The incompatibility within the context of modeling cannot be established simpliciter. The fact that modeling is understood as an activity whose representational power can only be partially established, may minimize the supposed existence of incompatible models. Indeed, it is argued from perspectivism that incompatibility can be dissolved, meaning that it becomes trivial or simply false due to the inherently pragmatic and partial nature of the act of representation and modeling. From this perspective, incompatibility can only be a consequence of a (...) misunderstanding of the very nature of modeling and representation In this sense, in order to tackle this strategy at its root from perspectivism, we will first need to outline the maximal perspectivism thesis, attempting to identify the possible escape routes that perspectivism could find in order to explain incompatibility as an illusory incompatibility. Then, we will analyze Valence Bond Model and Molecular Model of covalent bonds, and we will conclude that the dissuasive strategies used to minimize and/or disregard incompatibility prove to be fruitless. (shrink)

Inferentialists about scientific representation hold that an apparatus’s representing a target system consists in the apparatus allowing “surrogative inferences” about the target. I argue that a serious problem for inferentialism arises from the fact that many scientific theories and models contain internal inconsistencies. Inferentialism, left unamended, implies that inconsistent scientific models have unlimited representational power, since an inconsistency permits any conclusion to be inferred. I consider a number of ways that inferentialists can respond to this challenge before suggesting my own (...) solution. I develop an analogy to exploitable glitches in a game. Even though inconsistent representational apparatuses may in some sense allow for contradictions to be generated within them, doing so violates the intended function of the apparatus’s parts and hence violates representational “gameplay.”. (shrink)

The purpose of this dissertation is to contribute to our understanding of scientific theory pursuit by providing a detailed case study on the development of early quantum theory, from roughly 1900 to 1916. I first elaborate on why this case should be considered an instance of piecemeal pursuit by presenting the historical quantum conjectures that were being used in different contexts. These conjectures gave varied interpretations of quantization. By comparing these conjectures, I identify a general quantum postulate that captures the (...) underlying assumption common to all the cases. I argue that it is possible to consider a general postulate about quantization even when its proper application is ambiguous in a given context, and that the postulate can be separated from different elements of the framework being used to investigate it. I show that the quantum postulate can be deemed promising by analysing the support it gains using a Bayesian framework. I first defend the use of such a framework by considering the purported inconsistencies in Planck's introduction of his quantum conjecture and how we should handle these. I then explicate two cases of support for the postulate. First, I show how we can use a particular solution to the Bayesian problem of old evidence to interpret the support the quantum postulate received by accounting for phenomena that had no previous explanation. Finally, I show that the quantum postulate is also supported by a unification argument, where unification is interpreted as informational relevance between the different domains of inquiry. (shrink)

This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the features of their real-world target system. My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves.