Nudge Versus Boost: How Coherent are Policy and Theory?

Abstract

If citizens’ behavior threatens to harm others or seems not to be in their own interest (e.g., risking severe head injuries by riding a motorcycle without a helmet), it is not uncommon for governments to attempt to change that behavior. Governmental policy makers can apply established tools from the governmental toolbox to this end (e.g., laws, regulations, incentives, and disincentives). Alternatively, they can employ new tools that capitalize on the wealth of knowledge about human behavior and behavior change that has been accumulated in the behavioral sciences (e.g., psychology and economics). Two contrasting approaches to behavior change are nudge policies and boost policies. These policies rest on fundamentally different research programs on bounded rationality, namely, the heuristics and biases program and the simple heuristics program, respectively. This article examines the policy–theory coherence of each approach. To this end, it identifies the necessary assumptions underlying each policy and analyzes to what extent these assumptions are implied by the theoretical commitments of the respective research program. Two key results of this analysis are that the two policy approaches rest on diverging assumptions and that both suffer from disconnects with the respective theoretical program, but to different degrees: Nudging appears to be more adversely affected than boosting does. The article concludes with a discussion of the limits of the chosen evaluative dimension, policy–theory coherence, and reviews some other benchmarks on which policy programs can be assessed.

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Notes

  1. 1.

    According to the accuracy–effort trade-off, the less information, computation, or time a decision maker uses, the less accurate his or her judgments will be (see Payne et al. 1993). From this perspective, heuristics are construed to be less effortful, but never more accurate, than more complex strategies.

  2. 2.

    Some authors have suggested the term “educate” for these kinds of policies (Bond 2009; Katsikopolous 2014). In our view, boosting goes beyond education and the provision of information. For example, in order to boost decision makers’ skills, policy designers need to identify information representations that match the cognitive algorithms of the human mind, thus using the environment (e.g., external representations) as an ally to foster insight and decision-making skills. We therefore prefer the term “boost” to “educate”.

  3. 3.

    In the interest of full disclosure, let us point us that the second author has contributed to the SH program (see, e.g., Hertwig et al. 2013).

  4. 4.

    To be precise, the original Save More Tomorrow™ program consisted of two stages. In the first, a consultant discussed possible retirement plans with the employees, based on their own stated preferences. Only if they were reluctant to accept the consultant’s advice did the consultant switch to the program (Thaler and Benartzi 2004, p. 172).

  5. 5.

    Natural frequencies refer to the outcomes of natural sampling—that is, the acquisition of information by updating event frequencies without artificially fixing the marginal frequencies. Unlike probabilities and relative frequencies, natural frequencies are raw observations that have not been normalized with respect to the base rates of the event in question.

  6. 6.

    It could be argued that the collective approach is consistent with the legal approach of a hierarchy of nearest relatives to the extent that the legally assigned (or patient-designated) surrogate can always consult others. This is correct, but the surrogate is not obliged to consult anybody else, nor does he or she need to take others’ opinion into account should their opinion differ from his or hers.

  7. 7.

    The SH program does not endorse the strong language used by (some) proponents of the H&B approach to suggest that human reasoning is at times severely deficient (see Lopes 1991). One reason is that terms such as “cognitive illusions” presuppose the existence of a clear and unambiguous normative benchmark—an issue that has been hotly debated between the two programs (e.g., Gigerenzer 1996; Kahneman and Tversky 1996). Here, we use the more descriptive term “error” instead of “illusion” or “bias”.

  8. 8.

    Some proponents of nudge policies consider the possibility that some people may be immune to certain errors, thus admitting a kind of population heterogeneity. Asymmetric paternalism (Camerer et al. 2003), for example, assumes that some members of a population may be fully rational, and hence not need a nudge that others require. Consequently, it seeks to devise policies that affect only those whose judgments are erroneous. Yet even asymmetric paternalism assumes that those who are subject to error are affected in such a way that a uniform nudge can steer them toward their optimal option.

