Marcel Boumans

In this chapter, we discuss a specific kind of progress in economics, namely, progress that is pushed by the repeated use of mathematical models in most sub-branches of economics today. We adopt a functional account of progress to argue that progress in economics occurs via the use of what we call ‘common recipes’ and the use of model templates to define and solve problems of relevance for economists. We support our argument by discussing the case of twentieth-century business cycle research. By presenting this case study in detail, we show how model templates are not only re-applied to different phenomena. We also show how scientists come up with them in the first place and how – once they are considered less useful – they are replaced with new ones. Finally, our case also illustrates that it is not only the mathematical structure that is re-used but that such a re-use also requires a shared conceptual vision of core properties of the phenomenon. If that vision is not shared anymore among economists, a model template can become useless and has to be replaced – sometimes through overcoming resistance – with a different one.

This book brings together the research of philosophers and social scientists. It examines those areas of scientific practice where reliance on the subjective judgment of experts and practitioners is the main source of useful knowledge to address, and, possibly, bring solutions to social problems. A common phenomenon in applications of science is that objective evidence does not point to a single answer, or solution, to a problem. Reliance on subjective judgment, then, becomes necessary, despite the known fact that hunches, even those of putative experts, often provide information that is not very accurate, and that experts are prone to fallacies and biases. The book looks at how experts reach consensus in the social sciences, and which experts are relevant to which problems. This book offers the ingredients for building a normative theory of expertise on the basis of the evidence that social scientists and philosophers have uncovered.

Templates travel because they offer a tractable format that can be used for model-building in a variety of domains. It is often because of this quality that a particular template is chosen. But one cannot assume that there are always templates ready to model a new phenomenon, and moreover, templates have also been designed at some point. A critical aspect of this designing process is the choice of the mathematical objects with which one hopes to capture this phenomenon. This means that one has to assess which of the properties of different kinds of mathematical objects and which combinations of them are most appropriate. The selection of these objects is based on whether their properties enable the model to perform its function. This selection of mathematical objects is similar to the selection of materials in mechanical design.

Templates travel because they offer a tractable format that can be used for model-building in a variety of domains. It is often because of this quality that a particular template is chosen. But one cannot assume that there are always templates ready to model a new phenomenon, and moreover, templates have also been designed at some point. A critical aspect of this designing process is the choice of the mathematical objects with which one hopes to capture this phenomenon. This means that one has to assess which of the properties of different kinds of mathematical objects and which combinations of them are most appropriate. The selection of these objects is based on whether their properties enable the model to perform its function. This selection of mathematical objects is similar to the selection of materials in mechanical design.

The current expression of “flatten the curve” has similarities with mid-twentieth century macro-economic policy that can aptly be characterized as “shaping macro phenomena.” To the extent these similarities hold, the historical-epistemological analysis of this kind of macro-economic policy can provides us with a better understanding of the preconditions for the effectiveness of the current COVID-19 flatten-the-curve policy. Policy in terms of shaping a phenomenon presumes that the phenomenon in question exists and has a certain shape that can be moulded. This moulding, however, is not assumed to be performed directly on the shape itself, but by operating the mechanism that generates this shape, and which is also believed to exist. Therefore the precondition for this kind of policy is knowing the mechanism at work. The knowledge of this kind of intervention needed to change the shape in a desired direction, this knowledge about the mechanism, is assumed to be captured by a mathematical model on which several policies can be tried out to see which one leads to the desired shape. Mid-twentieth century macro-economic policy aimed at shaping the business cycle, and hence was based on the belief in the existence of a business cycle mechanism. With the loss of the belief in the existence of such a mechanism, the policy of shaping the business cycle disappeared with that too. This paper unpacks this history and shows how this policy based on mechanical reasoning, which originated in natural science, was gradually replaced by an approach that takes into account non-natural aspects of human behaviour.

