Skip to main content
SpringerLink
Log in

How choice proliferation affects revealed preferences

  • Published:
Theory and Decision Aims and scope Submit manuscript

Abstract

Whereas the literature on choice overload has shown that people tend to defer their choice or experience less satisfaction under choice proliferation, this paper aims to test how the profusion of choice directly affects individuals’ revealed preferences over options. To do so, we run an experiment where subjects have to compare familiar (i.e., easy, salient and relatively safe) and unfamiliar options under different choice contexts (Large or Small choice sets). We hypothesize that, as the choice set expands, the decisions become harder and more costly and subjects may find familiar items relatively more attractive. Our results provide clear evidence of set size dependence of revealed preferences: Subjects prefer familiar items more frequently in larger choice sets. This evidence is robust to a number of experimental variations and statistical controls.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Neither of these two phenomena is unambiguously established (see the meta-analyses in Scheibehenne et al. (2010) and Chernev et al. (2015), who come to rather opposite conclusions).

  2. Such an option may also represent a satisficing option à la Simon (1979), i.e., gives a sufficient level of satisfaction to choose it over an unfamiliar option, which would require more effort for a more uncertain outcome.

  3. In the experiment reported by Dean et al. (2017), the subjects are forced to choose an option. In line with their own interpretation of the results, retaining the default/status-quo option may be seen as a way of avoiding choice, rather than a deep change in one’s preferences.

  4. We assume there exists a minimal effort: e.g., read the name of option. We also assume that the representation of the option cannot be sure to make it possible uncertainty that is not due to information or knowledge about the options but to variations in the internal state of the decision-maker, variation in the context of consumption, etc.

  5. This property is easily generalized by considering the case where a and b belong to \({\widetilde{C}} \cap {\widehat{C}}\), where the former set is neither a subset nor a superset of the latter. However, we think that the text version is more intuitive, given our experimental design where subjects face one of two choice sets, one being a subset of the other.

  6. It is clear that the definition of rational preferences, which correspond “set size independence”, implies such a property but the contrary does not hold.

  7. A difference between the Dean et al. ’s (2017) model and our framework is the fact that they assume that the status quo is an option while it is a set of options in our context.

  8. A somewhat similar axiom, the Deferral Monotonicity axiom, is introduced by Gerasimou (2018). It states that a decision-maker chooses to defer her choice in a set C if she does it when facing a smaller set \({\widetilde{C}} \supset C\).

  9. It would be possible that enlarging a choice set makes more likely the preference reversals among a type of items. For instance, a decision-maker might prefer \(n_1\) over \(n_2\) when facing \(\{n_1,n_2\}\) but \(n_2\) over \(n_1\) when facing a larger set because more alternatives might entail higher decision costs or regret. As we do not consider this type of set dependence, our measure of choice set dependence might be underestimated.

  10. We do not present some theoretical mechanisms that although intuitively promising, actually fail to explain the growing attraction of familiarity with the set size: namely, errors, symmetric regret or incomplete preferences.

  11. The idea that individuals may feel regret and, therefore, violates the ‘standard’ theory of rational choice has been introduced by Loomes and Sugden (1982) or Sugden (1993).

  12. There is now an extensive body of literature on limited attention, consideration sets and their formation in consumer research, marketing and psychology (Hauser & Wernerfelt, 1990; Mehta & Srinivasan, 2003; Roberts & Lattin, 1991), and in theoretical and empirical economics as well (Dean et al., 2017; Geng & Özbay, 2020; Goeree, 2008; Masatlioglu & Nakajima, 2013; Masatlioglu et al., 2012).

  13. Note that it might be replaced by another unfamiliar item.

  14. These websites offer their own content, which excludes e-commerce websites and social networks.

  15. We checked this in the post-experiment socio-demographic questionnaire: our subjects spend an average of 1 hour 49 minutes on the web per day.

