期刊名称：Journal of Artificial Societies and Social Simulation
印刷版ISSN：1460-7425
出版年度：2008
卷号：11
期号：1
页码：1-22
出版社：University of Surrey, Department of Sociology
摘要：Human-subject market experiments have established in a wide variety of environments that the Continuous Double Auction (CDA) guarantees the maximum efficiency (100 percent) and the transaction prices converge quickly to the competitive equilibrium price. Since in human-subject experiments we can not control the agents' behaviour, one would like to know if these properties (quick price convergence and high market efficiency) hold for alternative agents' bidding strategies. We go a step farther: we substitute human agents by artificial agents to calibrate the agents' behaviour . In this paper we demonstrate that price convergence and allocative market efficiency in CDA markets depend on the proportion of the bidding strategies (Kaplan, Zero-Intelligence Plus, and GD) that agents have on both market sides. As a result, price convergence may not be achieved. The interesting question to ask is: can convergence be assured if the agents choose their bidding strategies? Since humans are frugal we explore two fast & frugal heuristics (imitation versus take-the-best) to choose one of three bidding strategies in order to answer this question. We find that the take-the-best choice performs much better than the imitation heuristic in the three market environments analyzed. Our experiment can be interpreted as a test to see whether an individual learning outperforms social learning or individual rationality (take-the-best) outperforms ecological rationality (imitation), for a given relevant institution (the CDA) in alternative environments.
关键词：Agent Based Models; Double Auction; Individual and Social Learning; Computational Organization; Bounded Rationality
