Ville Tanskanen (University of Helsinki)*; Chang Rajani (University of Helsinki); Homayun Afrabandpey (Nokia); Aini Putkonen (Aalto University); Aurélien Nioche (Aalto University); Arto Klami (University of Helsinki)PMLR Page
Abstract
Decision-theoretic models explain human behavior in choice problems involving uncertainty, in terms of individual tendencies such as risk aversion. However, many classical models of risk require knowing the distribution of possible outcomes (rewards) for all options, limiting their applicability outside of controlled experiments. We study the task of learning such models in contexts where the modeler does not know the distributions but instead can only observe the choices and their outcomes for a user familiar with the decision problems, for example a skilled player playing a digital game. We propose a framework combining two separate components, one for modeling the unknown decision-making environment and an- other for the risk behavior. By using environment models capable of learning distributions we are able to infer classical models of decision-making under risk from observations of the user’s choices and outcomes alone, and we also demonstrate alternative models for predic- tive purposes. We validate the approach on artificial data and demonstrate a practical use case in modeling risk attitudes of professional esports teams.