  16. The instructions (translated from the French) can be found in Appendix 1.

  17. The comments were drafted as follows: “I recommend this website/I do not recommend this website” plus an explanation. To control for the nature of the recommendations, 50% of the websites in a category had positive comments and 50% negative comments. Moreover, the positive and negative recommendations for a given website were reversed across sessions to produce a balanced design.

  18. See Lusk et al. (2008) for an evaluation of such a methodology.

  19. To a certain extent, our design, based on a real-consumption incentive mechanism, is simpler than what could be obtained with a monetary incentive scheme to study preferences for options within a choice set. Indeed, the latter would require us to introduce either a budget set, which can be a more demanding cognitive task, or payments in multiple dimensions (risk or uncertainty, time). Moreover, using everyday life consumption goods available for free (like content websites, music on the internet, etc.) is suitable for placing subjects in a simple and usual choice condition. To the best of our knowledge, no systematic experimental study has compared the relative performance of monetary and non-monetary incentive mechanisms.

  20. The average number of “likes” of the most popular websites in a choice set is around 346,000 (\(C^+\)) and 313,000 (\(C^-\)), with a certain heterogeneity from 117,000 (Economics and business) to 700,000 (General news). The least popular website has around 3,000 (\(C^+\)) and 5,000 (\(C^-\)) “likes”.

  21. One might argue that it should be better to use the reputation of websites (“Blockbuster”/middle/niche) rather than their familiarity. However, even if by construction a familiar item could be a “Blockbuster”, we argue that reputation is a less convincing criterion for evaluating choice overload. First, a “Blockbuster” website might be salient, but it is less likely to be easy and safe. If an individual heard about such a website but is not used to consult it, it can be difficult to form a precise belief about the satisfaction she might obtain from its consumption without having to exert much cognitive effort. Second, reputation is not a clear signal of quality. A “Blockbuster” or “superstar” item may not always provide much greater satisfaction than a less famous item (Rosen, 1981). Third, the choice of a “Blockbuster” item could come from social considerations outside of the lab, such as social norms or peer effects, that are hard to control.

  22. In the treatment where subjects face a small set without any information, a familiar item has 88.6% probability of being preferred to an unfamiliar one. It can be seen as supporting our experimental conjecture that familiar items give a sufficient and safe level of satisfaction when effort level is minimal, as it also rules out other motivations for choice, e.g., curiosity.

  23. The full distribution of individual frequencies with which familiar items are preferred to unfamiliar ones can also be represented by the empirical cumulative distribution functions. This is shown in Fig. 5, in Appendix 1. The conclusions drawn from the pooled frequencies seem to be valid at the individual level, where for all levels of information the distributions of the individual frequencies in which a familiar is preferred to an unfamiliar option in \(C^-\) (almost fully) dominate the distributions for (unrestricted) \(C^+\).

  24. For instance, for \(I^-\) in the unrestricted case, we use \(\mathrm {Prob}(f \succ _{C^+} n | n \succ _{C^-} f) = \mathrm {Prob}(f \succ _{C^+} n \cap n \succ _{C^-} f) / \mathrm {Prob}(n \succ _{C^-} f )\). Here, \(\mathrm {Prob}(f \succ _{C^+} n \cap n \succ _{C^-} f)\) is supposed to be equal to 0.349-0.284 = 0.075, i.e., we assume that \(n \succ _{C^+} f\) implies \(n \succ _{C^-} f\) (as implied by the Contraction axiom). Then: \(\mathrm {Prob}(f \succ _{C^+} n | n \succ _{C^-} f) = 0.075 / 0.349 = 0.2148\).

  25. This perceived complexity may come from having too many mental elements to weigh at the same time, and may yield an asymmetric effect in terms of subjective beliefs about individual options.

References

  • Andreoni, J., & Miller, J. (2002). Giving according to garp: An experimental test of the consistency of preferences for altruism. Econometrica, 70(2), 737–753.

