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Vitorino Ramos - Citations2016Jan

2016 – Up now, an overall of 1567 citations among 74 works (including 3 books) on GOOGLE SCHOLAR (https://scholar.google.com/citations?user=gSyQ-g8AAAAJ&hl=en) [with an Hirsh h-index=19, and an average of 160.2 citations each for any work on my top five] + 900 citations among 57 works on the new RESEARCH GATE site (https://www.researchgate.net/profile/Vitorino_Ramos).

Refs.: Science, Artificial Intelligence, Swarm Intelligence, Data-Mining, Big-Data, Evolutionary Computation, Complex Systems, Image Analysis, Pattern Recognition, Data Analysis.

Complete circuit diagram with pheromone - Cristian Jimenez-Romero, David Sousa-Rodrigues, Jeffrey H. Johnson, Vitorino Ramos; Figure – Neural circuit controller of the virtual ant (page 3, fig. 2). [URL: http://arxiv.org/abs/1507.08467 ]

Intelligence and decision in foraging ants. Individual or Collective? Internal or External? What is the right balance between the two. Can one have internal intelligence without external intelligence? Can one take examples from nature to build in silico artificial lives that present us with interesting patterns? We explore a model of foraging ants in this paper that will be presented in early September in Exeter, UK, at UKCI 2015. (available on arXiv [PDF] and ResearchGate)

Cristian Jimenez-Romero, David Sousa-Rodrigues, Jeffrey H. Johnson, Vitorino Ramos; “A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones“, UKCI 2015 Computational Intelligence – University of Exeter, UK, September 2015.

Abstract: A model of an Ant System where ants are controlled by a spiking neural circuit and a second order pheromone mechanism in a foraging task is presented. A neural circuit is trained for individual ants and subsequently the ants are exposed to a virtual environment where a swarm of ants performed a resource foraging task. The model comprises an associative and unsupervised learning strategy for the neural circuit of the ant. The neural circuit adapts to the environment by means of classical conditioning. The initially unknown environment includes different types of stimuli representing food (rewarding) and obstacles (harmful) which, when they come in direct contact with the ant, elicit a reflex response in the motor neural system of the ant: moving towards or away from the source of the stimulus. The spiking neural circuits of the ant is trained to identify food and obstacles and move towards the former and avoid the latter. The ants are released on a landscape with multiple food sources where one ant alone would have difficulty harvesting the landscape to maximum efficiency. In this case the introduction of a double pheromone mechanism (positive and negative reinforcement feedback) yields better results than traditional ant colony optimization strategies. Traditional ant systems include mainly a positive reinforcement pheromone. This approach uses a second pheromone that acts as a marker for forbidden paths (negative feedback). This blockade is not permanent and is controlled by the evaporation rate of the pheromones. The combined action of both pheromones acts as a collective stigmergic memory of the swarm, which reduces the search space of the problem. This paper explores how the adaptation and learning abilities observed in biologically inspired cognitive architectures is synergistically enhanced by swarm optimization strategies. The model portraits two forms of artificial intelligent behaviour: at the individual level the spiking neural network is the main controller and at the collective level the pheromone distribution is a map towards the solution emerged by the colony. The presented model is an important pedagogical tool as it is also an easy to use library that allows access to the spiking neural network paradigm from inside a Netlogo—a language used mostly in agent based modelling and experimentation with complex systems.

References:

[1] C. G. Langton, “Studying artificial life with cellular automata,” Physica D: Nonlinear Phenomena, vol. 22, no. 1–3, pp. 120 – 149, 1986, proceedings of the Fifth Annual International Conference. [Online]. Available: http://www.sciencedirect.com/ science/article/pii/016727898690237X
[2] A. Abraham and V. Ramos, “Web usage mining using artificial ant colony clustering and linear genetic programming,” in Proceedings of the Congress on Evolutionary Computation. Australia: IEEE Press, 2003, pp. 1384–1391.
[3] V. Ramos, F. Muge, and P. Pina, “Self-organized data and image retrieval as a consequence of inter-dynamic synergistic relationships in artificial ant colonies,” Hybrid Intelligent Systems, vol. 87, 2002.
[4] V. Ramos and J. J. Merelo, “Self-organized stigmergic document maps: Environment as a mechanism for context learning,” in Proceddings of the AEB, Merida, Spain, February 2002. ´
[5] D. Sousa-Rodrigues and V. Ramos, “Traversing news with ant colony optimisation and negative pheromones,” in European Conference in Complex Systems, Lucca, Italy, Sep 2014.
[6] E. Bonabeau, G. Theraulaz, and M. Dorigo, Swarm Intelligence: From Natural to Artificial Systems, 1st ed., ser. Santa Fe Insitute Studies In The Sciences of Complexity. 198 Madison Avenue, New York: Oxford University Press, USA, Sep. 1999.
[7] M. Dorigo and L. M. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem,” Universite Libre de Bruxelles, Tech. Rep. TR/IRIDIA/1996-5, ´ 1996.
[8] M. Dorigo, G. Di Caro, and L. M. Gambardella, “Ant algorithms for discrete optimization,” Artif. Life, vol. 5, no. 2, pp. 137– 172, Apr. 1999. [Online]. Available: http://dx.doi.org/10.1162/ 106454699568728
[9] L. M. Gambardella and M. Dorigo, “Ant-q: A reinforcement learning approach to the travelling salesman problem,” in Proceedings of the ML-95, Twelfth Intern. Conf. on Machine Learning, M. Kaufman, Ed., 1995, pp. 252–260.
[10] A. Gupta, V. Nagarajan, and R. Ravi, “Approximation algorithms for optimal decision trees and adaptive tsp problems,” in Proceedings of the 37th international colloquium conference on Automata, languages and programming, ser. ICALP’10. Berlin, Heidelberg: Springer-Verlag, 2010, pp. 690–701. [Online]. Available: http://dl.acm.org/citation.cfm?id=1880918.1880993
[11] V. Ramos, D. Sousa-Rodrigues, and J. Louçã, “Second order ˜ swarm intelligence,” in HAIS’13. 8th International Conference on Hybrid Artificial Intelligence Systems, ser. Lecture Notes in Computer Science, J.-S. Pan, M. Polycarpou, M. Wozniak, A. Carvalho, ´ H. Quintian, and E. Corchado, Eds. Salamanca, Spain: Springer ´ Berlin Heidelberg, Sep 2013, vol. 8073, pp. 411–420.
[12] W. Maass and C. M. Bishop, Pulsed Neural Networks. Cambridge, Massachusetts: MIT Press, 1998.
[13] E. M. Izhikevich and E. M. Izhikevich, “Simple model of spiking neurons.” IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, vol. 14, no. 6, pp. 1569–72, 2003. [Online]. Available: http://www.ncbi.nlm.nih. gov/pubmed/18244602
[14] C. Liu and J. Shapiro, “Implementing classical conditioning with spiking neurons,” in Artificial Neural Networks ICANN 2007, ser. Lecture Notes in Computer Science, J. de S, L. Alexandre, W. Duch, and D. Mandic, Eds. Springer Berlin Heidelberg, 2007, vol. 4668, pp. 400–410. [Online]. Available: http://dx.doi.org/10.1007/978-3-540-74690-4 41
[15] J. Haenicke, E. Pamir, and M. P. Nawrot, “A spiking neuronal network model of fast associative learning in the honeybee,” Frontiers in Computational Neuroscience, no. 149, 2012. [Online]. Available: http://www.frontiersin.org/computational neuroscience/10.3389/conf.fncom.2012.55.00149/full
[16] L. I. Helgadottir, J. Haenicke, T. Landgraf, R. Rojas, and M. P. Nawrot, “Conditioned behavior in a robot controlled by a spiking neural network,” in International IEEE/EMBS Conference on Neural Engineering, NER, 2013, pp. 891–894.
[17] A. Cyr and M. Boukadoum, “Classical conditioning in different temporal constraints: an STDP learning rule for robots controlled by spiking neural networks,” pp. 257–272, 2012.
[18] X. Wang, Z. G. Hou, F. Lv, M. Tan, and Y. Wang, “Mobile robots’ modular navigation controller using spiking neural networks,” Neurocomputing, vol. 134, pp. 230–238, 2014.
[19] C. Hausler, M. P. Nawrot, and M. Schmuker, “A spiking neuron classifier network with a deep architecture inspired by the olfactory system of the honeybee,” in 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011, 2011, pp. 198–202.
[20] U. Wilensky, “Netlogo,” Evanston IL, USA, 1999. [Online]. Available: http://ccl.northwestern.edu/netlogo/
[21] C. Jimenez-Romero and J. Johnson, “Accepted abstract: Simulation of agents and robots controlled by spiking neural networks using netlogo,” in International Conference on Brain Engineering and Neuro-computing, Mykonos, Greece, Oct 2015.
[22] W. Gerstner and W. M. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge: Cambridge University Press, 2002.
[23] J. v. H. W Gerstner, R Kempter and H. Wagner, “A neuronal learning rule for sub-millisecond temporal coding,” Nature, vol. 386, pp. 76–78, 1996.
[24] I. P. Pavlov, “Conditioned reflexes: An investigation of the activity of the cerebral cortex,” New York, 1927.
[25] E. J. H. Robinson, D. E. Jackson, M. Holcombe, and F. L. W. Ratnieks, “Insect communication: ‘no entry’ signal in ant foraging,” Nature, vol. 438, no. 7067, pp. 442–442, 11 2005. [Online]. Available: http://dx.doi.org/10.1038/438442a
[26] E. J. Robinson, D. Jackson, M. Holcombe, and F. L. Ratnieks, “No entry signal in ant foraging (hymenoptera: Formicidae): new insights from an agent-based model,” Myrmecological News, vol. 10, no. 120, 2007.
[27] D. Sousa-Rodrigues, J. Louçã, and V. Ramos, “From standard ˜ to second-order swarm intelligence phase-space maps,” in 8th European Conference on Complex Systems, S. Thurner, Ed., Vienna, Austria, Sep 2011.
[28] V. Ramos, D. Sousa-Rodrigues, and J. Louçã, “Spatio-temporal ˜ dynamics on co-evolved stigmergy,” in 8th European Conference on Complex Systems, S. Thurner, Ed., Vienna, Austria, 9 2011.
[29] S. Tisue and U. Wilensky, “Netlogo: A simple environment for modeling complexity,” in International conference on complex systems. Boston, MA, 2004, pp. 16–21.

