Abstract
Multi-agent social systems (MASSes) are systems of autonomous interdependent agents, each pursuing its own goals and interacting with other agents and environment. The dynamics of the MASS cannot be adequately modeled by the methods borrowed from statistical physics because these methods do not reflect the main feature of social systems, viz., their ability to percept, process, and use external information. This important quality of distributed (swarm) intelligence has to be directly taken into account in a correct theoretical description of social systems. However, discussion of distributed intelligence (DI) in the literature is mostly restricted to distributed tasks, information exchange, and aggregated judgment, i.e., to the “sum” or “average” of independent intellectual activities. This approach ignores the empirically well-known phenomenon of “collective insight” in a group, which is a specific manifestation of MASS DI. In this paper, the state of art in modeling social systems and investigating intelligence per se is briefly characterized and a new modular model of intelligence is proposed. This model makes it possible to reproduce the most important result of intellectual activity, viz., the creation of new information, which is not reflected in the contemporary schemes (e.g., neural networks). In the framework of the modular approach, the correspondence between individual intelligence and MASS DI is discussed and prospective directions for future research are outlined. The efficiency of DI is estimated numerically by computer simulation of a simple system of agents with variable kinematic parameters (ki) that move through a pathway with obstacles. Selection of fast agents with a positive mutation of the parameters provides ca. 20% reduction in the average passing time after 200–300 generations and creates a swarm movement whereby agents follow a leader and cooperatively avoid obstacles.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
In this paper, the terms “individual” and “agent” as applied to an “actor” of the social system are used as synonyms.
REFERENCES
Moussaid, M., Helbing, D., and Theraulaz, G., How simple rules determine pedestrian behavior and crowd disasters, Proc. Natl. Acad. Sci. USA, 2011, vol. 108, no. 17, pp. 6884–6888.
Gubko, M.V. and Novikov, D.A., Teoriya igr v upravlenii organizatsionnymi sistemami (Game Theory in Control of Organizational Systems), Moscow: Sinteg, 2005, 2nd ed.
Galam, S., Sociophysics: A Physicist’s Modeling of Phycho-Polytical Phenomena, Springer, 2012.
Zakharov, A.V., Models of political competition: A review, Econ. Math. Methods, 2009, vol. 45, no. 1, pp. 110–128.
Dorogovtsev, S.N., Lectures on Complex Networks, Clarendon: Oxford, 2010.
Newman, M.E.J., The structure and functions of complex networks, SIAM Rev., 2003, vol. 45, no. 2, pp. 167–225.
Bernovskii, M.M. and Kuzyurin, N.N., Random graphs, models, and generators of scale-free graphs, Tr. Inst. Sistemnogo Program. Ross. Akad. Nauk, 2012, vol. 22, pp. 419–432. doi 10.15514/ISPRAS-2012-22-22
Evin, I.A., Introduction to the theory of complex networks, Comput. Res. Model., 2010, vol. 2, no. 2, pp. 121–141.
Novikov, D.A., Models of strategic behavior, Autom. Remote Control, 2012, vol. 73, no. 1, pp. 1–19.
Kahneman, D., Maps of bounded rationality: Psychology for behavioral economics, Amer. Econ. Rev., 2003, vol. 93, no. 5, pp. 1449–1475.
Gasnikov, A.V., Ed., Vvedenie v matematicheskoe modelirovanie transportnykh potokov (Introduction to Mathematic Modeling of Traffic Flows), Moscow: MTsNMO, 2013.
Adamchuk, A.N. and Esipov, S.E., Collectively fluctuating assets in the presence of arbitrage opportunities and option pricing, Phys. Usp., 1997, vol. 167, no. 12, pp. 1295–1306.
Schelling, T., Dynamic models of segregation, J. Math. Sociol., 1971, vol. 1, no. 2, pp. 143–186.
Castellano, C., Fortunato, S., and Loreto, V., Statistical physics of social dynamics, Rev. Mod. Phys., 2009, vol. 81, no. 2, pp. 591–646.
