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Time-lapse imaging in live zebrafish embryos reveals that cerebellar granule cells migrate in chain-like structures as discovered by a recent article [1] [Köster et al., PLoS, Nov. 2009]. Figure above – Granule cells taken from the cerebellum of a pigeon (above, B) are shown in this 1899 drawing by legendary neuroscientist Santiago Ramón y Cajal.
Did talk about sticky objects and self-organization in the past, how positive and negative feedback’s stigmergic-like agents integrated could promote changes and learning over a complex system. Same happens to bacteria as also ants. On the other hand, we do know memes are also sticky (e.g. Chip Heath, Dan Heath, “Made to Stick: Why Some Ideas Survive and Others Die“, Random House, ISBN 978-1-4000-6428-1, January 2007). What’s new however, is that there are increasing proofs that our own brains my follow similar mechanisms (as Douglas Hofstadter in the past did made some analogies with how brains could work and how ant colonies raid different environments). In this recent new study, Köster and colleagues [1] [PLoS, Nov. 2009] reveal crucial pieces of this puzzle, showing how (neuronal) cells orient themselves to migrate together (like bacteria, above). The team studied zebrafish, one of the workhorses of developmental neurobiology, because its transparent body allows researchers to track movements of cells inside of it. As explained by Mason Inman [2]:
[...] Neurons in the developing brain complete their own self-organized waltz, coordinating with their neighbors to migrate to the right spots to form the cerebellum, visual cortex, or other parts of the brain. In this issue of PLoS Biology, Reinhard Köster and colleagues show that some of these brain cells behave much like slime molds, coordinating with other cells of the same type to migrate in a herd. They found that one particular protein called Cadherin-2 is crucial in allowing the cells to adhere to their neighbors so they can coordinate their movements and all wind up in the right spot. [...] Slime molds provide a textbook example of self-organization. They live as single cells until food becomes scarce. Then, they broadcast chemical signals that trigger their mass assembly into a fruiting body, with some cells forming a stalk and others turning into spores that cast about in the winds to spread far and wide. [...] Neurons in the developing brain complete their own self-organized waltz, coordinating with their neighbors to migrate to the right spots to form the cerebellum, visual cortex, or other parts of the brain. In this issue of PLoS Biology, Reinhard Köster and colleagues show that some of these brain cells behave much like slime molds, coordinating with other cells of the same type to migrate in a herd. They found that one particular protein called Cadherin-2 is crucial in allowing the cells to adhere to their neighbors so they can coordinate their movements and all wind up in the right spot.[...]
[...] But the mechanisms behind this coordinated movement – in particular, how each cell adjusts its inner workings to move to the right place at the right time – are only now starting to be revealed, using imaging that tracks these cells in live animals as they develop. [...] To figure out what triggers the cells to line up and move together, the authors looked at what other kinds of cells were in the neighborhood. Many studies have shown that support cells, known as glial cells, often help guide neurons during these kinds of migrations. But during the first few days of the zebrafish embryo’s development, Köster and colleagues found, there were no glial cells along the granular cells’ migration route. That means these cells must go it alone, the team reasoned, with their own mechanism for signaling between each other to line up into chains and make their move. [...] Although the study focused on just one type of brain cell, the findings could explain how many types of neurons find their way to their proper spots as the brain develops. There are still some pieces of the puzzle missing, however. While the findings explain how the granule cells are able to coordinate and follow their neighbors, it’s still not clear how the first few cells to head out on the journey – those at the front of the “conga line” – get oriented in the right direction. This suggests there must be some kind of signal from surrounding cells to get them headed in the right direction, the authors argue – yet another level of organization. [...] , in Mason Inman (Nov., 2009) Migrating Brain Cells Stick Together, PloS. [2]
[1] Rieger S, Senghaas N, Walch A, Köster RW (Nov., 2009) Cadherin-2 Controls Directional Chain Migration of Cerebellar Granule Neurons. PLoS Biology.
[2] Mason Inman (Nov., 2009) Migrating Brain Cells Stick Together, PloS Biology.
No one really knows what a meme is, nevertheless when a good one comes around, everybody recognizes it…!
[graphic tag cloud via Wordle]
(via Wikipedia - Meme Transmission) Life-forms can transmit information both vertically (from parent to child, via replication of genes) and horizontally (through viruses and other means). Memes can replicate vertically or horizontally within a single biological generation. They may also lie dormant for long periods of time. Memes spread by the behaviors that they generate in their hosts. Imitation counts as an important characteristic in the propagation of memes. Imitation often involves the copying of an observed behaviour of another individual, but memes may transmit from one individual to another through a copy recorded in an inanimate source, such as a book or a musical score. Researchers have observed memetic copying in just a few species on Earth, including hominids, dolphins and birds (which learn how to sing by imitating their parents or neighbors).
Some commentators have likened the transmission of memes to the spread of contagions. Social contagions such as fads, hysterias and copycat suicides exemplify memes seen as the contagious imitation of ideas. Observers distinguish the contagious imitation of memes from instinctively contagious phenomena such as yawning and laughing, which they consider innate (rather than socially learned) behaviors.
Aaron Lynch described seven general patterns of meme transmission, or “thought contagion”:
- Quantity of parenthood: an idea which influences the number of children one has. Children respond particularly receptively to the ideas of their parents, and thus ideas which directly or indirectly encourage a higher birthrate will replicate themselves at a higher rate than those that discourage higher birthrates.
- Efficiency of parenthood: an idea which increases the proportion of children who will adopt ideas of their parents. Cultural separatism exemplifies one practice in which one can expect a higher rate of meme-replication — because the meme for separation creates a barrier from exposure to competing ideas.
- Proselytic: ideas generally passed to others beyond one’s own children. Ideas that encourage the proselytism of a meme, as seen in many religious or political movements, can replicate memes horizontally through a given generation, spreading more rapidly than parent-to-child meme-transmissions do.
- Preservational: ideas which influence those that hold them to continue to hold them for a long time. Ideas which encourage longevity in their hosts, or leave their hosts particularly resistant to abandoning or replacing these ideas, enhance the preservability of memes and afford protection from the competition or proselytism of other memes.
- Adversative: ideas which influence those that hold them to attack or sabotage competing ideas and/or those that hold them. Adversative replication can give an advantage in meme transmission when the meme itself encourages aggression against other memes.
- Cognitive: ideas perceived as cogent by most in the population who encounter them. Cognitively transmitted memes depend heavily on a cluster of other ideas and cognitive traits already widely held in the population, and thus usually spread more passively than other forms of meme transmission. Memes spread in cognitive transmission do not count as self-replicating.
- Motivational: ideas that people adopt because they perceive some self-interest in adopting them. Strictly speaking, motivationally transmitted memes do not self-propagate, but this mode of transmission often occurs in association with memes self-replicated in the efficiency parental, proselytic and preservational modes.
In their book Made to Stick, Chip and Dan Heath describe characteristics of an idea that make it “sticky” (i.e. memorable or interesting).
Note: I personally recommend Cosma Shalizi “Memes” web entry. Center for the Study of Complex Systems, University of Michigan (26 September 1997).
[...] … I do not really believe that we shall succeed in creating life artificially; but after having reached the moon and landed a spaceship or two on Mars, I realize that this disbelief of mine means very little. But computers are totally different from brains, whose function is not primarily to compute but to guide and balance an organism and help it to stay alive. It is for this reason that the first step of nature toward an intelligent mind was the creation of life, and I think that should we artificially create an intelligent mind, we would have to follow the same path. [...], Karl Popper – Popper, K. R. and Eccles, J. C. (1983), The Self and its Brain: An Argument for Interactionism, Routledge & Kegan Paul plc, London.
