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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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.
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”.
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).
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).
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.
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.
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”.
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.
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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
- Bounded rationality
- Heuristics-and-biases program
- Simple heuristics program
- Ecological rationality