An algorithmic game theory framework for bilateral bargaining with uncertainty

Sofia Ceppi, Nicola Gatti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Bilateral bargaining is the most common economic transaction. Customarily, it is formulated as a non-cooperative game with uncertain-information and infinite actions (offers are real-value). Its automation is a long-standing open problem in artificial intelligence and no algorithmic methodology employable regardless of the kind of uncertainty is provided. In this paper, we provide the first step (with one-sided uncertainty) of an algorithmic game theory framework to solve bargaining with any kind of uncertainty. The idea behind our framework is to reduce, by analytical tools, a bargaining problem to a finite game and then to compute, by algorithmic tools, an equilibrium in this game.
Original languageEnglish
Title of host publicationAAMAS '10 Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems
PublisherACM
Pages1489-1490
Number of pages2
ISBN (Print)978-0-9826571-1-9
Publication statusPublished - 2010

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