David MS Rodrigues Reading the News Through its Structure New Hybrid Connectivity Based ApproachesFigure – Two simplicies a and b connected by the 2-dimensional face, the triangle {1;2;3}. In the analysis of the time-line of The Guardian newspaper (link) the system used feature vectors based on frequency of words and them computed similarity between documents based on those feature vectors. This is a purely statistical approach that requires great computational power and that is difficult for problems that have large feature vectors and many documents. Feature vectors with 100,000 or more items are common and computing similarities between these documents becomes cumbersome. Instead of computing distance (or similarity) matrices between documents from feature vectors, the present approach explores the possibility of inferring the distance between documents from the Q-analysis description. Q-analysis is a very natural notion of connectivity between the simplicies of the structure and in the relation studied, documents are connected to each other through shared sets of tags entered by the journalists. Also in this framework, eccentricity is defined as a measure of the relatedness of one simplex in relation to another [7].

David M.S. Rodrigues and Vitorino Ramos, “Traversing News with Ant Colony Optimisation and Negative Pheromones” [PDF], accepted as preprint for oral presentation at the European Conference on Complex SystemsECCS14 in Lucca, Sept. 22-26, 2014, Italy.

Abstract: The past decade has seen the rapid development of the online newsroom. News published online are the main outlet of news surpassing traditional printed newspapers. This poses challenges to the production and to the consumption of those news. With those many sources of information available it is important to find ways to cluster and organise the documents if one wants to understand this new system. Traditional approaches to the problem of clustering documents usually embed the documents in a suitable similarity space. Previous studies have reported on the impact of the similarity measures used for clustering of textual corpora [1]. These similarity measures usually are calculated for bag of words representations of the documents. This makes the final document-word matrix high dimensional. Feature vectors with more than 10,000 dimensions are common and algorithms have severe problems with the high dimensionality of the data. A novel bio inspired approach to the problem of traversing the news is presented. It finds Hamiltonian cycles over documents published by the newspaper The Guardian. A Second Order Swarm Intelligence algorithm based on Ant Colony Optimisation was developed [2, 3] that uses a negative pheromone to mark unrewarding paths with a “no-entry” signal. This approach follows recent findings of negative pheromone usage in real ants [4].

In this case study the corpus of data is represented as a bipartite relation between documents and keywords entered by the journalists to characterise the news. A new similarity measure between documents is presented based on the Q-analysis description [5, 6, 7] of the simplicial complex formed between documents and keywords. The eccentricity between documents (two simplicies) is then used as a novel measure of similarity between documents. The results prove that the Second Order Swarm Intelligence algorithm performs better in benchmark problems of the travelling salesman problem, with faster convergence and optimal results. The addition of the negative pheromone as a non-entry signal improves the quality of the results. The application of the algorithm to the corpus of news of The Guardian creates a coherent navigation system among the news. This allows the users to navigate the news published during a certain period of time in a semantic sequence instead of a time sequence. This work as broader application as it can be applied to many cases where the data is mapped to bipartite relations (e.g. protein expressions in cells, sentiment analysis, brand awareness in social media, routing problems), as it highlights the connectivity of the underlying complex system.

Keywords: Self-Organization, Stigmergy, Co-Evolution, Swarm Intelligence, Dynamic Optimization, Foraging, Cooperative Learning, Hamiltonian cycles, Text Mining, Textual Corpora, Information Retrieval, Knowledge Discovery, Sentiment Analysis, Q-Analysis, Data Mining, Journalism, The Guardian.

References:

[1] Alexander Strehl, Joydeep Ghosh, and Raymond Mooney. Impact of similarity measures on web-page clustering.  In Workshop on Artifcial Intelligence for Web Search (AAAI 2000), pages 58-64, 2000.
[2] David M. S. Rodrigues, Jorge Louçã, and Vitorino Ramos. From standard to second-order Swarm Intelligence  phase-space maps. In Stefan Thurner, editor, 8th European Conference on Complex Systems, Vienna, Austria,  9 2011.
[3] Vitorino Ramos, David M. S. Rodrigues, and Jorge Louçã. Second order Swarm Intelligence. In Jeng-Shyang  Pan, Marios M. Polycarpou, Micha l Wozniak, André C.P.L.F. Carvalho, Hector Quintian, and Emilio Corchado,  editors, HAIS’13. 8th International Conference on Hybrid Artificial Intelligence Systems, volume 8073 of Lecture  Notes in Computer Science, pages 411-420. Springer Berlin Heidelberg, Salamanca, Spain, 9 2013.
[4] Elva J.H. Robinson, Duncan Jackson, Mike Holcombe, and Francis L.W. Ratnieks. No entry signal in ant  foraging (hymenoptera: Formicidae): new insights from an agent-based model. Myrmecological News, 10(120), 2007.
[5] Ronald Harry Atkin. Mathematical Structure in Human A ffairs. Heinemann Educational Publishers, 48 Charles  Street, London, 1 edition, 1974.
[6] J. H. Johnson. A survey of Q-analysis, part 1: The past and present. In Proceedings of the Seminar on Q-analysis  and the Social Sciences, Universty of Leeds, 9 1983.
[7] David M. S. Rodrigues. Identifying news clusters using Q-analysis and modularity. In Albert Diaz-Guilera,  Alex Arenas, and Alvaro Corral, editors, Proceedings of the European Conference on Complex Systems 2013, Barcelona, 9 2013.

