Economic rationality under cognitive load

Drichoutis, Andreas C. and Nayga, Rodolfo (2017): Economic rationality under cognitive load.

Abstract

Economic analysis assumes that consumer behavior can be rationalized by a utility function. Previous research has shown that some decision-making quality can be captured by permanent cognitive ability but has not examined how a temporary load in subjects' working memory can affect economic rationality. In a controlled laboratory experiment, we exogenously vary cognitive load by asking subjects to memorize a number while they undertake an induced budget allocation task (Choi et al., 2007a,b). Using a number of manipulation checks, we verify that cognitive load has adverse affects on subjects' performance in reasoning tasks. However, we find no effect in any of the goodness-of-fit measures that measure consistency of subjects' choices with the Generalized Axiom of Revealed Preferences (GARP), despite having a sample size large enough to detect even small differences between treatments with 80% power. Our finding suggests that researchers need not worry about economic rationality breaking down when subjects are placed under temporary working memory load.

• Item Type: MPRA Paper
• Language: English
• Keywords: Cognitive load, rationality, revealed preferences, working memory, response times, laboratory experiment
• Subjects: C - Mathematical and Quantitative Methods > C9 - Design of Experiments > C91 - Laboratory, Individual Behavior; D - Microeconomics > D0 - General > D03 - Behavioral Microeconomics: Underlying Principles; D - Microeconomics > D1 - Household Behavior and Family Economics > D11 - Consumer Economics: Theory; D - Microeconomics > D1 - Household Behavior and Family Economics > D12 - Consumer Economics: Empirical Analysis; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions

References

Afriat, S. N. (1967). The construction of utility functions from expenditure data. International Economic Review 8 (1), 67-77.

Afriat, S. N. (1972). Efficiency estimation of production functions. International Economic Review 13 (3), 568-598.

Andreoni, J., M. A. Kuhn, and C. Sprenger (2013). On measuring time preferences. Working paper, University of California at San Diego.

Becker, G. S. (1962). Irrational behavior and economic theory. Journal of Political Economy 70 (1), 1-13.

Benjamin, D. J., S. A. Brown, and J. M. Shapiro (2013). Who is `behavioral'? Cognitive ability and anomalous preferences. Journal of the European Economic Association 11 (6), 1231-1255.

Bilker, W. B., J. A. Hansen, C. M. Brensinger, J. Richard, R. E. Gur, and R. C. Gur (2012). Development of abbreviated nine-item forms of the raven's standard progressive matrices test. Assessment 19 (3), 354-369.

Briz, T., A. C. Drichoutis, and R. M. Nayga (2016). Randomization to treatment failure in experimental auctions: The value of data from training rounds. Munich Personal RePEc Archive No. 70139 .

Bronars, S. G. (1987). The power of nonparametric tests of preference maximization. Econometrica 55 (3), 693-698.

Burghart, D. R., P. W. Glimcher, and S. C. Lazzaro (2013). An expected utility maximizer walks into a bar. Journal of Risk and Uncertainty 46 (3), 215-246.

Castillo, M., D. L. Dickinson, and R. Petrie (2017). Sleepiness, choice consistency, and risk preferences. Theory and Decision 82 (1), 41-73.

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

Choi, S., R. Fisman, D. M. Gale, and S. Kariv (2007b). Revealing preferences graphically: An old method gets a new tool kit. American Economic Review 97 (2), 153-158.

Choi, S., S. Kariv, W. Muller, and D. Silverman (2014). Who is (more) rational? American Economic Review 104 (6), 1518-50.

Cochran, W. G. and D. B. Rubin (1973). Controlling bias in observational studies: A review. Sankhya: The Indian Journal of Statistics, Series A 35 (4), 417-446.

Colom, R., I. Rebollo, A. Palacios, M. Juan-Espinosa, and P. C. Kyllonen (2004). Working memory is (almost) perfectly predicted by g. Intelligence 32 (3), 277-296.

Dean, M. and D. Martin (2016). Measuring rationality with the minimum cost of revealed preference violations. The Review of Economics and Statistics 98 (3), 524-534.

Deaton, A. and N. Cartwright (2016). Understanding and misunderstanding randomized controlled trials. National Bureau of Economic Research Working Paper No. 22595.

Deck, C. and S. Jahedi (2015). The effect of cognitive load on economic decision making: A survey and new experiments. European Economic Review 78, 97-119.

Echenique, F., S. Lee, and M. Shum (2011). The money pump as a measure of revealed preference violations. Journal of Political Economy 119 (6), 1201-1223.

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

Frederick, S. (2005). Cognitive reection and decision making. Journal of Economic Perspectives 19 (4), 25-42.

