A behavioral perspective on social choice

  • Anna Popova
  • Michel RegenwetterEmail author
  • Nicholas Mattei


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.


Behavioral social choice Consensus methods Inference Model dependence Voting paradoxes 

Mathematics Subject Classifications (2010)

91B10 91B12 91B14 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arrow, K.J.: Social Choice and Individual Values. Wiley, New York (1963)Google Scholar
  2. 2.
    Arrow, K.J., Sen, A.K., Suzumura, K. (eds.): Handbook of Social Choice and Welfare, vol. 1. North-Holland, Amsterdam (2002)Google Scholar
  3. 3.
    Bennett, J., Lanning, S.: The Netflix Prize. In: Proceedings of the KDD Cup and Workshop (2007).
  4. 4.
    Black, D.: On the rationale of group decision-making. J. Polit. Econ. 56(1), 23–34 (1948)CrossRefGoogle Scholar
  5. 5.
    Chamberlin, J.R., Cohen, J.L., Coombs, C.H.: Social choice observed: five presidential elections of the American Psychological Association. J. Polit. 46(2), 479–502 (1984)CrossRefGoogle Scholar
  6. 6.
    Condorcet, M.: Essai sur l’Application de l’Analyse à la Probabilité des Décisions Rendues à la Pluralité des Voix (Essai on the Application of the Probabilistic Analysis of Majority Vote Decisions). Imprimerie Royale, Paris (1785)Google Scholar
  7. 7.
    Conitzer, V., Walsh, T., Xia, L.: Dominating manipulations in voting with partial information. In: Proceedings of the 25th American Association of Artificial Intelligence Conference (AAAI 2011), pp. 638–643 (2011)Google Scholar
  8. 8.
    Erdélyi, G., Fernau, H., Goldsmith, J., Mattei, N., Raible, D., Rothe, J.: The complexity of probabilistic lobbying. In: Proceedings of the 1st International Conference on Algorithmic Decision Theory (ADT 2009), pp. 86–97 (2009)Google Scholar
  9. 9.
    Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L., Rothe, J.: The shield that never was: Societies with single-peaked preferences are more open to manipulation and control. Inf. Comput. 209(2), 89–107 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Felsenthal, D.S., Maoz, Z., A, R.: An empirical evaluation of six voting procedures: do they really make any difference? Br. J. Polit. Sci. 23, 1–27 (1993)CrossRefGoogle Scholar
  11. 11.
    Gehrlein, W.V.: Concorcet’s paradox. Theory Decis. 15, 161—197 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Gehrlein, W.V.: Condorcet efficiency of constant scoring rules for large electorates. Econ. Lett. 19, 13—15 (1985)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Gehrlein, W.V.: Condorcet efficiency of simple voting rules for large electorates. Econ. Lett. 40, 61—66 (1992)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Gehrlein, W.V.: Condorcet’s paradox and the likelihood of its occurrence: different perspectives on balanced preferences. Theory Decis. 52(2), 171–199 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Gehrlein, W.V., Fishburn, P.C.: Concorcet’s paradox and anonymous preference profiles. Public Choice 26, 1—18 (1976)CrossRefGoogle Scholar
  16. 16.
    Gehrlein, W.V., Fishburn, P.C.: The probability of the paradox of voting: a computable solution. J. Econ. Theory 13, 14–25 (1976)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Gehrlein, W.V., Fishburn, P.C.: Coincidence probabilities for simple majority and positional voting rules. Soc. Sci. Res. 7(3), 272–283 (1978)CrossRefGoogle Scholar
  18. 18.
    Gehrlein, W.V., Lepelley, D.: The probability that all weighted scoring rules elect the same winner. Econ. Lett. 66, 191–197 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Gibbard, A.: Manipulation of voting schemes: a general result. Econometrica 41(4), 587–601 (1973)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Hazon, N., Aumann, Y., Kraus, S., Wooldridge, M.: On the evaluation of election outcomes under uncertainty. Artif. Intell. 189, 1–18 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  21. 21.
    Mackie, G.: Democracy Defended. Cambridge University Press, New York (2003)CrossRefGoogle Scholar
  22. 22.
    Marlin, B., Zemel, R.: Collaborative prediction and ranking with non-random missing data. In: Proceedings of the 3rd ACM Conference on Recommender Systems (2009)Google Scholar
  23. 23.
    Niemi, R.: The occurrence of the paradox of voting in university elections. Public Choice 8(1), 91–100 (1970)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Nurmi, H.: Voting procedures: a summary analysis. Br. J. Polit. Sci. 13(2), 181–208 (1983)CrossRefGoogle Scholar
  25. 25.
    Regenwetter, M.: Perspectives on preference aggregation. Perspect. Psychol. Sci. 4, 403–407 (2009)CrossRefGoogle Scholar
  26. 26.
    Regenwetter, M., Grofman, B.: Approval voting, Borda winners and Condorcet winners: Evidence from seven elections. Manage. Sci. 44, 520–533 (1998)CrossRefzbMATHGoogle Scholar
  27. 27.
    Regenwetter, M., Grofman, B.: Choosing subsets: a size-independent probabilistic model and the quest for a social welfare ordering. Soc. Choice Welf. 15, 423–443 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  28. 28.
    Regenwetter, M., Rykhlevskaia, E.: On the (numerical) ranking associated with any finite binary relation. J. Math. Psychol. 48, 239–246 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  29. 29.
    Regenwetter, M., Rykhlevskaia, E.: A general concept of scoring rules: general definitions, statistical inference, and empirical illustrations. Soc. Choice Welf. 29, 211–228 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  30. 30.