In order to solve hard combinatorial optimization problems (e.g. optimally scheduling students and teachers along a week plan on several different classes and classrooms), one way is to computationally mimic how ants forage the vicinity of their habitats searching for food. On a myriad of endless possibilities to find the optimal route (minimizing the travel distance), ants, collectively emerge the solution by using stigmergic signal traces, or pheromones, which also dynamically change under evaporation.

Current algorithms, however, make only use of a positive feedback type of pheromone along their search, that is, if they collectively visit a good low-distance route (a minimal pseudo-solution to the problem) they tend to reinforce that signal, for their colleagues. Nothing wrong with that, on the contrary, but no one knows however if a lower-distance alternative route is there also, just at the corner. On his global search endeavour, like a snowballing effect, positive feedbacks tend up to give credit to the exploitation of solutions but not on the – also useful – exploration side. The upcoming potential solutions can thus get crystallized, and freeze, while a small change on some parts of the whole route, could on the other-hand successfully increase the global result.

Influence of Negative Pheromone in Swarm IntelligenceFigure – Influence of negative pheromone on kroA100.tsp problem (fig.1 – page 6) (values on lines represent 1-ALPHA). A typical standard ACS (Ant Colony System) is represented here by the line with value 0.0, while better results could be found by our approach, when using positive feedbacks (0.95) along with negative feedbacks (0.05). Not only we obtain better results, as we found them earlier.

There is, however, an advantage when a second type of pheromone (a negative feedback one) co-evolves with the first type. And we decided to research for his impact. What we found out, is that by using a second type of global feedback, we can indeed increase a faster search while achieving better results. In a way, it’s like using two different types of evaporative traffic lights, in green and red, co-evolving together. And as a conclusion, we should indeed use a negative no-entry signal pheromone. In small amounts (0.05), but use it. Not only this prevents the whole system to freeze on some solutions, to soon, as it enhances a better compromise on the search space of potential routes. The pre-print article is available here at arXiv. Follows the abstract and keywords:

Vitorino Ramos, David M. S. Rodrigues, Jorge Louçã, “Second Order Swarm Intelligence” [PDF], in Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, Springer-Verlag, Volume 8073, pp. 411-420, 2013.

Abstract: An artificial Ant Colony System (ACS) algorithm to solve general purpose combinatorial Optimization Problems (COP) that extends previous AC models [21] by the inclusion of a negative pheromone, is here described. Several Travelling Salesman Problem‘s (TSP) were used as benchmark. We show that by using two different sets of pheromones, a second-order co-evolved compromise between positive and negative feedbacks achieves better results than single positive feedback systems. The algorithm was tested against known NP complete combinatorial Optimization Problems, running on symmetrical TSPs. We show that the new algorithm compares favourably against these benchmarks, accordingly to recent biological findings by Robinson [26,27], and Grüter [28] where “No entry” signals and negative feedback allows a colony to quickly reallocate the majority of its foragers to superior food patches. This is the first time an extended ACS algorithm is implemented with these successful characteristics.

Keywords: Self-Organization, Stigmergy, Co-Evolution, Swarm Intelligence, Dynamic Optimization, Foraging, Cooperative Learning, Combinatorial Optimization problems, Symmetrical Travelling Salesman Problems (TSP).

Hybrid Artificial Intelligent Systems HAIS 2013 (pp. 411-420 Second Order Swarm Intelligence)Figure – Hybrid Artificial Intelligent Systems new LNAI (Lecture Notes on Artificial Intelligence) series volume 8073, Springer-Verlag Book [original photo by my colleague David M.S. Rodrigues].

New work, new book. Last week one of our latest works come out published on Springer. Edited by Jeng-Shyang Pan, Marios M. Polycarpou, Emilio Corchado et al. “Hybrid Artificial Intelligent Systems” comprises a full set of new papers on this hybrid area on Intelligent Computing (check the full articles list at Springer). Our new paper “Second Order Swarm Intelligence” (pp. 411-420, Springer books link) was published on the Bio-inspired Models and Evolutionary Computation section.

Nocturnal swarm

Nocturnal moth trails – Fluttering wings leave lacy trails as moths beat their way to a floodlight on a rural Ontario lawn. The midsummer night’s exposure, held for 20 seconds, captured some of the hundreds of insects engaged in a nocturnal swarm. [Photo: Steve Irvine, National Geographic, 2013, link]

Octavio Aburto David and Goliath CaboPulmo NatGeo2012

During several years, Octavio Aburto thought of one photo. Now, he finally got it. The recently published photograph by Aburto, titled “David and Goliath” (it his in fact David Castro, one of his research science colleagues at the center of this stunning image) has been widely shared over the last few weeks. It was taken at Cabo Pulmo National Park (Mexico) and submitted to the National Geographic photo contest 2012. Here, he captures the sheer size of fish aggregations in perspective with a single human surrounded by abundant marine life. On a recent interview, he explains:

[…] … this “David and Goliath” image is speaking to the courtship behavior of one particular species of Jack fish. […] Many people say that a single image is worth a thousand words, but a single image can also represent thousands of data points and countless statistical analyses. One image, or a small series of images can tell a complicated story in a very simple way. […] The picture you see was taken November 1st, 2012. But this picture has been in my mind for three years — I have been trying to capture this image ever since I saw the behavior of these fish and witnessed the incredible tornado that they form during courtship. So, I guess you could say this image took almost three years. […], in mission-blue.org , Dec. 2012.

Video – Behind the scenes of David and Goliath image. This photo was taken at Cabo Pulmo National Park and submitted to the National Geographic photo contest 2012. You can see more of his images from this place and about Mexican seas on Octavio‘s web link.

Fig. – (book cover) “Emergence: The Connected Lives of Ants, Brains, Cities, and Software” (additional link) is a book written by media theorist Steven Berlin Johnson, published in 2001,  Scribner. New York, NY.

The patterns are simple, but followed together, they make for a whole that is wiser than the sum of its parts. Go for a walk; cultivate hunches; write everything down, but keep your folders messy; embrace serendipity; make generative mistakes; take on multiple hobbies; frequent coffee houses and other liquid networks; follow the links; let others build on your ideas; borrow, recycle; reinvent. Build a tangled bank.” — Steven Johnson.

Recent research have increasingly being focused on the relationship between Human-Human interaction, social networks (no, not the Facebook) and other Human-activity areas, like health. Nicholas Christakis (Harvard Univ. research link) points us that, people are inter-connected, and so as well, their health is inter-connected. This research engages two types of phenomena: the social, mathematical, and biological rules governing how social networks form (“Connection“) and the biological and social implications of how they operate to influence thoughts, feelings, and behaviours (“Contagion“), as in the self-organized stigmergy-like dynamics of Cognitive Collective Perception (link).

Above, Nicholas Christakis (in a 56m. documentary lecture produced by The Floating University, Sept. 2011) discusses the obvious tension and delicate balance between agency (one individual choices and actions) and structure (our collective responsibility), where here, structure refers not only to our co-evolving dynamic societal environment as well as to the permanent unfolding entangled nature of topological structure on complex networks, such as in human-human social networks, while asking: If you’re so free, why do you follow others? The documentary (YouTube link) resume states:

If you think you’re in complete control of your destiny or even your own actions, you’re wrong. Every choice you make, every behaviour you exhibit, and even every desire you have finds its roots in the social universe. Nicholas Christakis explains why individual actions are inextricably linked to sociological pressures; whether you’re absorbing altruism performed by someone you’ll never meet or deciding to jump off the Golden Gate Bridge, collective phenomena affect every aspect of your life. By the end of the lecture Christakis has revealed a startling new way to understand the world that ranks sociology as one of the most vitally important social sciences.”

While cooperation is central to the success of human societies and is widespread, cooperation in itself, however, poses a challenge in both the social and biological sciences: How can this high level of cooperation be maintained in the face of possible exploitation? One answer involves networked interactions and population structure.

As perceived, the balance between homophily (where “birds of a feather flock together”) and heterophily (one where most of genotypes are negatively correlated), do requires further research. In fact, in humans, one of the most replicated findings in the social sciences is that people tend to associate with other people that they resemble, a process precisely known as homophily. As Christakis points out, although phenotypic resemblance between friends might partly reflect the operation of social influence, our genotypes are not materially susceptible to change. Therefore, genotypic resemblance could result only from a process of selection. Such genotypic selection might in turn take several forms. For short, let me stress you two examples. What follows are two papers, as well as a quick reference (image below) to a recent general-audience of his books:

1) Rewiring your network fosters cooperation:

“Human populations are both highly cooperative and highly organized. Human interactions are not random but rather are structured in social networks. Importantly, ties in these networks often are dynamic, changing in response to the behavior of one’s social partners. This dynamic structure permits an important form of conditional action that has been explored theoretically but has received little empirical attention: People can respond to the cooperation and defection of those around them by making or breaking network links. Here, we present experimental evidence of the power of using strategic link formation and dissolution, and the network modification it entails, to stabilize cooperation in sizable groups. Our experiments explore large-scale cooperation, where subjects’ cooperative actions are equally beneficial to all those with whom they interact. Consistent with previous research, we find that cooperation decays over time when social networks are shuffled randomly every round or are fixed across all rounds. We also find that, when networks are dynamic but are updated only infrequently, cooperation again fails. However, when subjects can update their network connections frequently, we see a qualitatively different outcome: Cooperation is maintained at a high level through network rewiring. Subjects preferentially break links with defectors and form new links with cooperators, creating an incentive to cooperate and leading to substantial changes in network structure. Our experiments confirm the predictions of a set of evolutionary game theoretic models and demonstrate the important role that dynamic social networks can play in supporting large-scale human cooperation.”, abstract in D.G. Rand, S. Arbesman, and N.A. Christakis, “Dynamic Social Networks Promote Cooperation in Experiments with Humans,” PNAS: Proceedings of the National Academy of Sciences (October 2011). [full PDF];

Picture – (book cover) Along with James Fowler, Christakis has authored also a general-audience book on social networks: Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives, 2011 (book link). For a recent book review, access here.

2) We are surrounded by a sea of our friends’ genes:

“It is well known that humans tend to associate with other humans who have similar characteristics, but it is unclear whether this tendency has consequences for the distribution of genotypes in a population. Although geneticists have shown that populations tend to stratify genetically, this process results from geographic sorting or assortative mating, and it is unknown whether genotypes may be correlated as a consequence of nonreproductive associations or other processes. Here, we study six available genotypes from the National Longitudinal Study of Adolescent Health to test for genetic similarity between friends. Maps of the friendship networks show clustering of genotypes and, after we apply strict controls for population strati!cation, the results show that one genotype is positively correlated (homophily) and one genotype is negatively correlated (heterophily). A replication study in an independent sample from the Framingham Heart Study veri!es that DRD2 exhibits signi!cant homophily and that CYP2A6 exhibits signi!cant heterophily. These unique results show that homophily and heterophily obtain on a genetic (indeed, an allelic) level, which has implications for the study of population genetics and social behavior. In particular, the results suggest that association tests should include friends’ genes and that theories of evolution should take into account the fact that humans might, in some sense, be metagenomic with respect to the humans around them.”, abstract in J.H. Fowler, J.E. Settle, and N.A. Christakis, “Correlated Genotypes in Friendship Networks,” PNAS: Proceedings of the National Academy of Sciences (January 2011). [full PDF].

Figure – A classic example of emergence: The exact shape of a termite mound is not reducible to the actions of individual termites. Even if, there are already computer models who could achieve it (Check for more on “Stigmergic construction” or the full current blog Stigmergy tag)

The world can no longer be understood like a chessboard… It’s a Jackson Pollack painting” ~ Carne Ross, 2012.

[…] As pointed by Langton, there is more to life than mechanics – there is also dynamics. Life depends critically on principles of dynamical self-organization that have remained largely untouched by traditional analytic methods. There is a simple explanation for this – these self-organized dynamics are fundamentally non-linear phenomena, and non-linear phenomena in general depend critically on the interactions between parts: they necessarily disappear when parts are treated in isolation from one another, which is the basis for any analytic method. Rather, non-linear phenomena are most appropriately treated by a synthetic approach, where synthesis means “the combining of separate elements or substances to form a coherent whole”. In non-linear systems, the parts must be treated in each other’s presence, rather than independently from one another, because they behave very differently in each other’s presence than we would expect from a study of the parts in isolation. […] in Vitorino Ramos, 2002, http://arxiv.org/abs/cs /0412077.

What follows are passages from an important article on the consequences for Science at the moment of the recent discovery of the Higgs boson. Written by Ashutosh Jogalekar, “The Higgs boson and the future of science” (link) the article appeared at the Scientific American blog section (July 2012). And it starts discussing reductionism or how the Higgs boson points us to the culmination of reductionist thinking:

[…] And I say this with a suspicion that the Higgs boson may be the most fitting tribute to the limitations of what has been the most potent philosophical instrument of scientific discovery – reductionism. […]

[…] Yet as we enter the second decade of the twenty-first century, it is clear that reductionism as a principal weapon in our arsenal of discovery tools is no longer sufficient. Consider some of the most important questions facing modern science, almost all of which deal with complex, multi factorial systems. How did life on earth begin? How does biological matter evolve consciousness? What are dark matter and dark energy? How do societies cooperate to solve their most pressing problems? What are the properties of the global climate system? It is interesting to note at least one common feature among many of these problems; they result from the build-up rather than the breakdown of their operational entities. Their signature is collective emergence, the creation of attributes which are greater than the sum of their constituent parts. Whatever consciousness is for instance, it is definitely a result of neurons acting together in ways that are not obvious from their individual structures. Similarly, the origin of life can be traced back to molecular entities undergoing self-assembly and then replication and metabolism, a process that supersedes the chemical behaviour of the isolated components. The puzzle of dark matter and dark energy also have as their salient feature the behaviour of matter at large length and time scales. Studying cooperation in societies essentially involves studying group dynamics and evolutionary conflict. The key processes that operate in the existence of all these problems seem to almost intuitively involve the opposite of reduction; they all result from the agglomeration of molecules, matter, cells, bodies and human beings across a hierarchy of unique levels. In addition, and this is key, they involve the manifestation of unique principles emerging at every level that cannot be merely reduced to those at the underlying level. […]

[…] While emergence had been implicitly appreciated by scientists for a long time, its modern salvo was undoubtedly a 1972 paper in Science by the Nobel Prize winning physicist Philip Anderson (link) titled “More is Different” (PDF), a title that has turned into a kind of clarion call for emergence enthusiasts. In his paper Anderson (who incidentally first came up with the so-called Higgs mechanism) argued that emergence was nothing exotic; for instance, a lump of salt has properties very different from those of its highly reactive components sodium and chlorine. A lump of gold evidences properties like color that don’t exist at the level of individual atoms. Anderson also appealed to the process of broken symmetry, invoked in all kinds of fundamental events – including the existence of the Higgs boson – as being instrumental for emergence. Since then, emergent phenomena have been invoked in hundreds of diverse cases, ranging from the construction of termite hills to the flight of birds. The development of chaos theory beginning in the 60s further illustrated how very simple systems could give rise to very complicated and counter-intuitive patterns and behaviour that are not obvious from the identities of the individual components. […]

