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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).

von Neumann

There is thus this completely decisive property of complexity, that there exists a critical size below which the process of synthesis is degenerative, but above which the phenomenon of synthesis, if properly arranged, can become explosive, in other words, where syntheses of automata can proceed in such a manner that each automaton will produce other automata which are more complex and of higher potentialities than itself“. ~ John von Neumann, in his 1949 University of Illinois lectures on the Theory and Organization of Complicated Automata [J. von Neumann, Theory of self-reproducing automata, 1949 Univ. of Illinois Lectures on the Theory and Organization of Complicated Automata, ed. A.W. Burks (University of Illinois Press, Urbana, IL, 1966).].

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]

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. […]

Figure (click to enlarge) – Time dependence of FAO Food Price Index from January 2004 to May 2011. Red dashed vertical lines correspond to beginning dates of “food riots” and protests associated with the major recent unrest in North Africa and the Middle East. The overall death toll is reported in parentheses [26-55]. Blue vertical line indicates the date, December 13, 2010, on which we submitted a report to the U.S. government, warning of the link between food prices, social unrest and political instability [56]. Inset shows FAO Food Price Index from 1990 to 2011. [From arXiv:1108.2455, page 3]

Poverty is the parent of revolution and crime.” ~ Aristotle.

By crossing data on food price, and food price peaks with an ongoing trend of increasing prices, as well as the date of riots around the world, 3 of my colleagues at NECSI – the New England Complex Systems Institute (link), Boston,  found out a specific food price threshold above which protests become likely. By doing so, unveiled a model that accurately explained why the waves of unrest that swept the world in 2008 and 2011 crashed when they did. That was the past. NECSI team however, expects a perilous trend in rising food prices to continue (link). Even before the extreme weather scrambled food prices this year, their 2011 report predicted that the next great breach would occur in August 2013, and that the risk of more worldwide rioting would follow. So, if trends hold, these complex systems model say we’re less than one year and counting from a fireball of global unrest riots.

The abstract and PDF link into their work follows:

[…] Social unrest may reflect a variety of factors such as poverty, unemployment, and social injustice. Despite the many possible contributing factors, the timing of violent protests in North Africa and the Middle East in 2011 as well as earlier riots in 2008 coincides with large peaks in global food prices. We identify a specific food price threshold above which protests become likely. These observations suggest that protests may reflect not only long-standing political failings of governments, but also the sudden desperate straits of vulnerable populations. If food prices remain high, there is likely to be persistent and increasing global social disruption. Underlying the food price peaks we also found an ongoing trend of increasing prices. We extrapolate these trends and identify a crossing point to the domain of high impacts, even without price peaks, in 2012-2013. This implies that avoiding global food crises and associated social unrest requires rapid and concerted action. […] in Marco Lagi, Karla Z. Bertrand and Yaneer Bar-Yam, “The Food Crises and Political Instability in North Africa and the Middle East“, arXiv:1108.2455, August 10, 2011. [PDF link]

Video lecture – In this new RSA Animate, Manuel Lima, senior UX design lead at Microsoft Bing, explores the power of network visualisation to help navigate our complex modern world. Taken from a lecture given by Manuel Lima as part of the RSA’s free public events programme.

Network visualization has experienced a meteoric rise in the last decade, bringing together people from various fields and capturing the interest of individuals across the globe. As the practice continues to shed light on an incredible array of complex issues, it keeps drawing attention back onto itself. Manuel Lima is a Senior UX Design Lead at Microsoft Bing and founder of VisualComplexity.com, and was nominated as ‘one of the 50 most creative and influential minds of 2009’ by Creativity Magazine. He visits the RSA to explore a critical paradigm shift in various areas of knowledge, as we stop relying on hierarchical tree structures and turn instead to networks in order to properly map the inherent complexities of our modern world. The talk will showcase a variety of captivating examples of visualization and also introduce the network topology as a new cultural meme. (from RSA, lecture link).

Figure (clik to enlarge) – Applying P(0)=0.6; r=4; N=100000; for(n=0;n<=N;n++) { P(n+1)=r*P(n)*(1-P(n)); } Robert May Population Dynamics equation [1974-76] (do check on Logistic maps) for several iterations (generations). After 780 iterations, P is attracted to 1 (max. population), and then suddenly, for the next generations the very same population is almost extinguish.

