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Computational and Neurobiological Foundations of Leadership Decisions

Micah G. Edelson, Rafael Polania, Christian C. Ruff, Ernst Fehr, and Todd A. Hare

Leadership decisions, across all levels of society from parent to president, directly affect the well-being of individuals, families, organizations, and entire nations. All of us will face numerous opportunities to lead or instead follow others throughout our lives. However, the internal decision-making processes driving those choices to lead or follow, as well as how and why these decisions may vary across individuals are currently unknown. We developed a computational model to examine these mechanisms. Computational modeling of two independent behavioral datasets revealed that common views holding that basic preferences for control, risk, loss, ambiguity, and social orientation are the drivers of leadership choices received no empirical support. Instead, we identify a new factor in the decision process that is a key determinant of choices to lead or follow. Furthermore, we show how, at the algorithmic level, this decision process can generate leadership seeking or avoidance behavior. By combining computational modeling of behavior with a dynamic brain-network-interaction model we also demonstrated that leadership choices are closely related to specific patterns of information transfer within a network of brain regions that individually encode separate lower-level components of the decision process. In ongoing work, find that how individuals learn about and change their decision-making strategies depends on whether or not they are responsible for others as well as whether or not the feedback was positive or negative. Taken together, these findings reveal the micro-foundations for decisions to lead and sources of individual variability in leadership behavior.