Excerpt – “Dartmouth and the Liberating Arts“: [...] Crucially, Deming (Edward Deming) then argued that this indispensable foundation of trust and shared commitment must be allied to a rigorous understanding of how complex systems work to produce desired results. (…) two sides of the educational mission set forth by my predecessors, a mission that in this historical moment is more vital than ever: on the one hand, the passionate commitment to making the world a better place; on the other, the practical understanding of complex systems required to deliver solutions on a global scale. Passion and practicality: Either without the other will be inadequate to tackle the challenges we face today. [...] Jim Yong Kim, “Passion and Practicality: Dartmouth and the Liberating Arts“, new President at Dartmouth Univ. at his inaugural address, Dartmouth Speeches, Sept. 2009.
Allow me to give you a starter. Albeit this is only the beginning. There is much more at stake over this 1 hour and 15 minutes movie drama documentary: [...] Did you read that the Japanese will be watching what’s going to be happening with American teenagers over the next 20 years, … and then they are going to decide to introduce GMO’s (Genetically Modified Organisms) into their food? [...]
Alternately teasing and terrifying, STRANGE CULTURE molds one man’s tragedy into an engrossing narrative. In 2004, Steve Kurtz (Thomas Jay Ryan), an associate professor of art at the State University of New York, Buffalo, was preparing an exhibition on genetically modified food for the Massachusetts Museum of Contemporary Art when his wife, Hope (Tilda Swinton), died in her sleep of heart failure. But when paramedics noticed petri dishes and other scientific paraphernalia in the home, they alerted the F.B.I.; within hours Mr. Kurtz found himself suspected of bioterrorism, his home quarantined and his wife’s body removed for autopsy. Filmmaker Lynn Hershman-Leeson bends the nonfiction form to her own unconventional will. The result is a fascinating collage of re-enactments, news clips and interviews, illuminating not only the implications of corporate meddling in the food chain but the ease with which innocent civilian behavior can become a suspicious act. [Text from the YouTube movie synopsis here]
On Bilateral Monopolies: [...] Mary has the world’s only apple, worth fifty cents to her. John is the world’s only customer for the apple, worth a dollar to him. Mary has a monopoly on selling apples, John has a monopoly (technically, a monopsony, a buying monopoly) on buying apples. Economists describe such a situation as bilateral monopoly. What happens? Mary announces that her price is ninety cents, and if John will not pay it, she will eat the apple herself. If John believes her, he pays. Ninety cents for an apple he values at a dollar is not much of a deal but better than no apple. If, however, John announces that his maximum price is sixty cents and Mary believes him, the same logic holds. Mary accepts his price, and he gets most of the benefit from the trade. This is not a fixed-sum game. If John buys the apple from Mary, the sum of their gains is fifty cents, with the division determined by the price. If they fail to reach an agreement, the summed gain is zero. Each is using the threat of the zero outcome to try to force a fifty cent outcome as favorable to himself as possible. How successful each is depends in part on how convincingly he can commit himself, how well he can persuade the other that if he doesn’t get his way the deal will fall through. Every parent is familiar with a different example of the same game. A small child wants to get her way and will throw a tantrum if she doesn’t. The tantrum itself does her no good, since if she throws it you will refuse to do what she wants and send her to bed without dessert. But since the tantrum imposes substantial costs on you as well as on her, especially if it happens in the middle of your dinner party, it may be a sufficiently effective threat to get her at least part of what she wants. Prospective parents resolve never to give in to such threats and think they will succeed. They are wrong. You may have thought out the logic of bilateral monopoly better than your child, but she has hundreds of millions of years of evolution on her side, during which offspring who succeeded in making parents do what they want, and thus getting a larger share of parental resources devoted to them, were more likely to survive to pass on their genes to the next generation of offspring. Her commitment strategy is hardwired into her; if you call her bluff, you will frequently find that it is not a bluff. If you win more than half the games and only rarely end up with a bargaining breakdown and a tantrum, consider yourself lucky.
Herman Kahn, a writer who specialized in thinking and writing about unfashionable topics such as thermonuclear war, came up with yet another variant of the game: the Doomsday Machine. The idea was for the United States to bury lots of very dirty thermonuclear weapons under the Rocky Mountains, enough so that if they went off, their fallout would kill everyone on earth. The bombs would be attached to a fancy Geiger counter rigged to set them off if it sensed the fallout from a Russian nuclear attack. Once the Russians know we have a Doomsday Machine we are safe from attack and can safely scrap the rest of our nuclear arsenal. The idea provided the central plot device for the movie Doctor Strangelove. The Russians build a Doomsday Machine but imprudently postpone the announcement they are waiting for the premier’s birthday until just after an American Air Force officer has launched a unilateral nuclear attack on his own initiative. The mad scientist villain was presumably intended as a parody of Kahn. Kahn described a Doomsday Machine not because he thought we should build one but because he thought we already had. So had the Russians. Our nuclear arsenal and theirs were Doomsday Machines with human triggers. Once the Russians have attacked, retaliating does us no good just as, once you have finally told your daughter that she is going to bed, throwing a tantrum does her no good. But our military, knowing that the enemy has just killed most of their friends and relations, will retaliate anyway, and the knowledge that they will retaliate is a good reason for the Russians not to attack, just as the knowledge that your daughter will throw a tantrum is a good reason to let her stay up until the party is over. Fortunately, the real-world Doomsday Machines worked, with the result that neither was ever used.
For a final example, consider someone who is big, strong, and likes to get his own way. He adopts a policy of beating up anyone who does things he doesn’t like, such as paying attention to a girl he is dating or expressing insufficient deference to his views on baseball. He commits himself to that policy by persuading himself that only sissies let themselves get pushed around and that not doing what he wants counts as pushing him around. Beating someone up is costly; he might get hurt and he might end up in jail. But as long as everyone knows he is committed to that strategy, other people don’t cross him and he doesn’t have to beat them up. Think of the bully as a Doomsday Machine on an individual level. His strategy works as long as only one person is playing it. One day he sits down at a bar and starts discussing baseball with a stranger also big, strong, and committed to the same strategy. The stranger fails to show adequate deference to his opinions. When it is over, one of the two is lying dead on the floor, and the other is standing there with a broken beer bottle in his hand and a dazed expression on his face, wondering what happens next. The Doomsday Machine just went off. With only one bully the strategy is profitable: Other people do what you want and you never have to carry through on your commitment. With lots of bullies it is unprofitable: You frequently get into fights and soon end up either dead or in jail. As long as the number of bullies is low enough so that the gain of usually getting what you want is larger than the cost of occasionally having to pay for it, the strategy is profitable and the number of people adopting it increases. Equilibrium is reached when gain and loss just balance, making each of the alternative strategies, bully or pushover, equally attractive. The analysis becomes more complicated if we add additional strategies, but the logic of the situation remains the same.
This particular example of bilateral monopoly is relevant to one of the central disputes over criminal law in general and the death penalty in particular: Do penalties deter? One reason to think they might not is that the sort of crime I have just described, a barroom brawl ending in a killing more generally, a crime of passion seems to be an irrational act, one the perpetrator regrets as soon as it happens. How then can it be deterred by punishment? The economist’s answer is that the brawl was not chosen rationally but the strategy that led to it was. The higher the penalty for such acts, the less profitable the bully strategy. The result will be fewer bullies, fewer barroom brawls, and fewer “irrational” killings. How much deterrence that implies is an empirical question, but thinking through the logic of bilateral monopoly shows us why crimes of passion are not necessarily undeterrable. [...]
in Chapter 8, David D. Friedman, “Law’s Order: What Economics Has to Do With Law and Why it Matters“, Princeton University Press, Princeton, New Jersey, 2000.
Note – Further reading should include David D. Friedman’s “Price Theory and Hidden Order“. Also, a more extensive treatment could be found on “Game Theory and the Law“, by Douglas G. Baird, Robert H. Gertner and Randal C. Picker, Cambridge, Mass: Harvard University Press, 1994.