Not only in research, but also in the everyday world of politics and economics, we would all be better off if more people realised that simple non-linear systems do not necessarily possess simple dynamical properties.” ~ Robert M. May, “Simple Mathematical models with very complicated Dynamics”, Nature, Vol. 261, p.459, June 10, 1976.

(…) The fact that the simple and deterministic equation (1) can possess dynamical trajectories which look like some sort of random noise has disturbing practical implications. It means, for example, that apparently erratic fluctuations in the census data for an animal population need not necessarily betoken either the vagaries of an unpredictable environment or sampling errors: they may simply derive from a rigidly deterministic population growth relationship such as equation (1). This point is discussed more fully and carefully elsewhere [1]. Alternatively, it may be observed that in the chaotic regime arbitrarily close initial conditions can lead to trajectories which, after a sufficiently long time, diverge widely. This means that, even if we have a simple model in which all the parameters are determined exactly, long term prediction is nevertheless impossible. In a meteorological context, Lorenz [15] has called this general phenomenon the “butterfly effect“: even if the atmosphere could be described by a deterministic model in which all parameters were known, the fluttering of a butterfly’s wings could alter the initial conditions, and thus (in the chaotic regime) alter the long term prediction. Fluid turbulence provides a classic example where, as a parameter (the Reynolds number) is tuned in a set of deterministic equations (the Navier-Stokes equations), the motion can undergo an abrupt transition from some stable configuration (for example, laminar flow) into an apparently stochastic, chaotic regime. Various models, based on the Navier-Stokes differential equations, have been proposed as mathematical metaphors for this process [15,40,41] . In a recent review of the theory of turbulence, Martin [42] has observed that the one-dimensional difference equation (1) may be useful in this context. Compared with the earlier models [15,40,41] it has the disadvantage of being even more abstractly metaphorical, and the advantage of having a spectrum of dynamical behaviour which is more richly complicated yet more amenable to analytical investigation. A more down-to-earth application is possible in the use of equation (1) to fit data [1,2,3,38,39,43] on biological populations with discrete, non-overlapping generations, as is the case for many temperate zone arthropods. (…) in pp. 13-14, Robert M. May, “Simple Mathematical models with very complicated Dynamics“, Nature, Vol. 261, p.459, June 10, 1976 [PDF link].

Video – Lynn Hoffman (social worker, link) talks about a shift that has been taking place in our world, a shift that simmered in the background for many years and has recently erupted onto the world stage. This shift is akin to a revolution, and often gives a renewed impetus to contemporary revolutionary movements. The shift is related to what Lynn sees as a move from the system metaphor, with its emphasis on symmetry, order and a return to the same, to the rhizome with its more messy and horizontal plane of endless relations.

Gregory Bateson and the Rhizome Century” is an interdisciplinary event inspired by the vision of family therapy pioneer, Lynn Hoffman. The conference is for anyone who: Appreciates the pressing significance of honoring the complexities of our interrelations with one another, with nature, and also with our technologies; Understands that a primary responsibility for our generation is to move beyond the individualism’s and negations so prominent in Western thought, towards a work that generates sustaining and sustainable webs of relationship. [http://www.therhizomecentury.com, Vancouver, Canada, Oct. 2012].

Fig. – (Organizational Complexity trough History) Four forms behind the Organization and Evolution of all societies (David Ronfeldt TIMN). Each form also seems to be triggered by major societal changes in communications and language. Oral speech enabled tribes (T), the written word enabled institutions (I), the printed word fostered regional and global markets (M), and the electric (digital) word is empowering worldwide networks (N). [in David Ronfeldt, “Tribes, Institutions, Markets, Networks: A framework about Societal Evolution“, RAND Corporation, Document Number: P-7967, (1996). PDF link]

