Abstract

Prediction markets (also known as information markets) are markets established to aggregate knowledge and opinions about the likelihood of future events. This chapter is intended to give an overview of the current research on computational aspects of these markets. We begin with a brief survey of prediction market research, and then give a more detailed description of models and results in three areas: the computational complexity of operating markets for combinatorial events; the design of automated market makers; and the analysis of the computational power and speed of a market as an aggregation tool. We conclude with a discussion of open problems and directions for future research.

Introduction: What Is a Prediction Market?

Consider the following mechanism design problem called the information aggregation problem. Suppose that an individual (“the aggregator”) would like to obtain a prediction about an uncertain variable, say the global average temperature in 2020. A number of individuals (“the informants”) each hold different and nonindependent sets of information bearing on the outcome of the variable. The goal is to design a mechanism that extracts the relevant information from the informants, aggregates the information appropriately, and provides a collective prediction or forecast. The forecast should ideally be equivalent to the omniscient forecast that has direct access to all the information available to all informants.