TY - JOUR
T1 - A behavioral perspective on social choice
AU - Popova, Anna
AU - Regenwetter, Michel
AU - Mattei, Nicholas
N1 - Funding Information:
Acknowledgements Regenwetter and Popova acknowledge funding under National Science Foundation grant No. SES-08-20009 (to Regenwetter, PI) and grant No. CCF-1216016 (to Regenwetter, PI). Mattei acknowledges support by the National Science Foundation under Grant No. IIS-1107011 (to Judy Goldsmith, PI) and CCF-1049360 (to Judy Goldsmith, PI). Much of this work was carried out while Mattei was still a graduate student at the University of Kentucky and we acknowledge their support. NICTA is funded by the Australian Government through the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program. We thank Sergey Popov for help and advice with programming and Netflix for releasing such valuable data. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation or the authors’ universities.
PY - 2013/7
Y1 - 2013/7
N2 - 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.
AB - 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.
KW - Behavioral social choice
KW - Consensus methods
KW - Inference
KW - Model dependence
KW - Voting paradoxes
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U2 - 10.1007/s10472-012-9326-6
DO - 10.1007/s10472-012-9326-6
M3 - Article
AN - SCOPUS:84890889048
SN - 1012-2443
VL - 68
SP - 5
EP - 30
JO - Annals of Mathematics and Artificial Intelligence
JF - Annals of Mathematics and Artificial Intelligence
IS - 1-3
ER -