Learning Strategies for Outsourcing Problems With asymmetric Information and Uncertain Execution

Alexander Herbst ()
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Alexander Herbst: University of Siegen

Chapter Chapter 63 in Operations Research Proceedings 2022, 2023, pp 531-537 from Springer

Abstract: Abstract In this contribution, we consider an outsourcing problem based on a specific principal–agent relationship with hidden characteristics. Under the assumption that the principal knows the probability distribution on the agent’s discrete type space, a standard solution technique for the resulting contracting problem is stochastic optimization on the set of incentive compatible menus of contracts from which the agent can choose a single contract according to the take-it-or-leave-it principle, respectively. Admittedly, this approach neglects any sort of uncertainties in the post-contract phase which is not realistic in many practical environments like production and logistics. To address this issue, we present a novel and holistic problem formulation that links the contracting phase to an uncertain execution phase in a logistical context containing the possibility of renegotiating contracts as a reaction to environmental changes. Since the resulting problem has the character of a multi-round game, we apply well-known concepts from the trendy AI-area of Deep Reinforcement Learning to exploit clever contracting strategies for the principal. Finally, we evaluate our approach inside a computational study.

Keywords: Agent systems; Artificial intelligence; Transportation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-24907-5_63

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DOI: 10.1007/978-3-031-24907-5_63

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