Market structure or agent rationality: How efficiency trades with belief updating?

Abstract

For the Bayesian learner, updating priors based on observation is the path that improves the beliefs and this, in turn, affects the efficiency of prices. Or so was believed until agents with no learning advantage, that is zero-intelligence, produced near-optimal levels of efficiency. We design a market environment characterized by model misspecification in which agents update their initial probabilistic guesses with increasing sophistication in incorporating observations. In this designed environment, the market penalizes divergence from the truth, measured in relative entropy. We show that a non-linear U-shaped relation brings together individual rationality in learning and quality of pricing (i.e. informative efficiency). Efficiency first takes a dip with learning sophistication, then converges toward the full information limit (i.e. Bayesian learning). We argue that the results emerge from the particular interaction of two evolutionary forces operating at different levels: learning (individual level) and selection (market level). JEL Classification: C60, D53, D81, D83, G11, G12