Learning to Be Epistemic Altruists

Suppose an epistemic agent has multiple strategies available to them. Among these, one strategy maximizes their expected accuracy, while another does not maximize their own expected accuracy, but instead maximizes the expected accuracy of the epistemic group they belong to. Call an agent that takes the latter strategy "altruistic." The role of epistemically altruistic agents has been highlighted in many results and phenomena studied in formal social epistemology, such as jury theorems, the wisdom of crowds, information cascades, opinion aggregation, and the exploration-exploitation tradeoff. But how do some agents come to be epistemically altruistic? In this paper, I demonstrate, using a self-assembling model with simple multi-agent reinforcement learning, how a group of agents can learn to adopt cooperative epistemic strategies through basic learning rules. Counterintuitively, I show that competitive incentives at the individual level lead to cooperative choices at the group level.