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[2602.20465] Prior-Agnostic Incentive-Compatible Exploration
Summary
The paper discusses a novel approach to incentive-compatible exploration in bandit settings, addressing the misalignment between principals and agents in dynamic environments with conflicting beliefs.
Why It Matters
This research is significant as it tackles the challenge of exploration in multi-agent systems where agents may have differing prior beliefs. By providing a framework that ensures agents follow recommendations, it enhances the effectiveness of online recommendation systems and other applications in machine learning and game theory.
Key Takeaways
- Exploration in bandit settings can lead to misalignment between principals and agents.
- The study provides weighted swap regret bounds that ensure agents follow forecasts.
- Dynamic environments with conflicting beliefs can still achieve approximate Bayes Nash equilibrium.
- Agents must have uncertainty about their rewards and arrival times for the model to work.
- Concrete algorithms are proposed to guarantee adaptive and weighted regret.
Computer Science > Computer Science and Game Theory arXiv:2602.20465 (cs) [Submitted on 24 Feb 2026] Title:Prior-Agnostic Incentive-Compatible Exploration Authors:Ramya Ramalingam, Osbert Bastani, Aaron Roth View a PDF of the paper titled Prior-Agnostic Incentive-Compatible Exploration, by Ramya Ramalingam and 2 other authors View PDF HTML (experimental) 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...