Truthful and Competitive Double Auctions
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Experimental Market Mechanism Design for Double Auction
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
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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.