Spatial Modeling of Agent-based Prediction Markets: Role of Individuals

Abstract

In this paper, we extend the spatial agent-based prediction market proposed by Yu and Chen at MABS 2011 into a spatial model in which agents choose their community (neighbors) by following Schelling’s proximity model. This extended model generalizes the spatial configuration of the original model and enables us to examine the validity of the Hayek hypothesis when the information distribution is determined by clusters of agents with heterogeneous identities. Specifically, we examine the role of the toleration capacity, the key parameter in the Schelling model, which generates the clusters of agents with different sizes, and the role of exploration capacity which determines how well an agent is informed about his local surroundings. We find that after taking into account market activity and price volatility, both the toleration capacity and exploration capacity have a positive effect on the prediction accuracy and enhance information polling and the information aggregation of markets. The results obtained in this agent-based simulation, therefore, add a qualification to the well-known Hayek hypothesis and point to the significance of individuals in information aggregation.