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Baudrillard Simulacra and Simulation 1981 book

According to Baudrillard, Simulacra are copies that depict things that either had no reality to begin with, or that no longer have an original. While, Simulation is the imitation of the operation of a real-world process or system over time. “Simulacres et Simulation” is a 1981 philosophical treatise by Jean Baudrillard seeking to interrogate the relationship among reality, symbols, and society:

[…] Simulacra and Simulation is most known for its discussion of symbols, signs, and how they relate to contemporaneity (simultaneous existences). Baudrillard claims that our current society has replaced all reality and meaning with symbols and signs, and that human experience is of a simulation of reality. Moreover, these simulacra are not merely mediations of reality, nor even deceptive mediations of reality; they are not based in a reality nor do they hide a reality, they simply hide that anything like reality is relevant to our current understanding of our lives. The simulacra that Baudrillard refers to are the significations and symbolism of culture and media that construct perceived reality, the acquired understanding by which our lives and shared existence is and are rendered legible; Baudrillard believed that society has become so saturated with these simulacra and our lives so saturated with the constructs of society that all meaning was being rendered meaningless by being infinitely mutable. Baudrillard called this phenomenon the “precession of simulacra”. […] (from Wikipedia)

Simulacra and Simulation” is definitely one of my best summer holiday readings I had this year. There are several connections to areas like Collective Intelligence and Perception, even Self-Organization as the dynamic and entangled use of symbols and signals, are recurrent on all these areas. Questions like the territory (cultural habitats) and metamorphose are also aborded. The book is an interesting source of new questions and thinking about our digital society, for people working on related areas such as Digital Media, Computer Simulation, Information Theory, Information and Entropy, Augmented Reality, Social Computation and related paradigms. I have read it in English for free [PDF] from a Georgetown Univ. link, here.

Image – Reese Inman, DIVERGENCE II (2008), acrylic on panel 30 x 30 in Remix (Boston, 2008), a solo exhibition of handmade computer art works by Reese Inman, Gallery NAGA in Boston.

Apophenia is the experience of seeing meaningful patterns or connections in random or meaningless data. The term was coined in 1958[1] by Klaus Conrad,[2] who defined it as the “unmotivated seeing of connections” accompanied by a “specific experience of an abnormal meaningfulness”, but it has come to represent the human tendency to seek patterns in random information in general (such as with gambling). In statistics, apophenia is known as a Type I error – the identification of false patterns in data.[7] It may be compared with a so called false positive in other test situations. Two correlated terms are synchronicity and pareidolia (from Wikipedia):

Synchronicity: Carl Jung coined the term synchronicity for the “simultaneous occurrence of two meaningful but not causally connected events” creating a significant realm of philosophical exploration. This attempt at finding patterns within a world where coincidence does not exist possibly involves apophenia if a person’s perspective attributes their own causation to a series of events. “Synchronicity therefore means the simultaneous occurrence of a certain psychic state with one or more external events which appear as meaningful parallels to a momentary subjective state”. (C. Jung, 1960).

Pareidolia: Pareidolia is a type of apophenia involving the perception of images or sounds in random stimuli, for example, hearing a ringing phone while taking a shower. The noise produced by the running water gives a random background from which the patterned sound of a ringing phone might be “produced”. A more common human experience is perceiving faces in inanimate objects; this phenomenon is not surprising in light of how much processing the brain does in order to memorize and recall the faces of hundreds or thousands of different individuals. In one respect, the brain is a facial recognition, storage, and recall machine – and it is very good at it. A by-product of this acumen at recognizing faces is that people see faces even where there is no face: the headlights & grill of an auto-mobile can appear to be “grinning”, individuals around the world can see the “Man in the Moon”, and a drawing consisting of only three circles and a line which even children will identify as a face are everyday examples of this.[15].

Video – Water has Memory (from Oasis HD, Canada; link): just a liquid or much more? Many researchers are convinced that water is capable of “memory” by storing information and retrieving it. The possible applications are innumerable: limitless retention and storage capacity and the key to discovering the origins of life on our planet. Research into water is just beginning.

Water capable of processing information as well as a huge possible “container” for data media, that is something remarkable. This theory was first proposed by the late French immunologist Jacques Benveniste, in a controversial article published in 1988 in Nature, as a way of explaining how homeopathy works (link). Benveniste’s theory has continued to be championed by some and disputed by others. The video clip above, from the Oasis HD Channel, shows some fascinating recent experiments with water “memory” from the Aerospace Institute of the University of Stuttgart in Germany. The results with the different types of flowers immersed in water are particularly evocative.

This line of research also remembers me back of an old and quite interesting paper by a colleague, Chrisantha Fernando. Together with Sampsa Sojakka, both have proved that waves produced on the surface of water can be used as the medium for a Wolfgang Maass’ “Liquid State Machine” (link) that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Amazingly, Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. What follows is the abstract of their paper entitled “Pattern Recognition in a Bucket“, as well a PDF link onto it:

Figure – Typical wave patterns for the XOR task. Top-Left: [0 1] (right motor on), Top-Right: [1 0] (left motor on), Bottom-Left: [1 1] (both motors on), Bottom-Right: [0 0] (still water). Sobel filtered and thresholded images on right. (from Fig. 3. in in Chrisantha Fernando and Sampsa Sojakka, “Pattern Recognition in a Bucket“, ECAL proc., European Conference on Artificial Life, 2003.