The conduct of most of social science occurs outside the laboratory. Such studies in field science explore phenomena that cannot for practical, technical, or ethical reasons be explored under controlled conditions. These phenomena cannot be fully isolated from their environment or investigated by manipulation or intervention. Yet measurement, including rigorous or clinical measurement, does provide analysts with a sound basis for discerning what occurs under field conditions, and why. In Science Outside the Laboratory, Marcel Boumans explores the state of measurement theory, its reliability, and the role expert judgment plays in field investigations from the perspective of the philosophy of science. Its discussion of the problems of passive observation, the calculus of observation, the two-model problem, and model-based consensus uses illustrations drawn primarily from economics. Rich in research and discussion, the volume clarifies the extent to which measurement provides valid information about objects and events in field sciences, but also has implications for measurement in the laboratory. Scholars in the fields of philosophy of science, social science, and economics will find Science Outside the Laboratory a compelling and informative read.

According to Suppes, measurement theory, like any scientific theory, should consist of two parts, a set-theoretical defined structure and the empirical interpretation of that structure. An empirical interpretation means the specification – ‘coordinating definitions’ – of a ‘hierarchy of models’ between the theory and the experimental results. But in the case of measurement theory, he defined the relationship between numerical structure and the empirical structure specifically in terms of homomorphism. This is rather a highly restrictive relation between models, and therefore he never succeeded in giving his measurement theory empirical content. This paper discusses what an empirical measurement theory will look like if we would use less restrictive ‘coordinating definitions’ to specify the relationships between the various models.

Scientific measurements are made objective through the use of reliable instruments. Instruments can have this function because they can - as material objects - be investigated independently of the specific measurements at hand. However, their materiality appears to be crucial for the assessment of their reliability. The usual strategies to investigate an instrument’s reliability depend on and assume possibilities of control, and control is usually specified in terms of materiality of the instrument and environment. The aim of this paper is to investigate the problem of reliability for non-material instruments, the instruments being applied in the social sciences. Any possible lack of reliability of the instrument hinders the measurements of ever becoming objective.

In the social sciences we hardly can create laboratory conditions, we only can try to find out which kinds of experiments Nature has carried out. Knowledge about Nature's designs can be used to infer conditions for reliable predictions. This problem was explicitly dealt with in Haavelmo's (1944) discussion of autonomous relationships, Friedman's (1953) as-if methodology, and Simon's (1961) discussions of nearly-decomposable systems. All three accounts take Marshallian partitioning as starting point, however not with a sharp ceteris paribus razor but with the blunt knife of negligibility assumptions. As will be shown, in each account reflection on which influences are negligible, for what phenomena and for how long, played a central role.

The kinds of models discussed in this paper function as measuring instruments. We will concentrate on two necessary steps for measurement: (1) the search of a mathematical representation of the phenomenon; (2) this representation should cover an invariant relationship between the properties of the phenomenon to be measured and observable accociated attributes of a measuring instrument. Therefore, the measuring instrument should function as a nomological machine. However, invariant relationships are not necessarily ceteris paribus regularities, but could also occur when the influence of the environment is negligible. Then we are able to achieve accurate measurements outside the laboratory.

In de economische wetenschap worden vragen beantwoord met behulp van modellen. Voor ieder type vraag bestaat een eigen klasse van modellen. Het type vraag bepaalt de eisen waaraan het model moet voldoen en geeft daarbij aan hoe de modellen gemaakt dienen te worden. Dit artikel behandelt de vraag die gesteld wordt om tot begrip te komen: de ‘hoezo-vraag’. Om duidelijk te maken welke modellen hoezo-vragen impliceren zal dit type vraag vergeleken worden met ‘waarom-vragen’ en ‘hoeveelvragen’. Het antwoord op een waarom-vraag is een verklaring. Het antwoord op een hoeveel-vraag is een meetresultaat

Glenn Harrison [Journal of Economic Methodology, 2013, 20, 103–117] discusses four related forms of methodological intolerance with respect to field experiments: field experiments should rely on some form of randomization, should be disconnected from theory, the concept of causality should only be defined in terms of observables, and the role of laboratory experiments is dismissed. As is often the case, the cause of intolerance is ignorance, as it is here. To acquire knowledge about potential influences, which we need for both the evaluation of internal and external validity of experimental results, we cannot do without theory. A purely empiricist methodology will be unable to give us sufficient understanding of the validity of these results. An account of causality only based on directly observed things, is an account based on factual influences only. This account will be too restricted, because it will not deal with the unobserved potential influences, which we need – again – for the evaluat...

