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Marcel Boumans [19]Marcel J. Boumans [10]
  1. Marcel J. Boumans, Built-in Justification.
    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 (...)
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  2. Marcel J. Boumans, Calibration of Models in Experiments.
    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 (...)
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  3. Marcel J. Boumans, Grey-Box Understanding in Economics.
    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 (...)
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  4. Marcel J. Boumans, Invariance and Calibration.
    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 (...)
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  5. Marcel J. Boumans, Observations of an Expert.
    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.
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  6. Marcel J. Boumans, The Difference Between Answering a 'Why' - Question and Answering a 'How Much' - Question.
    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 (...)
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  7. Marcel J. Boumans, Truth Versus Precision.
    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, (...)
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  8. Marcel J. Boumans, When Evidence is Not in the Mean.
    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 (...)
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  9. Mary S. Morgan & Marcel J. Boumans, Secrets Hidden by Two-Dimensionality: The Economy as a Hydraulic Machine.
    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.
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  10. Marcel Boumans (forthcoming). Measure for Measure: How Economists Model the World Into Numbers. Social Research.
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  11. Marcel Boumans, Giora Hon & Arthur Petersen (eds.) (forthcoming). Error and Uncertainty in Scientific Practice. Pickering & Chatto.
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  12. Marcel Boumans & Sabina Leonelli (2013). Introduction: On the Philosophy of Science in Practice. [REVIEW] Journal for General Philosophy of Science 44 (2):259-261.
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  13. Marcel Boumans (2012). Measurement in Economics. In Uskali Mäki, Dov M. Gabbay, Paul Thagard & John Woods (eds.), Philosophy of Economics. North Holland. 395.
  14. Marcel Boumans (2012). Modeling Strategies for Measuring Phenomena In-and Outside the Laboratory. In Henk W. de Regt (ed.), Epsa Philosophy of Science: Amsterdam 2009. Springer. 1--11.
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  15. Rachel Ankeny, Hasok Chang, Marcel Boumans & Mieke Boon (2011). Introduction: Philosophy of Science in Practice. [REVIEW] European Journal for Philosophy of Science 1 (3):303-307.
    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 (...)
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  16. Marcel Boumans (2011). Hoe economen begrijpen. Wijsgerig Perspectief 51 (2):22-31.
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  17. Marcel Boumans (2008). Battle in the Planning Office: Field Experts Versus Normative Statisticians. Social Epistemology 22 (4):389 – 404.
    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, (...)
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  18. Rens Bod, Mieke Boon & Marcel Boumans (2006). Introduction to the Symposium 'Applying Science'. International Studies in the Philosophy of Science 20 (1):1 – 3.
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  19. Marcel Boumans (2005). Measurement Outside the Laboratory. Philosophy of Science 72 (5):850-863.
    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 (...)
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  20. Marcel Boumans (2004). 13 Models in Economics. In John Bryan Davis & Alain Marciano (eds.), The Elgar Companion to Economics and Philosophy. Edward Elgar Pub.. 260.
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  21. Marcel Boumans (2004). Models in Economics. In John Bryan Davis & Alain Marciano (eds.), The Elgar Companion to Economics and Philosophy. Edward Elgar Pub.. 260--282.
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  22. Marcel Boumans (2004). The Reliability of an Instrument. Social Epistemology 18 (2 & 3):215 – 246.
    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 (...)
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  23. Marcel Boumans & Anne Beaulieu (2004). Foreword to 'Objects of Objectivity'. Social Epistemology 18 (2 & 3):105 – 108.
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  24. Marcel Boumans (2003). How to Design Galilean Fall Experiments in Economics. Philosophy of Science 70 (2):308-329.
    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 (...)
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  25. Paul Thagard, Kim Sterelny, Richard Richards, Denis M. Walsh, James W. McAllister, Marcel Boumans, Meir Hemmo, Orly Shenker & Matthew W. Parker (2003). 10. Response to Vollmer's Review of Minds and Molecules Response to Vollmer's Review of Minds and Molecules (Pp. 391-398). [REVIEW] Philosophy of Science 70 (2).
     
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  26. Marcel Boumans & Mary S. Morgan (2001). Ceteris Paribus Conditions: Materiality and the Application of Economic Theories. Journal of Economic Methodology 8 (1):11-26.
  27. Marcel Boumans (1999). Representation and Stability in Testing and Measuring Rational Expectations. Journal of Economic Methodology 6 (3):381-402.
    There are at least two elements of theory completion necessary for measurement: (1) a measurement formula and (2) standardization of that representation. Standardization is based on the search for stability. The more stable the correlation which the measurement formula represents is, the less influence other circumstances have. Then, the interconnection between testing, mathematical representation and standardization is of a hierarchical order. By testing a model one tries to find out to what extent the model covers the data of the phenomenon, (...)
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  28. Marcel J. Boumans, Measurement in Economic Systems.
    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 (...)
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