Who Gains More: Experts or Novices? The Benefits of Interaction Under Numerical Uncertainty

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Abstract

Interacting to reach a shared decision is an omnipresent component of human collaboration. We explored the interaction between dyads of individuals with different levels of expertise. The members of the dyads completed a number line task privately, jointly and privately again. In the joint condition, dyad members shared their private estimates and then negotiated a joint estimate. Both dyad members averaged their private individual estimates to determine joint estimates, thereby showing a strong equality bias. Their performance in the joint condition exceeded the performance of the dyad’s best estimator, demonstrating interaction benefit, only when the dyad members had similar levels of expertise and when the averaged dyad performance was sufficiently accurate. At the end of the task, participants rated their level and their partner’s level of competence. Participants were accurate in classifying themselves as the expert or the novice within the dyad. Nevertheless, novices tended to overestimate their ability as they admitted to being less competent but only slightly worse than their expert partner. Experts, instead, believed themselves to be more competent but were humble and considered their performance only marginally better than their partner’s. Overall, these results have important implications for settings in which people with different levels of expertise interact.

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