In several accounts of what models are and how they function a specific view dominates. This view contains the following characteristics. First, there is a clear-cut distinction between theories, models and data and secondly, empirical assessment takes place after the model is built. This view in which discovery and justification are disconnected is not in accordance with several practices of mathematical business-cycle model building. What these practices show is that models have to meet implicit criteria of adequacy, such as satisfying (...) theoretical, mathematical and statistical requirements, and be useful for policy. In order to be adequate, models have to integrate enough items to satisfy such criteria. These items include besides theoretical notions, policy views, mathematisations of the cycle and metaphors also empirical data and facts. So, the main thesis of this chapter is that the context of discovery is the successful integration of those items that satisfy the criteria of adequacy. Because certain items are empirical data and facts, justification can be built-in. (shrink)

A long-standing tradition presents economic activity in terms of the flow of fluids. This metaphor lies behind a small but influential practice of hydraulic modelling in economics. Yet turning the metaphor into a three-dimensional hydraulic model of the economic system entails making numerous and detailed commitments about the analogy between hydraulics and the economy. The most famous 3-D model in economics is probably the Phillips machine, the central object of this paper.

The Representational Theory of Measurement conceives measurement as establishing homomorphisms from empirical relational structures into numerical relation structures, called models. Models function as measuring instruments by transferring observations of an economic system into quantitative facts about that system. These facts are evaluated by their accuracy. Accuracy is achieved by calibration. For calibration standards are needed. Then two strategies can be distinguished. One aims at estimating the invariant (structural) equations of the system. The other is to use known stable facts about (...) the system to adjust the model parameters. For this latter strategy, the requirement of models as homomorphic mappings is not required anymore. (shrink)

Generally, simulations are carried out to answer specific questions. The assessment of the reliability of an answer depends on the kind of question investigated. The answer to a 'why' question is an explanation. The premises of an explanation have to include invariant relationships, and thus the reliability of such answer depends on whether the domain of invariance of the relevant relationships covers the domain of the question. The answer to a 'how much' question is a measurement. A measurement is reliable (...) when it is an output of a calibrated measuring instrument. (shrink)

A typical difference between social science and natural science is the degree in which control is possible. Strategies in both sciences to obtain true facts are consequently different. Measurement errors are due to background noise. Laboratories are environments in which background conditions can be controlled. As a result, accurate observations { measurement results close to the true values of the measurands { can only be obtained in laboratories. Therefore, measuring instruments are built such that they function as mini laboratories. However, (...) observations in social science are usually passive, in the sense that control of background conditions is impossible. Models are built to solve this problem of (lack of) control. They function as nonmaterial laboratories by aiming at precision, that is reducing the spread of the measurement errors. The application of models as measuring instruments necessitates a shift of the requirement of accuracy to the requirement of precision, which is a feature of the instrument and not of the environment. (shrink)

The assessment of models in an experiment depends on their material nature and their function in the experiment. Models that are used to make the phenomenon under investigation visible - sensors - are assessed by calibration. However, calibration strategies assume material intervention. The experiment discssed in this paper is an experiment in economics to measure the influence of technology shocks on business cycles. It uses immaterial, mathematical instruments. It appears that calibration did not work for these kinds of models, it (...) did not provide reliable evidence for the facts of the business cycle. (shrink)

In economics, models are built to answer specific questions. Each type of question requires its own type of models; in other words, it defines the requirements that a model should meet and thereby instructs how the models should be built. An explanation is an answer to a ‘why’-question. In economics, this answer is provided by a white -box model. To answer a ‘how much’-question, which is asking for a measurement, economists can make use of black-box models. Economic phenomena are often (...) so complex that white -box models that should function as explanations for them appear not to be intelligible. So, for questions concerning understanding - e.g. ‘Can you clarify this phenomenon‘’ ‘Can you tell me what would happen if‘’ - it would appear that grey-box models are more adequate. This implies a twofold revision of de Regt and Dieks’s criteria for understanding phenomena. First, whenever the term ‘theory’ is used it should be substituted by ‘model’. Secondly, the Criterion for the Intelligibility of Models is now simplified as: ‘A model M is intelligible for economists if they have built M as a grey-box construction’. A grey-box construction implies that they ‘can recognize qualitatively characteristic consequences of M without performing exact calculations’. (shrink)

The metrology literature neglects a strong empirical measurement tradition in economics, which is different from the traditions as accounted for by the formalist representational theory of measurement. This empirical tradition comes closest to Mari's characterization of measurement in which he describes measurement results as informationally adequate to given goals. In economics, one has to deal with soft systems, which induces problems of invariance and of self-awareness. It will be shown that in the empirical economic measurement tradition both problems have been (...) on the agenda for a long while, and that the proposed solutions to these problems provide clues for the directions in which one could develop a measurement theory that takes account of soft systems. (shrink)

This paper discusses the role of expert’s observations in different practices of decision making. In these practices it is never the case that the observations of one sole expert is being used, so discussing the role of expert’s observations implies a discussion of how these observations are combined.

When observing or measuring phenomena, errors are inevitable, one can only aspire to reduce these errors as much as possible. An obvious strategy to achieve this reduction is by using more precise instruments. Another strategy was to develop a theory of these errors that could indicate how to take them into account. One of the greatest achievements of statistics in the beginning of the 19th century was such a theory of error. This theory told the practitioners that the best thing (...) they could do is taking the arithmetical mean of their observations. This average would give them the most accurate estimate of the value they were searching for. Soon after its invention, this method made a triumphal march across various sciences. However, not in all sciences one stood waving aside. This method, namely, only worked well when the various observations were made under similar circumstances and when there were very many of them. And this was not the case for e.g. meteorology and actuarial science, the two sciences discussed in this paper. (shrink)