Published Online:https://doi.org/10.5465/amj.2018.0172

Heuristics are often viewed as inferior to “rational” strategies that exhaustively search and process information. Introducing the theoretical perspective of ecological rationality, we challenge this view and argue that, under conditions of uncertainty common to managerial decision making, managers can actually make better decisions using fast-and-frugal heuristics. Within the context of personnel selection, we show that a heuristic called Δ-inference can more accurately predict which of two job applicants would perform better in the future than can logistic regression, a prototypical rational strategy. Using data from 236 applicants at an airline company, we demonstrate, in Study 1, that, despite searching less than half of the cues, Δ-inference leads to more accurate selection decisions than logistic regression. After this existence proof, we examine, in Study 2, the ecological conditions under which the heuristic predicts more accurately than logistic regression using 1,728 simulated task environments. Finally, in Study 3, we show in an experiment that participants adapted their strategies to the characteristics of a task—and increasingly so the greater their previous experience in selection decisions. The aim of this article is to propose ecological rationality as an alternative to current views about the nature of heuristics in managerial decisions.

References

  • Arkes, H. R., Gigerenzer, G., & Hertwig, R. 2016. How bad is incoherence? Decision, 3: 20–39. Google Scholar
  • Artinger, F., Petersen, M., Gigerenzer, G., & Weibler, J. 2015. Heuristics as adaptive decision strategies in management. Journal of Organizational Behavior, 36(S1): S33–S52. Google Scholar
  • Baum, R. J., & Wally, S. 2003. Strategic decision speed and firm performance. Strategic Management Journal, 24: 1107–1129. Google Scholar
  • Bazerman, M. H., & Moore, D. A. 2008. Judgment in managerial decision making. New York, NY: Wiley. Google Scholar
  • Beach, L. R., & Mitchell, T. R. 1978. A contingency model for the selection of decision strategies. Academy of Management Review, 3: 439–449.LinkGoogle Scholar
  • Bettman, J. R., Johnson, E. J., & Payne, J. W. 1990. A componential analysis of cognitive effort in choice. Organizational Behavior and Human Decision Processes, 45: 111–139. Google Scholar
  • Bingham, C. B., & Eisenhardt, K. M. 2011. Rational heuristics: The “simple rules” that strategists learn from process experience. Strategic Management Journal, 32: 1437–1464. Google Scholar
  • Bingham, C., & Haleblian, J. 2012. How firms learn heuristics: Uncovering missing components of organizational learning. Strategic Entrepreneurship Journal, 6: 152–177. Google Scholar
  • Bobko, P., Roth, P. L., & Potosky, D. 1999. Derivation and implications of a meta-analytic matrix incorporating cognitive ability, alternative predictors, and job performance. Personnel Psychology, 52: 561–589. Google Scholar
  • Borgatti, S. P., & Foster, P. C. 2003. The network paradigm in organizational research: A review and typology. Journal of Management, 29: 991–1013. Google Scholar
  • Brighton, H., & Gigerenzer, G. 2012. Homo heuristicus: Less-is-more effects in adaptive cognition. Malaysian Journal of Medical Sciences, 19: 6–16. Google Scholar
  • Cohen, J. 1994. The earth is round (p < .05). American Psychologist, 49: 997–1003. Google Scholar
  • Cooksey, R. W. 1996. Judgment analysis: Theory, methods, and applications. San Diego, CA: Academic Press. Google Scholar
  • Czerlinski, J., Gigerenzer, G., & Goldstein, D. G. 1999. How good are simple heuristics? In G. GigerenzerP. M. Todd, & the ABC Research Group (Eds.), Simple heuristics that make us smart: 97–118. New York, NY: Oxford University Press. Google Scholar
  • Dane, E., & Pratt, M. G. 2007. Exploring intuition and its role in managerial decision making. Academy of Management Review, 32: 33–54.LinkGoogle Scholar
  • Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. 2009. Optimal structure, market dynamism, and the strategy of simple rules. Administrative Science Quarterly, 54: 413–452. Google Scholar
  • Dean, J. W., Jr., & Sharfman, M. P. 1993. Procedural rationality in the strategic decision‐making process. Journal of Management Studies, 30: 587–610. Google Scholar
  • Dean, J. W., Jr., & Sharfman, M. P. 1996. Does decision process matter? A study of strategic decision-making effectiveness. Academy of Management Journal, 39: 368–392.LinkGoogle Scholar
