Coevolution of Decision‐Making and Social Environments

Abstract

Abstract Social scientists have a longstanding theoretical interest in the relationship between individual behavior and social dynamics. A growing body of work demonstrates that, when human behavior is interdependent—that is, what one person does depends on the past, present, or anticipated future actions of others—there is not a simple or linear relationship between the choices of individuals and their collective consequences. Outside of the academy, policy makers are increasingly aware that well‐intentioned interventions can backfire if they fail to account for how people change their behavior in response to the intervention. This type of problem requires a systematic modeling approach. Our entry provides a brief introduction to a growing body of research that brings together two disparate literatures—studies of decision‐making and studies of the interplay between individuals' decisions and features of the social environment—through dynamic computational modeling. Cognitive scientists characterize human decision‐making under uncertainty using heuristics , rules‐of‐thumb that produce satisfactory choices quickly and with limited information. The heuristics we use and information samples we gather have profound consequences for the choices we make. At the same time, the social context defined by the choices of others feeds back to affect individual decision‐making. In recent years, there has been growing interest in methods such as agent‐based modeling and systems dynamics that can capture the dynamic interplay between individuals' choices and features of the environment. However, historically these approaches have not been grounded in cognitively plausible models of human behavior. We identify areas of high potential for future research, and lay out a preliminary framework to help guide understanding of the decision‐making process and its consequences in different social domains.