An algorithmic game theory framework for bilateral bargaining with uncertainty

Authors:
Sofia Ceppi;Nicola Gatti
Affiliations:
DEI, Politecnico di Milano, Milano, Italy;DEI, Politecnico di Milano, Milano, Italy
Venue:
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Year:
2010

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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.