[…] Many scientists and philosophers have contributed to considered critiques of reductionism and an appreciation of emergence since Anderson wrote his paper. (…) These thinkers make the point that not only does reductionism fail in practice (because of the sheer complexity of the systems it purports to explain), but it also fails in principle on a deeper level. […]

[…] An even more forceful proponent of this contingency-based critique of reductionism is the complexity theorist Stuart Kauffman who has laid out his thoughts in two books. Just like Anderson, Kauffman does not deny the great value of reductionism in illuminating our world, but he also points out the factors that greatly limit its application. One of his favourite examples is the role of contingency in evolution and the object of his attention is the mammalian heart. Kauffman makes the case that no amount of reductionist analysis could explain tell you that the main function of the heart is to pump blood. Even in the unlikely case that you could predict the structure of hearts and the bodies that house them starting from the Higgs boson, such a deductive process could never tell you that of all the possible functions of the heart, the most important one is to pump blood. This is because the blood-pumping action of the heart is as much a result of historical contingency and the countless chance events that led to the evolution of the biosphere as it is of its bottom-up construction from atoms, molecules, cells and tissues. […]

[…] Reductionism then falls woefully short when trying to explain two things; origins and purpose. And one can see that if it has problems even when dealing with left-handed amino acids and human hearts, it would be in much more dire straits when attempting to account for say kin selection or geopolitical conflict. The fact is that each of these phenomena are better explained by fundamental principles operating at their own levels. […]

[…] Every time the end of science has been announced, science itself proved that claims of its demise were vastly exaggerated. Firstly, reductionism will always be alive and kicking since the general approach of studying anything by breaking it down into its constituents will continue to be enormously fruitful. But more importantly, it’s not so much the end of reductionism as the beginning of a more general paradigm that combines reductionism with new ways of thinking. The limitations of reductionism should be seen as a cause not for despair but for celebration since it means that we are now entering new, uncharted territory. […]

Complex adaptive systems (CAS), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other CAS. Despite a wealth of data and descriptions concerning different CAS, there remain many unanswered questions about “steering” these systems. In Signals and Boundaries, John Holland (Wikipedia entry) argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering CAS through the mechanisms that generate their signal/boundary hierarchies. Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes.

in, Introduction to John H. Holland, “Signals and Boundaries – Building blocks for Complex Adaptive Systems“, Cambridge, Mass. : ©MIT Press, 2012.

I would like to thank flocks, herds, and schools for existing: nature is the ultimate source of inspiration for computer graphics and animation.” in Craig Reynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model“, (paper link) published in Computer Graphics, 21(4), July 1987, pp. 25-34. (ACM SIGGRAPH ’87 Conference Proceedings, Anaheim, California, July 1987.)

There is an entire genealogy to be written from the point of view of the challenge posed by insect coordination, by “swarm intelligence.” Again and again, poetic, philosophical, and biological studies ask the same question: how does this “intelligent,” global organization emerge from a myriad of local, “dumb” interactions?” — Alex Galloway and Eugene Thacker, The Exploit.

[…] The interest in swarms was intimately connected to the research on emergence and “superorganisms” that arose during the early years of the twentieth century, especially in the 1920s. Even though the author of the notion of superorganisms was the now somewhat discredited writer Herbert Spencer,63 who introduced it in 1898, the idea was fed into contemporary discourse surrounding swarms and emergence through myrmecologist William Morton Wheeler. In 1911 Wheeler had published his classic article “The Ant Colony as an Organism” (in Journal of Morphology), and similar interests continued to be expressed in his subsequent writings. His ideas became well known in the 1990s in discussions concerning artificial life and holistic swarm-like organization. For writers such as Kevin Kelly, mentioned earlier in this chapter, Wheeler’s ideas regarding superorganisms stood as the inspiration for the hype surrounding emergent behavior.64 Yet the actual context of his paper was a lecture given at the Marine Biological Laboratory at Woods Hole in 1910.65 As Charlotte Sleigh points out, Wheeler saw himself as continuing the work of holistic philosophers, and later, in the 1910s and 1920s, found affinities with Bergson’s philosophy of temporality as well.66 In 1926, when emergence had already been discussed in terms of, for example, emergent evolution, evolutionary naturalism, creative synthesis, organicism, and emergent vitalism, Wheeler noted that this phenomenon seemed to challenge the basic dualisms of determinism versus freedom, mechanism versus vitalism, and the many versus the one.67 An animal phenomenon thus presented a crisis for the fundamental philosophical concepts that did not seem to apply to such a transversal mode of organization, or agencement to use the term that Wheeler coined. It was a challenge to philosophy and simultaneously to the physical, chemical, psychological, and social sciences, a phenomenon that seemed to cut through these seemingly disconnected spheres of reality.

In addition to Wheeler, one of the key writers on emergence – again also for Kelly in his Out of Control 68 – was C. Lloyd Morgan, whose Emergent Evolution (1927) proposed to see evolution in terms of emergent “relatedness”. Drawing on Bergson and Whitehead, Morgan rejected a mechanistic dissecting view that the interactions of entities “whether physical or mental” always resulted only in “mixings” that could be seen beforehand. Instead he proposed that the continuity of the mechanistic relations were supplemented with sudden changes at times. At times reminiscent of Lucretius’s view that there is a basic force, clinamen, that is the active differentiating principle of the world, Morgan focused on how qualitative changes in direction could affect the compositions and aggregates. He was interested in the question of the new and how novelty is possible. In his curious modernization of Spinoza, Morgan argued for the primacy of relations – or “relatedness,” to be accurate.69 Instead of speaking of agencies or activities, which implied a self-enclosed view of interactions, in Emergent Evolution Morgan propagated in a way an ethological view of the world. Entities and organisms are characterized by relatedness, the tendency to relate to their environment and, for example, other organisms. So actually, what emerge are relations:

If it be asked: What is it that you claim to be emergent? the brief reply is: Some new kind of relation. Revert to the atom, the molecule, the thing (e.g. a crystal), the organism, the person. At each ascending step there is a new entity in virtue of some new kind of relation, or set of relations, within it, or, as I phrase it, intrinsic to it. Each exhibits also new ways of acting on, and reacting to, other entities. There are new kinds of extrinsic relatedness“.70

The evolutionary levels of mind, life, and matter are in this scheme intimately related, with the lower levels continuously affording the emergence of so-called higher functions, like those of humans. Different levels of relatedness might not have any understanding of the relations that define other levels of existence, but still these other levels with their relations affect the other levels. Morgan tried, nonetheless, to steer clear of the idealistic notions of humanism that promoted the human mind as representing a superior stage in emergence. His stance was much closer to a certain monism in which mind and matter are continuously in some kind of intimate correspondence whereby even the simplest expressions of life participate in a wider field of relatedness. In Emergent Evolution Morgan described relations as completely concrete. He emphasized that the issue is not only about relations in terms but as much about terms in relation, with concrete situations, or events, stemming from their relations.71 In a way, other views on emergence put similar emphasis on the priority of relations, expressing a kind of radical empiricism in the vein of William James. Drawing on E. G. Spaulding’s 1918 study The New Rationalism, Wheeler noted the unpredictable potentials in connectionism: a connected whole is more than (or at least nor reducible to) its constituent parts, implying the impossibility to find causal determination of aggregates. Whereas existing sciences might be able to recognize and track down certain relationships that they have normalized or standardized, the relations might still produce properties that are beyond those of the initial conditions – and thus also demand a vector of analysis that parts from existing theories – dealing with properties that open up only in relation to themselves (as a “law unto themselves”). 72 Instead, a more complicated mode of development was at hand, in which aggregates, or agencements, simultaneously involved various levels of reality. This also implied that aggregates, emergent orders, have no one direction but are constituted of relations that extend in various directions:

We must also remember that most authors artificially isolate the emergent whole and fail to emphasize the fact that its parts have important relations not only with one another but also with the environment and that these external relations may contribute effectively towards producing both the whole and its novelty“.73 […]

in (passage from), Jussi Parikka, “Insect Media: An Archaeology of Animals and Technology“, Chapter II – Genesis of Form: Insect Architecture and Swarms, (section) Emergence and Relatedness: A Radical Empiricism – take one, pp. 51-53, University of Minnesota Press, Minneapolis, 2011.

