Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming

We analyze the value of costly information in agent-based markets with nine distinct information levels. We use genetic programming where agents optimize how much information to buy and how to process it. We find that most agents first buy high information levels, but in equilibrium buy either complete or no information, with the respective shares depending on the information costs. When information is auctioned, markets are first inefficient, so agents raise their bids to buy the highest information levels, before they learn to bid amounts that they can cover with their trading profits. In equilibrium, markets are not fully efficient, but contain just enough noise to allow informed agents to earn their information costs.