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Title: Is genetic programming ";human-competitive";? The case of experimental double auction markets
Authors: Shu-Heng, Chen.;Kuo-Chuan, Shih
Contributors: 經濟學系
Keywords: Best strategy;Cognitive psychology;Double auction;Experimental markets;Human subjects;Human-Competitiveness;Learning performance;Optimal solutions;Two stage;Working memories;Autonomous agents;Commerce;Competition;Integer programming;Genetic programming
Date: 2011-09
Issue Date: 2015-10-23 17:33:58 (UTC+8)
Abstract: In this paper, the performance of human subjects is compared with genetic programming in trading. Within a kind of double auction market, we compare the learning performance between human subjects and autonomous agents whose trading behavior is driven by genetic programming (GP). To this end, a learning index based upon the optimal solution to a double auction market problem, characterized as integer programming, is developed, and criteria tailor-made for humans are proposed to evaluate the performance of both human subjects and software agents. It is found that GP robots generally fail to discover the best strategy, which is a two-stage procrastination strategy, but some human subjects are able to do so. An analysis from the point of view of cognitive psychology further shows that the minority who were able to find this best strategy tend to have higher working memory capacities than the majority who failed to do so. Therefore, even though GP can outperform most human subjects, it is not "human-competitive" from a higher standard. © 2011 Springer-Verlag.
Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Data Type: conference
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