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

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. 1 Motivation and Introduction How accurately the prediction market can predict, up to the present, is basically an empirical issue. However, empirical studies per se cannot articulate why sometimes the market for some events performed extremely well and sometimes it did not. While there are a number of studies trying to identify the factors contributing to its successes or failures, the explanations supporting the found causal links remain very verbal and informal, and a rigorous mechanism has not been explicitly spelled out. This is partially due to the limited analytical tractability of the prediction markets which operate in practice. In this article, we argue that, the spatial configuration, i.e., the distribution of information over agents, situated in different places, can matter for the prediction accuracy of the prediction markets. However, since the usual analytical model cannot effectively deal with these geographical variables, an agent-based spatial model of prediction markets is proposed to address the geographical significance. To begin with this line of research, our model is tailored to the future events related to political 2 Bin-Tzong Chie and Shu-Heng Chen elections only, normally known as the political futures. In other words, we shall show how geographical factors can be part of the functioning of the prediction accuracy of the political futures markets. The rest of the paper is organized as follows. Section 2 introduces our proposed spatial agent-based prediction markets and the two essential ingredients in the model, namely, toleration capacity and exploration capacity. Section 3 discusses the design of our simulation and shows the simulation results. Section 4 gives the concluding remarks.