I saw them hurrying from either side, and each shade kissed another, without pausing;  Each by the briefest society satisfied. (Ants in their dark ranks, meet exactly so, rubbing each other’s noses, to ask perhaps; What luck they’ve had, or which way they should go.)” — Dante, Purgatorio, Canto XXVI.

Video documentary: A 15-minute program produced from February 1949 to April 1952, Kieran’s Kaleidoscope presented its writer and host in his well-acquainted role as the learned and witty guide to the complexities of human knowledge (Production Company: Almanac Films). This is probably the most genuinely entertaining of all the John Kieran‘s Kaleidoscope films. On Ant City (1949) [Internet Archive] produced by Paul F. Moss, the poor ants are anthropomorphized to the nth degree; we even hear the Wedding March when the “queen” and her drone fly away from the nest. Kieran‘s patter has never been more meandering; he sounds like a befuddled uncle narrating home movies. Clumsy but enjoyable.

ECCS11 Spatio-Temporal Dynamics on Co-Evolved Stigmergy Vitorino Ramos David M.S. Rodrigues Jorge Louçã

Ever tried to solve a problem where its own problem statement is changing constantly? Have a look on our approach:

Vitorino Ramos, David M.S. Rodrigues, Jorge LouçãSpatio-Temporal Dynamics on Co-Evolved Stigmergy“, in European Conference on Complex Systems, ECCS’11, Vienna, Austria, Sept. 12-16 2011.

Abstract: Research over hard NP-complete Combinatorial Optimization Problems (COP’s) has been focused in recent years, on several robust bio-inspired meta-heuristics, like those involving Evolutionary Computation (EC) algorithmic paradigms. One particularly successful well-know meta-heuristic approach is based on Swarm Intelligence (SI), i.e., the self-organized stigmergic-based property of a complex system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. This line of research recognized as Ant Colony Optimization (ACO), uses a set of stochastic cooperating ant-like agents to find good solutions, using self-organized stigmergy as an indirect form of communication mediated by artificial pheromone, whereas agents deposit pheromone-signs on the edges of the problem-related graph complex network, encompassing a family of successful algorithmic variations such as: Ant Systems (AS), Ant Colony Systems (ACS), Max-Min Ant Systems (MaxMin AS) and Ant-Q.

Albeit being extremely successful these algorithms mostly rely on positive feedback’s, causing excessive algorithmic exploitation over the entire combinatorial search space. This is particularly evident over well known benchmarks as the symmetrical Traveling Salesman Problem (TSP). Being these systems comprised of a large number of frequently similar components or events, the principal challenge is to understand how the components interact to produce a complex pattern feasible solution (in our case study, an optimal robust solution for hard NP-complete dynamic TSP-like combinatorial problems). A suitable approach is to first understand the role of two basic modes of interaction among the components of Self-Organizing (SO) Swarm-Intelligent-like systems: positive and negative feedback. While positive feedback promotes a snowballing auto-catalytic effect (e.g. trail pheromone upgrading over the network; exploitation of the search space), taking an initial change in a system and reinforcing that change in the same direction as the initial deviation (self-enhancement and amplification) allowing the entire colony to exploit some past and present solutions (environmental dynamic memory), negative feedback such as pheromone evaporation ensure that the overall learning system does not stables or freezes itself on a particular configuration (innovation; search space exploration). Although this kind of (global) delayed negative feedback is important (evaporation), for the many reasons given above, there is however strong assumptions that other negative feedbacks are present in nature, which could also play a role over increased convergence, namely implicit-like negative feedbacks. As in the case for positive feedbacks, there is no reason not to explore increasingly distributed and adaptive algorithmic variations where negative feedback is also imposed implicitly (not only explicitly) over each network edge, while the entire colony seeks for better answers in due time.

In order to overcome this hard search space exploitation-exploration compromise, our present algorithmic approach follows the route of very recent biological findings showing that forager ants lay attractive trail pheromones to guide nest mates to food, but where, the effectiveness of foraging networks were improved if pheromones could also be used to repel foragers from unrewarding routes. Increasing empirical evidences for such a negative trail pheromone exists, deployed by Pharaoh’s ants (Monomorium pharaonis) as a ‘no entry‘ signal to mark unrewarding foraging paths. The new algorithm comprises a second order approach to Swarm Intelligence, as pheromone-based no entry-signals cues, were introduced, co-evolving with the standard pheromone distributions (collective cognitive maps) in the aforementioned known algorithms.

To exhaustively test his adaptive response and robustness, we have recurred to different dynamic optimization problems. Medium-size and large-sized dynamic TSP problems were created. Settings and parameters such as, environmental upgrade frequencies, landscape changing or network topological speed severity, and type of dynamic were tested. Results prove that the present co-evolved two-type pheromone swarm intelligence algorithm is able to quickly track increasing swift changes on the dynamic TSP complex network, compared to standard algorithms.

Keywords: Self-Organization, Stigmergy, Co-Evolution, Swarm Intelligence, Dynamic Optimization, Foraging, Cooperative Learning, Combinatorial Optimization problems, Dynamical Symmetrical Traveling Salesman Problems (TSP).


Fig. – Recovery times over several dynamical stress tests at the fl1577 TSP problem (1577 node graph) – 460 iter max – Swift changes at every 150 iterations (20% = 314 nodes, 40% = 630 nodes, 60% = 946 nodes, 80% = 1260 nodes, 100% = 1576 nodes). [click to enlarge]

ECCS11 From Standard to Second Order Swarm Intelligence Phase-Space Maps David Rodrigues Jorge Louçã Vitorino Ramos

David M.S. Rodrigues, Jorge Louçã, Vitorino Ramos, “From Standard to Second Order Swarm Intelligence Phase-space maps“, in European Conference on Complex Systems, ECCS’11, Vienna, Austria, Sept. 12-16 2011.

Abstract: Standard Stigmergic approaches to Swarm Intelligence encompasses the use of a set of stochastic cooperating ant-like agents to find optimal solutions, using self-organized Stigmergy as an indirect form of communication mediated by a singular artificial pheromone. Agents deposit pheromone-signs on the edges of the problem-related graph to give rise to a family of successful algorithmic approaches entitled Ant Systems (AS), Ant Colony Systems (ACS), among others. These mainly rely on positive feedback’s, to search for an optimal solution in a large combinatorial space. The present work shows how, using two different sets of pheromones, a second-order co-evolved compromise between positive and negative feedback’s achieves better results than single positive feedback systems. This follows the route of very recent biological findings showing that forager ants, while laying attractive trail pheromones to guide nest mates to food, also gained foraging effectiveness by the use of pheromones that repelled foragers from unrewarding routes. The algorithm presented here takes inspiration precisely from this biological observation.

