In recent years, policy makers worldwide have begun to acknowledge the potential value of insights from psychology and behavioral economics into how people make decisions. These insights can inform the design of nonregulatory and nonmonetary policy interventions-as well as more traditional fiscal and coercive measures. To date, much of the discussion of behaviorally informed approaches has emphasized "nudges," that is, interventions designed to steer people in a particular direction while preserving their freedom of choice. Yet behavioral science also provides support for a distinct kind of nonfiscal and noncoercive intervention, namely, "boosts." The objective of boosts is to foster people's competence to make their own choices-that is, to exercise their own agency. Building on this distinction, we further elaborate on how boosts are conceptually distinct from nudges: The two kinds of interventions differ with respect to (a) their immediate intervention targets, (b) their roots in different research programs, (c) the causal pathways through which they affect behavior, (d) their assumptions about human cognitive architecture, (e) the reversibility of their effects, (f) their programmatic ambitions, and (g) their normative implications. We discuss each of these dimensions, provide an initial taxonomy of boosts, and address some possible misconceptions.

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.

Libertarian Paternalism (LP) purports to be a kind of paternalism that is "liberty-preserving" and hence compatible with liberal principles. In this paper, I argue against this compatibility claim. I show that LP violates core liberal principles, first because it limits freedom, and secondly because it fails to justify these limitations in ways acceptable to liberal positions. In particular, Libertarian Paternalists argue that sometimes it is legitimate to limit people's liberties if it improves their welfare. A closer look at the welfare notions used, however, reveals that they respect neither the subjectivity nor the plurality of people's values. Thus its justification of the liberty-welfare trade-off is not compatible with liberal principles. I conclude that to justify LP policies, one must appeal to traditional paternalistic principles-and thus, there is no categorical difference between "libertarian" and other forms of paternalism.

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It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one's confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering such relevant possibilities may affect one's confidence in necessity or impossibility hypotheses. Thus, one can learn from minimal economic models.

The philosophical literature on simulations has increased dramatically during the past 40 years. Many of its main topics are epistemological. For example, philosophers consider how the results of simulations help explain natural phenomena. This essay's review treats mainly simulations in the social sciences. It considers the nature of simulations, the varieties of simulation, and uses of simulations for representation, prediction, explanation, and policy decisions. Being oriented toward philosophy of science, it compares simulations to models and experiments and considers whether simulations raise new methodological issues. The essay concludes that several features of simulations set them apart from models and experiments and make them novel scientific tools, whose powers and limits are not yet well understood.

Abstract:Proponents of behavioural policies seek to justify them as ‘evidence-based’. Yet they typically fail to show through which mechanisms these policies operate. This paper shows – at the hand of examples from economics and psychology – that without sufficient mechanistic evidence, one often cannot determine whether a given policy in its target environment will be effective, robust, persistent or welfare-improving. Because these properties are important for justification, policies that lack sufficient support from mechanistic evidence should not be called ‘evidence-based’.

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