References

  1. Akl, E. A., Oxman, A. D., Herrin, J., Vist, G. E., Terrenato, J., Sperati, F., Costiniuk C, Blank D., & Schünemann H. (2011). Using alternative statistical formats for presenting risks and risk reductions. Cochrane Database of Systematic Reviews, 3, CD006776.

    Google Scholar 

  2. Anderson, B. L., Gigerenzer, G., Parker, S., & Schulkin, J. (2012). Statistical literacy in obstetricians and gynecologists. Journal for Healthcare Quality, 36(1), 5–17.

    Article  Google Scholar 

  3. Arkes, H. R., Gigerenzer, G., & Hertwig, R. (2014). Coherence cannot be a universal criterion for rational behavior: An ecological perspective. Manuscript submitted for publication.

  4. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer Jr & T. K. Srull (Eds.), Handbook of social cognition (pp. 1–40). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  5. Beglinger, B., Rohacek, M., Ackermann, S., Hertwig, R., Karakoumis-Ilsemann, J., Boutellier, S., Geigy, N., Nickel, C., & Bingisser, R. (2015). The physician’s first clinical impression of emergency department patients with non-specific complaints is associated with morbidity and mortality. Medicine, 94(7), e374. doi:10.1097/MD.0000000000000374

  6. Berwick, D. M., Fineberg, H. V., & Weinstein, M. C. (1981). When doctors meet numbers. American Journal of Medicine, 71, 991–998.

    Article  Google Scholar 

  7. Beshears, J., Choi, J. J., Laibson, D., & Madrian, B. C. (2009). The importance of default options for retirement saving outcomes: Evidence from the United States. In J. R. Brown, J. B. Liebman, & D. A. Wise (Eds.), Social security policy in a changing environment (pp. 167–195). Chicago, IL: University of Chicago Press.

    Google Scholar 

  8. Bond, M. (2009). Risk school. Nature, 461, 1189–1192.

    Article  Google Scholar 

  9. Bornstein, B. H., & Emler, A. C. (2001). Rationality in medical decision making: A review of the literature on doctors’ decision-making biases. Journal of Evaluation in Clinical Practice, 7, 97–107.

    Article  Google Scholar 

  10. Börsch-Supan, A. (2004). Mind the gap: The effectiveness of incentives to boost retirement saving in Europe. OECD Economic Studies, 39, 111–144.

    Google Scholar 

  11. Bovens, L. (2008). The ethics of nudge. In T. Grüne-Yanoff & S. O. Hansson (Eds.), Preference change: Approaches from philosophy, economics and psychology (pp. 207–219). Berlin: Springer.

    Google Scholar 

  12. Brunswik, E. (1956). Perception and the representative design of psychological experiments. Berkeley, CA: University of California Press.

    Google Scholar 

  13. Camerer, C., Issacharoff, S., Loewenstein, G., O’Donoghue, T., & Rabin, M. (2003). Regulation for conservatives: Behavioral economics and the case for “asymmetric paternalism”. University of Pennsylvania Law Review, 151(3), 1211–1254.

    Article  Google Scholar 

  14. Cialdini, R. B. (2001). Influence: Science and practice (4th ed.). Boston: Allyn & Bacon.

    Google Scholar 

  15. Covey, J. (2007). A meta-analysis of the effects of presenting treatment benefits in different formats. Medical Decision Making, 27, 638–654.

    Article  Google Scholar 

  16. European Commission. (2011). Attitudes of European citizens towards the environment. (Special Eurobarometer 365). Retrieved from http://ec.europa.eu/environment/pdf/ebs_365_en.pdf.

  17. Fateh-Moghadam, B., & Gutmann, T. (2013). Governing [through] autonomy: The moral and legal limits of “soft paternalism”. Ethical Theory and Moral Practice, 17, 383–397.

    Article  Google Scholar 

  18. Fischer, J. E., Steiner, F., Zucol, F., Berger, C., Martignon, L., Bossart, W., et al. (2002). Use of simple heuristics to target macrolide prescription in children with community-acquired pneumonia. Archives of Pediatrics and Adolescent Medicine, 156, 1005–1008.

