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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.
External presentation
† indicates presentations by the coauthor
- Economic Science Association World Meeting† (Los Angeles, 2026)
- 3rd Annual BEE UK Conference† (Exeter, 2026)
- Beijing BEAT Conference (2026)
- Economic Science Association Asia-Pacific Meeting† (Melbourne, 2026)