On Incentivized Exploration beyond Bayesianism and Full-Information
Dimitar Chakarov, Lee Cohen, Nathan Srebro
cs.GT cs.LG
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.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.