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Pandora’s rules in the laboratory

Published online by Cambridge University Press:  14 March 2025

Efthymios Lykopoulos*
Affiliation:
Department of Economics, University of Cyprus, PO Box 20537, 1678 Nicosia, Cyprus
Georgios Voucharas*
Affiliation:
Department of Economics, University of Macedonia, Thessaloniki, Greece
Dimitrios Xefteris*
Affiliation:
Department of Economics, University of Cyprus, PO Box 20537, 1678 Nicosia, Cyprus

Abstract

In theory, search conditions are a key determinant of Pandora’s rule (i.e. of the optimal search pattern), and of the eventual payoffs (Weitzman, 1979; Doval, 2018). We compare different search conditions in the laboratory and find strong evidence that they affect subjects’ order of inspection and payoffs greatly. Subjects are more conservative at the beginning of the search, and earn less if they are constrained to choose only among the inspected alternatives, than if they are not. These findings reinforce the empirical pertinence of the formal search literature, and generate novel insights regarding relevant settings of applied interest (e.g. the impact of market digitization on consumer behavior).

Type
Original Paper
Copyright
Copyright © The Author(s), under exclusive licence to Economic Science Association 2022.

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Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s10683-022-09770-x.

We thank Laura Doval, Philippos Louis, two anonymous reviewers and the editor, John Duffy, for their valuable feedback and advice. This project has received funding from the University of Cyprus (Dimitrios Xefteris) under the IMVASY research grant. The replication material for the study is available at https://doi.org/10.7910/DVN/RE8SJN.

