Aggregating Dependency Graphs into Voting Agendas in Multi-Issue Elections

In Stephane Airiau, Ulle Endriss, Umberto Grandi, Daniele Porello & Joel Uckelman (eds.), {IJCAI} 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011. pp. 18--23 (2011)
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Many collective decision making problems have a combinatorial structure: the agents involved must decide on multiple issues and their preferences over one issue may depend on the choices adopted for some of the others. Voting is an attractive method for making collective decisions, but conducting a multi-issue election is challenging. On the one hand, requiring agents to vote by expressing their preferences over all combinations of issues is computationally infeasible; on the other, decomposing the problem into several elections on smaller sets of issues can lead to paradoxical outcomes. Any pragmatic method for running a multi-issue election will have to balance these two concerns. We identify and analyse the problem of generating an agenda for a given election, specifying which issues to vote on together in local elections and in which order to schedule those local elections.

Author Profiles

Ulle Endriss
University of Amsterdam
Daniele Porello
Università degli Studi di Genova


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