    Article  Google Scholar 

  19. Fodor, J. A. (1983). The modularity of mind. Cambridge, MA: MIT Press.

    Google Scholar 

  20. Fox, C. R., Rogers, B. A., & Tversky, A. (1996). Options traders exhibit subadditive decision weights. Journal of Risk and Uncertainty, 13, 5–17.

    MATH  Article  Google Scholar 

  21. Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic perspectives, 19(4), 25–42.

    Article  Google Scholar 

  22. Freeman, A. M, I. I. I. (1993). The measurement of environmental and resource values. Washington, DC: Resources for the Future.

    Google Scholar 

  23. Frey, R., Hertwig, R., & Herzog, S. M. (2014). Surrogate decision making: Do we have to trade off accuracy and procedural satisfaction? Medical Decision Making, 34, 258–269.

    Article  Google Scholar 

  24. García-Retamero, R., Galesic, M., & Gigerenzer, G. (2010). Do icon arrays help reduce denominator neglect? Medical Decision Making, 30, 672–684.

    Article  Google Scholar 

  25. Gerend, M. A., & Cullen, M. (2008). Effects of message framing and temporal context on college student drinking behavior. Journal of Experimental Social Psychology, 44(4), 1167–1173.

    Article  Google Scholar 

  26. Gigerenzer, G. (1996). On narrow norms and vague heuristics: A reply to Kahneman and Tversky (1996). Psychological Review, 103, 592–596.

    Article  Google Scholar 

  27. Gigerenzer, G. (2005). I think, therefore I err. Social Research: An International Quarterly, 72(1), 1–24.

    Google Scholar 

  28. Gigerenzer, G. (2010). Collective statistical illiteracy. Archives of Internal Medicine, 170, 468–469.

    Article  Google Scholar 

  29. Gigerenzer, G. (2014). Breast cancer screening pamphlets mislead women: All women and women’s organisations should tear up the pink ribbons and campaign for honest information. British Medical Journal, 348, g2636.

    Article  Google Scholar 

  30. Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143.

    Article  Google Scholar 

  31. Gigerenzer, G., & Edwards, A. (2003). Simple tools for understanding risks: From innumeracy to insight. British Medical Journal, 327, 741–744.

    Article  Google Scholar 

  32. Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L. M., & Woloshin, S. (2007). Helping doctors and patients make sense of health statistics. Psychological Science in the Public Interest, 8(2), 53–96.

    Google Scholar 

  33. Gigerenzer, G., Hertwig, R., & Pachur, T. (Eds.). (2011). Heuristics: The foundations of adaptive behavior. Oxford, UK: Oxford University Press.

    Google Scholar 

  34. Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102(4), 684–704.

    Article  Google Scholar 

  35. Gigerenzer, G., & Muir Gray, J. A. (Eds.). (2011). Better doctors, better patients, better decisions: Envisioning health care 2020. Cambridge, MA: MIT Press.

    Google Scholar 

  36. Gigerenzer, G., Todd, P. M., & the ABC Research Group. (1999). Simple heuristics that make us smart. New York, NY: Oxford University Press.

    Google Scholar 

  37. Gilovich, T., Griffin, D., & Kahneman, D. (Eds.). (2002). Heuristics and biases: The psychology of intuitive judgment. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  38. Glaeser, E. L. (2006). Paternalism and psychology. University of Chicago Law Review, 73, 133–156.

    Google Scholar 

  39. Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecolological rationality: The recognition heuristic. Psychological Review, 109, 75–90.

    Article  Google Scholar 

  40. Grüne-Yanoff, T. (2012). Old wine in new casks: Libertarian paternalism still violates liberal principles. Social Choice and Welfare, 38(4), 635–645.

    MathSciNet  MATH  Article  Google Scholar 

  41. Hausman, D. M., & Welch, B. (2010). Debate: To nudge or not to nudge. Journal of Political Philosophy, 18(1), 123–136.

    Article  Google Scholar 

  42. Hautz, W. E., Kämmer, J. E., Schauber, S. K., Spies, C. D., & Gaissmaier, W. (2015). Diagnostic performance by medical students working individually or in teams. JAMA, 313, 303–304.

