Der Begriff ‘Modell’ leitet sich vom Lateinischen ‘modulus’ (das Maß) ab, im Italienischen existiert seit dem 16. Jh. ‘modello’ und R. Descartes verwendet im 17. Jh. ‘modèlle’. Während der Begriff in Architektur und Kunst schon seit der Renaissance gängig ist, wird er in den Naturwissenschaften erst im 19. Jh. verwendet.1 Dort greifen wissenschaftliche Modelle die für eine gegebene Problemstellung als wesentlich erachteten Charakteristika (Eigenschaften, Beziehungen, etc.) eines Untersuchungsgegenstandes heraus und machen diesen so einem Verständnis bzw. einer weiterführenden Untersuchung zugänglich. Es (...) ist üblich, zwischen Skalenmodellen, Analogmodellen und theoretischen Modellen zu unterscheiden. (shrink)

Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...) These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. "Process" here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed case-study from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. (shrink)

This PhD thesis focuses on the philosophical foundations of Neuroeconomics, an innovative research program which combines findings and modelling tools from economics, psychology and neuroscience to account for human choice behaviour. The proponents of Neuroeconomics often manifest the ambition to foster radical modifications in the accounts of choice behaviour developed by its parent disciplines. This enquiry provides a philosophically informed appraisal of the potential for success and the relevance of neuroeconomic research for economics. My central claim is that neuroeconomists can (...) help other economists to build more predictive and explanatory models, yet are unlikely to foster revolutionary modifications in the economic theory of choice. The contents are organized as follows. In chapters 1-2, I present neuroeconomists’ investigative tools, distinguish the most influential approaches to neuroeconomic research and reconstruct the case in favour of a neural enrichment of economic theory. In chapters 3-7, I combine insights from neuro-psychology, economic methodology and philosophy of science to develop a systematic critique of Neuroeconomics. In particular, I articulate four lines of argument to demonstrate that economists are provisionally justified in retaining a methodologically distinctive approach to the modelling of decision making. My first argument points to several evidential and epistemological concerns which complicate the interpretation of neural data and cast doubt on the inferences neuroeconomists often make in their studies. My second argument aims to show that the trade-offs between the modelling desiderata that neuroeconomists and other economists respectively value severely constrain the incorporation of neural insights into economic models. My third argument questions neuroeconomists’ attempts to develop a unified theory of choice behaviour by identifying some central issues on which they hold contrasting positions. My fourth argument differentiates various senses of the term ‘revolution’ and illustrates that neuroeconomists are unlikely to provide revolutionary contributions to economic theory in any of these senses. (shrink)

The thesis proposes an account of the means of scientific representation focused on similarity, or more specifically, on the notion of “creative similarity”. I first distinguish between two different questions regarding the problem of representation: the question about the constituents and the question about the means of representation (following Suárez 2003; van Fraassen 2008). I argue that, although similarity is not a good candidate for constituent of representation, it can satisfactorily answer the question about the means of representation if adequately (...) characterized. To motivate this position, I dispute the main arguments offered against similarity as means of representation, namely the arguments from variety, vagueness, and misrepresentation, and contend that similarity plays a central epistemic role in practices of representing in science. The study of the role of similarity in scientific practice, I argue, requires the analysis of the uses of judgments of similarity in the construction of scientific models. I examine the cases of the Mississippi Basin Model and the San Francisco Bay Model to illustrate how judgments of similarity are directly involved in the production of epistemically fruitful models. Informed by the practical investigation developed throughout the thesis, I finally outline what I call the creative similarity account of the means of representation. The notion of “creative similarity” helps to capture the way in which similarity, as means of representation, intervenes in actual practices of representing, namely, in the form of a productive interplay of judgments of similarity and distortions (i.e. idealizations, abstractions, simplifications), which is employed by resourceful agents with the aim of understanding aspects of the natural world. (shrink)

Three metascientific concepts that have been object of philosophical analysis are the concepts oflaw, model and theory. The aim ofthis article is to present the explication of these concepts, and of their relationships, made within the framework of Sneedean or Metatheoretical Structuralism (Balzer et al. 1987), and of their application to a case from the realm of biology: Population Dynamics. The analysis carried out will make it possible to support, contrary to what some philosophers of science in general and of (...) biology in particular hold, the following claims: a) there are "laws" in biological sciences, b) many of the heterogeneous and different "models" of biology can be accommodated under some "theory", and c) this is exactly what confers great unifying power to biological theories. (shrink)

