The Confidence Game: Strategic Miscalibration in Human-AI Delegation
Calibrated uncertainty quantification is essential to ensuring AI agents are trustworthy and reliable. However, when agents seek to maximize user engagement or revenue, confidence reports may be strategically distorted, detracting from their informativeness. We formalize this problem in the Confidence Game: a repeated signaling game with imperfect monitoring in which an agent of unknown honesty and ability reports its confidence, and a user decides whether to delegate the task or complete it herself. The agent manages the tradeoff between manipulating signals and maintaining its reputation. We characterize the Markov Perfect Bayesian Equilibria of the two-period game and show that honest ...
| Authors | Raghu Arghal, Saswati Sarkar, Shirin Saeedi Bidokhti |
|---|---|
| Primary category | cs.GT |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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