Minds and Machines

, Volume 26, Issue 1–2, pp 61–86 | Cite as

Rational Foundations of Fast and Frugal Heuristics: The Ecological Rationality of Strategy Selection via Improper Linear Models



Research on “improper” linear models has shown that predetermined weighting schemes for the linear model, such as equally weighting all predictors, can be surprisingly accurate on cross-validation. We review recent advances that can characterize the optimal choice of an improper linear model. We extend this research to the understanding of fast and frugal heuristics, particularly to the ecologically rational goal of understanding in which task environments given heuristics are optimal. We demonstrate how to test this model using the Recognition Heuristic and Take the Best heuristic, show how the model reconciles with the ecological rationality program, and discuss how our prescriptive, computational approach could be approximated by simpler mental rules that might be more descriptive. Echoing the arguments of van Rooij et al. (Synthese 187:471–487, 2012), we stress the virtue of having a computationally tractable model of strategy selection, even if one proposes that cognizers use a simpler heuristic process to approximate it.


Improper linear models Heuristics Fast and frugal Adaptive toolbox Take the best Recognition heuristic Ecological rationality 



We thank Mirjam Jenny and Jean Whitmore for helpful comments.


  1. Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006). The priority heuristic: Making choices without trade-offs. Psychological Review, 113, 409–432.CrossRefGoogle Scholar
  2. Czerlinski, J., Gigerenzer, G., & Goldstein, D. G. (1999). How good are simple heuristics? In G. Gigerenzer, P. M. Todd, & The ABC Research Group (Eds.), Simple heuristics that make us smart (pp. 97-118). New York: Oxford University Press.Google Scholar
  3. Dana, J. (2008). What makes improper linear models tick? In J. Krueger (Ed.), Rationality and social responsibility: Essays in honor of Robyn Mason Dawes. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  4. Dana, J., & Dawes, R. M. (2004). The superiority of simple alternatives to regression for social science predictions. Journal of Educational and Behavioral Statistics, 3, 317–331.CrossRefGoogle Scholar
  5. Davis-Stober, C. P. (2011). A geometric analysis of when fixed weighting schemes will outperform ordinary least squares. Psychometrika, 76, 650–669.MathSciNetCrossRefMATHGoogle Scholar
  6. Davis-Stober, C. P., Dana, J., & Budescu, D. (2010a). A constrained linear estimator for multiple regression. Psychometrika, 75, 521–541.MathSciNetCrossRefMATHGoogle Scholar
  7. Davis-Stober, C. P., Dana, J., & Budescu, D. (2010b). Why recognition is rational: Optimality results on single-variable decision rules. Judgment and Decision Making, 5, 216–229.Google Scholar
  8. Dawes, R. M. (1979). The robust beauty of improper linear models. The American Psychologist, 34, 571–582.CrossRefGoogle Scholar
  9. Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81, 95–106.CrossRefGoogle Scholar
  10. Einhorn, H. J., & Hogarth, R. M. (1975). Unit weighting schemes for decision making. Organizational Behavior and Human Performance, 13, 171–192.CrossRefGoogle Scholar
  11. Fasolo, B., McClelland, G. H., & Todd, P. M. (2007). Escaping the tyranny of choice: When fewer attributes make choice easier. Marketing Theory, 7, 13–26.CrossRefGoogle Scholar
  12. Flury, B., & Riedwyl, H. (1985). T2 tests, the linear two-group discriminant function, and their computation by linear regression. The American Statistician, 39, 20–25.MATHGoogle Scholar
  13. Gigerenzer, G. (1991). From tools to theories: A heuristic of discovery in cognitive psychology. Psychological Review, 98, 254–267.CrossRefGoogle Scholar
  14. Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science, 3, 20–29.CrossRefGoogle Scholar
  15. Gigerenzer, G., & Brighton, H. (2009). Homo heuristics: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107–143.CrossRefGoogle Scholar
  16. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650–669.CrossRefGoogle Scholar
  17. Gigerenzer, G., Todd, P. M., & the ABC Research Group. (1999). Simple heuristics that make us smart. New York: Oxford University Press.Google Scholar
  18. Goldstein, D. G. (1997). Models of bounded rationality for inference. Doctoral thesis, The University of Chicago. Dissertation Abstracts International, 58(01), 435B. (University Microfilms No. AAT 9720040).Google Scholar
  19. Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75–90.CrossRefGoogle Scholar
  20. Goldstein, D. G., & Gigerenzer, G. (2009). Fast and frugal forecasting. International Journal of Forecasting, 25, 760–772.CrossRefGoogle Scholar
  21. Hertwig, R., Davis, J. N., & Sulloway, F. J. (2002). Parental investment: How an equity motive can produce inequality. Psychological Bulletin, 128, 728–745.CrossRefGoogle Scholar
  22. Hogarth, R. M., & Karelaia, N. (2005). Ignoring information in binary choice with continuous variables: When is less more? Journal of Mathematical Psychology, 49, 115–124.MathSciNetCrossRefMATHGoogle Scholar
  23. Hogarth, R. M., & Karelaia, N. (2006). “Take-The-Best” and other simple strategies: Why and when they work “well” with binary cues. Theory and Decision, 61, 205–249.CrossRefMATHGoogle Scholar
  24. Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.Google Scholar
  25. Katsikopoulos, K. V. (2011). Psychological heuristics for making inferences: Definition, performance, and the emerging theory and practice. Decision Analysis, 8, 10–29.MathSciNetCrossRefGoogle Scholar
  26. Katsikopoulos, K. V., Schooler, L. J., & Hertwig, R. (2010). The robust beauty of ordinary information. Psychological Review, 117, 1259–1266.CrossRefGoogle Scholar
  27. Lehmann, E. L., & Casella, G. (1998). Theory of point estimation (2nd ed.). New York: Springer.MATHGoogle Scholar
  28. Marden, J. I. (2013). Multivariate statistics old school. Department of Statistics, The University of Illinois at Urbana-Champagin.Google Scholar
  29. Martignon, L., & Hoffrage, U. (2002). Fast, frugal, and fit: Simple heuristics for paired comparison. Theory and Decision, 52, 29–71.CrossRefMATHGoogle Scholar
  30. Schmidt, F. L. (1971). The relative efficiency of regression and simple unit predictor weights in applied differential psychology. Educational and Psychological Measurement, 31, 699–714.CrossRefGoogle Scholar
  31. Shanteau, J., & Thomas, R. P. (2000). Fast and frugal heuristics: What about unfriendly environments? Behavioral and Brain Sciences, 23, 762–763.CrossRefGoogle Scholar
  32. van Rooij, I., Wright, C. D., & Wareham, T. (2012). Intractability and the use of heuristics in psychological explanations. Synthese, 187, 471–487.MathSciNetCrossRefMATHGoogle Scholar
  33. von Winterfeldt, D., & Edwards, W. (1973). Costs and payoffs in perceptual research. Technical Report, No. 011313-1-T, Engineering Psychology Laboratory, University of Michigan.Google Scholar
  34. Wainer, H. (1976). Estimating coefficients in linear models: It dont make no nevermind. Psychological Bulletin, 83, 213–217.CrossRefGoogle Scholar
  35. Wilks, S. S. (1938). Weighting systems for linear functions of correlated variables when there is no dependent variable. Psychometrika, 3, 23–40.CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.School of ManagementYale UniversityNew HavenUSA
  2. 2.219 McAlester Hall, Dept of Psychological SciencesUniversity of MissouriColumbiaUSA

Personalised recommendations