Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84602
Title: Alternating-offers bargaining in one-to-many and many-to-many settings
Authors: An, Bo
Gatti, Nicola
Lesser, Victor
Keywords: Automated negotiation
Equilibrium strategy
Issue Date: 2016
Source: An, B., Gatti, N., & Lesser, V. (2016). Alternating-offers bargaining in one-to-many and many-to-many settings. Annals of Mathematics and Artificial Intelligence, 77(1), 67-103.
Series/Report no.: Annals of Mathematics and Artificial Intelligence
Abstract: Automating negotiations in markets where multiple buyers and sellers operate is a scientific challenge of extraordinary importance. One-to-one negotiations are classically studied as bilateral bargaining problems, while one-to-many and many-to-many negotiations are studied as auctioning problems. This paper aims at bridging together these two approaches, analyzing agents’ strategic behavior in one-to-many and many-to-many negotiations when agents follow the alternating-offers bargaining protocol (Rubinstein Econometrica 50(1), 97–109, 33). First, we extend this protocol, proposing a novel mechanism that captures the peculiarities of these settings. Then, we analyze agents’ equilibrium strategies in complete information bargaining and we find that for a large subset of the space of the parameters, the equilibrium outcome depends on the values of a narrow number of parameters. Finally, we study incomplete information bargaining with one-sided uncertainty regarding agents’ reserve prices and we provide an algorithm based on the combination of game theoretic analysis and search techniques which finds agents’ equilibrium in pure strategies when they exist.
URI: https://hdl.handle.net/10356/84602
http://hdl.handle.net/10220/41875
ISSN: 1012-2443
DOI: http://dx.doi.org/10.1007/s10472-016-9506-x
Rights: © 2016 Springer International Publishing Switzerland. This is the author created version of a work that has been peer reviewed and accepted for publication by Annals of Mathematics and Artificial Intelligence, Springer International Publishing Switzerland. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1007/s10472-016-9506-x].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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