Abstract : A measurement result is never absolutely accurate: it is affected by an unknown “measurement error” which characterizes the discrepancy between the obtained value and the “true value” of the quantity intended to be measured. As a consequence, to be acceptable a measurement result cannot take the form of a unique numerical value, but has to be accompanied by an indication of its “measurement uncertainty”, which enunciates a state of doubt. What, though, is the value of measurement uncertainty? What (...) is its numerical value: how does one calculate it? What is its epistemic value: how one should interpret a measurement result? Firstly, we describe the statistical models that scientists make use of in contemporary metrology to perform an uncertainty analysis, and we show that the issue of the interpretation of probabilities is vigorously debated. This debate brings out epistemological issues about the nature and function of physical measurements, metrologists insisting in particular on the subjective aspect of measurement. Secondly, we examine the philosophical elaboration of metrologists in their technical works, where they criticize the use of the notion of “true value” of a physical quantity. We then challenge this elaboration and defend such a notion. The third part turns to a specific use of measurement uncertainty in order to address our thematic from the perspective of precision physics, considering the activity of the adjustments of physical constants. In the course of this activity, physicists have developed a dynamic conception of the accuracy of their measurement results, oriented towards a future progress of knowledge, and underlining the epistemic virtues of a never-ending process of identification and correction of measurement errors. (shrink)

Nature is complex, exceedingly so. A repercussion of this “complex world constraint” is that it is, in practice, impossible to connect words to the world in a foolproof manner. In this paper I explore the ways in which the complex world constraint makes vagueness, or more generally imprecision, in language in practice unavoidable, illuminates what vagueness comes to, and guides us to a sensible way of thinking about truth. Along the way we see that the problem of ceteris paribus laws (...) is exactly the problem of vagueness and susceptible to similar treatment. (shrink)

This Ph.D. thesis provides a pilosophical account of the structure of the evolutionary synthesis of the 1930s and 40s. The first, more historical part analyses how classical genetics came to be integrated into evolutionary thinking, highlighting in particular the importance of chromosomal mapping of Drosophila strains collected in the wild by Dobzansky, but also the work of Goldschmidt, Sumners, Timofeeff-Ressovsky and others. The second, more philosophical part attempts to answer the question wherein the unity of the synthesis consisted. I argue (...) that it exemplifies a weak of form of explanatory unification. (shrink)

The main thrust of the argument of this thesis is to show the possibility of articulating a method of construction or of synthesis--as against the most common method of analysis or division--which has always been (so we shall argue) a necessary component of scientific theorization. This method will be shown to be based on a fundamental synthetic logical relation of thought, that we shall call inversion--to be understood as a species of logical opposition, and as one of the basic monadic (...) logical operators. Thus the major objective of this thesis is to This thesis can be viewed as a response to Larry Laudan's challenge, which is based on the claim that ``the case has yet to be made that the rules governing the techniques whereby theories are invented (if any such rules there be) are the sorts of things that philosophers should claim any interest in or competence at.'' The challenge itself would be to show that the logic of discovery (if at all formulatable) performs the epistemological role of the justification of scientific theories. We propose to meet this challenge head on: a) by suggesting precisely how such a logic would be formulated; b) by demonstrating its epistemological relevance (in the context of justification) and c) by showing that a) and b) can be carried out without sacrificing the fallibilist view of scientific knowledge. OBJECTIVES: We have set three successive objectives: one general, one specific, and one sub-specific, each one related to the other in that very order. (A) The general objective is to indicate the clear possibility of renovating the traditional analytico-synthetic epistemology. By realizing this objective, we attempt to widen the scope of scientific reason or rationality, which for some time now has perniciously been dominated by pure analytic reason alone. In order to achieve this end we need to show specifically that there exists the possibility of articulating a synthetic (constructive) logic/reason, which has been considered by most mainstream thinkers either as not articulatable, or simply non-existent. (B) The second (specific) task is to respond to the challenge of Larry Laudan by demonstrating the possibility of an epistemologically significant generativism. In this context we will argue that this generativism, which is our suggested alternative, and the simplified structuralist and semantic view of scientific theories, mutually reinforce each other to form a single coherent foundation for the renovated analytico-synthetic methodological framework. (C) The third (sub-specific) objective, accordingly, is to show the possibility of articulating a synthetic logic that could guide us in understanding the process of theorization. This is realized by proposing the foundations for developing a logic of inversion, which represents the pattern of synthetic reason in the process of constructing scientific definitions. (shrink)

