataric, J.-A. Meyer, J. Pollack, & S. W. Wilson (Eds.), From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (pp. 506-515). Cambridge, MA: MIT Press.

[6] Cartlidge, J. and Bullock S., (2004), “Unpicking Tartan CIAO plots: Understanding irregular Co-Evolutionary Cycling“, Adaptive Behavior Journal, 12: 69-92, 2004.

[7] Ramos, Vitorino, (2007), “Co-Cognition, Neural Ensembles and Self-Organization“, extended abstract for a seminar talk at ISR – Institute for Systems and Robotics, Technical Univ. of Lisbon (IST), Lisbon, PORTUGAL. May 31, 2007.

[...] People should learn how to play Lego with their minds. Concepts are building bricks [...] V. Ramos, 2002.

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