Christos A. Ioannou

The theoretical literature thus far (see, for instance, Ostrovsky (2012)) has focused almost exclusively on agents who are unboundedly rational and have precise beliefs about all future events to find that information is aggregated for a class of securities, called separable, which includes the Arrow-Debreu securities.. However, both of these assumptions are unrealistic and highly stylized especially for events that are new or unfamiliar. In the forthcoming paper of Galanis, Ioannou, Kotronis (2022), the authors focus on agents who are still unboundedly rational but have imprecise beliefs about all future events. They show theoretically and confirm in a laboratory experiment that separable securities do not aggregate information when agents have imprecise beliefs. In addition, utilizing the (ambiguity aversion) framework of Gilboa and Schmeidler (1989) for imprecise beliefs, they identify a new class of securities, called strongly separable, that is shown both theoretically and experimentally to aggregate information in environments with ambiguity aversion. The objectives in this project are three-fold. First, to extend the results in the forthcoming paper by collaborating with the industry in order to deploy prediction markets in a field setting. Second, to understand theoretically under which conditions the prediction markets (and financial markets more generally) are an effective tool in aggregating information. Third, to design and conduct experiments in the laboratory to determine whether prediction markets aggregate information when traders are boundedly rational and their beliefs are either precise or imprecise.