Simulação Multiagente de uma Abordagem Evolutiva e Espacial para o Jogo do Ultimato
Resumo
Este trabalho apresenta uma simulação multiagente para uma abordagem evolucionária espacial do Jogo do Ultimato, implementada no software Netlogo. Considera-se uma população heterogênea de agentes com informação incompleta sobre as estratégias de outros agentes. Para a evolução das estratégias dos agentes, com o objetivo de auto regular as trocas proporcionadas pelo jogo, balanceando objetivos individuais e coletivos, é utilizado um algoritmo genético. Apresentam-se os resultados das simulações realizadas, com a comparação dos resultados em dois cenários diferentes.
Referências
Aguiar, M. S. (1998). Análise formal da complexidade de algoritmos genéticos. Master’s thesis, PPGC da UFRGS, Porto Alegre/RS.
Alexander, J. M. (2009). Evolutionary game theory. In Stanford Encyclopedia of Philosophy, Stanford. Stanford University.
Andreoni, J. and Miller, J. (2002). Giving according to garp: An experimental test of the consistency of preferences for altruism. Econometrica, 70(2):737–753.
Camerer, C. F., Ho, T.-H., and Chong, J. K. (2001). Behavioral game theory: Thinking, learning and teaching. JOURNAL OF RISK AND UNCERTAINTY, 19:7–42.
Dimuro, G. P., Costa, A. C. R., Gonçalves, L. V., and Pereira, D. R. (2011). Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies. Journal of the Brazilian Computer Society, 17(3):143–161.
Fehr, E. and Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114(3):817–868.
Fiani, R. (2006). Teoria dos Jogos. CAMPUS.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading, Massachusetts.
Kellermann, G. A. (2008). Aspectos estatísticos e dinâmicos do jogo do ultimato espacial e não espacial. Master’s thesis, PPGC da UFRGS, Porto Alegre/RS.
Laguna, M. and Moscato, P. (1996). Capitulo 3: Algoritmos geneticos. In Diaz, B. A., editor, Las Nuevas Técnicas Heurísticas y las Redes Neuronales, pages 67–103. Ed. Paraninfo, Madrid.
Lieberman, E., Hauert, C., and Nowak, M. A. (2005). Evolutionary dynamics on graphs. Nature, 433(7023):312–316.
Linden, R. (2008). Algoritmos Genéticos. Brasport, Rio de Janeiro/RJ, 2 edition.
Nowak, M. A., Page, K. M., and Sigmund, K. (2000). Fairness versus reason in the ultimatum game. Science, 289(5485):1773–1775.
Oosterbeek, H., Sloof, R., and van de Kuilen, G. (2004). Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental 0401003, EconWPA.
Page, K. M., Nowak, M. A., and Sigmund, K. (2000). The spatial ultimatum game. Proceedings of the Royal Society of London B Biological Sciences, 267(1458):2177–2182.
Piaget, J. (1995). Sociological Studies. Routlege, London.
Szaba, G. and Fáth, G. (2007). Evolutionary games on graphs. Physics Reports, 446:97–216.
von Neumann, J. and Morgenstern, O. (1944). Theory of games and economic behaviour. Princeton UP.
Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684):440–442.
Xianyu, B. (2010). Social preference, incomplete information, and the evolution of ultimatum game in the small world networks: An agent-based approach. Journal of Artificial Societies and Social Simulation, 13:2.
Alexander, J. M. (2009). Evolutionary game theory. In Stanford Encyclopedia of Philosophy, Stanford. Stanford University.
Andreoni, J. and Miller, J. (2002). Giving according to garp: An experimental test of the consistency of preferences for altruism. Econometrica, 70(2):737–753.
Camerer, C. F., Ho, T.-H., and Chong, J. K. (2001). Behavioral game theory: Thinking, learning and teaching. JOURNAL OF RISK AND UNCERTAINTY, 19:7–42.
Dimuro, G. P., Costa, A. C. R., Gonçalves, L. V., and Pereira, D. R. (2011). Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies. Journal of the Brazilian Computer Society, 17(3):143–161.
Fehr, E. and Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114(3):817–868.
Fiani, R. (2006). Teoria dos Jogos. CAMPUS.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading, Massachusetts.
Kellermann, G. A. (2008). Aspectos estatísticos e dinâmicos do jogo do ultimato espacial e não espacial. Master’s thesis, PPGC da UFRGS, Porto Alegre/RS.
Laguna, M. and Moscato, P. (1996). Capitulo 3: Algoritmos geneticos. In Diaz, B. A., editor, Las Nuevas Técnicas Heurísticas y las Redes Neuronales, pages 67–103. Ed. Paraninfo, Madrid.
Lieberman, E., Hauert, C., and Nowak, M. A. (2005). Evolutionary dynamics on graphs. Nature, 433(7023):312–316.
Linden, R. (2008). Algoritmos Genéticos. Brasport, Rio de Janeiro/RJ, 2 edition.
Nowak, M. A., Page, K. M., and Sigmund, K. (2000). Fairness versus reason in the ultimatum game. Science, 289(5485):1773–1775.
Oosterbeek, H., Sloof, R., and van de Kuilen, G. (2004). Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental 0401003, EconWPA.
Page, K. M., Nowak, M. A., and Sigmund, K. (2000). The spatial ultimatum game. Proceedings of the Royal Society of London B Biological Sciences, 267(1458):2177–2182.
Piaget, J. (1995). Sociological Studies. Routlege, London.
Szaba, G. and Fáth, G. (2007). Evolutionary games on graphs. Physics Reports, 446:97–216.
von Neumann, J. and Morgenstern, O. (1944). Theory of games and economic behaviour. Princeton UP.
Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684):440–442.
Xianyu, B. (2010). Social preference, incomplete information, and the evolution of ultimatum game in the small world networks: An agent-based approach. Journal of Artificial Societies and Social Simulation, 13:2.
Publicado
02/05/2012
Como Citar
MACEDO, Luís Felipe K.; RIBEIRO, Murian dos R.; BRIÃO, Stephanie L.; FONSECA, Celso N. da; AGUIAR, Marilton S. de; DIMURO, Graçaliz P..
Simulação Multiagente de uma Abordagem Evolutiva e Espacial para o Jogo do Ultimato. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 6. , 2012, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2012
.
p. 35-46.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2012.33133.