    Article  Google Scholar 

  • Bertrand, M., Karlan, D., Mullainathan, S., Shafir, E., & Zinman, J. (2010). What’s advertising content worth? Evidence from a consumer credit marketing field experiment. Quarterly Journal of Economics, 125(1), 263–306.

    Article  Google Scholar 

  • Beshears, J., Choi, J. J., Laibson, D., & Madrian, B. C. (2013). Simplification and saving. Journal of Economic Behavior and Organization, 95, 130–145.

    Article  Google Scholar 

  • Bhargava, S., Loewenstein, G., & Sydnor, J. (2017). Choose to lose: Health plan choices from a menu with dominated options. Quarterly Journal of Economics, 132(3), 1319–1372.

    Article  Google Scholar 

  • Boatwright, P., & Nunes, J. C. (2001). Reducing assortment: An attribute-based approach. Journal of Marketing, 65(3), 50–63.

    Article  Google Scholar 

  • Brocas, I., Carrillo, J. D., Combs, T. D., & Kodaverdian, N. (2019). Consistency in simple vs. complex choices by younger and older adults. Journal of Economic Behavior and Organization, 157, 580–601.

    Article  Google Scholar 

  • Buturak, G., & Evren, Ö. (2017). Choice overload and asymmetric regret. Theoretical Economics, 12(3), 1029–1056.

    Article  Google Scholar 

  • Chernev, A. (2003). When more is less and less is more: The role of ideal point availability and assortment in consumer choice. Journal of Consumer Research, 30(2), 170–183.

    Article  Google Scholar 

  • Chernev, A., Böckenholt, U., & Goodman, J. (2015). Choice overload: A conceptual review and meta-analysis. Journal of Consumer Psychology, 25(2), 333–358.

    Article  Google Scholar 

  • Choi, S., Fisman, R., Gale, D., & Kariv, S. (2007). Consistency and heterogeneity of individual behavior under uncertainty. The American Economic Review, 97(5), 1921–1938.

    Article  Google Scholar 

  • Cronqvist, H., & Thaler, R. H. (2004). Design choice in privatized social-security systems: Learning from the Swedish experience. The American Economic Review, 94(2), 424–428.

    Article  Google Scholar 

  • Dean, M., Kibris, Ö., & Masatlioglu, Y. (2017). Limited attention and status quo bias. Journal of Economic Theory, 169, 93–127.

    Article  Google Scholar 

  • Donner, A., & Klar, N. (1994). Methods for comparing event rates in intervention studies when the unit of allocation is a cluster. American Journal of Epidemiology, 140(3), 279–289.

    Article  Google Scholar 

  • Farrington, J. (2011). Seven plus or minus two. Performance Improvement Quarterly, 23(4), 113–116.

    Article  Google Scholar 

  • Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10(2), 171–178.

    Article  Google Scholar 

  • Geng, S., & Özbay, E. Y. (2020). Shortlisting with a limited capacity. Journal of Mathematical Economics, 94, 102447.

  • Gerasimou, G. (2018). Indecisiveness, undesirability and overload revealed through rational choice deferral. Economic Journal, 128(614), 2450–2479.

    Article  Google Scholar 

  • Goeree, M. S. (2008). Limited information and advertising in the US personal computer industry. Econometrica, 76(5), 1017–1074.

    Article  Google Scholar 

  • Harbaugh, W. T., Krause, K., & Berry, T. R. (2001). Garp for kids: On the development of rational choice behavior. The American Economic Review, 91(5), 1539–1545.

    Article  Google Scholar 

  • Hauser, J. R., & Wernerfelt, B. (1990). An evaluation cost model of consideration sets. Journal of Consumer Research, 16(4), 393–408.

    Article  Google Scholar 

  • Hausman, J. A., & Ruud, P. A. (1987). Specifying and testing econometric models for rank-ordered data. Journal of Econometrics, 34(1), 83–104.

