Norms in Conflict: Why AI Advisors Fail to Improve Human Coordination

Please note that this seminar takes place on Tuesday.

Abstract

Cooperation failures in social dilemmas persist because individually rational behavior yields inefficient collective outcomes. Advances in AI raise two possibilities: AI may improve outcomes by advising humans or by acting autonomously. We test both in a repeated threshold public-goods experiment with heterogeneous valuations of the public good. Such threshold games model a broad class of burden-sharing problems (crowdfunding, shared infrastructure, multilateral agreements) in which efficiency requires not only coordination but agreement on a cost-sharing norm. We compare a human-only benchmark to treatments with an AI advisor (OpenAI's GPT-5) and to treatments in which AI agents make allocations directly. AI Only groups outperform human groups, reaching the threshold more often. The mechanism is illuminating: AI agents contribute near-equal amounts largely independent of valuations, whereas humans scale contributions with valuations. In contrast, AI advisors do not improve human-only outcomes. We distinguish two bottlenecks to coordination—an information bottleneck, in which parties lack the calculations needed to condition on others' behavior, and a legitimacy bottleneck, in which parties reject cost-sharing rules that conflict with their fairness norms. AI overcomes the first but not the second.