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集市模型:少数者与多数者博弈演化分析 被引量:1

Fair Model: an Evolutionary Analysis of the Minority and Majority Game
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摘要 为比较主体不同行为策略对社会经济复杂系统演化的影响,基于酒吧模型提出了多数者博弈的集市模型A和少数者-多数者混合博弈的集市模型B。采用多主体建模方法,引入了主体的预期规则、策略选择、学习与适应等微观机制,模型模拟结果展现了人类社会系统所独有的预期的自我实现和/或自我毁灭现象。文章着重讨论了主体预期、行为策略对稳态参与人数均值的影响,发现主体的行为策略是决定系统演化结果的关键因素。 In order to study the influence of agents' different action strategies on the evolution of social economic system, this paper puts forward a fair model A and a fair model B inspired by the E1 Farol Model. Agents prefer to stay in the majority side in fair model A and they play mix-game in fair model B. We build agent-based models and introduce the microscopic mechanisms of expec- tation rules, strategy choice, learning process and adaptive behavior. The results show the emer- gent phenomena 'self fulfillment of expectation' and 'self destroying of expectation', which are unique to the human society. We emphatically discuss the impacts of expectation rules and action strategies on the long-time behavior of the three models as indicated by the convergence of the mean attendance in different parameters setting. We find that action strategy is a crucial factor in determining the system stable state.
出处 《复杂系统与复杂性科学》 EI CSCD 北大核心 2012年第3期82-89,共8页 Complex Systems and Complexity Science
基金 国家自然科学基金(708710132)
关键词 集市模型 多主体建模 复杂系统 少数者博弈 多数者博弈 fair model agent-based modeling complex system, minority game majority game
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参考文献13

  • 1Arthur W B. Inductive reasoning and bounded rationality (the El Farol problem) [J]. American Economic Review (Pa-pers and Proceedings) , 1994, 84(2) : 406.
  • 2Arthur W B. Complexity and the economy [J]. Science, 1999,284(2) : 107 - 109.
  • 3Marsili M,Challet D,Zecchina R. Exact solution of a modified El Faroes bar problem: efficiency and the role of marketimpact [J]. Physica A, 2000,280(3/4):522- 559.
  • 4Challet D, Zhang Y C. Emergence of cooperation and organization in an evolutionary game [J]. Physica A,1997,246(2) :407 -418.
  • 5Zhang Y C. Evolving models of financial markets [J]. Europhys News, 1998,21(29) : 51 - 52.
  • 6Farmer J A. Lo,frontiers of finance: evolution and efficient markets [J]. Proceedings of the National Academy of Sci-ences of the United States of America, 1999,96(18) : 9991 - 9992.
  • 7Arthur W B,Holland J H, LeBaron B, et al. Asset pricing under endogenous expectations in an artificial stock market[M] //Arthur B W, Lane D,Durlauf S N. The Economy as an Evolving Complex System II. Menlo-Park : Addison-Wes-ley,1997,15-44.
  • 8Marsili M. Market mechanism and expectations in minority and majority games [J]. Physica A,2001,299(1/2) : 93 -103.
  • 9Lus H, Aydin C O,Keten S,et al. El Farol revisited [J]. Physica A,2005,346(3/4) : 651 - 656.
  • 10Johnson N F,Jarvis S, Jonson R,et al. Volatility and agent adaptability in a self-organizing market [J]. Physica A,1998, 258(1/2): 230 -236.

同被引文献13

  • 1全宏俊,汪秉宏,罗晓曙.模仿经纪人演化少数者博奕模型中的合作效应[J].中国科学院研究生院学报,2003,20(2):191-195. 被引量:2
  • 2杨城,孙世新,曾繁华.非完备策略的少数者博弈[J].广西师范大学学报(自然科学版),2006,24(4):235-238. 被引量:2
  • 3ARTHUR W B. Inductive reasoning and bounded rationality[J]. Am Econ Rev, 1994, 84(2) : 406-411.
  • 4CHALLET D, ZHANG Yicheng. Emergence of cooperation and organization in an evolutionary game[J]. Physica A, 1997, 246(3-4): 407-418.
  • 5CHALLET D, ZHANG Yicheng. On the minority game:Analytical numerical studies[J]. Physica A, 1998, 256(3): 514-532.
  • 6ZHANG Yicheng. Toward a theory of marginally efficient markets[J']. Physica A, 1999, 269(1): 30-44.
  • 7HOLLAND J H. Emergence.. From chaos to order]-M]. Redwood City, CA.. Addison-Wesley, 1998.
  • 8JOHNSON N F, HUI P M, JONSON R, et al. Self-organized segregation within an evolving population[J]. Physical Review Letters, 1998, 82(16)~ 3360-3363.
  • 9FRANTISEK S. Harms and benefits from social imitation[J]. Physica A, 2001, 299(1-2).. 334-343.
  • 10CHEN Jiale, QUAN Hongjun. Effect of imitation in evolutionary minority game on small-word networks[J]. Physica A, 2009, 388(6): 945-952.

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