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Title: Multilateral bargaining for resource division
Authors: Fatima, Shaheen
Wooldridge, Michael
Issue Date: 2014
Publisher: © The Authors and IOS Press
Citation: FATIMA, S. and WOOLDRIDGE, M., 2014. Multilateral bargaining for resource division. IN: Schaub, T. et al. (eds.) The 21st European Conference on Artificial Intelligence (ECAI 2014), Prague Czech Republic, pp. 309 - 314.
Series/Report no.: Frontiers in Artificial Intelligence and Applications;263
Abstract: We address the problem of how a group of agents can decide to share a resource, represented as a unit-sized pie. We investigate a finite horizon non-cooperative bargaining game, in which the players take it in turns to make proposals on how the resource should be allocated, and the other players vote on whether or not to accept the allocation. Voting is modelled as a Bayesian weighted voting game with uncertainty about the players’ weights. The agenda, (i.e., the order in which the players are called to make offers), is defined exogenously. We focus on impatient players with heterogeneous discount factors. In the case of a conflict, (i.e., no agreement by the deadline), all the players get nothing. We provide a Bayesian subgame perfect equilibrium for the bargaining game and conduct an ex-ante analysis of the resulting outcome. We show that, the equilibrium is unique, computable in polynomial time, results in an instant Pareto optimal agreement, and, under certain conditions provides a foundation for the core of the Bayesian voting game. Our analysis also leads to insights on how an individual’s bargained share is in- fluenced by his position on the agenda. Finally, we show that, if the conflict point of the bargaining game changes, then the problem of determining a non-cooperative equilibrium becomes NP-hard even under the perfect information assumption.
Description: This is a conference paper from ECAI 2014. It is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License.
Sponsor: Michael Wooldridge was supported by the ERC under Advanced Grant 291528 (“RACE”).
Version: Published
DOI: 10.3233/978-1-61499-419-0-309
URI: https://dspace.lboro.ac.uk/2134/16726
Publisher Link: http://dx.doi.org/10.3233/978-1-61499-419-0-309
ISBN: 978-1-61499-419-0
Appears in Collections:Conference Papers and Presentations (Computer Science)

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