  • De Corte, W. 1999. Weighing job performance predictors to both maximize the quality of the selected workforce and control the level of adverse impact. Journal of Applied Psychology, 84: 695–702. Google Scholar
  • De Corte, W., Lievens, F., & Sackett, P. R. 2006. Predicting adverse impact and mean criterion performance in multistage selection. Journal of Applied Psychology, 91: 523–537. Google Scholar
  • De Corte, W., Lievens, F., & Sackett, P. R. 2007. Combining predictors to achieve optimal trade-offs between selection quality and adverse impact. Journal of Applied Psychology, 92: 1380–1393. Google Scholar
  • Dhami, M. K. 2003. Psychological models of professional decision making. Psychological Science, 14: 175–180. Google Scholar
  • Dougherty, T. W., Ebert, R. J., & Callender, J. C. 1986. Policy capturing in the employment interview. Journal of Applied Psychology, 71: 9–15. Google Scholar
  • Einhorn, H. J., & Hogarth, R. M. 1975. Unit weighting schemes for decision making. Organizational Behavior and Human Performance, 13: 171–192. Google Scholar
  • Farr, J. L., & Tippins, N. T. 2010. Handbook of employee selection. New York, NY: Routledge. Google Scholar
  • Fiedler, F. E. 1964. A contingency model of leadership effectiveness. Advances in Experimental Social Psychology, 1: 149–190. Google Scholar
  • Fific, M., & Gigerenzer, G. 2014. Are two interviewers better than one? Journal of Business Research, 67: 1771–1779. Google Scholar
  • Finch, D. M., Edwards, B. D., & Wallace, J. C. 2009. Multistage selection strategies: Simulating the effects on adverse impact and expected performance for various predictor combinations. Journal of Applied Psychology, 94: 318–340. Google Scholar
  • Friedman, M. 1953. Essays in positive economics. Chicago, IL: University of Chicago Press. Google Scholar
  • Garcia-Retamero, R., & Dhami, M. K. 2009. Take-the-best in expert–novice decision strategies for residential burglary. Psychonomic Bulletin & Review, 16: 163–169. Google Scholar
  • Gatewood, R., Feild, H. S., & Barrick, M. 2015. Human resource selection (8th ed.). Boston, MA: Cengage. Google Scholar
  • Geisser, S. 1993. Predictive inference: An introduction. Boston, MA: Springer. Google Scholar
  • Geman, S., Bienenstock, E., & Doursat, R. 1992. Neural networks and the bias/variance dilemma. Neural Computation, 4: 1–58. Google Scholar
  • Gigerenzer, G. 2008. Why heuristics work. Perspectives on Psychological Science, 3: 20–29. Google Scholar
  • Gigerenzer, G. 2016. Towards a rational theory of heuristics. In R. FrantzL. Marsh (Eds.), Minds, models, and milieux: Commemorating the centennial of the birth of Herbert Simon: 34–59. New York, NY: Palgrave Macmillan. Google Scholar
  • Gigerenzer, G., & Brighton, H. 2009. Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1: 107–143. Google Scholar
  • Gigerenzer, G., & Gaissmaier, W. 2011. Heuristic decision making. Annual Review of Psychology, 62: 451–482. Google Scholar
  • Gigerenzer, G., & Goldstein, D. G. 1996. Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103: 650–669. Google Scholar
  • Gigerenzer, G., & Goldstein, D. G. 2011. The recognition heuristic: A decade of research. Judgment and Decision Making, 6: 100–121. Google Scholar
  • Gigerenzer, G., & Selten, R. (Eds.) 2002. Bounded rationality: The adaptive toolbox. Cambridge, MA: MIT Press. Google Scholar
  • Gilovich, T., Griffin, D., & Kahneman, D. (Eds.) 2002. Heuristics and biases: The psychology of intuitive judgment. Cambridge, U.K.: Cambridge University Press. Google Scholar
  • Glöckner, A. 2009. Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method. Judgment and Decision Making, 4: 186–199. Google Scholar
  • Green, L., & Mehr, D. R. 1997. What alters physicians’ decisions to admit to the coronary care unit? Journal of Family Practice, 45: 219–226. Google Scholar
  • Guion, R. M. 2011. Assessment, measurement, and prediction for personnel decisions. New York, NY: Routledge. Google Scholar
  • Hammond, J. S., Keeney, R. L., & Raiffa, H. 1998. The hidden traps in decision making. Harvard Business Review, 76: 47–58. Google Scholar
  • Hertwig, R., Davis, J. N., & Sulloway, F. J. 2002. Parental investment: How an equity motive can produce inequality. Psychological Bulletin, 128: 728–745. Google Scholar
