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Profile: Phyllis McKay Illari (University of Kent at Canterbury)
  1. Lorenzo Casini, Phyllis Mckay Illari, Federica Russo & Jon Williamson (2011). Models for Prediction, Explanation and Control. Theoria 26 (1):5-33.
    The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular (...)
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  2. Phyllis McKay Illari (2011). Mechanistic Evidence: Disambiguating the Russo–Williamson Thesis. International Studies in the Philosophy of Science 25 (2):139 - 157.
    Russo and Williamson claim that establishing causal claims requires mechanistic and difference-making evidence. In this article, I will argue that Russo and Williamson's formulation of their thesis is multiply ambiguous. I will make three distinctions: mechanistic evidence as type vs object of evidence; what mechanism or mechanisms we want evidence of; and how much evidence of a mechanism we require. I will feed these more precise meanings back into the Russo?Williamson thesis and argue that it is both true and false: (...)
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  3. Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.) (2011). Causality in the Sciences. Oxford University Press.
    The book tackles these questions as well as others concerning the use of causality in the sciences.
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  4. Phyllis McKay Illari, Federica Russo & Jon Williamson (2011). Why Look at Causality in the Sciences? In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. OUP Oxford
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  5. Phyllis McKay Illari & Jon Williamson (2011). Mechanisms Are Real and Local. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. OUP Oxford
    Mechanisms have become much-discussed, yet there is still no consensus on how to characterise them. In this paper, we start with something everyone is agreed on – that mechanisms explain – and investigate what constraints this imposes on our metaphysics of mechanisms. We examine two widely shared premises about how to understand mechanistic explanation: (1) that mechanistic explanation offers a welcome alternative to traditional laws-based explanation and (2) that there are two senses of mechanistic explanation that we call ‘epistemic explanation’ (...)
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  6. Phyllis McKay Illari & Jon Williamson (2010). Function and Organization: Comparing the Mechanisms of Protein Synthesis and Natural Selection. Studies in History and Philosophy of Science Part C 41 (3):279-291.
    In this paper, we compare the mechanisms of protein synthesis and natural selection. We identify three core elements of mechanistic explanation: functional individuation, hierarchical nestedness or decomposition, and organization. These are now well understood elements of mechanistic explanation in fields such as protein synthesis, and widely accepted in the mechanisms literature. But Skipper and Millstein have argued that natural selection is neither decomposable nor organized. This would mean that much of the current mechanisms literature does not apply to the mechanism (...)
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