L1: Till Grüne-Yanoff: Behavioral public policy: one name, many types. A mechanistic perspective
Behavioral Public Policy (BPP) is often treated as a single kind, as witnessed for example in the popular use of the “nudge” label as encompassing all BPP, but also in the academic discussion of the pros and cons of BPP generally. This has led, firstly, to an unwarranted polarization in the debate; secondly, to a neglect of the context-sensitivity of these pro and con arguments; and thirdly, to a disregard of multiple stable kinds within the BPP category, that could capture these context-sensitivities. Against this uniformity assumption, we have argued that the BPP category contains multiple kinds, distinguished by mechanisms (Grüne-Yanoff and Hertwig 2016; Hertwig and Grüne-Yanoff 2017). Our main argument for this distinction is that there are systematic differences in the context-sensitivity both of the effectiveness and ethical evaluation of these mechanism-based kinds. Specifically, we claim that there are at least two kinds of behavioral policies, nudges and boosts, operating through different kinds of mechanisms. We do not claim, however, that these are the only kinds of BPP.
The main purpose of distinguishing kinds of BPP by mechanism is to provide a systematic base for the context-sensitive evaluations of their effectiveness and ethical acceptability, thus overcoming the current polarization. The argument therefore is not directed against nudge-type interventions. Instead, it criticizes those who treat BPP as of one kind, either to universally praise or to universally condemn them. Instead, it is argued that nudge and boost mechanisms have different moderators – thus explaining why the respective policy kinds exhibit different degrees of effectiveness in different contexts and different populations – and that they have different potential side-effects – thus explaining why the respective policy kinds exhibit different degrees of ethical permissibility in different contexts and different populations. Overcoming the polarization, and providing a more powerful tool to analyze which policy kind might fare better (either effectively or morally) in which environment, is what motivates our categorization proposal.
The talk begins with sketching the diversity of BPPs and arguing why this diversity matters. It then describes the notion of mechanism used in the analysis. Finally, it develops the distinction between nudges and boosts based on mechanisms and illustrates some of the uses of this categorization, both for evaluations of effectiveness and ethical acceptability.
L2: Roberto Fumagalli: Theories of well-being and normative welfare economics
This talk outlines and critically assesses the most prominent theories of well-being. It then explicates how these theories inform economists’ welfare analyses and policy evaluations, focusing on the conceptual foundations of normative welfare economics and the comparative merits of preference-based and opportunity-based approaches to normative welfare economics.
L3: Johanna Thoma: What is risk aversion? Recent work in philosophical decision theory and its relevance to economics
I provide an introduction to recent work in decision theory on the nature, rationality and formal modelling of non-neutral attitudes to risk, in particular risk aversion.
I highlight how this work builds on theories and insights from economics, and then argue that the different positions developed in the philosophical literature on the nature and normative status of risk attitudes are highly relevant for economics, in particular for the measurement of utility, welfare economics and social choice theory.
L4: Andre Hofmeyr: Real time elicitation and estimation of risk and time preferences
Risk and time preferences are primitives in economics, because they define how people respond to risk and uncertainty, and make choices over time. In this lecture, I will elicit students’ (and lecturers’) risk and time preferences. Participants simply need a computer and access to the internet to take part in the experiment. After a brief discussion of theories of choice under risk and over time, I explain how one can use the data that was just elicited to estimate structural models of risk and time preferences that are directly based on these theories. I will then estimate models of choice under risk and over time during the lecture using the data that was collected. This lecture will serve as a crash course in experimental economics, and show students how one can directly link theory to experimental design and statistical analysis.
L5: Conrad Heilmann: The philosophy of science of financial economics
Joint work with Melissa Vergara Fernández (Philosophy, EIPE) and Marta Szymanowska (Finance, RSM)
Financial economics has been steadily maturing as a subfield of economics for many decades now. Despite the importance of financial economics, there is not a lot of specific and sustained engagement with the models and theories in financial economics by philosophers of science.
