What is a mechanism? Thinking about mechanisms across the sciences

Illari, Phyllis; Williamson, Jon

doi:10.1007/s13194-011-0038-2

Cited by 332 publications

(165 citation statements)

References 34 publications

(38 reference statements)

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“…Psychologically, programmers are less likely to trust an arcane or unintelligible report than a transparent documentation of the entities and activities responsible for the error. This transparency merges a sense of adequate mechanistic explanation (Illari and Williamson 2012) with the logical community's sense of when a proof is both convincing and elegant.…”

Section: The Semantics Of Separation Logicmentioning

confidence: 94%

Why Separation Logic Works

2018

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One might poetically muse that computers have the essence both of logic and machines. Through the case of the history of Separation Logic, we explore how this assertion is more than idle poetry. Separation Logic works because it merges the software engineer's conceptual model of a program's manipulation of computer memory with the logical model that interprets what sentences in the logic are true, and because it has a proof theory which aids in the crucial problem of scaling the reasoning task. Scalability is a central problem, and some would even say the central problem, in applications of logic in computer science. Separation Logic is an interesting case because of its widespread success in verification tools. For these two senses of model-the engineering/conceptual and the logical-to merge in a genuine sense, each must maintain their norms of use from their home disciplines. When this occurs, both the logic and engineering benefit greatly. Seeking this intersection of two different senses of model provides a strategy for how computer scientists and logicians may be successful. Furthermore, the history of Separation Logic for analysing programs provides a novel case for philosophers of science of how software engineers and computer scientists develop models and the components of such models. We provide three contributions: an exploration of the extent of models merging that is necessary for success in computer science; an introduction to the technical details of Separation Logic, which can be used for reasoning about other exhaustible resources; and an introduction to (a subset of) the problems, process, and results of computer scientists for those outside the field.

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Section: The Semantics Of Separation Logicmentioning

confidence: 94%

Why Separation Logic Works

2018

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“…According to new mechanism, to explain a phenomenon φ is to elucidate the causal structure of the mechanism that gives rise to φ. While mechanisms are defined variously, the core idea is that they are organized systems, comprising causally relevant component parts and operations (or activities) thereof (for a recent review, see, e.g., Illari and Williamson 2011). Component parts of the mechanism interact, and their organized operation contributes to the capacity of the mechanism to exhibit φ.…”

Section: Mechanistic Integration Of Researchmentioning

confidence: 99%

Social intelligence: How to integrate research? A mechanistic perspective

Miłkowski

2017

AI & Soc

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Is there a field of social intelligence? Many various disciplines approach the subject and it may only seem natural to suppose that different fields of study aim at explaining different phenomena; in other words, there is no special field of study of social intelligence. In this paper, I argue for an opposite claim. Namely, there is a way to integrate research on social intelligence, as long as one accepts the mechanistic account to explanation. Mechanistic integration of different explanations, however, comes at a cost: mechanism requires explanatory models to be fairly complete and realistic, and this does not seem to be the case for many models concerning social intelligence, especially models of economical behavior. Such models need either be made more realistic, or they would not count as contributing to the same field. I stress that the focus on integration does not lead to ruthless reductionism; on the contrary, mechanistic explanations are best understood as explanatorily pluralistic.

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“…However, if asked which ontological position they take when thinking about causation, mechanistic or difference-making, most epidemiologists will probably agree that their notions of illness causation processes seem to fit current philosophical definitions of mechanism quite well (Glennan 1996, Machamer, Darden et al 2000, Bechtel and Abrahamsen 2005, Illari and Williamson 2012. One particularly attractive conception of mechanism is Garson's "functional sense of mechanism" (Garson 2013).…”

Section: Illness Causation Viewed From the Epidemiological Anglementioning

confidence: 99%

Evidence, illness, and causation: An epidemiological perspective on the Russo–Williamson Thesis

Fiorentino

Dammann

2015

Studies in History and Philosophy of Science Part C: Studies in

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According to the Russo-Williamson thesis, causal claims in the health sciences need to be supported by both difference-making and mechanistic evidence. In this article, we attempt to determine whether Evidence-based Medicine (EBM) can be improved through the consideration of mechanistic evidence. We discuss the practical composition and function of each RWT evidence type and propose that exposure-outcome evidence (previously known as difference-making evidence) provides associations that can be explained through a hypothesis of causation, while mechanistic evidence provides finer-grained associations and knowledge of entities that ultimately explains a causal hypothesis. We suggest that mechanistic evidence holds untapped potential to add value to the assessment of evidence quality in EBM and propose initial recommendations for the integration of mechanistic and exposure-outcome evidence to improve EBM by robustly leveraging available evidence in support of good medical decisions.

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What is a mechanism? Thinking about mechanisms across the sciences

Cited by 332 publications

References 34 publications

Why Separation Logic Works

Why Separation Logic Works

Social intelligence: How to integrate research? A mechanistic perspective

Evidence, illness, and causation: An epidemiological perspective on the Russo–Williamson Thesis

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