Till Grüne-Yanoff

Old wine in new casks: libertarian paternalism still violates liberal principles

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.

The Philosophy and Epistemology of Simulation: A Review

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.

The roles of stories in applying game theory

Game‐theoretic models consist of a formal game structure and an informal model narrative or story. When game theory is employed to model economic situations, the stories play a central role in interpreting, constructing and solving game structures. We analyse the architecture of game theory and distinguish between game models and the theory proper. We present the different functions of the model narrative in the application of game models to economic situations. In particular, we show how model narratives support the choice of solution concepts defined and provided by the theory proper. We further argue that the narratives role in interpretation, construction and solution makes it a necessary part of a game model that is intended to be a model of an economic situation. We conclude that game theory is not a universal theory of rationality, but only offers tools to model specific situations at varying degrees and kinds of rationality.

Nudge Versus Boost: How Coherent are Policy and Theory?

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.

The explanatory potential of artificial societies

It is often claimed that artificial society simulations contribute to the explanation of social phenomena. At the hand of a particular example, this paper argues that artificial societies often cannot provide full explanations, because their models are not or cannot be validated. Despite that, many feel that such simulations somehow contribute to our understanding. This paper tries to clarify this intuition by investigating whether artificial societies provide potential explanations. It is shown that these potential explanations, if they contribute to our understanding, considerably differ from potential causal explanations. Instead of possible causal histories, simulations offer possible functional analyses of the explanandum. The paper discusses how these two kinds explanatory strategies differ, and how potential functional explanations can be appraised.

WHY BEHAVIOURAL POLICY NEEDS MECHANISTIC EVIDENCE

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’.

Nudging and boosting: Steering or empowering good decisions

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.

Isolation Is Not Characteristic of Models

Modelling cannot be characterized as isolating, nor models as isolations. This article presents three arguments to that effect, against Uskali Mäkis account of models. First, while isolation proceeds through a process of manipulation and control, modelling typically does not proceed through such a process. Rather, modellers postulate assumptions, without seeking to justify them by reference to a process of isolation. Second, while isolation identifies an isolation base—a concrete environment it seeks to control and manipulate—modelling typically does not identify such a base. Rather, modellers construct their models without reference to concrete environments, and only later seek to connect their models to concrete situations of the real world. Third, Mäki argues that isolation employs idealization to control for disturbing factors, but does not affect the factors or mechanisms that are supposed to be isolated. However, models typically make idealizing assumptions about the factors and mechanisms that are the focus of investigation. Thus, even the product of modelling often cannot be characterized as isolation.

Agent-Based Models as Policy Decision Tools: The Case of Smallpox Vaccination

Agent-based simulation (ABS) studies have recently been employed to support policy decisions. This article addresses the particular potentials and problems that ABS faces in this usage. First, the author warns against taking “familiarity” with specific ABS as a criterion for having confidence in the model’s policy recommendations. Second, he shows that specific epistemic issues—in particular the high number of detailed simulated systems—require additional reflection on which decision rules to choose for policy decisions based on ABS. Third, the author points out directions in which the construction and uses of ABS in policy decision could be improved. Each of these issues is illustrated by simulation studies undertaken to investigate smallpox vaccination policies.

Genuineness resolved: a reply to Reiss' purported paradox

This response to Reiss ‘explanatory paradox’ argues that some economic models might be true, and that many economic models are not intended for providing how-actually explanations, but rather how-possibly explanations. Therefore, two assumptions of Reiss’ paradox are not true, and the paradox disappears.