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Is response time predictive of choice? An experimental study of threshold strategies

Published online by Cambridge University Press:  14 March 2025

Andrew Schotter  and
Isabel Trevino
Andrew Schotter*
Affiliation:
New York University, New York, USA
Isabel Trevino*
Affiliation:
University of California, San Diego, San Diego, USA
*
andrew.schotter@nyu.edu
itrevino@ucsd.edu
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Abstract

This paper investigates the usefulness of non-choice data, namely response times, as a predictor of threshold behavior in a simple global game experiment. Our results indicate that the signals associated to the highest or second highest response time at the beginning of the experiment are both unbiased estimates of the threshold employed by subjects at the end of the experiment. This predictive ability is lost when we move to the third or higher response times. Moreover, the response time predictions are better than the equilibrium predictions of the game. They are also robust, in the sense that they characterize behavior in an “out-of-treatment” exercise where we use the strategy method to elicit thresholds.

Keywords

Response timeThreshold strategiesGlobal games

JEL classification

D89: Other C9: Design of Experiments C71: Cooperative Games
Type
Original Paper
Information
Experimental Economics , Volume 24 , Issue 1 , March 2021 , pp. 87 - 117
Copyright
Copyright © 2020 Economic Science Association

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References

Alos-Ferrer, C, Granic, DG, Kern, J, & Wagner, AK (2016). Preference reversals: Time and again. Journal of Risk and Uncertainty, 52(1), 65–97. 10.1007/s11166-016-9233-zCrossRefGoogle Scholar
Busemeyer, JR (1985). Decision making under uncertainty: A comparison of simple scalability, fixed-sample, and sequential-sampling models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11(3), 538–564.Google ScholarPubMed
Caplin, A, & Dean, M (2015). Revealed preference, rational inattention, and costly information acquisition. American Economic Review, 105(7), 2183–2203. 10.1257/aer.20140117CrossRefGoogle Scholar
Carlsson, H, & van Damme, E (1993). Global games and equilibrium selection. Econometrica, 61(5), 989–1018. 10.2307/2951491CrossRefGoogle Scholar
Chabris, CF, Laibson, DI, Morris, CL, Schuldt, JP, & Taubinsky, D (2009). The allocation of time in decision-making. Journal of the European Economic Association, 7(2/3), 628–637. 10.1162/JEEA.2009.7.2-3.628CrossRefGoogle ScholarPubMed
Clithero, JA (2018). Improving out-of-sample predictions using response times and a model of the decision process. Journal of Economic Behavior & Organization, 148, 344–375. 10.1016/j.jebo.2018.02.007CrossRefGoogle Scholar
Fehr, E, & Rangel, A (2011). Neuroeconomic foundations of economic choice—Recent advances. Journal of Economic Perspectives, 25(4), 3–30. 10.1257/jep.25.4.3CrossRefGoogle Scholar
Fischbacher, U (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10(2), 171–178. 10.1007/s10683-006-9159-4CrossRefGoogle Scholar
Fudenberg, D, Strack, P, & Strzalecki, T (2018). Speed and the optimal timing of choices. American Economic Review, 108, 3651–3684. 10.1257/aer.20150742CrossRefGoogle Scholar
Gabaix, X, & Laibson, D (2005). Bounded rationality and directed cognition, Cambridge: Harvard University.Google Scholar
Gabaix, X, Laibson, D, Moloche, G, & Weinberg, S (2006). Costly information acquisition: Experimental analysis of a boundedly rational model. American Economic Review, 96, 1043–1068. 10.1257/aer.96.4.1043CrossRefGoogle Scholar
Gul, F, Pesendorfer, W Caplin, A, & Shotter, A (2008). The case for mindless economics. The foundations of positive and normative economics, Oxford: Oxford University Press.Google Scholar
Heinemann, F, Nagel, R, & Ockenfels, P (2004). The theory of global games on test: Experimental analysis of coordination games with public and private information. Econometrica, 72(5), 1583–1599. 10.1111/j.1468-0262.2004.00544.xCrossRefGoogle Scholar
Konovalov, A, & Krajbich, I (2019). Revealed strength of preference: Inference from response times. Judgment and Decision Making, 14(4), 381–394.CrossRefGoogle Scholar
Krajbich, I, Armel, C, & Rangel, A (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292–1298. 10.1038/nn.2635CrossRefGoogle ScholarPubMed
Krajbich, I, Oud, B, & Fehr, E (2014). Benefits of neuroeconomic modeling: New policy interventions and predictors of preference. American Economic Review: Papers and Proceedings, 104(5), 501–506. 10.1257/aer.104.5.501CrossRefGoogle Scholar
Milosavljevic, M, Malmaud, J, Huth, A, Koch, C, & Rangel, A (2010). The drift diffusion model can account for the accuracy and reaction times of value-based choice under high and low time pressure. Judgment and Decision Making, 5, 437.CrossRefGoogle Scholar
Morris, S., & Shin, H. (2003). “Global games: Theory and applications. In Eight world congress: Advances in economics and econometrics: Theory and applications Cambridge: Cambridge University Press.Google Scholar
Morris, S, & Shin, HS (1998). Unique equilibrium in a model of self-fulfilling currency attacks. American Economic Review, 88(3), 587–597.Google Scholar
Piovesan, M, & Wengstrom, E (2009). Fast or fair? A study of response times. Economic Letters, 105, 193–196. 10.1016/j.econlet.2009.07.017CrossRefGoogle Scholar
Ratcliff, R (1978). A theory of memory retrieval. Psychological Review, 85(2), 59 10.1037/0033-295X.85.2.59CrossRefGoogle Scholar
Ratcliff, R, & McKoon, G (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20, 873–922. 10.1162/neco.2008.12-06-420CrossRefGoogle ScholarPubMed
Rubinstein, A (2007). Instinctive and cognitive reasoning: A study of response times. The Economic Journal, 117, 1243–1259. 10.1111/j.1468-0297.2007.02081.xCrossRefGoogle Scholar
Rubinstein, A (2008). Comments on neuroeconomics. Economics and Philosophy, 24, 473–483. 10.1017/S0266267108002101CrossRefGoogle Scholar
Rubinstein, A (2013). Response time and decision making: An experimental study. Judgement and Decision Making, 8(5), 540–551.CrossRefGoogle Scholar
Spiliopoulos, L, & Ortmann, A (2014). The BCD of response time analysis in experimental economics. Experimental Economics, 21, 383–433. 10.1007/s10683-017-9528-1CrossRefGoogle Scholar
Szkup, M., & Trevino, I. (2019). Sentiments, strategic uncertainty, and information structures in coordination games, mimeo.CrossRefGoogle Scholar
Webb, R (2019). The (neural) dynamics of stochastic choice. Management Science, 64(1), 230–255. 10.1287/mnsc.2017.2931CrossRefGoogle Scholar
Wilcox, NT (1993). Lottery choice: Incentives, complexity, and decision time. The Economic Journal, 103, 1397–1417. 10.2307/2234473CrossRefGoogle Scholar
Woodford, M (2014). Stochastic choice: An optimizing neuroeconomic model. American Economic Review: Papers and Proceedings, 104(5), 495–500. 10.1257/aer.104.5.495CrossRefGoogle Scholar