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Incentives, Information, and Emergent Collective Accuracy


Scott E. Page, Departments of Political Science and Economics, and Center for the Study of Complex Systems University of Michigan, Ann Arbor, MI, USA. Email:


In this paper, we construct a framework within which we explore how incentives and information structures influence the ability of a collection of individuals to make an accurate aggregate prediction. In our framework, individuals of bounded ability predict outcomes that depend on the values of a set of attributes. Individual construct models consider only a subset of those attributes, and those models depend on their incentives and their information environments. We consider two types of incentive structures: one in which individuals get paid on the basis of accuracy and one based on market like, for example, parimutuel payoffs. We also consider two information environments: one in which individuals learn in isolation and another in which they can copy more successful predictors. We find that market incentives and isolated learning environments produce the most accurate aggregate predictions but that these same incentives and information structures also produce the least accurate individuals. Thus, the incentives and informational structures that produce collective wisdom may hinge on their ability to produce and maintain diversity. Copyright © 2012 John Wiley & Sons, Ltd.