Prior-Agnostic Incentive-Compatible Exploration

2026-02-24

AI Summary

This work addresses the challenge of incentive-compatible exploration in multi-agent online recommendation systems, where the platform seeks to maximize long-term cumulative rewards but individual agents may deviate from recommended actions due to heterogeneous and unknown prior beliefs and a lack of direct incentives. The authors propose a mechanism grounded in a weighted swap regret bound that achieves incentive compatibility solely through regret minimization, even when agents face uncertainty about their arrival times and hold unknown or conflicting priors. By integrating an adaptive weighted regret algorithm with an approximate Bayesian Nash equilibrium analysis, the approach establishes—within a stochastic bandit framework—the first provably incentive-compatible exploration strategy that requires no prior knowledge. This provides a theoretically sound and practically viable algorithmic solution for real-world recommendation systems.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Online Learning & Bandits
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In bandit settings, optimizing long-term regret metrics requires exploration, which corresponds to sometimes taking myopically sub-optimal actions. When a long-lived principal merely recommends actions to be executed by a sequence of different agents (as in an online recommendation platform) this provides an incentive misalignment: exploration is"worth it"for the principal but not for the agents. Prior work studies regret minimization under the constraint of Bayesian Incentive-Compatibility in a static stochastic setting with a fixed and common prior shared amongst the agents and the algorithm designer. We show that (weighted) swap regret bounds on their own suffice to cause agents to faithfully follow forecasts in an approximate Bayes Nash equilibrium, even in dynamic environments in which agents have conflicting prior beliefs and the mechanism designer has no knowledge of any agents beliefs. To obtain these bounds, it is necessary to assume that the agents have some degree of uncertainty not just about the rewards, but about their arrival time -- i.e. their relative position in the sequence of agents served by the algorithm. We instantiate our abstract bounds with concrete algorithms for guaranteeing adaptive and weighted regret in bandit settings.

Problem

• incentive compatibility
• bandit exploration
• prior-agnostic
• dynamic environments
• Bayesian equilibrium

Innovation

• prior-agnostic
• incentive-compatible exploration
• swap regret
• Bayes Nash equilibrium
• dynamic bandit