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Startups are commonly founded by teams rather than single individuals. We develop a computational model that examines the differential performance among alternative decision-making structures in entrepreneurial teams, when teams seek to exploit an opportunity of unknown quality, receive noisy information about the opportunity’s quality both prior to and following entry, and are potentially subject to multiple forms of confidence bias. We find that the optimal decision structure for an entrepreneurial team depends on what is known ex ante about the biases of team members. In particular, decision-making structures in which all team members must agree are advantageous when those team members initially over-estimate the likelihood of the new ventures’ success (i.e., are subject to overestimation bias). Similarly, decision-making structures in which all team members must agree are advantageous when all members have accurate initial beliefs, but update these beliefs too slowly (i.e., are subject to overprecision bias). By contrast, when team members’ estimation and precision biases are not known precisely ex ante but are drawn independently from distributions with mean zero bias, equal partnerships (in which all agents must agree) perform relatively poorly, and it is advantageous to pre-commit to a decision structure in which one agent can buy out the others at the end of the pre-entry learning period.
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