    Article  Google Scholar 

  43. Heath, C., Larrick, R. P., & Klayman, J. (1998). Cognitive repairs: How organizational practices can compensate for individual shortcomings. Research in Organizational Behavior, 20, 1–37.

    Google Scholar 

  44. Hertwig, R. (2012). Tapping into the wisdom of the crowd: With confidence. Science, 336(6079), 303–304.

    Article  Google Scholar 

  45. Hertwig, R., Buchan, H., Davis, D. A., Gaissmaier, W., Härter, M., Kolpatzik, K., Légaré, F., Schmacke, N., & Wormer, H. (2011). How will health care professionals and patients work together in 2020? A manifesto for change. In G. Gigerenzer & J. A. Muir Gray (Eds.), Better doctors, better patients, better decisions: Envisioning health care 2020 (pp. 317–337). Cambridge, MA: MIT Press.

    Google Scholar 

  46. Hertwig, R., & Erev, I. (2009). The description–experience gap in risky choice. Trends in Cognitive Sciences, 13, 517–523.

    Article  Google Scholar 

  47. Hertwig, R., & Gigerenzer, G. (1999). The “conjunction fallacy” revisited: How intelligent inferences look like reasoning errors. Journal of Behavioral Decision Making, 12, 275–305.

    Article  Google Scholar 

  48. Hertwig, R., Hoffrage, U., & the ABC Research Group. (2013). Simple heuristics in a social world. New York, NY: Oxford University Press.

    Google Scholar 

  49. Herzog, S. M., & Hertwig, R. (2009). The wisdom of many in one mind: Improving individual judgments with dialectical bootstrapping. Psychological Science, 20, 231–237.

    Article  Google Scholar 

  50. Herzog, S. M., & Hertwig, R. (2013). The crowd-within and the benefits of dialectical bootstrapping: A reply to White and Antonakis (2013). Psychological Science, 24, 117–119.

    Article  Google Scholar 

  51. Herzog, S. M., & Hertwig, R. (2014). Think twice and then: Combining or choosing in dialectical bootstrapping? Journal of Experimental Psychology: Learning, Memory, and Cognition, 40, 218–232.

    Google Scholar 

  52. Hoffrage, U., Lindsey, S., Hertwig, R., & Gigerenzer, G. (2000). Communicating statistical information. Science, 290, 2261–2262.

    Article  Google Scholar 

  53. House of Lords Science and Technology Select Committee. (2011). Behaviour change (2nd report of session 2010–12, HL paper 179). London, UK: The stationary office. Retrieved from http://www.publications.parliament.uk/pa/ld201012/ldselect/ldsctech/179/179.pdf.

  54. Jenny, M. A., Pachur, T., Williams, S. L., Becker, E., & Margraf, J. (2013). Simple rules for detecting depression. Journal of Applied Research in Memory and Cognition, 2, 149–157.

    Article  Google Scholar 

  55. Johnson, E. J., Bellman, S., & Lohse, G. L. (2002). Defaults, framing and privacy: Why opting in-opting out. Marketing Letters, 13(1), 5–15.

    Article  Google Scholar 

  56. Johnson, E., & Goldstein, D. (2003). Do defaults save lives? Science, 302, 1338–1339.

    Article  Google Scholar 

  57. Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. The American Economic Review, 93, 1449–1475.

    Article  Google Scholar 

  58. Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus & Giroux.

    Google Scholar 

  59. Kahneman, D., Diener, E., & Schwarz, N. (Eds.). (1999). Well-being: The foundations of hedonic psychology. New York, NY: Russell Sage Foundation.

    Google Scholar 

  60. Kahneman, D., & Krueger, A. B. (2006). Developments in the measurement of subjective well-being. Journal of Economic Perspectives, 20, 3–24.

    Article  Google Scholar 

  61. Kahneman, D., & Renshon, J. (2007). Why hawks win. Foreign Policy, 158, 34–38.

  62. Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. New York, NY: Cambridge University Press.

    Google Scholar 

  63. Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3, 430–454.

    Article  Google Scholar 

  64. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.

