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Intolerable nuisances: some laboratory evidence on survivor curve shapes

Published online by Cambridge University Press:  14 March 2025

Ciril Bosch-Rosa ,
Christina Aperjis ,
Daniel Friedman  and
Bernardo A. Huberman
Ciril Bosch-Rosa*
Affiliation:
Colegio Universitario de Estudios Financieros, Madrid, Spain Technische Universität Berlin, Berlin, Germany
Christina Aperjis
Affiliation:
Power Auctions, Washington, DC, USA
Daniel Friedman
Affiliation:
Economics Department, University of California Santa Cruz, Santa Cruz, CA, USA
Bernardo A. Huberman
Affiliation:
HP Labs, Palo Alto, CA, USA
*
cirilbosch@gmail.com
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Abstract

The fraction of a user population willing to tolerate nuisances of size x is summarized in the survivor curve S(x); its shape is crucial in economic decisions such as pricing and advertising. We report a laboratory experiment that, for the first time, estimates the shape of survivor curves in several different settings. Laboratory subjects engage in a series of six desirable activities, e.g., playing a video game, viewing a chosen video clip, or earning money by answering questions. For each activity and each subject we introduce a chosen level x∈[xmin,xmax] of a particular nuisance, and the subject chooses whether to tolerate the nuisance or to switch to a bland activity for the remaining time. New non-parametric techniques provide bounds on the empirical survivor curves for each activity. Parametric fits of the classic Weibull distribution provide estimates of the survivor curves’ shapes. The fitted shape parameter depends on the activity and nuisance, but overall the estimated survivor curves tend to be log-convex. An implication, given the model of Aperjis and Huberman (SSRN, doi: 10.2139/ssrn.1672820, 2011), is that introducing nuisances all at once will generally be more profitable than introducing them gradually.

Keywords

Internet monetizationOnline advertisingPricingReference pointsAdaptationLaboratory experiment

JEL classification

D40: General C91: Laboratory, Individual Behavior L11: Production, Pricing, and Market Structure • Size Distribution of Firms
Type
Original Paper
Information
Experimental Economics , Volume 20 , Issue 3 , September 2017 , pp. 601 - 621
Copyright
Copyright © 2016 Economic Science Association

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Footnotes

Electronic supplementary material The online version of this article (doi:https://doi.org/10.1007/s10683-016-9501-4) contains supplementary material, which is available to authorized users.