The new algorithm was exhaustively tested on a series of well-known benchmarks over hard NP-complete Combinatorial Optimization Problems (COP’s), running on symmetrical Traveling Salesman Problems (TSP). Different network topologies and stress tests were conducted over low-size TSP’s (eil51.tsp; eil78.tsp; kroA100.tsp), medium-size (d198.tsp; lin318.tsp; pcb442.tsp; att532.tsp; rat783.tsp) as well as large sized ones (fl1577.tsp; d2103.tsp) [numbers here referring to the number of nodes in the network]. We show that the new co-evolved stigmergic algorithm compared favorably against the benchmark. The algorithm was able to equal or majorly improve every instance of those standard algorithms, not only in the realm of the Swarm Intelligent AS, ACS approach, as in other computational paradigms like Genetic Algorithms (GA), Evolutionary Programming (EP), as well as SOM (Self-Organizing Maps) and SA (Simulated Annealing). In order to deeply understand how a second co-evolved pheromone was useful to track the collective system into such results, a refined phase-space map was produced mapping the pheromones ratio between a pure Ant Colony System (where no negative feedback besides pheromone evaporation is present) and the present second-order approach. The evaporation rate between different pheromones was also studied and its influence in the outcomes of the algorithm is shown. A final discussion on the phase-map is included. This work has implications in the way large combinatorial problems are addressed as the double feedback mechanism shows improvements over the single-positive feedback mechanisms in terms of convergence speed and on major results.

Keywords: Stigmergy, Co-Evolution, Self-Organization, Swarm Intelligence, Foraging, Cooperative Learning, Combinatorial Optimization problems, Symmetrical Traveling Salesman Problems (TSP), phase-space.

Fig. – Comparing convergence results between Standard algorithms vs. Second Order Swarm Intelligence, over TSP fl1577 (click to enlarge).

[…] Spontaneous orders should be paired with the contrasting ideal type of an instrumental organization, characterized by having a specifiable goal, unequal status ranked on the basis of service to that goal and ease of replacing, and openness to cooperative endeavors justified by their utility in serving that goal. Once this distinction is understood, it is possible to analyze symbiotic and confictual relations between spontaneous orders and the instrumental organizations within them. This approach can be used in more empirical studies of spontaneous orders and the organizations within them, such as corporations and markets or political parties and democracies or research organizations and science. […]; Weber‘s concept of spontaneous order as described by Reinhard Bendix, “Max Weber: An Intellectual Portrait“, 1959.

[…] It is an old idea that society is in a number of respects similar to an organism, a living system with its cells, metabolic circuits and systems. As an example, the army functions like an immune system, protecting the organism from invaders, while the government functions like the brain, steering the whole and making decisions. In this metaphor, different organizations or institutions play the role of organs, each fulfilling its particular function in keeping the system alive, an idea that can be traced back at least as far as Aristotle, being a major inspiration for the founding fathers of sociology, such as Comte, Durkheim and especially Spencer […], in Vitorino Ramos, On the Implicit and on the Artificial – Morphogenesis and Emergent Aesthetics in Autonomous Collective Systems, in ARCHITOPIA Book, Art, Architecture and Science, INSTITUT D’ART CONTEMPORAIN, J.L. Maubant et al. (Eds.), pp. 25-57, Chapter 2, ISBN 2905985631 – EAN 9782905985637, France, Feb. 2002.

With an eye for detail and an easy style, Peter Miller explains why swarm intelligence has scientists buzzing.” — Steven Strogatz, author of Sync, and Professor of Mathematics, Cornell University.

From the introduction of, Peter Miller, “Smart Swarms – How Understanding Flocks, Schools and Colonies Can Make Us Better at Communicating, Decision Making and Getting Things Done“. (…) The modern world may be obsessed with speed and productivity, but twenty-first century humans actually have much to learn from the ancient instincts of swarms. A fascinating new take on the concept of collective intelligence and its colourful manifestations in some of our most complex problems, Smart Swarm introduces a compelling new understanding of the real experts on solving our own complex problems relating to such topics as business, politics, and technology. Based on extensive globe-trotting research, this lively tour from National Geographic reporter Peter Miller introduces thriving throngs of ant colonies, which have inspired computer programs for streamlining factory processes, telephone networks, and truck routes; termites, used in recent studies for climate-control solutions; schools of fish, on which the U.S. military modelled a team of robots; and many other examples of the wisdom to be gleaned about the behaviour of crowds-among critters and corporations alike. In the tradition of James Surowiecki‘s The Wisdom of Crowds and the innovative works of Malcolm Gladwell, Smart Swarm is an entertaining yet enlightening look at small-scale phenomena with big implications for us all. (…)

(…) What do ants, bees, and birds know that we don’t? How can that give us an advantage? Consider: • Southwest Airlines used virtual ants to determine the best way to board a plane. • The CIA was inspired by swarm behavior to invent a more effective spy network. • Filmmakers studied flocks of birds as models for armies of Orcs in Lord of the Rings battle scenes. • Defense agencies sponsored teams of robots that can sense radioactivity, heat, or a chemical device as easily as a school of fish can locate food. Find out how “smart swarms” can teach us how to make better choices, create stronger networks, and organize our businesses more effectively than we ever thought possible. (…)

Book – Carlo and Luigi Usai, “Stigmergy – The ultimate fantasy tale“, UniBook, Italy, 2011.

(in Italian from UniBook) […] Un libro destinato a lasciare un segno nelle Saghe Fantasy: il piccolo Curado si trova, suo malgrado, immerso in una serie di avventurose vicende attraverso il regno della Magia di un mondo Fantasy che richiama i migliori dei racconti della serie. Questo è il primo di una serie di volumi. Dal genio degli scrittori Fantasy Carlo Usai e Luigi Usai. Stigmergy è il primo episodio di una saga che è destinata a non lasciare indifferenti gli appassionati del genere. Stigmergy è stato già tradotto in lingua inglese, francese, spagnola e portoghese. Sono in corso le traduzioni in lingua cinese mandarino, arabo, tedesco, albanese, persiano e rumeno.
Luigi Usai è nato a Cagliari e vive a Verona, dove attualmente sta conseguendo la laurea Magistralis in Scienze Filosofiche. Ha già pubblicato “Riflessioni sul metodo cartesiano e la pratica musicale” col Gruppo Albatros. Stigmergy è il suo secondo lavoro. Carlo Usai è nato a Cagliari e vive a Verona, dove attualmente, dopo un lungo periodo di studi sul mondo Fantasy, decide di dare il suo contributo a questo filone narrativo. […]

[…] Dumb parts, properly connected into a swarm, yield smart results. […] ~ Kevin Kelly. / […] “Now make a four!” the voice booms. Within moments a “4” emerges. “Three.” And in a blink a “3” appears. Then in rapid succession, “Two… One…Zero.” The emergent thing is on a roll. […], Kevin Kelly, Out of Control, 1994.

video -‘Swarm Showreel’ by SwarmWorks Ltd. December 2009 (EVENTS ZUM SCHWÄRMEN – Von Entertainment bis Business).