    MATH  Article  Google Scholar 

  65. Kahneman, D., & Tversky, A. (1996). On the reality of cognitive illusions: A reply to Gigerenzer’s critique. Psychological Review, 103, 582–591.

    Article  Google Scholar 

  66. Katsikopoulos, K. (2014). Bounded rationality: The two cultures. Journal of Economic Methodology,. doi:10.1080/1350178X.2014.965908.

    Google Scholar 

  67. Klein, J. G. (2005). Five pitfalls in decisions about diagnosis and prescribing. British Medical Journal, 330, 781–783.

    Article  Google Scholar 

  68. Koehler, J. J. (1996). The base rate fallacy reconsidered: Descriptive, normative and methodological challenges. Behavioral and Brain Sciences, 19, 1–53.

    Article  Google Scholar 

  69. Kruglanski, A. W., & Gigerenzer, G. (2011). Intuitive and deliberate judgments are based on common principles. Psychological Review, 118, 97–109.

    Article  Google Scholar 

  70. Kunreuther, H., & Michel-Kerjan, E. (2011). People get ready: Disaster preparedness. Issues in Science and Technology, 28, 1–7.

    Google Scholar 

  71. Kurz-Milcke, E., Gigerenzer, G., & Martignon, L. (2008). Transparency in risk communication: Graphical and analog tools. Annals of the New York Academy of Sciences, 1128, 18–28.

    Article  Google Scholar 

  72. Kurz-Milcke, E., Gigerenzer, G., & Martignon, L. (2011). Risiken durchschauen: Grafische und analoge Werkzeuge [Understanding risk: Graphical and analog tools]. Stochastik in der Schule, 31, 8–16.

    Google Scholar 

  73. Latten, S., Martignon, L., Monti, M., & Multmeier, J. (2011). Die Förderung erster Kompetenzen für den Umgang mit Risiken bereits in der Grundschule: ein Projekt von RIKO-STAT und dem Harding Center [Teaching risk literacy in elementary school: A project of RIKO-STAT and the Harding Center]. Stochastik in der Schule, 31, 17–25.

    Google Scholar 

  74. Levin, I. P., & Gaeth, G. J. (1988). How consumers are affected by the framing of attribute information before and after consuming the product. Journal of Consumer Research, 15, 374–378.

    Article  Google Scholar 

  75. Lindsey, S., Hertwig, R., & Gigerenzer, G. (2003). Communicating statistical DNA evidence. Jurimetrics: The Journal of Law, Science, and Technology, 43, 147–163.

    Google Scholar 

  76. Loewenstein, G., & Prelec, D. (1992). Anomalies in intertemporal choice: Evidence and an interpretation. Quarterly Journal of Economics, 107, 573–597.

    Article  Google Scholar 

  77. Lopes, L. L. (1991). The rhetoric of irrationality. Theory & Psychology, 1, 65–82.

    Article  Google Scholar 

  78. Luan, S., Schooler, L. J., & Gigerenzer, G. (2011). A signal detection analysis of fast-and-frugal trees. Psychological Review, 118, 316–338.

    Article  Google Scholar 

  79. Malmendier, U., & Tate, G. (2005). CEO overconfidence and corporate investment. Journal of Finance, 60, 2661–2700.

  80. Marewski, J. N., & Schooler, L. J. (2011). Cognitive niches: An ecological model of strategy selection. Psychological Review, 118, 393–437.

    Article  Google Scholar 

  81. Martignon, L., Katsikopoulos, K. V., & Woike, J. K. (2008). Categorization with limited resources: A family of simple heuristics. Journal of Mathematical Psychology, 52, 352–361.

    MathSciNet  MATH  Article  Google Scholar 

  82. Martignon, L., Vitouch, O., Takezawa, M., & Forster, M. (2003). Naïve and yet enlightened: From natural frequencies to fast and frugal decision trees. In D. Hardman & L. Macchi (Eds.), Thinking: Psychological perspectives on reasoning, judgment, and decision making (pp. 189–211). Chichester, UK: Wiley.

