New research explores probabilistic service options and demand management

This paper explores how service providers can manage demand by offering probabilistic service options, which display predicted service levels at the time of booking. The challenge arises because truthful probabilities can depend on the system state and are difficult for customers to verify, creating an incentive for providers to manipulate these probabilities. The authors model this as a sequential decision process, define truthful probabilities recursively, and introduce transparency constraints. They propose an adapted reinforcement learning algorithm to approximate these probabilities and a sampling-based mixed-integer program to optimize information disclosure, with a computational study motivated by out-of-home delivery services. AI

IMPACT This research could inform how AI systems manage user expectations and resource allocation in service-based applications.

RANK_REASON The item is an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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