Negotiation among autonomous computational agents: principles, analysis and challenges
Artificial Intelligence Review
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TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
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ACM Transactions on Sensor Networks (TOSN)
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Negotiations have been extensively studied theoretically throughout the years. A well-known bilateral approach is the ultimatum game, where two agents negotiate on how to split a surplus or a "dollar"-the proposer makes an offer and responder can choose to accept or reject. In this paper a natural extension of the ultimatum game is presented, in which both agents can negotiate with other opponents in case of a disagreement. This way the basics of a competitive market are modeled, where, for instance, a buyer can try several sellers before making a purchase decision. The game is investigated using an evolutionary simulation. The outcomes appear to depend largely on the information available to the agents. We find that if the agents' number of remaining bargaining opportunities is commonly known, the proposer has the advantage. If this information is held private, however, the responder can obtain a larger share of the surplus. For the first case we also provide a game-theoretic analysis and compare the outcome with evolutionary results. Furthermore, the effects of search costs, uncertainty about future opportunities, and allowing multiple issues to be negotiated simultaneously are investigated