The Representational Theory of Measurement conceives measurement as establishing homomorphisms from empirical relational structures into numerical relation structures, called models. There are two different approaches to deal with the justification of a model: an axiomatic and an empirical approach. The axiomatic approach verifies whether a given relational structure satisfies certain axioms to secure homomorphic mapping. The empirical approach conceives models to function as measuring instruments by transferring observations of a phenomenon under investigation into quantitative facts about that phenomenon. These facts are evaluated by their accuracy and precision. Precision is generally achieved by least squares methods and accuracy by calibration. For calibration standards are needed. Then two polar strategies can be distinguished: white-box modeling and black-box modeling. The first strategy of modeling aims at estimating the invariant equations of the phenomenon, thereby fulfilling Hertz’s correctness requirement. The latter strategy of modeling is to use known stable facts about the phenomenon to adjust the model parameters, thereby fulfilling Hertz’s appropriateness requirement. For this latter strategy, the requirement of models as homomorphic mappings has been dropped. Where one will find the axiomatic approach more often used for measurement in the laboratory, the empirical approach is more appropriate for measurement outside the laboratory. The reason for this is that for measurement of phenomena outside the laboratory, one also needs to take account of the environment to achieve accurate results. Environments are generally too relation-rich for an axiomatic approach, which are only applicable for relation-poor systems. The white-box modeling strategy, reflecting the complexity of the environment due to its correctness requirement, will, however, lead to immensely large models. To avoid this problem, modular design is an appropriate strategy to reduce this complexity. Modular design is a grey-box modeling strategy. Grey-box models are assemblies of modules; these are black boxes with standard interface. It should be noted that the structure of the assemblage need not be homomorphic to the relations describing the interaction between phenomenon and environment. These three modeling strategies map out the possible designs for computer simulations as measuring instruments. Whether a simulation is based on a white-box, grey-box or black-box model is only determined by the relationship between the phenomenon and its environment and not by e.g. its materiality or physicality

The practice of economic science is dominated by model building. To evaluate economic policy, models are built and used to produce numbers to inform us about economic phenomena. Although phenomena are detected through the use of observed data, they are in general not directly observable. To 'see' them we need instruments. More particularly, to obtain numerical facts of the phenomena we need measuring instruments. This paper will argue that in economics models function as such instruments of observation, more specific as measuring instruments. In measurement theory, measurement is a mapping of some class of aspects of characteristics of the empirical world into a set of numbers. The paper's view is that economic modelling is a specific kind of mapping to which the standard account on how models are obtained and assessed does not apply. Models are not easily or simply derived from theories and subsequently tested against empirical data. Instruments are constructed by integrating several theoretical and empirical ideas and requirements in such a way that their performance meets a beforehand chosen standard. The empirical requirement is that the model should take account of the phenomenological facts, so that the reliability of the model is not assessed by post-model testing but obtained by calibration

Since the February 2020 publication of the article ‘Flattening the curve’ in The Economist, political leaders worldwide have used this expression to legitimize the introduction of social distancing measures in fighting Covid-19. In fact, this expression represents a complex combination of three components: the shape of the epidemic curve, the social distancing measures and the reproduction number \. Each component has its own history, each with a different history of control. Presenting the control of the epidemic as flattening the curve is in fact flattening the underlying natural-social complexity. The curve that needs to be flattened is presented as a bell-shaped curve, implicitly suggesting that the pathogen’s spread is subject only to natural laws. The \ value, however, is, fundamentally, a metric of how a pathogen behaves within a social context, namely its numerical value is affected by sociopolitical influences. The jagged and erratic empirical curve of Covid-19 illustrates this. Although the virus has most likely infected only a small portion of the total susceptible population, it is clear its shape has changed drastically. This changing shape is largely due to sociopolitical factors. These include shifting formal laws and policies, shifting individual behaviors as well as shifting various other social norms and practices. This makes the course of Covid-19 curve both erratic and unpredictable.