References

Abeler, J, Falk, A, Goette, L, & Huffman, D (2011). Reference points and effort provision. The American Economic Review, 101(2), 470–492. 10.1257/aer.101.2.470CrossRefGoogle Scholar
Aperjis, C, & Huberman, BA (2011). Adaptation and the provider’s dilemma. SSRN.Google Scholar
Aperjis, C., & Huberman, B. A. (2012). Introducing revenue-generating features. US Patent, 8271312.Google Scholar
Ariely, D (1998). Combining experiences over time: The effects of duration, intensity changes, and on-line measurements on retrospective pain evaluations. Journal of Behavioral Decision Making, 11, 19–45. 10.1002/(SICI)1099-0771(199803)11:1<19::AID-BDM277>3.0.CO;2-B3.0.CO;2-B>CrossRefGoogle Scholar
Bagnoli, M, & Bergstrom, T (2005). Log-concave probability and its applications. Economic Theory, 26(2), 445–469. 10.1007/s00199-004-0514-4CrossRefGoogle Scholar
Baucells, M, Weber, M, & Welfens, F (2011). Reference-point formation and updating. Management Science, 57(3), 506–519. 10.1287/mnsc.1100.1286CrossRefGoogle Scholar
Chen, HA, Levy, D, Ray, S, & Bergen, M (2008). Asymmetric price adjustment in the small. Journal of Monetary Economics, 55(4), 728–737. 10.1016/j.jmoneco.2008.05.002CrossRefGoogle Scholar
Fibich, G, Gavious, A, & Lowengart, O (2003). Explicit solutions of optimization models and differential games with nonsmooth (asymmetric) reference-price effects. Operations Research, 51(5), 721–734. 10.1287/opre.51.5.721.16758CrossRefGoogle Scholar
Fibich, G, Gavious, A, & Lowengart, O (2005). The dynamics of price elasticity of demand in the presence of reference price effects. Journal of the Academy of Marketing Science, 33(1), 66–78. 10.1177/0092070304267108CrossRefGoogle Scholar
Frederick, S. Loewenstein, G. (1999). Hedonic adaptation. In Kahneman, D. & Diener, E. (Eds.), Well-being: The foundations of hedonic psychology (pp. 302–329).Google Scholar
Furyk, JS, O’Kane, CJ, Aitken, PJ, Banks, CJ, & Kault, DA (2009). Fast versus slow bandaid removal: A randomised trial. Medical Journal of Australia, 191, 682–683.CrossRefGoogle ScholarPubMed
Gneezy, Uri (2005). Deception: The role of consequences. The American Economic Review, 95(1), 384–394. 10.1257/0002828053828662CrossRefGoogle Scholar
Goldstein, L. (2000). How to boil a live frog. Analysis, 60(266), 170–178, April 2000. ISSN 1467-8284. http://onlinelibrary.wiley.com/. doi:10.1111/1467-8284.00220/abstract.CrossRefGoogle Scholar
Heffetz, O., & List, J. A. (2011). Is the endowment effect a reference effect?. NBER Working Paper 16715.CrossRefGoogle Scholar
Hyde, D. (2011). Sorites paradox. In Zalta, E. N. (Ed.), The stanford encyclopedia of philosophy (Winter 2011 ed).CrossRefGoogle Scholar
Johnson, N. L., Kotz, S., & Balakrishnan, N. (1994). Continuous univariate distributions. Wiley series in probability and mathematical statistics (2nd ed). New York: Wiley. ISBN 0471584959 0471584940.Google Scholar
Kahneman, D, Fredrickson, BL, Schreiber, CA, & Redelmeier, DA (1993). When more pain is preferred to less: Adding a better end. Psychological Science, 4(6), 401–405. 10.1111/j.1467-9280.1993.tb00589.xCrossRefGoogle Scholar
Kahneman, D, & Tversky, A (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292. 10.2307/1914185CrossRefGoogle Scholar
Kalyanaram, G, & Winer, RS (1995). Empirical generalizations from reference price research. Marketing Science, 14(3), 161–169. 10.1287/mksc.14.3.G161CrossRefGoogle Scholar
Kőszegi, B, & Rabin, M (2006). A model of reference-dependent preferences.. The Quarterly Journal of Economics, 121(4), 1133–1165.Google Scholar
Kopalle, PK, Rao, AG, & Assuncao, JL (1996). Asymmetric reference price effects and dynamic pricing policies. Marketing Science, 15(1), 60–85. 10.1287/mksc.15.1.60CrossRefGoogle Scholar
Mazumdar, T, Raj, SP, & Sinha, I (2005). Reference price research. Journal of Marketing, 69, 84–102. 10.1509/jmkg.2005.69.4.84CrossRefGoogle Scholar
Nasiry, J., & Popescu, I. (2010). Dynamic pricing with loss averse consumers and peak-end anchoring. INSEAD Working Paper No. 2010/102/DS.Google Scholar
Popescu, I, & Yaozhong, W (2007). Dynamic pricing strategies with reference effects. Operations Research, 55(3), 413–429. 10.1287/opre.1070.0393CrossRefGoogle Scholar
Quinn, WS (1990). The puzzle of the self-torturer. Philosophical Studies, 59(1), 79–90. 10.1007/BF00368392CrossRefGoogle Scholar
Rinne, H (2008). The Weibull distribution: A handbook, Boca Raton: CRC Press 10.1201/9781420087444CrossRefGoogle Scholar
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