Scientific inquiry has led to immense explanatory and technological successes, partly as a result of the pervasiveness of scientific theories. Relativity theory, evolutionary theory, and plate tectonics were, and continue to be, wildly successful families of theories within physics, biology, and geology. Other powerful theory clusters inhabit comparatively recent disciplines such as cognitive science, climate science, molecular biology, microeconomics, and Geographic Information Science (GIS). Effective scientific theories magnify understanding, help supply legitimate explanations, and assist in formulating predictions. Moving from their (...) knowledge-producing representational functions to their interventional roles (Hacking 1983), theories are integral to building technologies used within consumer, industrial, and scientific milieus. -/- This entry explores the structure of scientific theories from the perspective of the Syntactic, Semantic, and Pragmatic Views. Each of these views answers questions such as the following in unique ways. What is the best characterization of the composition and function of scientific theory? How is theory linked with world? Which philosophical tools can and should be employed in describing and reconstructing scientific theory? Is an understanding of practice and application necessary for a comprehension of the core structure of a scientific theory? How are these three views ultimately related? (shrink)

This paper proposes an account of accurate scientific representation in terms of techniques that produce data from a target phenomenon. I consider an approach to accurate representation that abstracts from such epistemic factors, justified by a thesis I call Ontic Priority. This holds that criteria for representational accuracy depend on a pre-established account of the nature of the relation between a model and its target phenomenon. I challenge Ontic Priority, drawing on the observation that many working scientists do not have (...) access to information allowing them to describe this relation between model and target or compare them accordingly. I critique the ability of an ontic-first approach to provide accuracy criteria in such cases and present historical support for an alternative according to which a model is accurate if and only if integrating the model into a theory of the data acquisition process yields well-fitting predictions of patterns in the data. (shrink)

Benacerraf has presented two problems for the philosophy of mathematics. These are the problem of identification and the problem of representation. This paper aims to reconstruct the latter problem and to unpack its undermining bearing on the version of Ontic Structural Realism that frames scientific representations in terms of abstract structures. I argue that the dichotomy between mathematical structures and physical ones cannot be used to address the Benacerraf problem but strengthens it. I conclude by arguing that versions of OSR (...) that do not rely on mathematical frameworks for representational purposes need not be vulnerable to Benacerraf’s second problem. (shrink)

Inspired by Ronald Giere’s cognitive approach to scientific models, Cognitive Structural Realism has presented a naturalist account of scientific representation. CSR characterises the structure of theories in terms of cognitive structures. These are informational structures embodied in the brains of scientists. CSR accounts for scientific representation in terms of the dynamical relationship between the organism and its environment. The proposal has been criticised on account of its negligence of social aspects of scientific practice. The present paper aims to chart out (...) a reply to the objection. It shows that cognitive structures do not need to be put inside the brains of single individuals. Cognitive structures are redefined as extended structures in distributed cognitive systems under Free Energy Principle. (shrink)

Probabilistic realism and syntactical positivism were two among outdated theories that Feigl criticised on account of their semantical poverty. In this paper, I argue that a refined version of probabilistic realism, which relies on what Feigl specified as the pragmatic description of the symbolic behaviour of scientists’ estimations and foresight, is defendable. This version of statistical realism does not need to make the plausibility of realist thesis dependent on the conventional acceptance of a constructed semantic metalanguage. I shall rely on (...) the Prediction Error Minimisation theory to support my probabilistic version of realism with a scientifically-informed and naturalistically plausible statistical account of the theories-world relationship which has a pragmatic ring to it. (shrink)