Efficiency of continuous double auctions under individual evolutionary learning with full or limited information

Publisher:
Springer
Publication Type:
Journal Article
Citation:
Journal of Evolutionary Economics, 2013, 23 (3), pp. 539 - 573
Issue Date:
2013-01
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In this paper we explore how specific aspects of market transparency and agentsï½ behavior affect the efficiency of the market outcome. In particular, we are interested whether learning behavior with and without information about actions of other participants improves market efficiency. We consider a simple market for a homogeneous good populated by buyers and sellers. The valuations of the buyers and the costs of the sellers are given exogenously. Agents are involved in consecutive trading sessions, which are organized as a continuous double auction with order book. Using Individual Evolutionary Learning agents submit price bids and offers, trying to learn the most profitable strategy by looking at their realized and counterfactual or ï½foregoneï½ payoffs. We find that learning outcomes heavily depend on information treatments. Under full information about actions of others, agentsï½ orders tend to be similar, while under limited information agents tend to submit their valuations/costs. This behavioral outcome results in higher price volatility for the latter treatment. We also find that learning improves allocative efficiency when compared to outcomes with Zero-Intelligent traders.
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