Co-evolving Trading Strategies to Analyze Bounded Rationality in Double Auction Markets

We investigate double-auction (DA) market behavior under traders with different degrees of rationality (intelligence or cognitive ability). The rationality of decision making is implemented using genetic programming (GP), where each trader evolves a population of strategies to conduct an auction. By assigning the GP traders different population sizes to differentiate their cognitive ability, through a series of simulations, we find that increasing the traders’ intelligence improves the market’s efficiency. However, increasing the number of intelligent traders in the market leads to a decline in the market’s efficiency. By analyzing the individual GP traders’ strategies and their co-evolution dynamics, we provide explanations to these emerging market phenomena. While auction markets are gaining popularity on the Internet, the insights can help market designers devise robust and efficient auction e-markets.