Marcel Boumans

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.

Generally, rational decision-making is conceived as arriving at a decision by a correct application of the rules of logic and statistics. If not, the conclusions are called biased. After an impressive series of experiments and tests carried out in the last few decades, the view arose that rationality is tough for all, skilled field experts not excluded. A new type of planner's counsellor is called for: the normative statistician, the expert in reasoning with uncertainty par excellence. To unravel this view, the paper explores a specific practice of clinical decision-making, namely Evidence-Based Medicine. This practice is chosen, because it is very explicit about how to rationalize practice. The paper shows that whether a decision-making process is rational cannot be assessed without taking into account the environment in which the decisions have to be taken. To be more specific, the decision to call for new evidence should be rational too. This decision and the way in which this evidence is obtained are crucial to validate the base rates. Rationality should be model-based, which means that not only the isolated decision-making process should take a Bayesian updating process as its norm, but should also model the acquisition of evidence (priors and tests results) as a rational process.

Notwithstanding the fact that a lot, if not most, of science is done outside the laboratory, most literature in the history and philosophy of science, when discussing the experimental method, focus only on experimentation “within the walls of a laboratory” . To fill this embarrassing gap, Astrid Schwarz has written an excellent book on field experimentation. The field, however, is a much more messy site than a clean lab. In an introduction to a special issue of Osiris on field science, Kuklick and Kohler list a number of the problems related to science in the field: As scientific rigor is defined by the standards of the laboratory, the field is considered to be “a site of compromised work: field sciences have dealt with problems that resist tidy solutions, and they have not excluded amateur participants” . To discuss science in the field, we will have to take account of a methodological tension between laboratory and field standards of evidence and reasoning. ..

Introduction: philosophy of science in practice Content Type Journal Article Category Editorial Article Pages 303-307 DOI 10.1007/s13194-011-0036-4 Authors Rachel Ankeny, School of History & Politics, University of Adelaide, Napier Building, The University of Adelaide, Adelaide, SA 5005, Australia Hasok Chang, Department of History and Philosophy of Science, University of Cambridge, Free School Lane, Cambridge, CB2 3RH UK Marcel Boumans, Faculty of Economics and Business, University of Amsterdam, Valckenierstraat 65-67, 1018 XE Amsterdam, The Netherlands Mieke Boon, Department of Philosophy, University of Twente, Postbox 217, 7500 AE Enschede, The Netherlands Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 3

Unlike basic sciences, scientific research in advanced technologies aims to explain, predict, and describe not phenomena in nature, but phenomena in technological artefacts, thereby producing knowledge that is utilized in technological design. This article first explains why the covering‐law view of applying science is inadequate for characterizing this research practice. Instead, the covering‐law approach and causal explanation are integrated in this practice. Ludwig Prandtl’s approach to concrete fluid flows is used as an example of scientific research in the engineering sciences. A methodology of distinguishing between regions in space and/or phases in time that show distinct physical behaviours is specific to this research practice. Accordingly, two types of models specific to the engineering sciences are introduced. The diagrammatic model represents the causal explanation of physical behaviour in distinct spatial regions or time phases; the nomo‐mathematical model represents the phenomenon in terms of a set of mathematically formulated laws.