Expert Selection in Prediction Markets With Homological Invariants

• Main Authors: Carlos Bousono-Calzon, Harold Molina-Bulla, Jose Joaquin Escudero-Garzas, Francisco J. Herrera-Galvez
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Artificial markets; behavioral classification; Betti numbers; group decision-making; expert selection; insurance sector
• Online Access: https://ieeexplore.ieee.org/document/8386656/

Group decision making is a topic of growing interest in today's complex societies. One of the key technologies in this area is the prediction market, where a group of experts plays a fake stock market with assets that represent the outcomes of an uncertain event. The particular problem we address in this paper is the expert selection in these markets to improve their reliability. To aggregate decisions from a particular group of experts, instead of using prices as is typically done, we define a market deconstruction considering player portfolios. This decision technology makes the behaviors of experts toward their decisions available through their portfolios evolution. Our main contribution is the identification of two Persistent Homological Invariants able to classify experts in groups based on the histories of their portfolios. Interestingly, this translates into the definition of essentially two dominant groups. A simulation of the Prediction Market with artificial agents allow us to interpret these two classes as rational and irrational players, following the Microeconomic jargon. Four experiments with experts in the insurance sector help us to illustrate the relationship between these two player types with the prediction reliability of the market.