    Article  Google Scholar 

  • Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. The American Economic Review, 92(5), 1644–1655.

    Article  Google Scholar 

  • Huber, J., Payne, J. W., & Puto, C. (1982). Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis. Journal of Consumer Research, 9(1), 90–98.

    Article  Google Scholar 

  • Irons, B., & Hepburn, C. (2007). Regret theory and the tyranny of choice. Economic Record, 83(261), 191–203.

    Article  Google Scholar 

  • Iyengar, S., & Kamenica, E. (2010). Choice proliferation, simplicity seeking, and asset allocation. Journal of Public Economics, 94(7–8), 530–539.

    Article  Google Scholar 

  • Iyengar, S., & Lepper, M. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995–1006.

    Article  Google Scholar 

  • Le Lec, F., & Tarroux, B. (2020). On attitudes to choice: Some experimental evidence on choice aversion. Journal of the European Economic Association, 18(5), 2108–2134.

    Article  Google Scholar 

  • Leonard, T. C., Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Constitutional Political Economy, 19(4), 356–360.

    Article  Google Scholar 

  • Lleras, J. S., Masatlioglu, Y., Nakajima, D., & Ozbay, E. Y. (2017). When more is less: Limited consideration. Journal of Economic Theory, 170, 70–85.

    Article  Google Scholar 

  • Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. The Economic Journal, 92(368), 805–824.

    Article  Google Scholar 

  • Lusk, J. L., Fields, D., & Prevatt, W. (2008). An incentive compatible conjoint ranking mechanism. American Journal of Agricultural Economics, 90(2), 487–498.

    Article  Google Scholar 

  • Manzini, P., & Mariotti, M. (2012). Categorize then choose: Boundedly rational choice and welfare. Journal of the European Economic Association, 10(5), 1141–1165.

    Article  Google Scholar 

  • Masatlioglu, Y., & Nakajima, D. (2013). Choice by iterative search. Theoretical Economics, 8, 701–728.

    Article  Google Scholar 

  • Masatlioglu, Y., Nakajima, D., & Ozbay, E. Y. (2012). Revealed attention. The American Economic Review, 102(5), 2183–2205.

    Article  Google Scholar 

  • Mehta, S. R. N., & Srinivasan, K. (2003). Price uncertainty and consumer search: A structural model of consideration set formation. Marketing Science, 22(1), 58–84.

    Article  Google Scholar 

  • Reutskaja, E., & Hogarth, R. M. (2009). Satisfaction in choice as a function of the number of alternatives: When goods satiate. Psychology and Marketing, 26(3), 197–203.

    Article  Google Scholar 

  • Reutskaja, E., Lindner, A., Nagel, R., Andersen, R. A., & Camerer, C. F. (2018). Choice overload reduces neural signatures of choice set value in dorsal striatum and anterior cingulate cortex. Nature Human Behaviour, 2, 925–935.

    Article  Google Scholar 

  • Reutskaja, E., Nagel, R., Camerer, C. F., & Rangel, A. (2011). Search dynamics in consumer choice under time pressure: An eye-tracking study. The American Economic Review, 101(2), 906–926.

    Article  Google Scholar 

  • Roberts, J. H., & Lattin, J. M. (1991). Development and testing of a model of consideration set composition. Journal of Marketing Research, 28(4), 429–440.

    Article  Google Scholar 

  • Rosen, S. (1981). The economics of superstars. The American Economic Review, 71(5), 845–58.

    Google Scholar 

  • Salant, Y., & Rubinstein, A. (2008). (a, f): Choice with frames. Review of Economic Studies, 75, 1287–1296.

    Article  Google Scholar 

  • Sarver, T. (2008). Anticipating regret: Why fewer options may be better. Econometrica, 76(2), 263–305.

    Article  Google Scholar 

  • Scheibehenne, B., Greifeneder, R., & Todd, P. (2010). Can there ever be too many options? A meta-analytic review of choice overload. Journal of Consumer Research, 37, 409–425.