Video – Awesome choice by Tim Burton. It fits him like a glove. Here is the official Tim Burton’s Alice in Wonderland teaser trailer (just uploaded yesterday). Alice in Wonderland is directed by visionary director Tim Burton, of everything from Pee-Wee’s Big Adventure to Beetlejuice to Batman to Edward Scissor hands to Mars Attacks to Sleepy Hollow to Charlie and the Chocolate Factory to Sweeney Todd most recently. This is based on Lewis Carroll’s beloved series of books that were first published in 1865. Disney is bringing Tim Burton’s Alice in Wonderland to both digital 3D and 2D theaters everywhere on March 5th, 2010 early next year (more). Finally, just one personal thought. Soon, Tim Burton’s will stand for cinema, as what Jules Verne represented in literature.
In 1973, under several ongoing works on Co-Evolution and Evolutionary theory, L. van Alen proposed a new hypothesis: the Red Queen effect [1]. According to him, several different species will migth propably undergo and submit themselves to a continuous re-adapation [2,3], being it genetic or synaptic, only to end themselves at the point they started. A kind of arms races between species [4], potentially leading to specialization, as well as evolutionary Punctuated equilibria [5,6].
Van Alen chose the name “Red Queen” in allusion to the romance “Alice in Wonderland”, from Charles Lutwidge Dodgson (better known as Lewis Carroll) published in 1865. Over this country (Wonderland) it was usual to run as quick as you could, just to end yourself at the same place. The dialogs between Alice and the Red Queen are sintomatic:
[...] ‘Now! Now!’ cried the Queen. ‘Faster! Faster!’ And they went so fast that at last they seemed to skim through the air, hardly touching the ground with their feet, till suddenly, just as Alice was getting quite exhausted, they stopped, and she found herself sitting on the ground, breathless and giddy. The Queen propped her up against a tree, and said kindly, ‘You may rest a little, now. Alice looked round her in great surprise. ‘Why, I do believe we’ve been under this tree the whole time! Everything’s just as it was!’ ‘Of course it is,’ said the Queen. ‘What would you have it?’. ‘Well, in our country, said Alice, still panting a little, ‘you’d generally get to somewhere else – if you ran very fast for a long time as we’ve been doing.’ ‘A slow sort of country!’ said the Queen. ‘Now, here, I see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!‘ [...]
Meanwhile, since 2007 (even much earlier!) I have taken Alice into my own arms. In fact, she is not heavy at all. If you feel you should keep running, some should, have a read on “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), May 31, 2007. Written at Granada University, Spain, 29 May 2007.
[1] van Alen, L. (1973), “A New Evolutionary Law“, Evolutionary Theory, 1, pp. 1-30.
[2] Cliff D., Miller G.F. (1995), “Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations“, in F. Moran, A. Moreno, J.J. Merelo and P. Cachon (editors) Advances in Artificial Life: Proceedings of the Third European Conference on Artificial Life (ECAL95). Lecture Notes in Artificial Intelligence 929, Springer- Verlag, pp.200-218.
[3] Cliff D., Miller G.F. (1996), “Co-Evolution of Pursuit and Evasion II: Simulation Methods and Results“. In P. Maes et al. (Eds.), From Animals to Animats IV, Procs. of the Fourth Int. Conf. on Simulation of Adaptive Behaviour, MIT Press, pp. 506-515.
[4] Dawkins R., Krebs J.R. (1979), “Arms Races between and within Species“. In Procs. of the Royal Society of London: Biological Sciences, nº. 205, pp. 489-511.
[5] Eldredge, N., Gould, S. J., “Punctuated equilibria: an alternative to phyletic gradualism“. In: Models In Paleobiology (Ed. by T. J. M. Schopf), 1972.
[6] Gould, S. J., & Eldredge, N., “Punctuated equilibria: the tempo and mode of evolution reconsidered“. Paleobiology, 3, 115-151, 1977.
Figure – My first Swarm Painting SP0016 (Jan. 2002). This was done attaching the following algorithm into a robotic drawing arm. In order to do it however, pheromone distribution by the overall ant colony were carefully coded into different kinds of colors and several robotic pencils (check “The MC2 Project [Machines of Collective Conscience]“, 2001, and “On the Implicit and on the Artificial“, 2002). On the same year when the computational model appeared (2000) the concept was already extended into photography (check original paper) – using the pheromone distribution as photograms (“Einstein to Map” in the original article along with works like “Kafka to Red Ants” as well as subsequent newspaper articles). Meanwhile, in 2003, I was invited to give an invited talk over these at the 1st Art & Science Symposium in Bilbao (below). Even if I was already aware of Jeffrey Ventrella outstanding work as well as Ezequiel Di Paolo, it was there where we first met physically.
[] Vitorino Ramos, Self-Organizing the Abstract: Canvas as a Swarm Habitat for Collective Memory, Perception and Cooperative Distributed Creativity, in 1st Art & Science Symposium – Models to Know Reality, J. Rekalde, R. Ibáñez and Á. Simó (Eds.), pp. 59, Facultad de Bellas Artes EHU/UPV, Universidad del País Vasco, 11-12 Dec., Bilbao, Spain, 2003.
Many animals can produce very complex intricate architectures that fulfil numerous functional and adaptive requirements (protection from predators, thermal regulation, substrate of social life and reproductive activities, etc). Among them, social insects are capable of generating amazingly complex functional patterns in space and time, although they have limited individual abilities and their behaviour exhibits some degree of randomness. Among all activities by social insects, nest building, cemetery organization and collective sorting, is undoubtedly the most spectacular, as it demonstrates the greatest difference between individual and collective levels. Trying to answer how insects in a colony coordinate their behaviour in order to build these highly complex architectures, scientists assumed a first hypothesis, anthropomorphism, i.e., individual insects were assumed to possess a representation of the global structure to be produced and to make decisions on the basis of that representation. Nest complexity would then result from the complexity of the insect’s behaviour. Insect societies, however, are organized in a way that departs radically from the anthropomorphic model in which there is a direct causal relationship between nest complexity and behavioural complexity. Recent works suggests that a social insect colony is a decentralized system composed of cooperative, autonomous units that are distributed in the environment, exhibit simple probabilistic stimulus-response behaviour, and have only access to local information. According to these studies at least two low-level mechanisms play a role in the building activities of social insects: Self-organization and discrete Stigmergy, being the latter a kind of indirect and environmental synergy. Based on past and present stigmergic models, and on the underlying scientific research on Artificial Ant Systems and Swarm Intelligence, while being systems capable of emerging a form of collective intelligence, perception and Artificial Life, done by Vitorino Ramos, and on further experiences in collaboration with the plastic artist Leonel Moura, we will show results facing the possibility of considering as “art”, as well, the resulting visual expression of these systems. Past experiences under the designation of “Swarm Paintings” conducted in 2001, not only confirmed the possibility of realizing an artificial art (thus non-human), as introduced into the process the questioning of creative migration, specifically from the computer monitors to the canvas via a robotic harm. In more recent self-organized based research we seek to develop and profound the initial ideas by using a swarm of autonomous robots (ARTsBOT project 2002-03), that “live” avoiding the purpose of being merely a simple perpetrator of order streams coming from an external computer, but instead, that actually co-evolve within the canvas space, acting (that is, laying ink) according to simple inner threshold stimulus response functions, reacting simultaneously to the chromatic stimulus present in the canvas environment done by the passage of their team-mates, as well as by the distributed feedback, affecting their future collective behaviour. In parallel, and in what respects to certain types of collective systems, we seek to confirm, in a physically embedded way, that the emergence of order (even as a concept) seems to be found at a lower level of complexity, based on simple and basic interchange of information, and on the local dynamic of parts, who, by self-organizing mechanisms tend to form an lived whole, innovative and adapting, allowing for emergent open-ended creative and distributed production.