  • Highhouse, S. 2008. Stubborn reliance on intuition and subjectivity in employee selection. In Society for Industrial and Organizational Psychology (Ed.), Industrial and organizational psychology: Vol. 1—Perspectives on science and practice: 333–342. Cambridge, U.K.: Cambridge University Press. Google Scholar
  • Highhouse, S., Dalal, R. S., & Salas, E. 2013. Judgment and decision making at work. New York, NY: Routledge. Google Scholar
  • Hodgkinson, G. P., Sadler-Smith, E., Burke, L. A., Claxton, G., & Sparrow, P. R. 2009. Intuition in organizations: Implications for strategic management. Long Range Planning, 42: 277–297. Google Scholar
  • Hogarth, R. M. 2001. Educating intuition. Chicago, IL: University of Chicago Press. Google Scholar
  • Hogarth, R. M., & Karelaia, N. 2007. Heuristic and linear models of judgment: Matching rules and environments. Psychological Review, 114: 733–758. Google Scholar
  • Jenny, M. A., Hertwig, R., Ackermann, S., Messmer, A. S., Karakoumis, J., Nickel, C. H., Bingisser, R., & Gaddis, G. 2015. Are mortality and acute morbidity in patients presenting with nonspecific complaints predictable using routine variables? Academic Emergency Medicine, 22: 1155–1163. Google Scholar
  • Katsikopoulos, K. V., Schooler, L. J., & Hertwig, R. 2010. The robust beauty of ordinary information. Psychological Review, 117: 1259–1266. Google Scholar
  • Kausel, E. E., Culbertson, S. S., & Madrid, H. P. 2016. Overconfidence in personnel selection: When and why unstructured interview information can hurt hiring decisions. Organizational Behavior and Human Decision Processes, 137: 27–44. Google Scholar
  • Kausel, E. E., & Slaughter, J. E. 2013. Employee selection decisions. In S. HighhouseR. S. DalalE. Salas (Eds.), Judgment and decision making at work: 77–99. New York, NY: Routledge. Google Scholar
  • Keller, N., & Katsikopoulos, K. V. 2016. On the role of psychological heuristics in operational research; and a demonstration in military stability operations. European Journal of Operational Research, 249: 1063–1073. Google Scholar
  • Knight, F. H. 1921. Risk, uncertainty and profit. Boston, MA: Houghton Mifflin. Google Scholar
  • Kohli, R., & Jedidi, K. 2007. Representation and inference of lexicographic preference models and their variants. Marketing Science, 26: 380–399. Google Scholar
  • Kruglanski, A., & Gigerenzer, G. 2011. Intuitive and deliberate judgments are based on common principles. Psychological Review, 118: 97–109. Google Scholar
  • Lazer, D., Kennedy, R., King, G., & Vespignani, A. 2014. Big data. The parable of Google flu: Traps in big data analysis. Science, 343: 1203–1205 (Appendix). Google Scholar
  • Leonard, K. 2011. Talent acquisition factbook 2011: Benchmarks and trends of spending, staffing and key talent metrics (Report). Oakland, CA: Bersin & Associates. Google Scholar
  • Lewandowsky, S., & Farrell, S. 2011. Computational modeling in cognition: Principles and practice. Thousand Oaks, CA: SAGE. Google Scholar
  • Lievens, F., Highhouse, S., & De Corte, W. 2005. The importance of traits and abilities in supervisors’ hirability decisions as a function of method of assessment. Journal of Occupational and Organizational Psychology, 78: 453–470. Google Scholar
  • Luan, S., & Reb, J. 2017. Fast-and-frugal trees as noncompensatory models of performance-based personnel decisions. Organizational Behavior and Human Decision Processes, 141: 29–42. Google Scholar
  • Luan, S., Schooler, L. J., & Gigerenzer, G. 2014. From perception to preference and on to inference: An approach–avoidance analysis of thresholds. Psychological Review, 121: 501–525. Google Scholar
  • Manimala, M. J. 1992. Entrepreneurial heuristics: A comparison between high PL (pioneering-innovative) and low PI ventures. Journal of Business Venturing, 7: 477–504. Google Scholar
  • Marewski, J. N., & Schooler, L. J. 2011. Cognitive niches: An ecological model of strategy selection. Psychological Review, 118: 393–437. Google Scholar
  • Martignon, L., & Hoffrage, U. 1999. Why does one-reason decision making work? In G. GigerenzerP. M. Todd, & the ABC Research Group (Eds.), Simple heuristics that make us smart: 119–140. New York, NY: Oxford University Press. Google Scholar
  • Martignon, L., & Hoffrage, U. 2002. Fast, frugal, and fit: Simple heuristics for paired comparison. Theory and Decision, 52: 29–71. Google Scholar
  • McCrae, R. R., Martin, T. A., & Costa, P. T. 2005. Age trends and age norms for the NEO Personality Inventory-3 in adolescents and adults. Assessment, 12: 363–373. Google Scholar