    Regenwetter, M., Tsetlin, I.: Approval voting and positional voting methods: inference, relationship, examples. Soc. Choice Welf. 22, 539–566 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  31. 31.
    Regenwetter, M., Adams, J., Grofman, B.: On the (sample) Condorcet efficiency of majority rule: an alternative view of majority cycles and social homogeneity. Theory Decis. 53, 153–186 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  32. 32.
    Regenwetter, M., Grofman, B., Marley, A.A.J.: On the model dependence of majority preferences reconstructed from ballot or survey data. Math. Soc. Sci. (Special issue on random utility theory and probabilistic measurement theory) 43, 453–468 (2002)Google Scholar
  33. 33.
    Regenwetter, M., Marley, A.A.J., Grofman, B.: A general concept of majority rule. Math. Soc. Sci. (Special issue on random utility theory and probabilistic measurement theory) 43, 407–430 (2002)Google Scholar
  34. 34.
    Regenwetter, M., Marley, A.A.J., Grofman, B.: General concepts of value restriction and preference majority. Soc. Choice Welf. 21, 149–173 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  35. 35.
    Regenwetter, M., Grofman, B., Marley, A.A.J., Tsetlin, I.M.: Behavioral Social Choice: Probabilistic Models, Statistical Inference, and Applications. Cambridge University Press, New York (2006)Google Scholar
  36. 36.
    Regenwetter, M., Ho, M.H., Tsetlin, I.: Sophisticated approval voting, ignorance priors, and plurality heuristics: a behavioral social choice analysis in a Thurstonian framework. Psychol. Rev. 114, 994–1014 (2007)CrossRefGoogle Scholar
  37. 37.
    Regenwetter, M., Kim, A., Kantor, A., Ho, M.H.: The unexpected empirical consensus among consensus methods. Psychol. Sci. 18, 559–656 (2007)CrossRefGoogle Scholar
  38. 38.
    Regenwetter, M., Grofman, B., Popova, A., Messner, W., Davis-Stober, C.P., Cavagnaro, D.R.: Behavioural social choice: a status report. Philos. Trans. R. Soc. Lond., B Biol. Sci. 364, 833–843 (2009)CrossRefGoogle Scholar
  39. 39.
    Riker, W.H.: Liberalism v. Populism. W. H. Freeman, San Fransisco (1982)Google Scholar
  40. 40.
    Rivest, R.L., Shen, E.: An optimal single-winner preferential voting system based on game theory. In: Proceedings of the 3rd International Workshop on Computational Social Choice (COMSOC 2010), pp. 399–410 (2010)Google Scholar
  41. 41.
    Roberts, F.S.: Measurement Theory. Addison-Wesley, London (1979)zbMATHGoogle Scholar
  42. 42.
    Roberts, F.: Applications of the theory of meaningfulness to psychology. J. Math. Psychol. 29, 311–332 (1985)CrossRefzbMATHGoogle Scholar
  43. 43.
    Roberts, F.S.: Meaningless statements. In: Graham, R.L., Kratochvil, J., Nesetril, J., Roberts, F.S. (eds.) The Future of Discrete Mathematics. American Mathematical Society, Providence (1998)Google Scholar
  44. 44.
    Roberts, F., Rosenbaum, Z.: Scale type, meaningfulness and the possible psychophysical laws. Math. Soc. Sci. 12, 77–95 (1986)CrossRefzbMATHMathSciNetGoogle Scholar
  45. 45.
    Saari, D.: Geometry of Voting. Springer, New York (1994)CrossRefzbMATHGoogle Scholar
  46. 46.
    Satterthwaite, M.: Strategy-proofness and Arrow’s conditions: existence and correspondence theorems for voting procedures and social welfare functions. J. Econ. Theory 10(2), 187–216 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  47. 47.
    Sen, A.K.: A possibility theorem on majority decisions. Econometrica 34(2), 491–499 (1966)CrossRefzbMATHGoogle Scholar
  48. 48.
    Shepsle, K., Bonchek, M.: Analyzing Politics. Norton, New York (1997)Google Scholar
  49. 49.
    Shoham, Y., Leyton-Brown, K.: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, New York (2009)Google Scholar
  50. 50.
    Tideman, N.: Collective Decisions and Voting: The Potential for Public Choice. Ashgate Publishing, Aldershot (2006)Google Scholar
  51. 51.
    Tideman, N., Plassmann, F.: Modeling the outcomes of vote-casting in actual elections. In: Felsenthal, D., Machover, M. (eds.) Electoral Systems: Paradoxes, Assumptions, and Procedures. Springer, New York (2012)Google Scholar
  52. 52.
    Tsetlin, I., Regenwetter, M.: On the probability of correct or incorrect majority preference relations. Soc. Choice Welf. 20, 283–306 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  53. 53.
    Tsetlin, I., Regenwetter, M., Grofman, B.: The impartial culture maximizes the probability of majority cycles. Soc. Choice Welf. 21, 387–398 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  54. 54.
    Walsh, T.: An empirical study of the manipulability of single transferable voting. In: Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010), pp. 257–262 (2010)Google Scholar
  55. 55.
    Xia, L., Conitzer, V.: Determining possible and necessary winners under common voting rules given partial orders. J. Artif. Intell. Res. 41(2), 25–67 (2011)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Anna Popova
    • 1
  • Michel Regenwetter
    • 2
    Email author
  • Nicholas Mattei
    • 3
  1. 1.Department of PsychologyUniversity of Illinois at Urbana-ChampaignChampaignUSA
  2. 2.Department of Psychology and Department of Political ScienceUniversity of Illinois at Urbana-ChampaignChampaignUSA
  3. 3.NICTA and University of New South WalesSydneyAustralia

Personalised recommendations