Mark Newman

In this book, Peter Vickers argues that inconsistency in science has been massively exaggerated by philosophers. In his view, inconsistent science is neither as rampant nor as damaging as many have supposed. To argue his point, he develops a specific method he calls theory eliminativism and applies it to four case studies from the history of physics and mathematics .The method is original and convincing, and the case studies well researched and compelling. Vickers’ monograph provides a challenge to any philosopher of science who takes inconsistency claims seriously, while also introducing a potentially very useful methodology for analyzing away problematic ‘theory’ discourse in other philosophical debates. Overall then, this is a very creative text and useful for both those directly interested in ..

In this article I develop a model of theoretical understanding in science. This is a philosophical theory that specifies the conditions that are both necessary and sufficient for a scientist to satisfy the construction ‘S understands theory T ’. I first consider how this construction is preferable to others, then build a model of the requisite conditions on the basis of examples from elementary physics. I then show how this model of theoretical understanding can be made philosophically robust and provide a more sophisticated account than we see from models of a similar kind developed by those working in the psychology of physics and artificial intelligence. 1 Introduction2 The Explicandum/Analysandum3 Analysis of ‘S understands T’ 4 The Inferential Model4.1 Which problems are we talking about?4.2 Does the solution have to be true?4.3 Does each cycle have to be correct for S to understand T?4.4 What is problem-solving reliability?4.5 Does every specific inference of a cycle have to be correct?4.6 Does each inference have to use a principle that is part of the theory?5 Theoretical Understanding and Conceptual Expertise: Empirical Considerations6 Conclusion: Inferential Model of Scientific Understanding and Our Traditional Problems.

In this article, I use a mental models computational account of representation to illustrate some details of my previously presented inferential model of scientific understanding. The hope is to shed some light on possible mechanisms behind the notion of scientific understanding. I argue that if mental models are a plausible approach to modelling cognition, then understanding can best be seen as the coupling of specific rules. I present our beliefs as ?ordinary? conditional rules, and the coupling process as one where the consequent of one ordinary rule (OR) matches and activates the antecedent of the rule to which it is coupled in virtue of the activation of an intermediate ?inference? rule. I argue that on this approach knowledge of an explanation is the activation of ORs in a cognitive hierarchy, while understanding is achieved when those activated ORs are also coupled via correct inference rules. I do not directly address issues regarding the plausibility of mental models themselves. This article should therefore be seen as an exercise in refining the inferential model within an already presupposed computational setting, not one of arguing for the psychological adequacy of computational approaches

The Explanatory Model of Scientific Understanding is a deflationary thesis recently advocated by Kareem Khalifa. EMU is committed to two key ideas: all understanding-relevant knowledge is propositional in nature; and the abilities we use to generate understanding are merely our usual logical reasoning skills. In this paper I provide an argument against both ideas, suggesting that scientific understanding requires a significant amount of non-propositional knowledge not captured by logical relations. I use the Inferential Model of Scientific Understanding to reveal how we can better represent what constitutes understanding a scientific event. In particular, this model accounts for not only logical and probabilistic inferences, but also those conceptual associations and categorizations we must make to comprehend an explanation.

John Worrall recently provided an account of epistemic structural realism, which explains the success of science by arguing for the correct mathematical structure of our theories. He accounts for the historical failures of science by pointing to bloated ontological interpretations of theoretical terms. In this paper I argue that Worrall’s account suffers from five serious problems. I also show that Pierre Cruse and David Papineau have developed a rival structural realism that solves all of the problems faced by Worrall. This Ramsey-sentence realism is a significant advance in the debate, but still ultimately fails for its incomplete account of reference.

The No-Miracles Argument (NMA) is often used to support scientific realism. We can formulate this argument as an inference to the best explanation this accusation of circularity by appealing to reliabilism, an externalist epistemology. In this paper I argue that this retreat fails. Reliabilism suffers from a potentially devastating difficulty known as the Generality Problem and attempts to solve this problem require adopting both epistemic and metaphysical assumptions regarding local scientific theories. Although the externalist can happily adopt the former, if he adopts the latter then the Generality Problem arises again, but now at the level of scientific methodology. Answering this new version of the Generality Problem is impossible for the scientific realist without making the important further assumption that there exists the possibility of a unique rule of Doing this however would make the NMA viciously premise circular

In this article I argue that two current accounts of scientific understanding are incorrect and I propose an alternative theory. My new account draws on recent research in cognitive psychology which reveals the importance of making causal and logical inferences on the basis of incoming information. To understand a phenomenon we need to make particular kinds of inferences concerning the explanations we are given. Specifically, we come to understand a phenomenon scientifically by developing mental models that incorporate the correct causal and logical properties responsible for the causes or logical properties of the phenomenon being explained

In this paper I argue that singularist approaches to solving the Pessimistic Induction, such as Structural Realism, are unacceptable, but that when a pluralist account of methodological principles is adopted this anti-realist argument can be dissolved. The proposed view is a contextual methodological pluralism in the tradition of Normative Naturalism, and is justified by appeal to meta-methodological principles that are themselves justified via an externalist epistemology. Not only does this view provide an answer to the Pessimistic Induction, it can also accommodate our strongest intuitions regarding the progress of science.