Slovokhotov, Yu.L., Physics vs. sociophysics, Probl. Upr., 2012, no. 3, pp. 2–34.
Kipyatkov, V.E., Mir obshchestvennykh nasekomykh (World of Social Insects), Moscow: Librokom, 2009, 3rd ed.
Engelbtecht, A.P., Fundamentals of Computational Swarm Intelligence, New York: Wiley, 2005.
Falikman, M.V., Basic approaches in cognitive science. http://www.soc-phys.chem.msu.ru/rus/prev/zas-2017 -02-09/presentation.pdf
Velichkovskii, B.M., Kognitivnaya nauka: osnovy psikhologii poznaniya (Cognitive Science: Foundations of Cognition Psychology), 2 vols., Moscow: Smysl, Academiya, 2006.
Shaib-Draa, B., Moulin, B., Mandiau, P., and Millot, P., Trends in distributed artificial intelligence, Artif. Intell. Rev., 1992, vol. 6, no. 1, pp. 35–66.
Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998, 2nd ed.
Petukhov, V.V., Psikhologiya myshleniya: uchebno-metodicheskoe posobie (Psychology of Thinking: A Textbook), Moscow: Mosk. Gos. Univ., 1987.
Al'tshuller, G.S., Naiti ideyu. Vvedenie v TRIZ – teoriyu resheniya izobretatel’skikh zadach (To Find the Idea. Introduction to TRIZ: A Theory of Solving Inventional Problems), Moscow: Al’pina Pablisherz, 2011, 4th ed.
Chernavskii, D.S., Sinergetika i informatsiya: dinamicheskaya teoriya informatsii (Synergetics and Information: Dynamic Theory of Information), Moscow: Librokom, 2009, 3rd ed.
Anokhin, K.V., Cognitom: Mind as a physical and mathematical structure. http://www.soc-phys.chem. msu.ru/rus/prev/zas-2016-09-27/presentation.pdf Accessed April 2, 2018.
Shumskii, S.A., Modeling of brain activity: State of art and prospects. http://www.soc-phys.chem.msu.ru/rus /prev/zas-2015-03-31/presentation.pdf
Ohlsson, S., Information-processing explanations of insight and related phenomena, Advances in the Psychology of Thinking, Keane, M.T. and Gilhooly, K.J., Eds., New York: Harvester Wheatsheaf, 1992, pp. 1–44.
Fodor, J.A., The Modularity of Mind, MIT Press, 1983.
Kurganskii, A.V., Internal representation in cognitive neuroscience. http://www.soc-phys.chem.msu.ru/rus /prev/zas-2017-02-28/presentation.pdf
Dandurand, F., Shultz, T.R., and Rivest, F., Complex problem solving with reinforcement learning, Proc 6th IEEE Int. Conf. Development and Learning, 2007, pp. 157–162.
Hebb, D.O., The Organization of Behavior: A Neuropsychological Theory, Wiley, 1949.
Mossaid, M., Garnieer, S., Theraulaz, G., and Helbing, D., Collective information processing and pattern formation in swarms, flocks, and crowds, Topics Cogn. Sci., 2009, vol. 1, pp. 469–497.
Becker, J., Brackbill, D., and Centola, D., Proc. Natl. Acad. Sci. USA, 2017, vol. 114, pp. E5070–E5076.
ACKNOWLEDGMENTS
Yu.L. Slovokhotov is grateful to M.V. Falikman (Dr. Sci. (Psychol.), National Research University Higher School of Economics) for extensive references on cognitive research and stimulating discussions.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Translated by Yu. Kornienko
Rights and permissions
About this article
Cite this article
Slovokhotov, Y.L., Neretin, I.S. Toward Constructing a Modular Model of Distributed Intelligence. Program Comput Soft 44, 499–507 (2018). https://doi.org/10.1134/S0361768818060166
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0361768818060166