When making a decision under uncertainty, individuals aim to make the best choice. In general, the best solution, defined as the optimal solution, is the solution maximizing expected benefits. However, the quality of a decision can also be assessed based on the accuracy of the decision. While most of the time an accurate decision leads to the optimal decision, different types of successes and errors rewarded asymmetrically induce a divergence between accuracy and optimality. In this situation, individuals may face a trade-off between maximizing accuracy and maximizing expected benefits. Using Signal Detection Theory as a normative benchmark, we highlight this optimality-accuracy trade-off and study its origins using two laboratory experiments on perceptual decision-making. The first experiment confirms the existence of the trade-off with a leading role of accuracy. It also gives evidence on the prevalence of a payoff approach over an utility approach to assess optimality. The second experiment explains the trade-off by the concern of people for being right.


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