Humans depart from optimal computational models of interactive decision-making during competition under partial information

Abstract

Decision making under uncertainty in multiagent settings is of increasing interest in decision science. The degree to which human agents depart from computationally optimal solutions in socially interactive settings is generally unknown. Such understanding provides insight into how social contexts affect human interaction and the underlying contributions of Theory of Mind. In this paper, we adapt the well-known ‘Tiger Problem’ from artificial-agent research to human participants in solo and interactive settings. Compared to computationally optimal solutions, participants gathered less information before outcome-related decisions when competing than cooperating with others. These departures from optimality were not haphazard but showed evidence of improved performance through learning. Costly errors emerged under conditions of competition, yielding both lower rates of rewarding actions and accuracy in predicting others. Taken together, this work provides a novel approach and insights into studying human social interaction when shared information is partial.

Decision Making in Networks: A Model of Awareness Raising

“…The interest in developing such a mathematical model of awareness is manifold. From a theoretical point of view, it provides a reference for evaluating the impact of personal relations on awareness raising, thus allowing us to analyze it from both the individual and the social perspectives [3]. Moreover, it can be used to investigate the pros and cons of using artificial intelligence in human decision making and identify new directions for integrating a prototypical consciousness into AI algorithms [4,5].…”