Fig. – Knight, Death and the Devil (1513). This is one of three metal engravings by Albrecht Dürer in a series called Meisterstiche (since I have started this blog, I have also chosen a woodcut engraving done by Dürer, – his Rhinoceros – for several reasons, one being that it appeared in Europe for the fisrt time trough Lisbon in 1515). The others are Melancholia I and Saint Jerome in His Study. The engraving is dated 1513, two hundred years after the dissolution of the Knights Templar in 1313. We see a skull in the bottom left corner; the night in full armour (shining armor?) carries a lance; behing him is a pig-snouted horned devil and he is passing Death on his pale horse, who is carrying an hourglass. Under the knight’s horse runs a long-haired retriever, a hunting dog. Dürer called this picture Reuter, which is, Rider. (source).
“Every evil leaves a sorrow in the memory, until the supreme evil, death,
wipes out all memories together with all life“. Leonardo da Vinci.
Carlos Gershenson (Complexes blog), some days ago just uploaded a short (5 pp.) philosophical essay about life, death and artificial life (*) (aLife), which I vividly recommend. He starts his “What Does Artificial Life Tell Us About Death?” with this precise Leonardo’s quote (above). Among other passages it’s interesting to see how different notions of death are deduced from a limited set of different notions of life (in many situations, opposing terms could be used to define each other). Carlos points us out to six currents, or lines of thought:
• If we consider life as self-production (Varela et al., 1974; Maturana and Varela, 1980, 1987; Luisi, 1998), then death will the the loss of that self-production ability.
• If we consider life as what is common to all living beings (De Duve, 2003, p. 8), then death implies the termination of that commonality, distinguishing it from other living beings.
• If we consider life as computation (Hopfield, 1994), then death will be the end (halting?) of that computing process.
• If we consider life as supple adaptation (Bedau, 1998), death implies the loss of that adaptation.
• If we consider life as a self-reproducing system capable of at least one thermodynamic work cycle (Kauffman, 2000, p. 4), death will occur when the system will be unable to perform thermodynamic work.
• If we consider life as information (a system) that produces more of its own information than that produced by its environment (Gershenson, 2007), then death will occur when the environment will produce more information than that produced by the system.
I was aware of Kauffman’s “blender thought experiment”, however Gershenson adds much more into it. A variation. He goes on like this. Nice reading:
[...] Focussing on our understanding of death, this will depend necessarily on our understanding of life, and vice versa. Throughout history there have been several explanations to both life and death, and it seems unfeasible that a consensus will be reached. Thus, we are faced with multiple notions of life, which imply different notions of death. However, generally speaking, if we describe life as a process, death can be understood as the irreversible termination of that process. The general notion of life as a process or organization (Langton, 1989; Sterelny and Griffiths, 1999; Korzeniewski, 2001) has expelled vitalism from scientific worldviews. Moreover, there are advantages in describing living systems from a functional perspective, e.g. it makes the notion of life independent of its implementation. This is crucial for artificial life. Also, we know that there is a constant flow of matter and energy in living systems, i.e. their physical components can change while the identity of the organism is preserved. In this respect, one can make a variation of Kauffman’s “blender thought experiment” (Kauffman, 2000): if you put a macroscopic living system in a blender and press “on”, after some seconds you will have the same molecules that the living system had. However, the organization of the living system is destroyed in the blending. Thus, life is an organizational aspect of living systems, not so much a physical aspect. Death occurs when this organization is lost. [...]
(*) even if, I do not recommend this Wikipedia entry. Extremely poor.
Fig. – Illusion created by Prof. Akiyoshi Kitaoka (Dep. of Psychology, Ritsumeikan Univ., Kyoto, Japan). If you don’t see any illusion at all, don’t worry. That’s exactly why this optical illusion is so great. The illusion is not there, or is it?! Meanwhile over his page, Akiyoshi warns: This page contains some works of “anomalous motion illusion”, which might make sensitive observers dizzy or sick. Should you feel dizzy, you had better leave this page immediately (more).
Where’s the illusion, right? Well,… what if I just tell you that no blue at all is used over this picture! No matter how strongly you want to believe you are seeing blue and green spirals here, there is no blue color in this image. There is only green, red and orange. What you think is blue is actually green. Don’t worry, … you are not daltonic. I mean, I’m a little bit but, you could check this out through Paint Shop Pro or Photoshop, if you need an affirmation. Indeed, these are just “Vain speculation undeceived by the senses” (1670’s Scilla’s treatise) .
In fact, Relations here, between different colors (green, red and orange), are more important than each color by itself. Relations plus context are the key (more here over Generative Art, and here over Swarm Intelligence based Pattern Recognition). Through these relations, much probably using Gestalt’s principles (the German word Gestalt could be translated into “configuration or pattern”), here Akiyoshi manages to emerge us the blue color over our perception. This does not cheat a computer of course, however could cheat our own eyes. In other areas the opposite could also be found. For instance, Humans can easily recognize a car over background trees (segment it, in just tiny lapses of a second), while this natural task could be extremely painful for computers over some cases (here is one example).
Born in Prague (inspired by 1890’s works of Christian von Ehrenfels, Austrian philosopher), then later absorbed by a great and tremendous intellectual period occurred from Germany back to Austria (Bauhaus), the Gestalt Laws of Organization have guided the study of how people perceive visual components as organized patterns or wholes, instead of many different parts. I would say that most certainly some Wertheimer’s gestaltic principles were used in here: Figure and Ground, Similarity, Proximity or Contiguity, Continuity, Closure, Area, and Symmetry (check Gestalt Theory of Visual Perception). We could see this happening also in other areas, … in Music for instance:
[...] Gestalt theory first arose in 1890 as a reaction to the prevalent psychological theory of the time – atomism. Atomism examined parts of things with the idea that these parts could then be put back together to make wholes. Atomists believed the nature of things to be absolute and not dependent on context. Gestalt theorists, on the other hand, were intrigued by the way our mind perceives wholes out of incomplete elements [1, 2]. “To the Gestaltists, things are affected by where they are and by what surrounds them…so that things are better described as “more than the sum of their parts.” [1, p. 49]. Gestaltists believed that context was very important in perception. An essay by Christian von Ehrenfels discussed this belief using a musical example. Take a 12 note melody. Play it in one key, say the key of C. Now change to another key, say the key of A flat. There might not be any notes the same in the two songs, yet a person listening to it knows that it is the same tune. It is the relationships between the notes that give us the tune, the whole, not which notes make up the tune. [...], from “Gestalt Principles of Perception“, Bonnie Skaalid, Univ. of Saskatchewan, Canada, 1999.
Care for an contemporary example? Well, … the first thing that comes to my mind is DUB music genre. In fact, I do have several albums from different musicians over my house. Dub music evolved in Jamaica (1968) from early rastafarian instrumental reggae music and versions that incorporated fairly primitive reverbs and echo sound effects, being found by accident (engineer Byron Smith left the vocal track out by accident). Over decades, it inspired immense groups of musicians from well-known bands such as The Police, The Clash, UB40 up to reputed musicians such as Bill Laswell. Of course !, it was not far from what John Cage have made for the solo piano Music of Changes, to determine which notes should be used and when they should sound. In the fifty’s, Cage start it to use the mechanism of the I Ching (Chinese “Book of Changes”) in the composition of his music in order to provide a framework for his uses of chance.
Other most recent bands include, Leftfield, Massive Attack, Bauhaus, The Beastie Boys, Asian Dub Foundation, Underworld, Thievery Corporation, Gorillaz, Kruder & Dorfmeister, and DJ Spooky. But what is then so special about Dub? Well, one of this genre’s most striking features is the fact that some if not all musical sentences are incomplete. Those special sentences (Gestaltic, let me add), are normally followed by a pause. The most amazing thing however, is that us, Humans could perceive the entire sentence being formed on the back of our minds! So the music is not there, and at the same time, we are listening to two adjacent simultaneous melodies, as we were a composer. By just using relations among a few notes, we soon start to emerge a perception for the whole sentence, as if they were self-organizing! Being it extremely rhythmic, this often could lead us to a sweet soft state of overwhelming emotion, or exalted organic feel to the music .