    Google Scholar 

  83. Mata, J., Frank, R., & Gigerenzer, G. (2014). Symptom recognition of heart attack and stroke in nine European countries: A representative study. Health Expectations, 17, 376–387.

    Article  Google Scholar 

  84. McKenzie, C. R. M., Liersch, M. J., & Finkelstein, S. R. (2006). Recommendations implicit in policy defaults. Psychological Science, 17, 414–420.

    Article  Google Scholar 

  85. McLeod, P., & Dienes, Z. (1996). Do fielders know where to go to catch the ball, or only how to get there? Journal of Experimental Psychology: Human Perception and Performance, 22, 531–543.

    Google Scholar 

  86. Meyvis, T., Ratner, R. K., & Levav, J. M. (2010). Why don’t we learn to accurately forecast feelings? How misremembering our predictions blinds us to forecasting errors. Journal of Experimental Psychology: General, 139, 579–589.

    Article  Google Scholar 

  87. Mitchell, G. (2005). Libertarian paternalism is an oxymoron. Northwestern University Law Review, 99(3), 1245–1277.

    Google Scholar 

  88. Nelson, W., Reyna, V. F., Fagerlin, A., Lipkus, I., & Peters, E. (2008). Clinical implications of numeracy: Theory and practice. Annals of Behavioral Medicine, 35(3), 261–274.

    Article  Google Scholar 

  89. Pachur, T., & Hertwig, R. (2006). On the psychology of the recognition heuristic: Retrieval primacy as a key determinant of its use. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 983–1002.

    Google Scholar 

  90. Park, C. W., Jun, S. Y., & MacInnis, D. J. (2000). Choosing what I want versus eliminating what I don’t want: The effects of additive versus subtractive product option framing on consumer decision making. Journal of Marketing Research, 37(2), 187–202.

    Article  Google Scholar 

  91. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge, UK: Cambridge University.

    Google Scholar 

  92. Payne, J. W., Bettman, J. R., & Schade, D. A. (1999). Measuring constructed preferences: Towards a building code. Journal of Risk and Uncertainty, 19, 243–270.

    MATH  Article  Google Scholar 

  93. Pichert, D., & Katsikopoulos, K. V. (2008). Green defaults: Information presentation and pro-environmental behavior. Journal of Environmental Psychology, 28, 63–73.

    Article  Google Scholar 

  94. Posner, R. A. (1979). Some uses and abuses of economics in law. The University of Chicago Law Review, 46(2), 281–306.

    Article  Google Scholar 

  95. Rebonato, R. (2012). Taking liberties: A critical examination of libertarian paternalism. New York, NY: Palgrave Macmillan.

    Google Scholar 

  96. Reyna, V. F., Nelson, W. L., Han, P. K., & Dieckmann, N. F. (2009). How numeracy influences risk comprehension and medical decision making. Psychological Bulletin, 135(6), 943–973.

    Article  Google Scholar 

  97. Rieskamp, J., & Otto, P. E. (2006). SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135, 207–236.

    Article  Google Scholar 

  98. Rothman, A. J., Bartels, R. D., Wlaschin, J., & Salovey, P. (2006). The strategic use of gain-and loss-framed messages to promote healthy behavior: How theory can inform practice. Journal of Communication, 56(s1), S202–S220.

    Article  Google Scholar 

  99. Rottenstreich, Y., & Tversky, A. (1997). Unpacking, repacking, and anchoring: Advances in support theory. Psychological Review, 104, 406–415.

    Article  Google Scholar 

  100. Saghai, Y. (2014). Salvaging the concept of nudge. Journal of Medical Ethics, 38, 487–493.

    Google Scholar 

  101. Sarfati, D., Howden-Chapman, P., Woodward, A., & Salmond, C. (1998). Does the frame affect the picture? A study into how attitudes to screening for cancer are affected by the way benefits are expressed. Journal of Medical Screening, 5, 137–140.

    Article  Google Scholar 

  102. Sedlmeier, P., & Gigerenzer, G. (2001). Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology: General, 130, 380–400.

