On Incentivized Exploration beyond Bayesianism and Full-Information

2026-07-14

AI Summary

This work addresses the limitations of traditional Bayesian incentive compatibility, which fails when agents possess private information, lack a common prior, or operate in incomplete information environments. The paper proposes a more general incentive exploration framework that dispenses with Bayesian assumptions and requirements of complete information, allowing agents to act according to any undominated strategy. By introducing a novel definition of incentive compatibility that does not rely on a common prior and integrating multi-prior robust optimization with non-Bayesian decision theory, the framework effectively handles action ties. This approach substantially extends the applicability and robustness of incentive-compatible mechanisms under information asymmetry and uncertainty.

Abstract

We extend Incentive Compatible Exploration beyond the Bayesian full-information setting of Kremer et al. [2014]. We consider agents that may possess external information unknown to the principal. We show such settings require new notions of incentivized exploration, as well as going beyond a Bayesian perspective, and we introduce a definition where agents choose any reasonable (undominated) action. Furthermore, our framework provides for a more robust treatment of ties, and extends to settings where agents lack a single common prior and instead only know that reward distributions belong to a collection of potential priors.

Problem

• Incentivized Exploration
• Bayesianism
• Full-Information
• Common Prior
• Undominated Action

Innovation

• Incentivized Exploration
• Beyond Bayesianism
• Undominated Actions
• Robust Tie-breaking
• Multiple Priors