[…] Organizational complexity is defined as the amount of differentiation that exists within different elements constituting the organization. This is often operationalized as the number of different professional specializations that exist within the organization. For example, a school would be considered a less complex organization than a hospital, since a hospital requires a large diversity of professional specialties in order to function. Organizational complexity can also be observed via differentiation in structure, authority and locus of control, and attributes of personnel, products, and technologies. Contingency theory states that an organization structures itself and behaves in a particular manner as an attempt to fit with its environment. Thus organizations are more or less complex as a reaction to environmental complexity. An organization’s environment may be complex because it is turbulent, hostile, diverse, technologically complex, or restrictive. An organization may also be complex as a result of the complexity of its underlying technological core. For example, a nuclear power plant is likely to have a more complex organization than a standard power plant because the underlying technology is more difficult to understand and control. There are numerous consequences of environmental and organizational complexity. Organizational members, faced with overwhelming and/or complex decisions, omit, tolerate errors, queue, filter, abstract, use multiple channels, escape, and chunk in order to deal effectively with the complexity. At an organizational level, an organizational will respond to complexity by building barriers around its technical core; by smoothing input and output transactions; by planning and predicting; by segmenting itself and/or becoming decentralized; and by adopting rules.
Complexity science offers a broader view of organizational complexity – it maintains that all organizations are relatively complex, and that such complexity arises that complex behavior is not necessarily the result of complex action on the behalf of a single individual’s effort; rather, complex behavior of the whole can be the result of loosely coupled organizational members behaving in simple ways, acting on local information. Complexity science posits that most organizational behavior is the result of numerous events occurring over extended periods of time, rather than the result of some smaller number of critical incidents. […] in Dooley, K. (2002), “Organizational Complexity,” International Encyclopedia of Business and Management, M. Warner (ed.), London: Thompson Learning, p. 5013-5022. (PDF link)

The Internet has given us a glimpse of the power of networks. We are just beginning to realize how we can use networks as our primary form of living and working. David Ronfeldt has developed the TIMN framework to explain this – Tribal (T); Institutional (I); Markets (M); Networks (N). The TIMN framework shows how we have evolved as a civilization. It has not been a clean progression from one organizing mode to the next but rather each new form built upon and changed the previous mode. He sees the network form not as a modifier of previous forms, but a form in itself that can address issues that the three other forms could not address. This point is very important when it comes to things like implementing social business (a network mode) within corporations (institutional + market modes). Real network models (e.g. wirearchy) are new modes, not modifications of the old ones.

Another key point of this framework is that Tribes exist within Institutions, Markets and Networks. We never lose our affinity for community groups or family, but each mode brings new factors that influence our previous modes. For example, tribalism is alive and well in online social networks. It’s just not the same tribalism of several hundred years ago. Each transition also has its hazards. For instance, while tribal societies may result in nepotism, networked societies can lead to deception. Ronfeldt states that the initial tribal form informs the other modes and can have a profound influence as they evolve:

Balanced combination is apparently imperative: Each form (and its realm) builds on its predecessor(s). In the progression from T through T+I+M+N, the rise of a new form depends on the successes (and failures) achieved through the earlier forms. For a society to progress optimally through the addition of new forms, no single form should be allowed to dominate any other, and none should be suppressed or eliminated. A society’s potential to function well at a given stage, and to evolve to a higher level of complexity, depends on its ability to integrate these inherently contradictory forms into a well-functioning whole. A society can constrain its prospects for evolutionary growth by elevating a single form to primacy — as appears to be a tendency at times in market-mad America. [in David Ronfeldt, “Tribes, Institutions, Markets, Networks: A framework about Societal Evolution“, RAND Corporation, Document Number: P-7967, (1996). PDF link]

Finally, on these areas (far behind the strict topic of organizational topology and complex networks), let me add two books. One his from José Fonseca, a friend researcher I first met in 2001, during a joint interview for the Portuguese Idéias & Negócios Magazine, for his 5th anniversary (old link) embracing innovation in Portugal. His book entitled “Complexity & Innovation in Organizations” (above) was published in December that year, 2001 by Routledge. The other one is more recent and from Ralph Stacey, “Complexity and Organizational Reality: Uncertainty and the Need to Rethink Management After the Collapse of Investment Capitalism” (below), Routledge, 2010. Even if, Ralph as many other past seminal books on this topic. Both, have worked together at the Hertfordshire University.

(…) The Four Mists of Chaos, the North, the East, the West, and the South, went to visit Chaos himself. He treated them all very kindly and when they were thinking of leaving, they consulted among themselves how they might repay his hospitality. Since they had noticed that he had no holes in his body, as they each had (eyes, nose, mouth, ears, etc.), they decided each day to provide him with an opening. At the end of seven days, Kwang-tse tells us, Chaos died. (…)  in Indeterminacy – Ninety Stories by John Cage (Transcript of story number 27), With Music, ca. 26’00” to 27’00”, From John Cage’s [1958] Lecture ‘Indeterminacy’, 26’00” to 27’00”, in Die Reihe No. 5, English edition on p.120.

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 Scoop.it 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” (scoop.it 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.

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.

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).

Picture – The European Conference on Complex Systems (ECCS’11 – link) at one of the main Austrian newspapers Der Standard: “Die ganze Welt als Computersimulation” (link), Klaus Taschwer, Der Standard, 14 September [click to enlarge – photo taken at the conference on Sept. 15, Vienna 2011].

Take Darwin, for example: would Caltech have hired Darwin? Probably not. He had only vague ideas about some of the mechanisms underlying biological Evolution. He had no way of knowing about genetics, and he lived before the discovery of mutations. Nevertheless, he did work out, from the top down, the notion of natural selection and the magnificent idea of the relationship of all living things.” Murray Gell-Mann in “Plectics“, excerpted from The Third Culture: Beyond the Scientific Revolution by John Brockman (Simon & Schuster, 1995).

To be honest, I didn’t enjoy this title, but all of us had a fair share with journalists, now and then by now. After all, 99% of us don’t do computer simulation. We are all after the main principles, and their direct applications.

During 5 days (12-16 Sept.), with around 700 attendees the Vienna 2011 conference evolved around main important themes as Complexity & Networks (XNet), Current Trends in Game Theory, Complexity in Energy Infrastructures, Emergent Properties in Natural and Artificial Complex Systems (EPNACS), Complexity and the Future of Transportation Systems, Econophysics, Cultural and Opinion Dynamics, Dynamics on and of Complex Networks, Frontiers in the Theory of Evolution, and – among many others – Dynamics of Human Interactions.

For those who know me (will definitely understand), I was mainly attending those sessions underlined above, the last one (Frontiers in Evolution) being one of my favorites, among all these ECCS years. All in all, the conference had highly quality works (daily, we had about 3-4 works I definitely think should be followed in the future) and to those, more attention should be deserved (my main critics to the conference organization goes in here). Naturally, the newspaper article also reflects on the FuturICT, being historically one of the major scientific European projects ever done (along, probably, with the Geneva LHC), which teams spread across Europe, including Portugal with a representative team of 7 members present on the conference, led by Jorge Louçã, the former editor and organizer on the previous ECCS’10 last year in Lisbon.

Video – “… they forgot to say: in principle!“. Ricard Solé addressing the topic of a Morphospace for Biological Computation at ECCS’11 (European Conference on Complex Systems), while keeping is good humor on.

Let me draw anyway your attention to 4 outstanding lectures: Peter Schuster (link) on the first day, dissected on the source of Complexity in Evolution, battling among – as he puts it – two paradoxes: (1) Evolution is an enormously complex process, and (2) biological evolution on Earth proceeds from lower towards higher complexity. Earlier on that morning – opening the conference -, Murray Gell-Mann (link) who co-founded the Santa Fe Institute in 1984, gave a wonderful lecture on Generalized Entropies. Besides his age, the 1969 Nobel Prize in physics for his work on the theory of elementary particles, gladly turned his interest in the 1990s to the theory of Complex Adaptive Systems (CAS). Next, Albert-László Barabási (link), tamed Complexity on Controlling Networks. Finally, at the last day, closing the conference in pure gold, Ricard Solé (link) addressed the topic of a Morphospace for Biological Computation, an amazing lecture with a powerful topic to which – nevertheless – I felt he had little time (20 minutes), for such a rich endeavor. However – by no means -, he have lost his good humor during the talk (check my video above). Next year, the conference will be held in Brussels, and by just judging at the poster design, it promises. Go ants, go … !

Picture – The European Conference on Complex Systems (ECCS’12 – link) poster design for next year in Brussels.

Darwin by Peter Greenaway (1993) – Although British director Peter Greenaway is best known for feature films like The Cook, the Thief, His Wife and Her Lover, Prospero’s Books, and The Pillow Book, he has also completed several highly respected projects for television, including this 53-minute exploration (now free) of the life and work of Charles Darwin. Darwin is structured around 18 separate tableaux, each focusing on another chapter in the naturalist’s life, and each consisting of just one long uninterrupted shot. Other than the narrator’s voice-over, there is no dialogue.

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

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