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arXiv Machine Learning By Daniela Sailer, Robert Klein, Claudius Steinhardt

Honesty is the Best Policy ... or is it? Demand Management for Probabilistic Service Options

Read the original on arXiv Machine Learning →

The paper proposes a framework for service providers to display probabilistic service level information at booking time, aiming to inform customers and redistribute demand. It models the challenge as a sequential decision process, introduces recursive definitions of truthful probabilities, and imposes transparency constraints to limit manipulation. Using reinforcement learning and a sampling‑based mixed‑integer program, the authors approximate truthful probabilities and optimize the communicated information, demonstrating a trade‑off between individual transparency and overall customer utility in out‑of‑home delivery scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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