Preference Handling in Combinatorial Domains: From AI to Social Choice


  • Yann Chevaleyre LAMSADE, Université Paris-Dauphine
  • Ulle Endriss ILLC, University of Amsterdam
  • Jérôme Lang LAMSADE, Université Paris-Dauphine
  • Nicolas Maudet LAMSADE, Université Paris-Dauphine



In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multi-attribute) structure. We give an introduction to preference handling in combinatorial domains in the context of collective decision making, and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains, and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, known as computational social choice, that brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

Author Biographies

Yann Chevaleyre, LAMSADE, Université Paris-Dauphine


Maître de Conférence

Ulle Endriss, ILLC, University of Amsterdam

Institute for Logic, Language, and Computation

Assistant Professor

Jérôme Lang, LAMSADE, Université Paris-Dauphine



Nicolas Maudet, LAMSADE, Université Paris-Dauphine

Computer Science Department

Assistant Professor




How to Cite

Chevaleyre, Y., Endriss, U., Lang, J., & Maudet, N. (2008). Preference Handling in Combinatorial Domains: From AI to Social Choice. AI Magazine, 29(4), 37.