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Learning to Recommend in Unknown Games

NeurIPS 2026 · Multi-agent systems and games › Games and equilibria · approach: Cooperation and social dilemmas · 0 citations (OpenAlex)

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Studies how a moderator who issues correlated-equilibrium-style recommendations can learn agents' unknown normal-form game utilities from best-response or quantal-response action feedback, giving learnability results and an online low-regret cutting-plane algorithm.

Problem

Platforms issuing action recommendations to strategic agents with unknown utilities must learn utilities (or enough of them) and keep recommendations compliant, given only observed follow/deviate actions.

Approach

Treats recommendations as active probes: under quantal-response feedback the sign of incentive-to-deviate reveals utility-difference vectors up to scale, enabling identifiability up to positive affine equivalence with binary search; under best-response feedback the indistinguishable set is characterized via normal fans of polarized utility polytopes; a cutting-plane method with center-of-gravity q

Key results

Surprising findings

Limitations

Experimental setup

Metrics: regret sample complexity

Builds on: correlated equilibrium contextual search quantal response equilibrium

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