We will review the philosophy of science of finance in three steps: (1) We begin with a potted historical description of financial economics. To do so, we consider relevant aspects of how financial economics became a separate research field and describe important characteristics of its current scientific practice. (2) The assessment of the epistemic performance of financial economics is central. We ask: which strategies do financial economists employ to make claims about empirical reality (knowledge claims)? What is the content and import of the knowledge claims they typically make? What are the main problems related to obtaining adequate evidence for these knowledge claims? (3) We also briefly highlight the topic of non-epistemic values for financial economics. We ask: to what extent does financial economics take into account and discuss ethical, political, and social values?
We will argue that financial economics should be characterized particularly by its focus on the use of models and by its performative nature. We suggest that the model-based and performative character of financial economics is: (1) visible in the history of its emergence and its current scientific practice, (2) central to the kind of knowledge claims it makes and the challenges that arise from making them, and (3) key to understand what kind of ethical values are and should be at play.
L6: Caterina Marchionni: Responsible economic modelling
Economics has immense power and epistemic authority both among policy makers and the public. For this reason, its models have the potential to affect our behavior in ways that are not neither intended nor foreseen. This phenomenon, to which I refer as reactivity, affects both the adequacy of the models as well as the scope of economists’ responsibilities. In this talk I examine the interplay between epistemic and ethical aspects of responsible practices of modelling reactive phenomena in economics.
L7: Emma Tieffenbach: The explanatory power of unintended consequences
To offer an invisible-hand explanation (hereafter IHE) is to think of the various choices that, over several successive stages, many individuals would have to make for a certain social outcome to obtain, without the latter figuring among these individuals’ objectives. The italic restriction in this definition is crucial. For the success of an IHE centrally depends on whether it dispenses with an intention to produce the outcome to be explained on the part of those who bring it into being. Commentators agree on IHE having more explicatory value than their intentional-design rivals. But they also tend to avoid any precise claims about the explicatory benefit that is thus provided. They leave it at one’s guess the explanatory gain of assuming, while offering an explanation of S, that those who contribute to S are not aware of S, let alone intend S. What is it that we get to know about S that is concealed when S is featured as intended? An answer to that question is missing, and it is the goal of the present talk to attempt to supply it.
L8: Harold Kincaid: Some developments in causal inference and their relevance to economics
There has been steady progress over the last twenty years in the logic of causal inference. My goal is to sketch the basics and describe some recent developments. I then look at the relevance for economics and discuss the extent to which advances in causal inference have or have not been taken up in econometrics.
L9: Erik Angner: Why science needs philosophy – and vice versa
In 1976, Mario Bunge advocated a “vigorous and symmetrical interaction between science and philosophy … to close the gap between the two camps and to develop a scientific philosophy and a science with philosophical awareness.” The aim of this paper is to defend both parts of Bunge’s thesis, viz., that philosophical conclusions are relevant to empirical research – and, more controversially, that empirical research is relevant to philosophical conclusions. Drawing on a series of fine-grained examples from behavioral economics and the economics of happiness, I will outline various ways in which the science depends on philosophical assumptions. In addition, I will review a number of ways in which the relevant bits of philosophy depend on empirical premises. The upshot is that the relationship between the relevant science and philosophy is remarkably symmetric: just like scientists cannot avoid making philosophical assumptions, philosophers often cannot help but proceed from empirical premises. I conclude by endorsing Bunge’s recommendation that “philosophers should become apprentices rather than lawgivers, and participants rather than onlookers” – and that the same thing is true for social and behavioral scientists. The argument suggests a picture according to which science and philosophy stand in a symbiotic relationship, with scientists and philosophers engaging in a mutually beneficial exchange of ideas for the advancement of the general knowledge. The argument also has implications for the way we teach and practice both economics and philosophy – in particular, the responsible organization of the relevant epistemic communities.