Efficiency of Continuous Double Auctions under Individual Evolutionary Learning with Full or Limited Information

In this paper we explore how specific aspects of market transparency and agents' 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 electronic 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 "foregone" payoffs. We find that learning outcomes heavily depend on information treatments. Under full information about actions of others, agents' 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 with to outcomes with Zero-Intelligent traders.

UNDER-DIVERSIFICATION AND THE ROLE OF BEST REPLY TO PATTERN

Three experiments are presented that compare alternative explanations to the coexistence of risk aversion and under-diversification in investment decisions. The participants were asked to select one of several assets under two feedback conditions. In each case, one asset was a weighted combination of the other assets, allowing for lower volatility. The frequency of choice of the composite asset was highly sensitive to feedback condition. The composite asset was the least popular asset when the feedback included information concerning forgone payoffs, and increased in frequency when the feedback was limited to the obtained payoff. These results support the assertion that under-diversification can be a product of learning from feedback and in particular best reply to pattern.Risk; Diversification; Learning

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

In this paper we explore how specific aspects of market transparency and agents’ 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 “foregone” payoffs. We find that learning outcomes heavily depend on information treatments. Under full information about actions of others, agents’ 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 Zero-Intelligent traders