A behavioral perspective on social choice

Authors:
Anna Popova;Michel Regenwetter;Nicholas Mattei
Affiliations:
Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, USA;Department of Psychology and Department of Political Science, University of Illinois at Urbana-Champaign, Champaign, USA;NICTA and University of New South Wales, Sydney, Australia
Venue:
Annals of Mathematics and Artificial Intelligence
Year:
2013

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Abstract

We discuss what behavioral social choice can contribute to computational social choice. An important trademark of behavioral social choice is to switch perspective away from a traditional sampling approach in the social choice literature and to ask inference questions: Based on limited, imperfect, and highly incomplete observed data, what inference can we make about social choice outcomes at the level of a population that generated those observed data? A second important consideration in theoretical and behavioral work on social choice is model dependence: How do theoretical predictions and conclusions, as well as behavioral predictions and conclusions, depend on modeling assumptions about the nature of human preferences and/or how these preferences are expressed in ratings, rankings, and ballots of various kinds? Using a small subcollection from the Netflix Prize dataset, we illustrate these notions with real movie ratings from real raters. We highlight the key roles that inference and behavioral modeling play in the analysis of such data, particularly for sparse data like the Netflix ratings. The social and behavioral sciences can provide a supportive role in the effort to develop behaviorally meaningful and robust studies in computational social choice.