Robust Monopoly Pricing: The Case of Regret

We consider a robust version of the classic problem of optimal monopoly pricing with incomplete information. The robust version of the problem is distinct in two aspects: (i) the seller minimizes regret rather than maximizes revenue, and (ii) the seller only knows that the true distribution of the valuations is in a neighborhood of a given model distribution. We characterize the robust pricing policy as the solution to a minimax problem for small and large neighborhoods. In contrast to the classic monopoly policy which is a single deterministic price, the robust policy is always a random pricing policy, or equivalently, a multi-item menu policy. The responsiveness of the robust policy to an increase in risk is determined by the curvature of the static profit function.

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