Optimal Pricing with Unreliable Signals
This addresses a pricing challenge in AI-assisted decision-making, introducing a new mechanism design paradigm that leverages the other side's knowledge about information reliability, which is incremental but novel in its specific application.
The paper tackles the problem of optimal pricing when a seller has unreliable side information about a buyer's valuation, and the buyer knows the reliability of that information. It characterizes the tradeoff between performance under accurate and hallucinatory signals, showing that keeping the signal private yields better outcomes than public benchmarks, with mechanisms achieving perfect consistency and nontrivial robustness guarantees, such as 1-consistency and 1-robustness under certain prior conditions.
We study a single-buyer pricing problem with unreliable side information, motivated by the increasing use of AI-assisted decision-making and LLM-based predictions. The seller observes a private sample that may be either accurate (coinciding with the buyer's valuation), or hallucinatory (an independent draw from the prior), without knowing which case has realized. The buyer does not observe the realized signal, yet knows whether it is accurate or hallucinatory. This creates a higher-order informational asymmetry: the seller is uncertain about the reliability of his own side information, while the buyer has private information about that reliability. Adopting a consistency-robustness framework, we characterize the exact Pareto frontier of tradeoffs between consistency (performance under an accurate signal) and robustness (performance under a hallucinatory signal). We show that keeping the unreliable signal private generates substantial value, yielding tradeoffs that strictly dominate any public-signal benchmark. We further show that perfect consistency does not preclude meaningful protection against hallucination: for every prior, there exists a mechanism achieving perfect consistency together with a nontrivial robustness guarantee of $\frac{1}{2}$. Moreover, if the prior has an infinite mean or a mean of at most its monopoly price, we provide a mechanism that is simultaneously 1-consistent and 1-robust. Our results illustrate a new mechanism design paradigm: rather than relying only on information directly possessed by the designer, mechanisms can be built to leverage the other side's knowledge about the reliability of the designer's information.