Fortune Favors Fools : How Confidence Can Compensate for Competence in Learning

An important strategic challenge facing entrepreneurs and managers is the need to make choices across a set of policy alternatives (e.g., strategies, technologies, product designs), the merits of which are not initially well known. The efficacy of these choices is an increasing function of knowledge — competence that takes the form of accurate beliefs about the relative merits of the alternatives. Yet knowledge has a second, equally important but underexplored, dimension related to confidence in those beliefs. We examine confidence from a behavioral theory perspective that points to its role in learning processes, rather than a psychological perspective. We employ a formal computational model of learning where uncertainty may make any particular experience with an alternative misleading or unrepresentative. While our model is general in its applicability, we apply it to the case of an entrepreneur pursuing a particular market opportunity. Confidence acts to moderate an entrepreneur's willingness to disregard feedback that conflicts with her beliefs. We find that there are conditions (e.g., high uncertainty) under which confidence in beliefs, rather than the accuracy of those beliefs, is the primary driver of the efficacy of entrepreneurial choice. One implication of this observation is that a less competent (less accurate) but more confident entrepreneur may outperform a highly competent entrepreneur lacking confidence in her accurate beliefs