A Bayesian Theory of Conformity in Collective Decision Making

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Koosha Khalvati, Saghar Mirbagheri, Seongmin A. Park, Jean-Claude Dreher, Rajesh PN Rao


In collective decision making, members of a group need to coordinate their actions in order to achieve a desirable outcome. When there is no direct communication between group members, one should decide based on inferring others' intentions from their actions. The inference of others' intentions is called "theory of mind" and can involve different levels of reasoning, from a single inference on a hidden variable to considering others partially or fully optimal and reasoning about their actions conditioned on one's own actions (levels of “theory of mind”). In this paper, we present a new Bayesian theory of collective decision making based on a simple yet most commonly observed behavior: conformity. We show that such a Bayesian framework allows one to achieve any level of theory of mind in collective decision making. The viability of our framework is demonstrated on two different experiments, a consensus task with 120 subjects and a volunteer's dilemma task with 29 subjects, each with multiple conditions.