    Article  Google Scholar 

  103. Sedrakyan, A., & Shih, C. (2007). Improving depiction of benefits and harms: Analyses of studies of well-known therapeutics and review of high-impact medical journals. Medical Care, 45, 523–528.

    Article  Google Scholar 

  104. Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63, 129–138.

    Article  Google Scholar 

  105. Simon, H. A. (1978). Rationality as process and as product of thought. American Economic Review, 68, 1–16.

    Google Scholar 

  106. Simon, H. A. (1990). Invariants of human behavior. Annual Review of Psychology, 41, 1–19.

    Article  Google Scholar 

  107. Skinner, B. F. (1971). Beyond freedom and dignity. New York, NY: Knopf.

    Google Scholar 

  108. Skinner, B. F. (1975). Walden two. Indianapolis, IN: Hackett.

  109. Smith, N. C., Goldstein, D. G., & Johnson, E. J. (2013). Choice without awareness: Ethical and policy implications of defaults. Journal of Public Policy & Marketing, 32, 159–172.

    Article  Google Scholar 

  110. Staddon, J. (1995) On responsibility and punishment. The Atlantic Monthly, 275(2), 88–94.

  111. Sunstein, C. R. (2014). Why nudge? The politics of libertarian paternalism. New Haven, CN: Yale University Press.

    Google Scholar 

  112. Thaler, R. H. (1991). Quasi rational economics. New York, NY: Sage.

    Google Scholar 

  113. Thaler, R., & Benartzi, S. (2004). Save more tomorrow: Using behavioral economics to increase employee savings. Journal of Political Economy, 112, 164–187.

    Article  Google Scholar 

  114. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.

    Google Scholar 

  115. Todd, P. M., Gigerenzer, G., & the ABC Research Group. (2012). Ecological rationality: Intelligence in the world. New York, NY: Oxford University Press.

    Google Scholar 

  116. Tversky, A. (1996). Contrasting rational and psychological principles in choice. In R. J. Zeckhauser, R. L. Keeny, & J. K. Sebenius (Eds.), Wise choices: Decisions, games and negotiations (pp. 5–21). Boston, MA: Harvard Business School Press.

    Google Scholar 

  117. Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological Bulletin, 76(2), 105.

    Article  Google Scholar 

  118. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

    Article  Google Scholar 

  119. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.

    MATH  Article  Google Scholar 

  120. Veetil, V. P. (2011). Libertarian paternalism is an oxymoron: An essay in defence of liberty. European Journal of Law and Economics, 31(3), 321–334.

    Article  Google Scholar 

  121. Volz, K. G., Schooler, L. J., Schubotz, R. I., Raab, M., Gigerenzer, G., & von Cramon, D. Y. (2006). Why you think Milan is larger than Modena: Neural correlates of the recognition heuristic. Journal of Cognitive Neuroscience, 18(11), 1924–1936.

    Article  Google Scholar 

  122. Wegwarth, O., & Gigerenzer, G. (2013). Trust-your-doctor: A simple heuristic in need of a proper social environment. In R. Hertwig, U. Hoffrage, & the ABC Research Group (Eds.), Simple heuristics in a social world (pp. 67–102). New York, NY: Oxford University Press.

    Google Scholar 

  123. White, M. (2013). The manipulation of choice. Ethics and libertarian paternalism. New York, NY: Palgrave MacMillan.

    Google Scholar 

  124. Wilkinson, T. M. (2013). Nudging and manipulation. Political Studies, 61(2), 341–355.

    Article  Google Scholar 

  125. World Health Organization. (2013). Obesity and overweight. Factsheet N°311. Geneva, Switzerland: Author. Retrieved from http://www.who.int/mediacentre/factsheets/fs311/en/.

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Correspondence to Till Grüne-Yanoff.

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Grüne-Yanoff, T., Hertwig, R. Nudge Versus Boost: How Coherent are Policy and Theory?. Minds & Machines 26, 149–183 (2016). https://doi.org/10.1007/s11023-015-9367-9

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Keywords

  • Bounded rationality
  • Nudging
  • Heuristics-and-biases program
  • Simple heuristics program
  • Ecological rationality
  • Defaults