As you will probably know by now, the same could happen over misplaced letters over an entire phrase. Even if some letters are not at their right proper place, at each word, we could still perceive the whole sentence meaning. Up to your gestaltic neurons to decipher.
Next time you go to a rave party (I never did, neither pretend to), do think about the title of this post, the figure above, as well as on all those great past musicians, along with – unfortunately – awkward current DJ’s, who pass on for hours strident music mixes without knowing at all what Gestalt is all about! Oh, … by the way, should you feel extremely dizzy, do follow Akiyoshi’s advice: If you start feeling unwell when using this website (rave party), immediately cover one eye with your hand and then leave the page (leave the party). Do not close your both eyes because that can make the attack worse!
a) Dynamic Optimization Problems (DOP) tackled by Swarm Intelligence (in here a quick snapshot of the dynamic environment)
b) Swarm adaptive response over time, under severe dynamics, over the dynamic environment on the left (a).
Figs. – Check animated pictures in here. (a) A 3D toroidal fast changing landscape describing a Dynamic Optimization (DO) Control Problem (8 frames in total). (b) A self-organized swarm emerging a characteristic flocking migration behaviour surpassing in intermediate steps some local optima over the 3D toroidal landscape (left), describing a Dynamic Optimization (DO) Control Problem. Over each foraging step, the swarm self-regulates his population and keeps tracking the extrema (44 frames in total).
[] Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, On Self-Regulated Swarms, Societal Memory, Speed and Dynamics, in Artificial Life X – Proc. of the Tenth Int. Conf. on the Simulation and Synthesis of Living Systems, L.M. Rocha, L.S. Yaeger, M.A. Bedau, D. Floreano, R.L. Goldstone and A. Vespignani (Eds.), MIT Press, ISBN 0-262-68162-5, pp. 393-399, Bloomington, Indiana, USA, June 3-7, 2006.
PDF paper.
Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective “swarm” intelligence. Termite colonies – for instance – build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS’s were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able to maintain a number of different solutions, while adapting to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes; the possibility to spontaneously create and maintain different sub-populations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents (division of labor) over dramatic changing periods, and economizing those foraging resources over periods of intermediate stabilization. Finally, results illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work.
Abraham, Ajith; Grosan, Crina; Ramos, Vitorino (Eds.), Stigmergic Optimization, Studies in Computational Intelligence (series), Vol. 31, Springer-Verlag, ISBN: 3-540-34689-9, 295 p., Hardcover, 2006.
TABLE OF CONTENTS (short /full) / CHAPTERS:
[1] Stigmergic Optimization: Foundations, Perspectives and Applications.
[2] Stigmergic Autonomous Navigation in Collective Robotics.
[3] A general Approach to Swarm Coordination using Circle Formation.
[4] Cooperative Particle Swarm Optimizers: a powerful and promising approach.
[5] Parallel Particle Swarm Optimization Algorithms with Adaptive
Simulated Annealing.
[6] Termite: a Swarm Intelligent Routing algorithm for Mobile
Wireless ad-hoc Networks.
[7] Linear Multiobjective Particle Swarm Optimization.
[8] Physically realistic Self-Assembly Simulation system.
[9] Gliders and Riders: A Particle Swarm selects for coherent Space-time Structures in Evolving Cellular Automata.
[10] Stigmergic Navigation for Multi-agent Teams in Complex Environments.
[11] Swarm Intelligence: Theoretical proof that Empirical techniques are Optimal.
[12] Stochastic Diffusion search: Partial function evaluation in Swarm Intelligence Dynamic Optimization.
Fig. – The famous frontispiece from Scilla’s treatise of 1670 defending the organic nature of fossils. The solid young man, representing the truth of sensory experience, shows a fossil sea urchin in his right hand to a wraithlike figure representing the former style of speculative thinking. With his left hand, the solid figure points to other fossils found in Sicily. The text proclaims: “Vain speculation undeceived by the senses.” (from, Stephen Jay Gould, “The Structure Of Evolutionary Theory”, The Belknap Press of Harvard University Press”, Cambridge, Massachusetts, 2002).
Exaptation: 1. The use of a biological structure or function for a purpose other than that for which it initially evolved. 2. An evolutionary process in which a given adaptation is first naturally selected for, and subsequently used by the organism for something other than its original, intended purpose. 3. Exaptations – Features (such as feathers) that evolved by selection for one purpose (such as warmth) and were later adapted to a new purpose (such as flight). [more]. Exaptive: to show exaptation – featuring it.
Last year, at the beginning of October I decided to dedicate my second post on financial markets (I, II) to Black Swans. Swans are beautiful animals, but while white swans are vulgar and omnipresent at every pond, black swans are rare! Meanwhile, 2 days ago (June 1) the Wall Street Journal comes with this very awkward - and by all means for that precise reason - interesting article written by journalist Scott Patterson, where Mr. Taleb’s name pops-up again (image below).
Well, … let’s face it: you could put your money in the bank and have – let’s say – a 3% revenue at the end of your fiscal year. Or you could apply it to raise a new fancy gourmet restaurant at your local vicinity. Restaurants and local food stores are known to have 5-7% revenues in one year, not to speak on the immense burden they represent as well as for some associated risks – specially these days. But then you may think – better than banks, right? Right! Or, just to give you another example on this increasing scale - raising a little bit the risk -, on the other hand you could apply your money in stock markets. Main financial indexes (Dow Jones, NASDAQ, etc) are known to have an annual average revenue of 10-12% (since 1918). Not these days of course, where high volatility and entropy in the markets are installed. Well, emergent countries like China are raising themselves at 12%/year also. We could go on and on with so many other examples. Some say that Eolic parks could achieve 40%. Normally the cost of one eolic tower is around 1 million euros, which could be paid back after one year producing energy trough wind at normal operating conditions. The rest are maintenance costs, as well as initial investment in terrains, etc. So, what’s new? Consider this. For moments imagine yourself having 100% in revenues, just last year, at this precise dramatic context. That’s 10 times what the market does in regular years, 20 times what your favorite restaurant does. Moreover, there is a substantial difference between all these examples. If you keep dropping money at the restaurant (for instance the revenue you have earned in the last year), still liquid revenues will be the same in the next year (unless you open a new dinner room next to the first one, while the awful burden keeps increasing). Some business are static and linear in time while others are exponential. As Alice in the wonderland, you will need to keep running twice as faster in order to be at the same place. Amazing those differences, no? Well, not for those “lovely” animal creatures known as Black Swans. According to Patterson, … Funds run by Universa, which is managed and owned by Mr. Taleb’s long-time collaborator Mark Spitznagel, last year gained more than 100% thanks to its bearish bets. Universa now runs about $6 billion, up from the $300 million it began with in January 2007. Excerpts from the Wall Street Journal article (Black Swan Fund Makes a Big Bet on Inflation) follow below. So, why the hell I do not feel at all surprised by this?! Really, I am not. Let me just say, I do have my own reasons:
[...] A hedge fund firm that reaped huge rewards betting against the market last year is about to open a fund premised on another wager: that the massive stimulus efforts of global governments will lead to hyperinflation. The firm, Universa Investments L.P., is known for its ties to gloomy investor Nassim Nicholas Taleb, author of the 2007 bestseller “The Black Swan,” which describes the impact of extreme events on the world and financial markets.
Funds run by Universa, which is managed and owned by Mr. Taleb’s long-time collaborator Mark Spitznagel, last year gained more than 100% thanks to its bearish bets. Universa now runs about $6 billion, up from the $300 million it began with in January 2007. Earlier this year, Mr. Spitznagel closed several funds to new investors….