Gerhardt, H., G. P. Biele, H. R. Heekeren, and H. Uhlig (2016). Cognitive load increases risk aversion. SFB 649 Discussion Paper 2016-011.

Gray, J. R., C. F. Chabris, and T. S. Braver (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience 6 (3), 316-322.

Gray, J. R. and P. M. Thompson (2004). Neurobiology of intelligence: science and ethics. Nature Reviews Neuroscience 5 (6), 471-482.

Greiner, B. (2015). Subject pool recruitment procedures: Organizing experiments with ORSEE. Journal of the Economic Science Association 1 (1), 114-125.

Gross, J. and D. Kaiser (1996). Two simple algorithms for generating a subset of data consistent with warp and other binary relations. Journal of Business & Economic Statistics 14 (2), 251-255.

Heufer, J. and P. Hjertstrand (2015). Consistent subsets: Computationally feasible methods to compute the Houtman-Maks-index. Economics Letters 128, 87-89.

Hinson, J. M., T. L. Jameson, and P. Whitney (2003). Impulsive decision making and working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition 29 (2), 298-306.

Ho, D. E., K. Imai, G. King, and E. A. Stuart (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 15 (3), 199-236.

Houtman, M. and J. Maks (1985). Determining all maximal data subsets consistent with revealed preference. Kwantitatieve Methoden 19, 89-104.

Imbens, G. W. and D. B. Rubin (2016). Causal Inference for Statistics, Social, and Biomedical Sciences, An introduction. Cambridge and New York: Cambridge University Press.

Imbens, G. W. and J. M. Wooldridge (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47 (1), 5-86.

Kahneman, D. (2011). Thinking, fast and slow (1st ed.). New York: Farrar, Straus and Giroux.

Kupper, L. L. and K. B. Hafner (1989). How appropriate are popular sample size formulas? The American Statistician 43 (2), 101-105.

Lehr, R. (1992). Sixteen s-squared over d-squared: A relation for crude sample size estimates. Statistics in Medicine 11 (8), 1099-1102.

Moher, D., S. Hopewell, K. F. Schulz, V. Montori, P. C. Gtzsche, P. J. Devereaux, D. Elbourne, M. Egger, and D. G. Altman (2010). CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ 340.

Mutz, D. C. and R. Pemantle (2015). Standards for experimental research: Encouraging a better understanding of experimental methods. Journal of Experimental Political Science 2 (2), 192-215.

Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66 (5), 688-701.

Rubin, D. B. (1990). [On the application of probability theory to agricultural experiments. Essay on principles. Section 9.] Comment: Neyman (1923) and causal inference in experiments and observational studies. Statistical Science 5 (4), 472-480.

Shiv, B. and A. Fedorikhin (1999). Heart and mind in conict: the interplay of affect and cognition in consumer decision making. Journal of Consumer Research 26 (3), 278-292.

Sims, C. A. (2003). Implications of rational inattention. Journal of Monetary Economics 50 (3), 665-690.

Sims, C. A. (2006). Rational inattention: Beyond the linear-quadratic case. American Economic Review 96 (2), 158-163.

Smeulders, B., L. Cherchye, F. C. R. Spieksma, and B. De Rock (2013). The money pump as a measure of revealed preference violations: A comment. Journal of Political Economy 121 (6), 1248-1258.

Speed, T. P. (1990). Introductory remarks on Neyman (1923). Statistical Science 5 (4), 463-464.

Splawa-Neyman, J., D. M. Dabrowska, and T. P. Speed (1990). On the application of probability theory to agricultural experiments. essay on principles. section 9. Statistical Science 5 (4), 465-472.

Stanovich, K. E. and R. F. West (2001). Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences 23 (5), 645-665.

StataCorp (2013). Stata user's guide release 13. College Station, Texas, USA: Stata Press.

van Belle, G. (2008). Statistical Rules of Thumb (2nd ed.). John Wiley & Sons, Inc.

van Bruggen, P. and J. Heufer (2017). Afriat in the lab. Journal of Economic Theory 169, 546-550.

Varian, H. (1993). Goodness-of-fit for revealed preference tests. University of Michigan, Mimeo.

Varian, H. R. (1982). The nonparametric approach to demand analysis. Econometrica 50 (4), 945-973.

Varian, H. R. (1988). Revealed preference with a subset of goods. Journal of Economic Theory 46 (1), 179-185.

Varian, H. R. (1990). Goodness-of-fit in optimizing models. Journal of Econometrics 46 (1), 125-140.

Varian, H. R. (1996). Efficiency in production and consumption. In H. R. Varian (Ed.), Computational Economics and Finance: Modeling and Analysis with Mathematica, pp. 131-142. New York: Springer-Verlag.

Warshall, S. (1962). A theorem on boolean matrices. Journal of the ACM 9 (1), 11-12.