Unificatory Explanation

Nathan, Marco J.

doi:10.1093/bjps/axv022

Cited by 7 publications

(6 citation statements)

References 43 publications

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“…Classic references on the merits of unification in science are (Kitcher 1989, Sober 2003. This topic is again intensively debated today in various disciplines (for a few recent examples, see Colombo and Hartmann 2017, Nathan 2017, Blanchard 2018. The third problem we will consider, the Causation Problem (P-C), has perhaps the longest history, and involves a cluster of issues concerning the concept of cause.…”

Section: P-c) or The 'Causation Problem'mentioning

confidence: 99%

Deep Learning Applied to Scientific Discovery: A Hot Interface with Philosophy of Science

et al. 2023

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We scrutinize publications in automated scientific discovery using deep learning, with the aim of shedding light on problems with strong connections to philosophy of science, of physics in particular. We show that core issues of philosophy of science, related, notably, to the nature of scientific theories; the nature of unification; and of causation loom large in scientific deep learning. Therefore advances in deep learning could, and ideally should, have impact on philosophy of science, and vice versa. We suggest lines of further research, and highlight the role 'theory-driven' AI could have in future developments of the field.

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Section: P-c) or The 'Causation Problem'mentioning

confidence: 99%

Deep Learning Applied to Scientific Discovery: A Hot Interface with Philosophy of Science

et al. 2023

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“…Thus, integrative efforts are necessary for cognitive science to remain interdisciplinary. These efforts are usually required for explanatory purposes, and collaborations could even be quite ephemeral, without establishing any long-term institutionalized fields or disciplines (Nathan 2017). 3 But they could also coalesce to form new interfield theories (Darden and Maull 1977).…”

Section: Integrative Coordinationmentioning

confidence: 99%

Explanations in cognitive science: unification versus pluralism

Miłkowski

Hohol

2020

Synthese

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The debate between the defenders of explanatory unification and explanatory pluralism has been ongoing from the beginning of cognitive science and is one of the central themes of its philosophy. Does cognitive science need a grand unifying theory? Should explanatory pluralism be embraced instead? Or maybe local integrative efforts are needed? What are the advantages of explanatory unification as compared to the benefits of explanatory pluralism? These questions, among others, are addressed in this Synthese's special issue. In the introductory paper, we discuss the background of the questions, distinguishing integrative theorizing from building unified theories. On the one hand, integrative efforts involve collaboration between various disciplines, fields, approaches, or theories. These efforts could even be quite temporary, without establishing any long-term institutionalized fields or disciplines, but could also contribute to developing new interfield theories. On the other hand, unification can rely on developing complete theories of mechanisms and representations underlying all cognition, as Newell's "unified theories of cognition", or may appeal to grand principles, as predictive coding. Here, we also show that unification in contemporary cognitive science goes beyond reductive unity, and may involve various forms of joint efforts and division of explanatory labor. This conclusion is one of the themes present in the content of contributions constituting the special issue.

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“…While I have discussed this case in the context of early quantum theory in which multiple domains were unified by their incorporation of a quantization parameter into their physical description, there exist a multitude of ways of following such a strategy. For instance, Myrvold (2017) and Nathan (2017) provide different interpretations of unification. Furthermore, Morrison (2000) argues by examining several historical cases that there is no unified account of theoretical unification.…”

Section: Relation To Robustness Reasoningmentioning

confidence: 99%

Unification beyond justification: a strategy for theory development

Kao

2017

Synthese

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This paper considers the importance of unification in the context of developing scientific theories. I argue that unifying hypotheses are not valuable simply because they are supported by multiple lines of evidence. Instead, they can be valuable because they guide experimental research in different domains in such a way that the results from those experiments inform the scope of the theory being developed. I support this characterization by appealing to the early development of quantum theory. I then draw some comparisons with discussions of robustness reasoning.

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Unificatory Explanation

Cited by 7 publications

References 43 publications

Deep Learning Applied to Scientific Discovery: A Hot Interface with Philosophy of Science

Deep Learning Applied to Scientific Discovery: A Hot Interface with Philosophy of Science

Explanations in cognitive science: unification versus pluralism

Unification beyond justification: a strategy for theory development

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