Mr. Taleb doesn’t have an ownership interest in the Santa Monica, Calif., firm, but he has significant investments in it and helps shape its strategies. The term “black swan,” which has become a market catchphrase in the last few years, alludes to the once-widespread belief in the West that all swans are white. The notion was proven false when European explorers discovered black swans in Australia. A black-swan event, according to Mr. Taleb, is something that is extreme and highly unexpected. … [...]
A couple of years ago, while G.W. Bush was comfortably seated at the White House, Gore Vidal, the American novelist, was invited by the Canadian TV show THE HOUR (June 6, 2007). What follows is an amazing first response by Gore Vidal to a trivial question. I suspect that Gore Vidal on his spare time, do thinks a lot on the nature of what complex systems really are. Here is an extract from the introductory part of that interview:
THE HOUR – Here’s Mr. Gore Vidal
Gore Vidal (GV) – Nice too see you. See you good.
THE HOUR – How are things?
GV – … Things, … aaahhhh, … fall apart. That’s what things do. And we are things… so I promise not to crumble on the program.
(…) later on;
GV – … they have demonized the word “liberal”. We were a liberal republic, to start with. What does the liberal word mean? It comes from those who favour legislation tending towards greater democracy.
For some seconds, just imagine having these 50 m² – 8 meters tall artifact constructed (above) by tiny Giant Architects in a plaza over a big city near you. Over this youtube video several scientists have filled the big city unearthed with 10 tens of cement during 3 days. Then calmly (taking several weeks), have digg it to the bone. To have a clue on what I mean just imagine having all these at Times Square plaza in New York! or at the front-door of the Frank Gehry’s Guggenheim Museum in Bilbao (in fact a giant spider is also there – check photo below). Colonies of eu-social insects use stigmergy in order to do this, being a good reference the work done by Karsai back in 1999 at the Artificial Life MIT Press Journal (here is the abstract – unfornately I have it on paper but not scanned):
# István Karsai, “Decentralized Control of Construction Behavior in Paper Wasps: An Overview of the Stigmergy Approach“, Spring 1999, Vol. 5, No. 2, Pages 117-136.
Grassé [26] coined the term stigmergy (previous work directs and triggers new building actions) to describe a mechanism of decentralized pathway of information flow in social insects. In general, all kinds of multi-agent groups require coordination for their effort and it seems that stigmergy is a very powerful means to coordinate activity over great spans of time and space in a wide variety of systems. In a situation in which many individuals contribute to a collective effort, such as building a nest, stimuli provided by the emerging structure itself can provide a rich source of information for the working insects. The current article provides a detailed review of this stigmergic paradigm in the building behavior of paper wasps to show how stigmergy influenced the understanding of mechanisms and evolution of a particular biological system. The most important feature to understand is how local stimuli are organized in space and time to ensure the emergence of a coherent adaptive structure and to explain how workers could act independently yet respond to stimuli provided through the common medium of the environment of the colony.
Another interesting paper (available online) is the more recent work by Mason at the 8th Artificial Life conference, in 2002. Below I have selected part of the introductory text:
# Zachary Mason ,”Programming with Stigmergy: Using Swarms for Construction“, in Artificial Life VIII Conf., Standish, Abbass, Bedau (eds)(MIT Press), New South Wales, Australia, pp. 371-375, 2002.
(…) Termite nests are large and complex. A nest may be as much as 104 or 105 times as large as an individual termite (Boneabeau et al. 1997) a ratio unparalleled in the animal kingdom. The nests of the African termite sub-family Macrotermitinae are composed of many substructures, such as protective bulwarks, pillared brood chambers, spiral cooling vents, galleries of fungus gardens and royal chambers. For all the architectural sophistication of termite nests, termites themselves are blind, weak and apparently not responsive to a coordinating authority. This work attempts to borrow and generalize the termite construction-algorithm, permitting artificial, decentralized swarms to be programmed to build complex, composable structures.
How do small, blind termites manage to build (relatively) huge, intricate nests? Work on this question includes a simple, decentralized building model (Grasse 1959) (Grasse 1984), an empirical study of termite building behavior (Bruinsma 1979), a mathematical model of the synthesis of pillars in termite nests (Deneubourg 1977), and a model explaining how modest environmental variation can cause the same termite behaviors to generate qualitatively different structures (Boneabeau et al. 1997). Most relevant to this work is (Bruinsma 1979), which records three feedback mechanisms governing termite behavior. In the first, a termite picks up a soil pellet, masticates it into a paste and injects a termiteattracting pheremone into it. When the pellet is deposited, the pheremone stimulates nearby termites to pellet-gathering behavior and makes them more likely to deposit their pellets nearby. Second, small obstacles in the terrain stimulate pellet deposits and can seed pillars. Finally, a trail pheremone allows more workers to be drawn to a construction site. Termites and many social insects interact stigmergically - that is, communication is mediated through changes in the environment rather than direct signal transmission. Computer simulations have used stigmergy to reproduce termite’s pillar-making behavior and ant’s foraging and the spontaneous cemetery building. These applications rely of qualitative stigmergy | individual agents react to a continuous variations in the environment. An example of quantitative stigmergy is (G. Theraulaz 1995), a simulation of wasp nest building. Wasps build nests by depositing cells on a lattice. Whether an empty cell is lled depends on the adjacent cells. Because all wasps have the same deposit-triggers, multiple wasps are able to simultaneously work on a single nest without without ruining each others work. A set of deposit-triggers is coherent if each no stage in the building process can be confused with an earlier stage by making only local observations, thus obviating the need for centralized control.
The goal of this work is to generalize the construction methodologies of the social insects and create a language for stigmergically assembling complex structures. Such a language permit swarms of agents to erect interesting architectures without benefit of a central controller or explicit inter-agent communication. The primary advantage of this approach is that stigmergically controlled swarms have minimal communication and no coordination overhead. Also, very little processing is demanded of agents, and the swarm can tolerate a degree of agent error. On a more abstract plane, this work is an example of designing emergent behavior. (…)
Back in 2003 I was photographed by João Bracourt, a friend and professional photograph which among other things (web design + painting) travels around the world within big professional surf events (he is right now on it’s way to Indonesia), covering it for main surf magazines. Back then (Sept. 2003) we were enjoying ourselves with a big group late nigth at Bairro Alto, the main bar and restaurant district in Lisbon.
The t-shirt I’m wearing here is from COSI – Complexity in Social Sciences Summer School. One month earlier have been invited among other people to give a lecture in Spain about my work, there at COSI (Baeza, Andaluzia). After all these years the PPT file (Stigmergy as a possible exploratory walk up to collective life-like complexity and behaviour) is still available. As well as those from Gerard Weisbuch (Research Director of the Complex Networks and Cognitive Systems Team within the Statistical Physics Laboratory of the l’Ecole Normale Supérieure in Paris, France) and Rosaria Conte (head of the Division of Artificial Intelligence, Cognitive Modelling & Interaction at the Institute of Psychology of the Italian National Research Council), among others. Many other research materials concerning complexity and social sciences are still available at COSI’s 2003 main site.
(at Bairro Alto, Lisbon, Sept. 2003 - taken by João Bracourt)
(at Bairro Alto, Lisbon, Sept. 2003 - taken by João Bracourt)
Gum election in the public streets of Berlin – “Who sucks the worst? Vote with your gum“. Several weeks before the election on United States, this rather simple but extraordinary concept spread from NY city to San Francisco, from St. Louis to São Paulo, from Berlin to Sydney within a few days. This kind of remembers me one of my friend’s (Ivo et al’s) project – Stick Me!, due to some similar features. Even nowadays my own refrigerator has one Stick Me! sticker over it and I really enjoyed participating on it in the past via one very quick and humble “Stick Me Mate” proposal, while playing blitz chess with friends at a bar nearby my house.
A bunch of people (promoting Collective Intelligence?) is using the environment as a way to communicate (like over any chessboard). Communication is indirect, but still they communicate through the alterations and patterns they impose on the environment itself. Meanwhile, imposing a mark or sign somewhere, increases the probability of a second response later in time – a response to a stimulus (as ants put their pheromone marks on the ground). Though here however (on both projects) only positive feedback is used.
In fact, Mother Nature has conceived a very outstandingly simple and better strategy: their signs and cues vanish in time, simple as that! For instance, pheromone, a chemical substance segregated by ants and termites evaporates in time. Over here however, there is no evaporation at all working on (societal agents are not entitled to use negative feedbacks or using vanishing marks), which can curse it’s own dynamic – unless someone destroys the posters, of course. Amazon book recommendation system, works as well this way, that is by uniquely making use of positive feedbacks (people that bought this X book also as bought Y, etc). Unfortunately, Amazon system along with his wish lists could not integrate that someone who bought the X book did not bought Z (while others have done it), which basically leads to a snow-balling effect that does not self-organize in time (adapts) to new potential good-reading books. What you end up seeing is just the overall majority consensus, the “minimum common multiple” as I sometimes call it, who tends to over-look and underestimate some high potential new-coming solutions (over this precise context, good books coming in). Amazon should instead look carefully to some scientific works on collaborative filtering. Instead the consequences are this: check here for a real user feedback on what Amazon is suggesting, or in fact not suggesting at all.
Not only their system tends to adapt slowly, since the only thing it’s promoting is nothing else but memory (exploitation, which could be achieved by positive feedbacks), as he is not learning (exploration, which could be achieved by negative feedbacks), when we know that on the contrary, a delicate compromise between both is in fact of huge importance. The difficult but possible systemic trick is to remember the past as simultaneously innovating. If as a whole the system only remembers the past, no innovation is possible causing dramatic consequences when the “environment” changes. This could lead to stagnation. On the other hand, if too much systemic pressure is put on innovation itself, energy is lost, leading the system to explore the universe of possible solutions in a quite “”stupid” trial-and-error like random manner. Some dynamics between one thing (memory) and the other (learning) could be checked here (figs. 4,5,6,7 and 19), along with their speed.
After all a gum or a sticker is nothing else than a tag -as web blogging tags and internet tag clouds are. My question is – Could they vanish over time as I believe and propose they should? Having that question in mind, while looking at these precise public street projects, there are also other conceptual bridges we may found, as far as I recognize.
Let me refer at least 4, with the help of some passages below from other texts: (1) Hobo signs and codes (as well as the bottom-up like emergence of norms and ethical codes between them), (2) the role of Positive and Negative feedbacks briefly discussed above, (3) Swarm Intelligence and of course, (4) Stigmergy. In what specifically regards Hobo signs let me say that they are quite clever. Since they are done with chalk! So, rain and erosion could erase them, little by little, day by day. Thus, solutions that were good in the past, but no longer exist or that are partially vanished over time, tend to be replaced by new fresh ones, appropriated for the present, only loosing part of the whole systemic memory, serving us with new stimulus (we tend to respond to those fresh ones), allowing a continuous adaptation to reality. As I said in the past over a scientific invited lecture (not the right place to say it, though!), signs, quotes, delayed desynchronized dialogues and phrases over the doors of public bathrooms follow similar trends and tend to be stigmergic. In what regards the following four passages, I leave to you the connection between them (sorry for this now long food for thought post):
(1) [...] 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 quote “the whole is greater than the sum of its parts” (Aristotle, in Metaphysics), 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 by Grassé 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 typical case of environmental synergy. Grassé showed that the coordination and regulation of building activities 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. Another illustration of how stimergy and self-organization can be combined into more subtle adaptive behaviors is recruitment in social insects. 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); that is stigmergy. [...],
in Vitorino Ramos, Juan J. Merelo, Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning, in AEB´2002 – 1st Spanish Conference on Evolutionary and Bio-Inspired Algorithms, E. Alba, F. Herrera, J.J. Merelo et al. (Eds.), pp. 284-293, Centro Univ. de Mérida, Mérida, Spain, 6-8 Feb. 2002.
(2) [...] To cope with the difficulty of hobo life, hobos developed a system of symbols, or a code. Hobos would write this code with chalk or coal to provide directions, information, and warnings to other hobos. Some signs included “turn right here”, “beware of hostile railroad police”, “dangerous dog”, “food available here”, and so on. For instance: a cross signifies “angel food,” that is, food served to the hobos after a party. A triangle with hands signifies that the homeowner has a gun. Sharp teeth signify a mean dog. A square missing its top line signifies it is safe to camp in that location. A top hat and a triangle signify wealth. A spearhead signifies a warning to defend oneself. A circle with two parallel arrows means to get out fast, as hobos are not welcome in the area. Two interlocked humans signify handcuffs. (i.e. hobos are hauled off to jail). A Caduceus symbol signifies the house has a medical doctor living in it. A cat signifies that a kind lady lives here. A wavy line (signifying water) above an X means fresh water and a campsite. Three diagonal lines means it’s not a safe place. A square with a slanted roof (signifying a house) with an X through it means that the house has already been “burned” or “tricked” by another hobo and is not a trusting house. Two shovels, signifying work was available (Shovels, because most hobos did manual labor). [...], in Hobo, Wikipedia.
(3) [...] Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model (Stan Franklin, Coordination without Communication, talk at Memphis Univ., USA, 1996). [...] (here)
Hobo or tramp markings at Algiers entrance to Canal Street Ferry across Mississippi River, New Orleans. Ferry is free for pedestrians or on bicycle. "X" means "OK", slashed circle "Good way to go". (via Wikipedia above).
(4) [...] – Positive feedback, f+: in contrast to negative feedback, positive feedback generally promotes changes in the system (the majority of SO systems use them). The ex-plosive growth of the human population provides a familiar example of the effect of positive feedback. The snowballing auto catalytic effect of f+ takes an initial change in a system (due to amplification of fluctuations; a minimal and natural local cluster of objects could be a starting point) and reinforces that change in the same direction as the initial deviation. Self-enhancement, amplification, facilitation, and auto catalysis are all terms used to describe positive feedback. Another example could be provided by the clustering or aggregation of individuals. Many birds, such as seagulls nest in large colonies. Group nesting evidently provides individuals with certain benefits, such as better detection of predators or greater ease in finding food. The mechanism in this case is imitation : birds preparing to nest are attracted to sites where other birds are already nesting, while the behavioral rule could be synthesized as “I nest close where you nest“. The key point is that aggregation of nesting birds at a particular site is not purely a consequence of each bird being attracted to the site per se. Rather, the aggregation evidently arises primarily because each bird is attracted to others. On social insect societies, f+ could be illustrated by the pheromone reinforcement on trails, allowing the entire colony to exploit some past and present solutions. Generally, as in the above cases, positive feedback is imposed implicitly on the system and locally by each one of the constituent units. Fireflies flashing in synchrony follow the rule, “I signal when you signal”, fish traveling in schools abide by the rule, “I go where you go”, and so forth. In humans, the “infectious” quality of a yawn of laughter is a familiar example of positive feedback of the form, “I do what you do“. Seeing a person yawning , or even just thinking of yawning, can trigger a yawn. There is however one associated risk, generally if f+ acts alone without the presence of negative feedbacks, which per si can play a critical role keeping under control this snowballing effect, providing inhibition to offset the amplification and helping to shape it into a particular pattern. Indeed, the amplifying nature of f+ means that it has the potential to produce destructive explosions or implosions in any process where it plays a role. Thus the behavioral rule may be more complicated than initially suggested, possessing both an autocatalytic as well as an antagonistic aspect. In the case of fish, the minimal behavioral rule could be “I nest where others nest, unless the area is overcrowded“. In this case both the positive and negative feedback may be coded into the behavioral rules of the fish. Finally, in other cases one finds that the inhibition arises automatically, often simply from physical constraints. Since in SO systems their organization arises entirely from multiple interactions, it is of critical importance to question how organisms acquire and act upon information. Basically through two forms: a) information gathered from one’s neighbors, and b) information gathered from work in progress, that is, stigmergy. In the case of animal groups, these internal interactions typically involve information transfers between individuals. Biologists have recently recognized that information can flow within groups via two distinct pathways – signals and cues. Signals are stimuli shaped by natural selection specifically to convey information, whereas cues are stimuli that convey information only incidentally. The distinction between signals and cues is illustrated by the difference ant and deer trails. The chemical trail deposited by ants as they return from a desirable food source is a signal. Over evolutionary time such trails have been molded by natural selection for the purpose of sharing with nestmates information about the location of rich food sources. In contrast, the rutted trails made by deer walking through the woods is a cue, not shaped by natural selection for communication among deer but are a simple by-product of animals walking along the same path. SO systems are based on both, but whereas signals tends to be conspicuous, since natural selection has shaped signals to be strong and effective displays, information transfer via cues is often more subtle and based on incidental stimuli in an organism’s social environment. [...], in Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes.
