Individual Evolutionary Learning in the market environment with full or limited information ∗

In this paper we explore how specific aspects of mechanism design affect the efficiency of the market outcome. In particular, we are interested whether the availability of information about actions of other participants improves market efficiency. We consider a simple market of 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 the consequent trading sessions, which are organized either as a call auction or as a continuous double auction with electronic book. Using Individual Evolutionary Learning mechanism agents submit price bids and offers, trying to learn the most profitable strategy by looking at their realized and counterfactual or “forgone” payoffs. In this setting we compare the outcomes of the call auction and the continuous double auction under the open and closed book treatments, focusing both on the informational and allocative efficiency of markets and also on the individual learning outcome.