Integrating Incomplete Information With Imperfect Advice

Abstract

When our own knowledge is limited, we often turn to others for information. However, social learning does not guarantee accurate learning or better decisions: Other people's knowledge can be as limited as our own, and their advice is not always helpful. This study examines how human learners put two "imperfect" heads together to make utility-maximizing decisions. Participants played a card game where they chose to "stay" with a card of known value or "switch" to an unknown card, given an advisor's advice to stay or switch. Participants used advice strategically based on which cards the advisor could see (Experiment 1), how helpful the advisor was (Experiment 2), and what strategy the advisor used to select advice (Experiment 3). Overall, participants benefited even from imperfect advice based on incomplete information. Participants' responses were consistent with a Bayesian model that jointly infers how the advisor selects advice and the value of the advisor's card, compared to an alternative model that weights advice based on the advisor's accuracy. By reasoning about others' minds, human learners can make the best of even noisy, impoverished social information.