References

Bergemann, D., & Valimaki, J. (2006). Bandit problems. Cowles Foundation Discussion Paper, No.1551.Google Scholar
Botti, S., & Hsee, C. K. (2010). Dazed and confused by choice: How the temporal costs of choice freedom lead to undesirable outcomes. Organizational Behavior Human Decision Processes, 112(2), 161171. 10.1016/j.obhdp.2010.03.002CrossRefGoogle Scholar
Braunstein, Y. M., & Schotter, A. (1982). Labor market search: An experimental study. Economic Inquiry, 20(1), 133144. 10.1111/j.1465-7295.1982.tb01146.xCrossRefGoogle Scholar
Brown, M., Flinn, C. J., & Schotter, A. (2011). Real-time search in the laboratory and the market. American Economic Review, 101(2), 948–74 10.1257/aer.101.2.948CrossRefGoogle Scholar
Caplin, A., Dean, M., & Martin, D. (2011). Search and satisficing. American Economic Review, 101(7), 28992922. 10.1257/aer.101.7.2899CrossRefGoogle Scholar
Casner, B. (2021). Learning while shopping: An experimental investigation into the effect of learning on consumer search. Experimental Economics, 24, 238273. 10.1007/s10683-020-09659-7CrossRefGoogle ScholarPubMed
Cox, J. C., & Oaxaca, R. L. (1989). Laboratory experiments with a finite-horizon job-search model. Journal Risk Uncertainty, 2(3), 301329. 10.1007/BF00209391CrossRefGoogle Scholar
Doval, L. (2018). Whether or not to open pandora’s box. Journal Economic Theory, 175, 127158. 10.1016/j.jet.2018.01.005CrossRefGoogle Scholar
Fischbacher, U. (2007). z-tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10(2), 171178. 10.1007/s10683-006-9159-4CrossRefGoogle Scholar
Fong, N. M. (2017). How targeting affects customer search: A field experiment. Management Science, 63(7), 23532364. 10.1287/mnsc.2016.2447CrossRefGoogle Scholar
Fox, E. J., & Hoch, S. J. (2005). Cherry-picking. Journal Marketing, 69(1), 4662. 10.1509/jmkg.69.1.46.55506CrossRefGoogle Scholar
Gabaix, X., Laibson, D., Moloche, G., & Weinberg, S. (2006). Costly information acquisition: Experimental analysis of a boundedly rational model. American Economic Review, 96(4), 10431068. 10.1257/aer.96.4.1043CrossRefGoogle Scholar
Ge, X., Brigden, N., & Häubl, G. (2015). The preference-signaling effect of search. Journal Consumer Psychology, 25(2), 245256. 10.1016/j.jcps.2014.09.003CrossRefGoogle Scholar
Gittins, J. C. (1979). Bandit processes and dynamic allocation indices. Journal Royal Statistical Society: Series B (Methodological), 41(2), 148164.CrossRefGoogle Scholar
Gronau, R. (1971). Information and frictional unemployment. American Economic Review, 61(3), 290301.Google Scholar
Hansen, B. E. (2000). Sample splitting and threshold estimation. Econometrica, 68(3), 575603. 10.1111/1468-0262.00124CrossRefGoogle Scholar
Jain, V., & Whitmeyer, M. (2021). Search and competition with flexible investigations arXiv:2104.13159.Google Scholar
Kim, J. B., Albuquerque, P., & Bronnenberg, B. J. (2010). Online demand under limited consumer search. Marketing Science, 29(6), 10011023. 10.1287/mksc.1100.0574CrossRefGoogle Scholar
Liu, L., & Dukes, A. (2016). Consumer search with limited product evaluation. Journal Economics Management Strategy, 25(1), 3255. 10.1111/jems.12131Google Scholar
Louis, P., Troumpounis, O., Tsakas, N., & Xefteris, D. (2022). Coordination with preferences over the coalition size. Journal Economic Behavior Organization, 194, 105123. 10.1016/j.jebo.2021.12.010CrossRefGoogle Scholar
McCall, J. J. (1970). Economics of information and job search. Quarterly Journal of Eco- nomics: 113126.Google Scholar
Moorthy, S., Ratchford, B. T., & Talukdar, D. (1997). Consumer information search revisited: Theory and empirical analysis. Journal of Consumer Research, 23(4), 263277. 10.1086/209482CrossRefGoogle Scholar
Mortensen, D. T. (1970). Job search, the duration of unemployment, and the phillips curve. American Economic Review, 60(5), 847862.Google Scholar
Mortensen, D. T. (1986). Job search and labor market analysis. Handbook Labor Economics, 2, 849919. 10.1016/S1573-4463(86)02005-9CrossRefGoogle Scholar
Pan, X., Ratchford, B. T., & Shankar, V. (2004). Price dispersion on the internet: a review and directions for future research. Journal Interactive Marketing, 18(4), 116135. 10.1002/dir.20019CrossRefGoogle Scholar
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal Experimental Psychology: Learning, Memory, Cognition, 14(3), 534.Google Scholar
Robbins, H. (1952). Some aspects of the sequential design of experiments. Bulletin American Mathematical Society, 58(5), 527535. 10.1090/S0002-9904-1952-09620-8CrossRefGoogle Scholar
Rothschild, M. (1974). A two-armed bandit theory of market pricing. Journal Economic Theory, 9(2), 185202. 10.1016/0022-0531(74)90066-0CrossRefGoogle Scholar
Schotter, A., & Braunstein, Y. M. (1981). Economic search: An experimental study. Economic Inquiry, 19(1), 125. 10.1111/j.1465-7295.1981.tb00600.xCrossRefGoogle Scholar
Shah, A. M., & Wolford, G. (2007). Buying behavior as a function of parametric variation of number of choices. Psychological Science, 18(5), 369370. 10.1111/j.1467-9280.2007.01906.xCrossRefGoogle ScholarPubMed
Slonim, R. (1994). Learning in a search-for-the-best-alternative experiment. Journal Economic Behavior Organization, 25(2), 141165. 10.1016/0167-2681(94)90008-6CrossRefGoogle Scholar
Stigler, G. J. (1961). The economics of information. Journal Political Economy, 69(3), 213225. 10.1086/258464CrossRefGoogle Scholar
Stigler, G. J. (1962). Information in the labor market. Journal Political Economy, 70(5, part 2), 94105. 10.1086/258727CrossRefGoogle Scholar
Urbany, J. E. (1986). An experimental examination of the economics of information. Journal Consumer Research, 13(2), 257271. 10.1086/209065CrossRefGoogle Scholar
Weitzman, M. L. (1979). Optimal search for the best alternative. Econometrica: Journal Econometric Society, 47, 641654. 10.2307/1910412CrossRefGoogle Scholar
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