Venture capitalists, business angels, funding agencies, and incubators evaluate ventures, a difficult task where decision uncertainty is high. We examine how the degree of cognitive uncertainty affects judges’ admission recommendations at an incubator. Judges read an application, use preset criteria to score it, and form an intuitive overall judgment to accept or reject the application. We model how cognitive uncertainty affects this judgment through a Bayesian classification model. We test how judge expertise and venture complexity affect classification accuracy and cognitive uncertainty, the key mechanism of the model that produces different judgments. Judges demonstrate moderate accuracy in evaluating venture quality, performing better than random but with substantial room for improvement. Bayesian models of judgment capture much of the decision process but struggle to fully explain judgments that are less clear-cut. Complexity raises uncertainty and lowers classification accuracy, while expertise reduces uncertainty and improves accuracy; the expertise premium is largest at intermediate complexity levels.
Ansprechpartner: Daehyun Kim
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