[…] In a darkened Las Vegas conference room, a cheering audience waves cardboard wands in the air. Each wand is red on one side, green on the other. Far in back of the huge auditorium, a camera scans the frantic attendees. The video camera links the color spots of the wands to a nest of computers set up by graphics wizard Loren Carpenter. Carpenter’s custom software locates each red and each green wand in the auditorium. Tonight there are just shy of 5,000 wandwavers. The computer displays the precise location of each wand (and its color) onto an immense, detailed video map of the auditorium hung on the front stage, which all can see. More importantly, the computer counts the total red or green wands and uses that value to control software. As the audience wave the wands, the display screen shows a sea of lights dancing crazily in the dark, like a candlelight parade gone punk. The viewers see themselves on the map; they are either a red or green pixel. By flipping their own wands, they can change the color of their projected pixels instantly.
Loren Carpenter boots up the ancient video game of Pong onto the immense screen. Pong was the first commercial video game to reach pop consciousness. It’s a minimalist arrangement: a white dot bounces inside a square; two movable rectangles on each side act as virtual paddles. In short, electronic ping-pong. In this version, displaying the red side of your wand moves the paddle up. Green moves it down. More precisely, the Pong paddle moves as the average number of red wands in the auditorium increases or decreases. Your wand is just one vote.
Carpenter doesn’t need to explain very much. Every attendee at this 1991 conference of computer graphic experts was probably once hooked on Pong. His amplified voice booms in the hall, “Okay guys. Folks on the left side of the auditorium control the left paddle. Folks on the right side control the right paddle. If you think you are on the left, then you really are. Okay? Go!”
The audience roars in delight. Without a moment’s hesitation, 5,000 people are playing a reasonably good game of Pong. Each move of the paddle is the average of several thousand players’ intentions. The sensation is unnerving. The paddle usually does what you intend, but not always. When it doesn’t, you find yourself spending as much attention trying to anticipate the paddle as the incoming ball. One is definitely aware of another intelligence online: it’s this hollering mob.
The group mind plays Pong so well that Carpenter decides to up the ante. Without warning the ball bounces faster. The participants squeal in unison. In a second or two, the mob has adjusted to the quicker pace and is playing better than before. Carpenter speeds up the game further; the mob learns instantly.
“Let’s try something else,” Carpenter suggests. A map of seats in the auditorium appears on the screen. He draws a wide circle in white around the center. “Can you make a green ‘5’ in the circle?” he asks the audience. The audience stares at the rows of red pixels. The game is similar to that of holding a placard up in a stadium to make a picture, but now there are no preset orders, just a virtual mirror. Almost immediately wiggles of green pixels appear and grow haphazardly, as those who think their seat is in the path of the “5” flip their wands to green. A vague figure is materializing. The audience collectively begins to discern a “5” in the noise. Once discerned, the “5” quickly precipitates out into stark clarity. The wand-wavers on the fuzzy edge of the figure decide what side they “should” be on, and the emerging “5” sharpens up. The number assembles itself.
“Now make a four!” the voice booms. Within moments a “4” emerges. “Three.” And in a blink a “3” appears. Then in rapid succession, “Two… One…Zero.” The emergent thing is on a roll.
Loren Carpenter launches an airplane flight simulator on the screen. His instructions are terse: “You guys on the left are controlling roll; you on the right, pitch. If you point the plane at anything interesting, I’ll fire a rocket at it.” The plane is airborne. The pilot is…5,000 novices. For once the auditorium is completely silent. Everyone studies the navigation instruments as the scene outside the windshield sinks in. The plane is headed for a landing in a pink valley among pink hills. The runway looks very tiny. There is something both delicious and ludicrous about the notion of having the passengers of a plane collectively fly it. The brute democratic sense of it all is very appealing. As a passenger you get to vote for everything; not only where the group is headed, but when to trim the flaps.
But group mind seems to be a liability in the decisive moments of touchdown, where there is no room for averages. As the 5,000 conference participants begin to take down their plane for landing, the hush in the hall is ended by abrupt shouts and urgent commands. The auditorium becomes a gigantic cockpit in crisis. “Green, green, green!” one faction shouts. “More red!” a moment later from the crowd. “Red, red! REEEEED !” The plane is pitching to the left in a sickening way. It is obvious that it will miss the landing strip and arrive wing first. Unlike Pong, the flight simulator entails long delays in feedback from lever to effect, from the moment you tap the aileron to the moment it banks. The latent signals confuse the group mind. It is caught in oscillations of overcompensation. The plane is lurching wildly. Yet the mob somehow aborts the landing and pulls the plane up sensibly. They turn the plane around to try again.
How did they turn around? Nobody decided whether to turn left or right, or even to turn at all. Nobody was in charge. But as if of one mind, the plane banks and turns wide. It tries landing again. Again it approaches cockeyed. The mob decides in unison, without lateral communication, like a flock of birds taking off, to pull up once more. On the way up the plane rolls a bit. And then rolls a bit more. At some magical moment, the same strong thought simultaneously infects five thousand minds: “I wonder if we can do a 360?”
Without speaking a word, the collective keeps tilting the plane. There’s no undoing it. As the horizon spins dizzily, 5,000 amateur pilots roll a jet on their first solo flight. It was actually quite graceful. They give themselves a standing ovation. The conferees did what birds do: they flocked. But they flocked self- consciously. They responded to an overview of themselves as they co-formed a “5” or steered the jet. A bird on the fly, however, has no overarching concept of the shape of its flock. “Flockness” emerges from creatures completely oblivious of their collective shape, size, or alignment. A flocking bird is blind to the grace and cohesiveness of a flock in flight.
At dawn, on a weedy Michigan lake, ten thousand mallards fidget. In the soft pink glow of morning, the ducks jabber, shake out their wings, and dunk for breakfast. Ducks are spread everywhere. Suddenly, cued by some imperceptible signal, a thousand birds rise as one thing. They lift themselves into the air in a great thunder. As they take off they pull up a thousand more birds from the surface of the lake with them, as if they were all but part of a reclining giant now rising. The monstrous beast hovers in the air, swerves to the east sun, and then, in a blink, reverses direction, turning itself inside out. A second later, the entire swarm veers west and away, as if steered by a single mind. In the 17th century, an anonymous poet wrote: “…and the thousands of fishes moved as a huge beast, piercing the water. They appeared united, inexorably bound to a common fate. How comes this unity?”
A flock is not a big bird. Writes the science reporter James Gleick, “Nothing in the motion of an individual bird or fish, no matter how fluid, can prepare us for the sight of a skyful of starlings pivoting over a cornfield, or a million minnows snapping into a tight, polarized array….High-speed film [of flocks turning to avoid predators] reveals that the turning motion travels through the flock as a wave, passing from bird to bird in the space of about one-seventieth of a second. That is far less than the bird’s reaction time.” The flock is more than the sum of the birds.
In the film Batman Returns a horde of large black bats swarmed through flooded tunnels into downtown Gotham. The bats were computer generated. A single bat was created and given leeway to automatically flap its wings. The one bat was copied by the dozens until the animators had a mob. Then each bat was instructed to move about on its own on the screen following only a few simple rules encoded into an algorithm: don’t bump into another bat, keep up with your neighbors, and don’t stray too far away. When the algorithmic bats were run, they flocked like real bats.
The flocking rules were discovered by Craig Reynolds, a computer scientist working at Symbolics, a graphics hardware manufacturer. By tuning the various forces in his simple equation a little more cohesion, a little less lag time. Reynolds could shape the flock to behave like living bats, sparrows, or fish. Even the marching mob of penguins in Batman Returns were flocked by Reynolds’s algorithms. Like the bats, the computer-modeled 3-D penguins were cloned en masse and then set loose into the scene aimed in a certain direction. Their crowdlike jostling as they marched down the snowy street simply emerged, out of anyone’s control. So realistic is the flocking of Reynolds’s simple algorithms that biologists have gone back to their hi-speed films and concluded that the flocking behavior of real birds and fish must emerge from a similar set of simple rules. A flock was once thought to be a decisive sign of life, some noble formation only life could achieve. Via Reynolds’s algorithm it is now seen as an adaptive trick suitable for any distributed vivisystem, organic or made. […] in Kevin Kelly, “Out of Control – the New Biology of Machines, Social Systems and the Economic World“, pp. 11-12-13, 1994 (full pdf book)

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

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