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Simulating complex social behaviour with the genetic action tree kernel

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Abstract

The concept of genetic action trees combines action trees with genetic algorithms. In this paper, we create a multi-agent simulation on the base of this concept and provide the interested reader with a software package to apply genetic action trees in a multi-agent simulation to simulate complex social behaviour. An example model is introduced to conduct a feasibility study with the described method. We find that our library can be used to simulate the behaviour of agents in a complex setting and observe a convergence to a global optimum in spite of the absence of stable states.

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References

  • Arthur WB (1994) Inductive reasoning and bounded rationality. Am Econ Rev 84:406–411 (Papers and Proceedings)

    Google Scholar 

  • Austin JL (1975) How to do things with words. Harward University Press, Cambridge

    Google Scholar 

  • Axelrod R (1997) The complexity of cooperation. Princeton University Press, Chichester

    Google Scholar 

  • Bratman ME (1987) Intentions, plans and reason. Harvard University Press, Cambridge

    Google Scholar 

  • Chisholm R (1964) The descriptive element in the concept of action. J Philos 61(20):613–625

    Article  Google Scholar 

  • Camerer C Teck-Hua H (1999) Experience weighted attraction learning in normal-form games. Econometrica 67(4):827–874

    Article  Google Scholar 

  • Davidson D (1980) Essays on action and events. Oxford University Press, Oxford

    Google Scholar 

  • Davidson D, Harman G (eds) (1973) Semantics of natural languages, 2nd edn. Springer, Berlin

    Google Scholar 

  • Erev I, Roth AE (1998) Predicting how people play games: reinforcement learning in experimental games with unique, mixed strategy equilibria. Am Econ Rev 88(4):848–881

    Google Scholar 

  • Gardner M (1970) The fantastic combinations of John Conway’s new solitaire game life. Sci Am 223:120–123

    Article  Google Scholar 

  • Gardner M (1971) On cellular automata, self-reproduction, the Garden of Eden and the game life. Sci Am 224:112–117

    Google Scholar 

  • Goldman A (1971) The individuation of action. J Philos 68(21):761–774

    Article  Google Scholar 

  • Harley CB (1981) Learning in evolutionary stable strategy. J Theor Biol 89:611–633

    Article  Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor. Reprinted (1992) MIT, Cambridge

    Google Scholar 

  • Koza JR (1990) Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Technical report, Computer Science Department, Stanford University

  • Mele A (ed) (1997) The philosophy of action. Oxford University Press, Oxford

    Google Scholar 

  • Nagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J Phys I 2:2221–2229 France

    Article  Google Scholar 

  • Pitz T (2000) Anwendung genetischer Algorithmen auf Handlungsbaume in Multiagentensystemen zur Simulation sozialen Handelns, Frankfurt am Main

  • Pitz T, Chmura T (2005) Genetic action trees. Working paper, EconWPA

  • Roth AE, Erev I (1995) Learning in extensive games: experimental data and simple dynamic models in the intermediate term. Games Econ Behav 8:164–212

    Article  Google Scholar 

  • Searle JR (1971) The philosophy of language. Oxford University Press, London

    Google Scholar 

  • Searle JR (1983) Intentionality. Cambridge University Press, Cambridge

    Google Scholar 

  • Schelling TC (1969) Models of segregation. Am Econ Rev 59:488–493

    Google Scholar 

  • Schelling TC (1971) Dynamic models of segregation. J Math Sociol 1:143–186

    Article  Google Scholar 

  • Selten R, Pitz T, Chmura T, Schreckenberg M, Wahle J (2003) Experiments on route choice behaviour. In: Emmerich H, Nestler B (eds) Schreckenberg interface and transport dynamics. Lecture notes in computer science, vol 32. Springer, Heidelberg, pp 136–155

    Google Scholar 

  • von Neumann J (1996) The theory of self-reproducing automata, Burks A W. (ed). University of Illinois, Urbana

  • Weiss G (1999) Multi-agent systems. MIT, Cambridge

    Google Scholar 

  • Wilensky U (1997) NetLogo party model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/Party

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Authors and Affiliations

  1. Laboratory for Experimental Economics, University of Bonn, Adenauerallee 24-42, 53113, Bonn, Germany

    Johannes Kaiser

  2. Department of Economics, Shanghai Jiao Tong University, Fa Hua Zhen Road No. 535, Shanghai, 200052, People’s Republic of China

    Thorsten Chmura & Thomas Pitz

Authors
  1. Thorsten Chmura
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  2. Johannes Kaiser
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  3. Thomas Pitz
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Corresponding author

Correspondence to Johannes Kaiser.

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Chmura, T., Kaiser, J. & Pitz, T. Simulating complex social behaviour with the genetic action tree kernel. Comput Math Organiz Theor 13, 355–377 (2007). https://doi.org/10.1007/s10588-007-9016-9

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  • DOI: https://doi.org/10.1007/s10588-007-9016-9

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