  • Milkman, K. L., Chugh, D., & Bazerman, M. H. 2009. How can decision making be improved? Perspectives on Psychological Science, 4: 379–383. Google Scholar
  • Münsterberg, H. 1912. Psychologie und Wirtschaftsleben: Ein Beitrag zur angewandten Experimental-Psychologie. Leipzig, Germany: Barth. Google Scholar
  • Newell, A., & Simon, H. A. 1972. Human problem solving. Englewood Cliffs, NJ: Prentice-Hall. Google Scholar
  • Pachur, T., & Marinello, G. 2013. Expert intuitions: How to model the decision strategies of airport customs officers? Acta Psychologica, 144: 97–103. Google Scholar
  • Payne, J. W., Bettman, J. R., & Johnson, E. J. 1993. The adaptive decision maker. Cambridge, U.K.: Cambridge University Press. Google Scholar
  • Phillips, N. D., Neth, H., Woike, J. K., & Gaissmaier, W. 2017. FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees. Judgment and Decision Making, 12: 344–368. Google Scholar
  • Rieskamp, J., & Otto, P. E. 2006. SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology. General, 135: 207–236. Google Scholar
  • Roth, P. L., Bobko, P., Switzer, F. S., & Dean, M. A. 2001. Prior selection causes biased estimates of standardized ethnic group differences: Simulation and analysis. Personnel Psychology, 54: 591–617. Google Scholar
  • Roth, P. L., Switzer, F. S., III, Van Iddekinge, C. H., & Oh, I. S. 2011. Toward better meta‐analytic matrices: How input values can affect research conclusions in human resource management simulations. Personnel Psychology, 64: 899–935. Google Scholar
  • Ryan, A. M., & Ployhart, R. E. 2014. A century of selection. Annual Review of Psychology, 65: 693–717. Google Scholar
  • Sackett, P. R., & Lievens, F. 2008. Personnel selection. Annual Review of Psychology, 59: 419–450. Google Scholar
  • Savage, L. J. 1954. The foundations of statistics. New York, NY: John Wiley and Sons. Google Scholar
  • Schmidt, F. L., & Hunter, J. E. 1998. The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124: 262–274. Google Scholar
  • Schmidt, F. L., & Hunter, J. 2004. General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86: 162–173. Google Scholar
  • Schwartz, J., Collins, L., Stockton, H., Wagner, D., & Walsh, B. 2017. Rewriting the rules for the digital age: Deloitte global human capital trends. Vancouver, BC, Canada: Deloitte University Press. Google Scholar
  • Simon, H. A. 1947. Administrative behavior: A study of decision-making processes in administrative organizations. New York, NY: Macmillan. Google Scholar
  • Simon, H. A. 1955. A behavioral model of rational choice. Quarterly Journal of Economics, 69: 99–118. Google Scholar
  • Simon, H. A. 1957. Administrative behavior: A study of decision-making processes in administrative organizations (2nd ed.). New York, NY: Macmillan. Google Scholar
  • Simon, H. A. 1971. Designing organizations for an information-rich world. In M. Greenberger (Ed.), Computers, communications and the public interest: 37–72. Baltimore, MD: Johns Hopkins Press. Google Scholar
  • Simon, H. A. 1990. Invariants of human behavior. Annual Review of Psychology, 41: 1–20. Google Scholar
  • Şimşek, Ö., & Buckmann, M. 2015. Learning from small samples: An analysis of simple decision heuristics. Advances in Neural Information Processing Systems, 2015: 3159–3167. Google Scholar
  • Stone, M. 1974. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B. Methodological, 36: 111–147. Google Scholar
  • Thaler, R. H., & Sunstein, C. R. 2008. Nudge. New Haven, CT: Yale University Press. Google Scholar
  • Todd, P. M., Gigerenzer, G., & The ABC Research Group (Eds.) 2012. Ecological rationality: Intelligence in the world. New York, NY: Oxford University Press. Google Scholar
  • Tversky, A. 1969. Intransitivity of preferences. Psychological Review, 76: 31–48. Google Scholar
  • Tversky, A. 1972. Elimination by aspects: A theory of choice. Psychological Review, 79: 281–299. Google Scholar
  • Tversky, A., & Kahneman, D. 1974. Judgment under uncertainty: Heuristics and biases. Science, 185: 1124–1131. Google Scholar
  • Vroom, V. H., & Jago, A. G. 2007. The role of the situation in leadership. American Psychologist, 62: 17–24. Google Scholar
  • Wegwarth, O., Gaissmaier, W., & Gigerenzer, G. 2009. Smart strategies for doctors and doctors-in-training: Heuristics in medicine. Medical Education, 43: 721–728. Google Scholar
Academy of Management
  Academy of Management
  555 Pleasantville Road, Suite N200
  Briarcliff Manor, NY 10510-8020, USA
  Phone: +1 (914) 326-1800
  Fax: +1 (914) 326-1900