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Multilateral bargaining for resource division

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conference contribution
posted on 13.02.2015, 09:53 authored by Syeda FatimaSyeda Fatima, Michael Wooldridge
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.


Michael Wooldridge was supported by the ERC under Advanced Grant 291528 (“RACE”).



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Published in

The 21st European Conference on Artificial Intelligence (ECAI 2014)


309 - 314 (6)


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.


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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.



Book series

Frontiers in Artificial Intelligence and Applications;263




Prague Czech Republic

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