Stick Me! sticker in plain nature over Aljezur, Algarve (South of Portugal). Unknow author. "Copyrigthed nature" or a way of saying "I was here. I am connected. You could also be connected. We are all connected" ?!
August 1, 2007
August 10, 2007 (the beginning)
December 20, 2007
January 15, 2008
May 15, 2008
July 10, 2008
September 15, 2008 (post-Lehman)
October 10, 2008 (biggest DJIA point drop in history)
With a short empirical investigation, Reginald Smith (MIT – Sloan School of Management) have come to some interesting complex networks (nodes in here are financial stocks) over time, since the beginning of the financial crisis in August 10, 2007, till today. His rather simple econophysics study (draft PDF link) somehow demonstrates that the losses in certain markets, in this case the US equity markets, follow a cascade or “epidemic” flow like model along the correlations of various stocks. His networks shows the correlation (similar rise and fall movements) among the stocks in the S&P 500 and NASDAQ-100 using the latest stocks in the index (as of 10/10/2008). The abbreviations are the ticker symbols. Network edges here connect stocks (nodes) based on their correlations. More then 500 tickers were used. After correlations among any two stocks were calculated (J.C. Gower, Biometrika, 1966), a distance metric is computed. Finally these distances are used to create a minimal spanning tree. For the graphics and animations Reginald have used the python-graph module, pydot and Graphviz. Extra details and a F.A.Q. is here as well as some movies. If the stock share price return had a return (minus dividends) greater than or equal to -10% the nodes are green, less than -10% but greater than -25% yellow, and less than or equal to -25% red.
In what relates red nodes over time, I now wonder what would be the probability distribution of vertex connectivity change (is it scale-free?!), the characteristic path length L as well as the clustering coefficient C. It would be quite funny to know.
It’s not everyday we see a 40 year ideology collapsing through a dramatic act of contrition. It has happened just a few hours ago (check video above), yesterday in the “financial crisis” congressional hearing in Washington (23. Oct. 2008). Moreover, what seems remarkable, is that the recognition comes from one of his most universally respected founding fathers and defenders.
Alan Greenspan was the longest serving chairman in the Federal Reserve board history (1987-2006), and during this 18-year period of time he were perhaps the leading proponent of de-regulation along with libertarian capitalism, vividly expressed on his “The Age of Turbulence – Adventures in a New World” 2007 book, advocating above all issues, Adam Smith’s “Invisible Hand” that markets can regulate themselves. As it’s known, for his whole adult life, the former Fed chairman has been a devotee of the philosophy of Ayn Rand, who celebrated free-market capitalism as the world’s most moral economic order and advocated a strict laissez-faire approach to government regulation of the marketplace. Ironically, he was a regulator that did not believed in any regulation at all.
It is now quite a remarkable historic moment seeing former Federal Reserve chairman, a lifelong champion of free markets, publicly questioning the philosophy that guided him throughout his years as the world’s most powerful economic policymaker. A philosophy followed and strongly defended by him (along with many others like Margaret Thatcher and Ronald Reagan), at least in the last 40 years, as he himself acknowledged yesterday. Asked by the congressional committee chairman, whether his free-market convictions pushed him to make wrong decisions, especially his failure to rein in unsafe mortgage lending practices, Greenspan replied that indeed he had found a flaw in his ideology, one that left him very distressed. “In other words, you found that your view of the world, your ideology was not right?” he was asked:
“Absolutely, precisely“, replied Greenspan. “That’s precisely the reason I was shocked, because I have been going for 40 years or more with very considerable evidence it was working exceptionally well“. Albeit he was surely one of the most influential voices for de-regulation: “There is nothing in Federal regulation that makes it superior to Market regulation”, said Greenspan back in 1994, in one among many of his past radical free-market statements.
I presume we now all wonder, where was Greenspan, when back in 2003 one of the most prestigiously recognized and legendary financial investors such as Warren Buffet, called credit default obligations and derivatives “weapons of financial mass destruction“? Or where was he when Princeton Professor of Economics, Paul Krugman – the recent Nobel laureate – said back in 2006 that “If anyone is to blame on the current situation (sub prime) is Mr Greenspan who poopooed warnings about an emerging bubble and did nothing to crack down on irresponsible lending“. Or what did he, Greenspan itself, said just a few days after ENRON collapsed?
People working in complex systems – and surely financial markets are one of them (yes, for the past 4-5 years including these present turbulent times I am working hard in this area as well) – for long know that any systemic structure could collapse if only positive-feedbacks are injected into them, creating an auto-catalytic snow-ball effect, leading among other things to a power-law like Black Swan. Indeed power-laws are a striking powerful signature. This is specially true when we address self-organization (read it in the present context as self-regulation). In order to be a truly self-regulated system, financial markets should also be embedded with negative-feedbacks as well, as I have addressed in a post about finance and complex systems one month ago. In fact, in order to emerge as a truly self-organized system, self-interest, should constitute just one among many of the ingredients over the entire financial system, and not the isolated unique ingredient. Self-interest promotes amplification and positive feedback, which is – as I recognize – necessary. However, left alone, promotes instead dramatic snowballing drifts over chaotic regimes, due to it’s intrinsic amplification. What’s amazing (at least for me), is that Alan Greenspan just recognized that in a tiny few seconds along his current discourse (check video above), pointing it to the precise key-word:
[...] I made a mistake in presuming that the self-interest of organizations, specifically banks and others, was such that they were best capable of protecting their own shareholders. [...] So the problem, here is something which looked to be as a very solid edifice, and indeed a critical pillar to market competition and free-markets, did breakdown and I think that, as I said, shock me. I still do not fully understand why it happened, and obviously to the extend, that I figure out where it happened and why, … aaaaaa, … I will change my views. If the facts change I will change. [...]
As a result, “the whole intellectual edifice” of risk management collapsed, Greenspan said. In what regards his unexpected words yesterday at the congressional hearing, at least, I frankly praise him for his huge intellectual courage and present honesty. In the end, it seems that during the past 18 years, former FED chair was nothing else then a simple-man driven by his own blind faith on markets, from which he apparently comes out now. Unfortunately, only now at a very high price. Meanwhile as we know, severe consequences are here to stay, as was already evident when Greenspan addressed the House Financial Services Committee on 2003 (video below). Let’s hope that all these will not be forgotten in 3 decades from now (though, I doubt it – after all, nothing really serious came out from the entitled 3-man dream-team Bush-Sarkozy-Barroso “new global finance order” summit at Camp David last weekend, as expected):
“You have told the American people that you support a trade policy which is selling them out.” – Rep. Bernard Sanders to Federal Reserve Chairman Alan Greenspan on 7/16/03. Rep. Bernard Sanders (Independent-Vermont), now a US Senator, dresses down Federal Reserve Chairman Alan Greenspan in front of the House Financial Services Committee on 7/16/03.
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