Forecasting under Uncertainty: How Network Composition Shapes Future-Oriented Cognition

Abstract

Future migration is central to contemporary politics, but we know little of how citizens and policy-makers perceive and predict migratory trends. I analyze migration forecasting in a representative sample of the population of France, using survey data and administrative records to document differences in the accuracy of forecasting among groups of individuals. The article takes an interdisciplinary approach to future-oriented thinking, conceiving it as a distributed cognitive process, and showing that educational attainment and migratory background shape one’s ability to predict short-term trends. My analysis stresses the importance of accounting for sociodemographic characteristics and social networks in forecasting: I show that social diversity can improve predictions and extend studies based on the Delphi methodology by discussing the relevant expertise to forecast in different realms.