[…] Abstract. This paper demonstrates that the waves produced on the surface of water can be used as the medium for a “Liquid State Machine” that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Interference between waves allows non-linear parallel computation upon simultaneous sensory inputs. Temporal patterns of stimulation are converted to spatial patterns of water waves upon which a linear discrimination can be made. Whereas Wolfgang Maass’ Liquid State Machine requires fine tuning of the spiking neural network parameters, water has inherent self-organising properties such as strong local interactions, time-dependent spread of activation to distant areas, inherent stability to a wide variety of inputs, and high complexity. Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. An analogy is made between water molecules and neurons in a recurrent neural network. […] in Chrisantha Fernando and Sampsa Sojakka, Pattern Recognition in a Bucket“, ECAL proc., European Conference on Artificial Life, 2003. [PDF link]

Fig. – A Symbolical Head (phrenological chart) illustrating the natural language of the faculties. At the Society pages / Economic Sociology web page.

You have much probably noticed by now how is emerging as a powerful platform for those collecting interesting research papers. There are several good examples, but let me stress one entitled “Bounded Rationality and Beyond” ( web page) curated by Alessandro Cerboni (blog). On a difficult research theme, Alessandro is doing a great job collecting nice essays and wonderful articles, whenever he founds them. One of those articles I really appreciated was John Conlisk‘s “Why Bounded Rationality?“, delivering into the field several important clues, for those who (like me) work in the area. What follows, is an excerpt from the article as well as part of his introductory section. The full (PDF) paper could be retrieved here:

In this survey, four reasons are given for incorporating bounded rationality in economic models. First, there is abundant empirical evidence that it is important. Second, models of bounded rationality have proved themselves in a wide range of impressive work. Third, the standard justifications for assuming unbounded rationality are unconvincing; their logic cuts both ways. Fourth, deliberation about an economic decision is a costly activity, and good economics requires that we entertain all costs. These four reasons, or categories of reasons, are developed in the following four sections. Deliberation cost will be a recurring theme.

Why bounded rationality? In four words (one for each section above): evidence, success, methodology, and scarcity. In more words: Psychology and economics provide wide-ranging evidence that bounded rationality is important (Section I). Economists who include bounds on rationality in their models have excellent success in describing economic behavior beyond the coverage of standard theory (Section II). The traditional appeals to economic methodology cut both ways; the conditions of a particular context may favor either bounded or unbounded rationality (Section III). Models of bounded rationality adhere to a fundamental tenet of economics, respect for scarcity. Human cognition, as a scarce resource, should be treated as such (Section IV). The survey stresses throughout that an appropriate rationality assumption is not something to decide once for all contexts. In principle, we might suppose there is an encompassing single theory which takes various forms of bounded and unbounded rationality as special. cases. As with other model ingredients, however, we in practice want to work directly with the most convenient special case which does justice to the context. The evidence and models surveyed suggest that a sensible rationality assumption will vary by context, depending on such conditions as deliberation cost, complexity, incentives, experience, and market discipline. Beyond the four reasons given, there is one more reason for studying bounded rationality. It is simply a fascinating thing to do. We can mix some Puck with our Hamlet.

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.

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.


Dynamic Optimization Problems (DOP) solved by Swarm Intelligence (dynamic environment) - Vitorino Ramos

a) Dynamic Optimization Problems (DOP) tackled by Swarm Intelligence (in here a quick snapshot of the dynamic environment)

Swarm adaptive response over time, under sever dynamics

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. 


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.

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 ?!

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" ?!


Transition behavior of one Artificial Ant Colony in presence of a sudden change in his artificial digital image Habitat, between two different Digital Grey Images (face of Einstein and a Map). Created with an Artificial Ant Colony, that uses images as Habitats, being sensible to their gray levels [in, V. Ramos, F. Almeida, “Artificial Ant Colonies in Digital Image Habitats – a mass behavior effect study on Pattern Recognition“, ANTS’00 Conf., Brussels, Belgium, 2000].

After “Einstein face” is injected as a substrate at t=0, 100 iterations occur. At this point you could recognize the face. Then, a new substrate (a new “environmental condition”) is imposed (Map image). The colony then adapts quickly to this new situation, losing their collective memory of past contours.

In white, the higher levels of pheromone (a chemical evaporative sugar substance used by swarms on their orientation trough out the trails). It’s exactly this artificial evaporation and the computational ant collective group synergy reallocating their upgrades of pheromone at interesting places, that allows for the emergence of adaptation and “perception” of new images. Only some of the 6000 iterations processed are represented. The system does not have any type of hierarchy, and ants communicate only in indirect forms, through out the successive alteration that they found on the Habitat. If you however, inject Einstein image again as a substrate, the whole ant society will converge again to it, but much faster than the first time, due to the residual memory distributed in the environment.

