The paper presents an argument for treating certain types of computer simulation as having the same epistemic status as experimental measurement. While this may seem a rather counterintuitive view it becomes less so when one looks carefully at the role that models play in experimental activity, particularly measurement. I begin by discussing how models function as “measuring instruments” and go on to examine the ways in which simulation can be said to constitute an experimental activity. By focussing on the connections (...) between models and their various functions, simulation and experiment one can begin to see similarities in the practices associated with each type of activity. Establishing the connections between simulation and particular types of modelling strategies and highlighting the ways in which those strategies are essential features of experimentation allows us to clarify the contexts in which we can legitimately call computer simulation a form of experimental measurement. (shrink)

The philosophy of measurement studies the conceptual, ontological, epistemic, and technological conditions that make measurement possible and reliable. A new wave of philosophical scholarship has emerged in the last decade that emphasizes the material and historical dimensions of measurement and the relationships between measurement and theoretical modeling. This essay surveys these developments and contrasts them with earlier work on the semantics of quantity terms and the representational character of measurement. The conclusions highlight four characteristics of the emerging research program in (...) philosophy of measurement: it is epistemological, coherentist, practice oriented, and model based. (shrink)

This work develops an epistemology of measurement, that is, an account of the conditions under which measurement and standardization methods produce knowledge as well as the nature, scope, and limits of this knowledge. I focus on three questions: (i) how is it possible to tell whether an instrument measures the quantity it is intended to? (ii) what do claims to measurement accuracy amount to, and how might such claims be justified? (iii) when is disagreement among instruments a sign of error, (...) and when does it imply that instruments measure different quantities? Based on a series of case studies conducted in collaboration with the US National Institute of Standards and Technology (NIST), I argue for a model-based approach to the epistemology of physical measurement. To measure a physical quantity, I argue, is to estimate the value of a parameter in an idealized model of a physical process. Such estimation involves inference from the final state (‘indication’) of a process to the value range of a parameter (‘outcome’) in light of theoretical and statistical assumptions. Contrary to contemporary philosophical views, measurement outcomes cannot be obtained by mapping the structure of indications. Instead, measurement outcomes as well as claims to accuracy, error and quantity individuation can only be adjudicated relative to a choice of idealized modelling assumptions. (shrink)

Whereas computer simulations involve no direct physical interaction between the machine they are run on and the physical systems they are used to investigate, they are often used as experiments and yield data about these systems. It is commonly argued that they do so because they are implemented on physical machines. We claim that physicality is not necessary for their representational and predictive capacities and that the explanation of why computer simulations generate desired information about their target system is only (...) to be found in the detailed analysis of their semantic levels. We provide such an analysis and we determine the actual consequences of physical implementation for simulations. (shrink)

Through a study of a sophisticated contemporary scientific experiment, it is shown how and why use of the term 'observation' in reference to that experiment departs from ordinary and philosophical usages which associate observation epistemically with perception. The role of "background information" is examined, and general conclusions are arrived at regarding the use of descriptive language in and in talking about science. These conclusions bring out the reasoning by which science builds on what it has learned, and, further, how that (...) process of building consists not only in adding to our substantive knowledge, but also in increasing our ability to learn about nature, by extending our ability to observe it in new ways. The argument of this paper is thus a step toward understanding how it is that all our knowledge of nature rests on observation. (shrink)

As a device used by scientists in the course of performing research, the digital computer might be considered a scientific instrument. But if so, what is it an instrument for? This paper explores a number of answers to this question, focusing on the use of computers in a simulating mode.

This book develops an explanation for the roles of observation and theory in scientific endeavor that occupies the middle ground between empiricism and rationalism, and captures the strengths of both approaches. Brown argues that philosophical theories have the same epistemological status as scientific theories and constructs an epistemological theory that provides an account of the role that theory and instruments play in scientific observation. His theory of perception yields a new analysis of objectivity that combines the traditional view of observation (...) as the foundation of scientific objectivity with the contemporary recognition that observation is theory-dependent. (shrink)

Bas C. van Fraassen presents an original exploration of how we represent the world.

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)

Morrison points out many similarities between the roles of simulation models and other sorts of models in science. On the basis of these similarities she claims that running a simulation is epistemologically on a par with doing a traditional experiment and that the output of a simulation therefore counts as a measurement. I agree with her premises but reject the inference. The epistemological payoff of a traditional experiment is greater (or less) confidence in the fit between a model and a (...) target system. The source of this payoff is the existence of a causal interaction with the target system. A computer experiment, which does not go beyond the simulation system itself, lacks any such interaction. So computer experiments cannot confer any additional confidence in the fit (or lack thereof) between the simulation model and the target system. (shrink)

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. (shrink)

A careful analysis of the role of observation in the natural sciences, with particular attention to the epistemic evaluation and evidential contribution of observations, is used as the basis for an argument that the opportunities for meaningful observations in studies of the human past are no fewer and no less important that in the natural sciences. Observation is described in terms of the acquisition of information through interaction with the world, a description which brings out the significant epistemic features of (...) observation in science while avoiding the controversial and misleading issue of distinguishing the observable from the unobservable. This description applies as effectively and with equal epistemological sensitivity to empirical studies of the human past and it shows that they are not disadvantaged with respect to the sciences in terms of their ability to observe, directly or indirectly, the objects of study. (shrink)

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. (shrink)

This paper discusses features of an epistemically valuable form of evidence that raise troubles for received and new epistemological treatments of experimental evidence.