This article argues that nudges can often be turned into self-nudges: empowering interventions that enable people to design and structure their own decision environments – that is, to act as citizen choice architects. Self-nudging applies insights from behavioral science in a way that is practicable and cost-effective, but that sidesteps concerns about paternalism or manipulation. It has the potential to expand the scope of application of behavioral insights from the public to the personal sphere (e.g., homes, offices, families). It is a tool for reducing failures of self-control and enhancing personal autonomy; specifically, self-nudging can mean designing one's proximate choice architecture to alleviate the effects of self-control problems, engaging in education to understand the nature and causes of self-control problems and employing simple educational nudges to improve goal attainment in various domains. It can even mean self-paternalistic interventions such as winnowing down one's choice set by, for instance, removing options. Policy-makers could promote self-nudging by sharing knowledge about nudges and how they work. The ultimate goal of the self-nudging approach is to enable citizen choice architects’ efficient self-governance, where reasonable, and the self-determined arbitration of conflicts between their mutually exclusive goals and preferences.

It has recently been argued that successful evidence-based policy should rely on two kinds of evidence: statistical and mechanistic. The former is held to be evidence that a policy brings about the desired outcome, and the latter concerns how it does so. Although agreeing with the spirit of this proposal, we argue that the underlying conception of mechanistic evidence as evidence that is different in kind from correlational, difference-making or statistical evidence, does not correctly capture the role that information about mechanisms should play in evidence-based policy. We offer an alternative account of mechanistic evidence as information concerning the causal pathway connecting the policy intervention to its outcome. Not only can this be analyzed as evidence of difference-making, it is also to be found at any level and is obtainable by a broad range of methods, both experimental and observational. Using behavioral policy as an illustration, we draw the implications of this revised understanding of mechanistic evidence for debates concerning policy extrapolation, evidence hierarchies, and evidence integration.

Nudge and boost are two competing approaches to applying the psychology of reasoning and decision making to improve policy. Whereas nudges rely on manipulation of choice architecture to steer people towards better choices, the objective of boosts is to develop good decision-making competences. Proponents of both approaches claim capacity to enhance social welfare through better individual decisions. We suggest that such efforts should involve a more careful analysis of how individual and social welfare are related in the policy context. First, individual rationality is not always sufficient or necessary for improving collective outcomes. Second, collective outcomes of complex social interactions among individuals are largely ignored by the focus of both nudge and boost on individual decisions. We suggest that the design of mechanisms and social norms can sometimes lead to better collective outcomes than nudge and boost, and present conditions under which the three approaches (nudge, boost, and design) can be expected to enhance social welfare.

In many daily life situations, people face decisions involving a trade-off between exploring new options and exploiting known ones. In these situations, observing the decisions of others can influence people’s decisions. Whereas social information often helps making better decisions, research has suggested that under certain conditions it can be detrimental. How precisely social information influences decision strategies and impacts performance is, however, disputed. Here we study how social information influences individuals’ exploration-exploitation trade-off and show that this adaptation can undermine their performance. Using a minimal experimental paradigm, we find that participants tend to copy the solution of other individuals too rapidly, thus decreasing the likelihood of discovering a better solution. Approximating this behavior with a simple model suggests, that individuals’ willingness to explore only depends on the value of known existing solutions. Our results allow for a better understanding of the interplay between social and individual factors in individual decision-making.

We argue that the appraisal of models in social epistemology requires conceiving of them as argumentative devices, taking into account the argumentative context and adopting a family-of-models perspective. We draw up such an account and show how it makes it easier to see the value and limits of the use of models in social epistemology. To illustrate our points, we document and explicate the argumentative role of epistemic landscape models in social epistemology and highlight their limitations. We also claim that our account could be fruitfully used in appraising other models in philosophy and science.