Theoretical models are an important tool for many aspects of scientific activity. They are used, i.a., to structure data, to apply theories or even to construct new theories. But what exactly is a model? It turns out that there is no proper definition of the term "model" that covers all these aspects. Thus, I restrict myself here to evaluate the function of models in the research process while using "model" in the loose way physicists do. To this end, I distinguish (...) four kinds of models. These are (1) models as special theories, (2) models as a substitute for a theory, (3) toy models and (4) developmental models. I argue that models of the types (3) and (4) are considerably useful in the process of theory construction. This will be demonstrated in an extended case-study from High-Energy Physics. (shrink)

Criteria of empirical significance are supposed to state conditions under which reference to an unobservable object or property is “empirically meaningful.” The intended kind of empirical meaningfulness should be necessary for admissibility into the selective contexts of scientific inquiry. I defend Justus’s recent argument that the reasons generally given for rejecting the project of defining a significance criterion are unpersuasive. However, as I show, this project remains wedded to an overly narrow conception of its subject matter. Even the most cutting (...) edge significance criteria identify empirical significance with predictive power, and thereby rule out vocabulary with legitimate scientific functions. In a nutshell, the problem is that there are terms that reduce the computational burden of extracting predictions from theory, and that may therefore be scientifically useful, but that do not add to the theory’s observational consequences, and so are ruled scientifically inadmissibility by existing significance criteria. I spell out this objection by specifying shortcut terms that are ruled inadmissible by Creath’s and Schurz’s criteria. Having objected in this way to extant criteria, and to the equation of empirical significance with predictive power in general, I discuss an approach to defining empirical significance that is capable of avoiding my objection and, more ambitiously, that may break the cycle of “punctures and patches” that has plagued the project from the beginning. I gloss Goldfarb and Ricketts’s idea of “case-by-case” delineations of empirically significant terms as the provision of special rather than general explications of the informal concept of empirical significance. (shrink)

This dissertation consists of three parts. Part I is a defense of an artificial language methodology in philosophy and a historical and systematic defense of the logical empiricists' application of an artificial language methodology to scientific theories. These defenses provide a justification for the presumptions of a host of criteria of empirical significance, which I analyze, compare, and develop in part II. On the basis of this analysis, in part III I use a variety of criteria to evaluate the scientific (...) status of intelligent design, and further discuss confirmation, reduction, and concept formation. (shrink)

Resumen: Usualmente se ha asumido que una única distinción puede dar cuenta del rol que cumplen los conceptos en una teoría respecto de la contrastación y respecto de la explicación. Intentaremos mostrar que esta asunción es incorrecta. Por una parte, no hay razones para considerar que esta coincidencia deba darse, y por otra, como se intentará mostrar a partir de varios ejemplos, de hecho, no se da. La base de contrastación de una teoría no tiene por qué coincidir con el (...) explanandum de la teoría. Para defender este punto se asumirá el estructuralismo metateórico. Se extraerán consecuencias para la concepción metateórica presupuesta. Palabras claves: Explicación; contrastación; T-teoricidad; Estructuralismo metateórico; Distinción teórico observacional -/- Title: Explain and test Abstract: It is usually held that one distinction can account for the role that concepts play in a theory regarding both test and explanation. We will demonstrate that this assumption is incorrect. On the one hand, there is no reason to think that this coincidence should exist. On the other, this is not the case, as we will show analysing several examples. The testing basis of a theory does not have to coincide with the explanandum of the theory. To defend this point we will endorse de metatheoretical structuralism. In addition, we will consider some repercussions that this discussion has for the assumed metatheoretical framework. Key-words: Explanation; Test; T-theoreticity; Metatheoretical Structuralism; Theoretical observational distinction . (shrink)