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Switch to: References
Citations of:
Scientific Explanation: Putting Communication First
Philosophy of Science 83 (5):721-732 (2016)
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In this paper, I first outline the view developed in my recent book on the role of idealization in scientific understanding. I discuss how this view leads to the recognition of a number of kinds of variability among scientific representations, including variability introduced by the many different aims of scientific projects. I then argue that the role of idealization in securing understanding distances understanding from truth, but that this understanding nonetheless gives rise to scientific knowledge. This discussion will clarify how (...) |
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How can a reflective scientist put forward an explanation using a model when they are aware that many of the assumptions used to specify that model are false? This paper addresses this challenge by making two substantial assumptions about explanatory practice. First, many of the propositions deployed in the course of explaining have a non-representational function. In particular, a proposition that a scientist uses and also believes to be false, i.e. an “idealization”, typically has some non-representational function in the practice, (...) |
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This article addresses Simon Lohse’s and Daniel Little’s responses to my article “Is Social Ontology Prior to Social Scientific Methodology?.” In that article, I present a pragmatic and deflationary view of the priority of social ontology to social science methodology where social ontology is valued for its ability to promote empirical success and not because it yields knowledge of what furnishes the social world. First, in response to Lohse, I argue that my view is compatible with a role for ontological (...) |
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We argue that the robustness analysis of idealized models can have confirmational power. This responds to concerns recently raised in the literature, according to which the robustness analysis of models whose idealizations are not discharged is unable to confirm the causal mechanisms underlying these models, and the robustness analysis of models whose idealizations are discharged is unnecessary. In response, we make clear that, where idealizations sweep out, in a specific way, the space of possibilities— which is sometimes, though not always, (...) |
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I draw upon speech act theory to understand the speech acts appropriate to the multiple aims of scientific practice and the role of nonepistemic values in evaluating speech acts made relative to those aims. First, I look at work that distinguishes explaining from describing within scientific practices. I then argue speech act theory provides a framework to make sense of how explaining, describing, and other acts have different felicity conditions. Finally, I argue that if explaining aims to convey understanding to (...) |
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Taking a cue from remarks Thomas Kuhn makes in 1990 about the historical turn in philosophy of science, I examine the history of history and philosophy of science within parts of the British philosophical context in the 1950s and early 1960s. During this time, ordinary language philosophy's influence was at its peak. I argue that the ordinary language philosophers' methodological recommendation to analyze actual linguistic practice influences several prominent criticisms of the deductive-nomological model of scientific explanation and that these criticisms (...) |
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We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: Are networks explainable, and if so, what does it mean to explain the output of a network? And what does it mean for a network to be interpretable? We argue that accounts of “explanation” tailored specifically to neural networks have ineffectively reinvented the wheel. In (...) |
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Explainable artificial intelligence (XAI) seeks to produce explanations for those machine learning methods which are deemed opaque. However, there is considerable disagreement about what this means and how to achieve it. Authors disagree on what should be explained (topic), to whom something should be explained (stakeholder), how something should be explained (instrument), and why something should be explained (goal). In this paper, I employ insights from means-end epistemology to structure the field. According to means-end epistemology, different means ought to be (...) |
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Scientific realism is a thesis about the success of science. Most traditionally: science has been so successful at prediction and guiding action because its best theories are true (or approximately true or increasing in their degree of truth). If science is in the business of doing its best to generate true theories, then we should turn to those theories for explanatory knowledge, predictions, and guidance of our actions and decisions. Views that are popular in contemporary philosophy of science about scientific (...) |
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Philosophers of psychology debate, among other things, which psychological models, if any, are (or provide) mechanistic explanations. This should seem a little strange given that there is rough consensus on the following two claims: 1) a mechanism is an organized collection of entities and activities that produces, underlies, or maintains a phenomenon, and 2) a mechanistic explanation describes, represents, or provides information about the mechanism producing, underlying, or maintaining the phenomenon to be explained (i.e. the explanandum phenomenon) (Bechtel and Abrahamsen (...) |
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In his chapter in this volume, Christopher Pincock develops an argument for scientific realism based on scientific understanding, and he argues that Giere’s (2006) and my (2017, 2020) commitment to the context-dependence of scientific understanding or knowledge renders our views unable to account for an essential step in how scientists come to know. Meanwhile, in my chapter in this volume, I motivate a view that I call "causal pattern realism." In this response to Pincock's chapter, I will sketch a revised (...) |
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These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018. |
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