As a whole, the system is constantly trying to establish a proper compromise between memory (past solutions – via pheromone reinforcement) and novel ones in order to adapt (new conditions on the habitat, through pheromone evaporation). The right compromise, ables the system to tackle two contradictory situations: keeping some memory while learning something radically new. Antagonist features such as exploration and exploitation are tackled this way.

Image Classification of Shellfish Larvae Digital Images using Swarm Intelligence. On the left a compendium of 9 raw images (out of 20 samples) used in the present study. Respective segmented images on the rigth.

Image Classification of Shellfish Larvae Digital Images using Swarm Intelligence. On the left a compendium of 9 raw images (out of 20 samples) used in the present project. Respective segmented images on the rigth.

[] Vitorino Ramos, Jonathan Campbell, John Slater, John Gillespie, Ivan F. Bendezu and Fionn Murtagh, Swarming around Shellfish Larvae Images, in WCLC-05, 2nd World Congress on Lateral Computing, Bangalore, India, 16-18 Dec., 2005.

The collection of wild larvae seed as a source of raw material is a major sub industry of shellfish aquaculture. To predict when, where and in what quantities wild seed will be available, it is necessary to track the appearance and growth of planktonic larvae. One of the most difficult groups to identify, particularly at the species level are the Bivalvia. This difficulty arises from the fact that fundamentally all bivalve larvae have a similar shape and colour. Identification based on gross morphological appearance is limited by the time-consuming nature of the microscopic examination and by the limited availability of expertise in this field. Molecular and immunological methods are also being studied. We describe the application of computational pattern recognition methods to the automated identification and size analysis of scallop larvae. For identification, the shape features used are binary invariant moments; that is, the features are invariant to shift (position within the image), scale (induced either by growth or differential image magnification) and rotation. Images of a sample of scallop and non-scallop larvae covering a range of maturities have been analysed. In order to overcome the automatic identification, as well as to allow the system to receive new unknown samples at any moment, a self-organized and unsupervised ant-like clustering algorithm based on Swarm Intelligence is proposed, followed by simple k-NNR nearest neighbour classification on the final map. Results achieve a full recognition rate of 100% under several situations (k =1 or 3).

(to obtain the respective PDF file follow link above or visit

[] Vitorino Ramos, Filipe Almeida, Artificial Ant Colonies in Digital Image Habitats – A Mass Behaviour Effect Study on Pattern Recognition, Proceedings of ANTS´2000 – 2nd International Workshop on Ant Algorithms (From Ant Colonies to Artificial Ants), Marco Dorigo, Martin Middendorf & Thomas Stüzle (Eds.), pp. 113-116, Brussels, Belgium, 7-9 Sep. 2000.

Figure - Transition behaviour of one Artificial Ant Colony in presence of a sudden change in his artificial digital image Habitat, between two different Digital Grey Images. Created with an Artificial Ant Colony, that uses images as Habitats, being sensible to their gray levels. At the second row, Kafka image is replaced as a substrate, by Red Ant. In black, the higher levels of pheromone (a chemical evaporative sugar substance used by swarms on their orientation trought out the trails). It’s exactly this artificial evaporation and the computational ant collective group sinergy realocating their upgrades of pheromone at interesting places, that allows for the emergence of adaptation and perception of new images. Only some of the 6000 iterations processed are represented. The system does not have any type of hierarchy, and ants communicate only in indirect forms, through out the sucessive alteration that they found on the Habitat.

Figure - Transition behaviour of one Artificial Ant Colony in presence of a sudden change in his artificial digital image Habitat, between two different Digital Grey Images. Created with an Artificial Ant Colony, that uses images as Habitats, being sensible to their gray levels. At the second row, "Kafka" image is replaced as a substrate, by "Red Ant". In black, the higher levels of pheromone (a chemical evaporative sugar substance used by swarms on their orientation trought out the trails). It’s exactly this artificial evaporation and the computational ant collective group sinergy realocating their upgrades of pheromone at interesting places, that allows for the emergence of adaptation and "perception" of new images. Only some of the 6000 iterations processed are represented. The system does not have any type of hierarchy, and ants communicate only in indirect forms, through out the sucessive alteration that they found on the Habitat.

Some recent studies have pointed that, the self-organization of neurons into brain-like structures, and the self-organization of ants into a swarm are similar in many respects. If possible to implement, these features could lead to important developments in pattern recognition systems, where perceptive capabilities can emerge and evolve from the interaction of many simple local rules. The principle of the method is inspired by the work of Chialvo and Millonas who developed the first numerical simulation in which swarm cognitive map formation could be explained. From this point, an extended model is presented in order to deal with digital image habitats, in which artificial ants could be able to react to the environment and perceive it. Evolution of pheromone fields point that artificial ant colonies could react and adapt appropriately to any type of digital habitat.


(to obtain the respective PDF file follow link above or visit





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

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