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Phyllis Illari
University College London
  1.  77
    Mechanistic Evidence: Disambiguating the Russo–Williamson Thesis.Phyllis McKay Illari - 2011 - 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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  2. Function and Organization: Comparing the Mechanisms of Protein Synthesis and Natural Selection.Phyllis McKay Illari & Jon Williamson - 2010 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 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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  3. Mechanisms Are Real and Local.Phyllis McKay Illari & Jon Williamson - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
    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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  4. Why Look at Causality in the Sciences?Phyllis McKay Illari, Federica Russo & Jon Williamson - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
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  5.  20
    Models for Prediction, Explanation and Control: Recursive Bayesian Networks.Lorenzo Casini, Phyllis McKay Illari, Federica Russo & Jon Williamson - 2011 - Theoria : An International Journal for Theory, History and Fundations of Science 26 (1):5-33.
    The Recursive Bayesian Net 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 how (...)
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  6.  68
    Causality in the Sciences.Illari Phyllis McKay, Russo Federica & Williamson Jon (eds.) - 2011 - Oxford University Press.
    The book tackles these questions as well as others concerning the use of causality in the sciences.
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  7.  57
    Why Theories of Causality Need Production : An Information Transmission Account.Phyllis McKay Illari - 2011 - Philosophy and Technology 24 (2):95-114.
    In this paper, I examine the comparatively neglected intuition of production regarding causality. I begin by examining the weaknesses of current production accounts of causality. I then distinguish between giving a good production account of causality and a good account of production. I argue that an account of production is needed to make sense of vital practices in causal inference. Finally, I offer an information transmission account of production based on John Collier’s work that solves the primary weaknesses of current (...)
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