The subject of information aggregation has long been a focus of enquiry in economics and finance. In particular, how can we effectively aggregate disparate pieces of information that are spread among many different individuals? The academic study of prediction markets (speculative markets designed to aggregate information for the purpose of forecasting) is, however, relatively recent. Yet the insights to be gained have many potentially valuable applications in terms of economic and social policy. Notably, the effective use of these markets has the potential not only to help forecast future events at a national and international level, but also to assist companies in providing, for example, improved estimates of the potential market size for a new product idea or the launch date of new products and services.
The markets have already been used to forecast uncertain outcomes ranging from influenza to the spread of infectious diseases, to the demand for hospital services, to the box office success of movies, climate change, vote shares and election outcomes, to the probability of meeting project deadlines. The insights gained also have many potentially valuable applications for public policy more generally, whether alone or as a supplement to other mechanisms like surveys, group deliberations and expert opinion. Moreover, they can be applied at a macroeconomic and microeconomic level to yield information that is valuable for government and commercial policy-makers and which can be used for a number of social purposes.
Outputs from previous studies have already been published at the top end of the international journal rankings, including the American Economic Review, the Journal of Economic Perspectives and the Quarterly Journal of Economics. Special Issues of Economica and the Southern Economic Journal (both edited by the proposer) have also been devoted to this theme. The proposer is also editor of the ‘Journal of Prediction Markets’, editor of ‘Prediction Markets: Theory and Applications’ (Routledge, 2012), and author of ‘Prediction Markets, Social Media and Information Efficiency’ (Kyklos, 2016).