    Article  Google Scholar 

  • Sen, A. (1993). Internal consistency of choice. Econometrica, 61(3), 495–521.

    Article  Google Scholar 

  • Shah, A. M., & Wolford, G. (2007). Buying behavior as a function of parametric variation of number of choices. Psychological Science, 18(5), 369–370.

    Article  Google Scholar 

  • Shin, J., & Ariely, D. (2004). Keeping doors open: The effect of unavailability on incentives to keep options viable. Management Science, 50(5), 575–586.

    Article  Google Scholar 

  • Simon, H. A. (1957). Models of Man: Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. New York: Wiley.

    Google Scholar 

  • Simon, H. A. (1979). Rational decision making in business organizations. The American Economic Review, 69(4), 493–513.

    Google Scholar 

  • Simonson, I. (1989). Choice based on reasons: The case of attraction and compromise effects. Journal of Consumer Research, 16(2), 158–174.

    Article  Google Scholar 

  • Sippel, R. (1997). An experiment on the pure theory of consumer’s behaviour. The Economic Journal, 107(444), 1431–1444.

    Article  Google Scholar 

  • Sugden, R. (1993). An axiomatic foundation for regret theory. Journal of Economic Theory, 60(1), 159–180.

    Article  Google Scholar 

  • Sunstein, C. R., & Thaler, R. H. (2003). Libertarian paternalism is not an oxymoron. The University of Chicago Law Review, 70(4), 1159–1202.

    Article  Google Scholar 

  • Tversky, A., & Shafir, E. (1992). Choice under conflict: The dynamics of deferred decision. Psychological Science, 3(6), 358–361.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Elven Priour for programming the experiment. The authors also thank Dorothea Kubler, Marie Claire Villeval and seminar participants at the CREM (Univ. Rennes), the CEPN (Univ. Paris 13), the GATE (Univ. Lyon), the ASFEE conference, the ACEI conference (Montreal), the ESA European Meeting (Prague) and the OSE-PSE meeting for helpful comments. This manuscript has also benefited from useful comments of a coordinating editor of Theory and Decision and a reviewer. Financial support from the MSHB and the LabEx ICCA is gratefully acknowledged. This paper is part of the research project ValFree (The Value of Choice, ANR-16-CE41-0002-01) of the French National Agency for Research (ANR), whose financial support is gratefully acknowledged. It was also performed within the framework of the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program “Investissements d’Avenir” (ANR-11-IDEX-007) operated by the ANR.

Author information

Authors and Affiliations

  1. LEM UMR 9221, University of Lille, Cité scientifique, 59491, Villeneuve d’Ascq, France

    Fabrice Le Lec

  2. University of Angers, GRANEM EA 7456 and LabEx ICCA, 13 allée F. Mitterrand, 49000, Angers, France

    Marianne Lumeau

  3. GATE UMR 5824, University of Lyon, Université Lumière Lyon 2, 69130, Ecully, France

    Benoît Tarroux

Authors
  1. Fabrice Le Lec
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marianne Lumeau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benoît Tarroux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benoît Tarroux.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1: Additional statistics and figures

Appendix 1: Additional statistics and figures

See Figs. 4 and 5.

Fig. 4
figure 4

Difference in item familiarity between sub-treatments

Full size image
Fig. 5
figure 5

Empirical cumulative distribution functions of the individual frequencies of a familiar being preferred to an unfamiliar option

Full size image
Table 6 Average frequencies with which a familiar is preferred to an unfamiliar item (in %)
Full size table
Table 7 Rank-ordered logit estimates with control variables
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Le Lec, F., Lumeau, M. & Tarroux, B. How choice proliferation affects revealed preferences. Theory Decis 93, 331–358 (2022). https://doi.org/10.1007/s11238-021-09848-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11238-021-09848-7

Keywords

Search

Navigation