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Luca Rivelli
Université Catholique de Louvain
  1.  96
    Digital Literature Analysis for Empirical Philosophy of Science.Oliver M. Lean, Luca Rivelli & Charles H. Pence - 2021 - British Journal for the Philosophy of Science.
    Empirical philosophers of science aim to base their philosophical theories on observations of scientific practice. But since there is far too much science to observe it all, how can we form and test hypotheses about science that are sufficiently rigorous and broad in scope, while avoiding the pitfalls of bias and subjectivity in our methods? Part of the answer, we claim, lies in the computational tools of the digital humanities, which allow us to analyze large volumes of scientific literature. Here (...)
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  2. Editorial introduction to “Digital Studies of Digital Science”.Charles H. Pence & Luca Rivelli - 2022 - Synthese 200:328.
    (Editorial introduction to a special issue of Synthese.).
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    Multilevel Ensemble Explanations: A Case from Theoretical Biology.Luca Rivelli - 2019 - Perspectives on Science 27 (1):88-116.
    In this paper I will reconstruct and analyze a famous argument by Stuart Kauffman about complex systems and evolution, in order to highlight the use in theoretical biology of a kind of non-mechanistic and non-causal explanation which I propose to call, following Kauffman, ensemble explanation. The aim is to contribute to the ongoing philosophical debate about non-causal explanations in the special sciences, kinds of explanation apparently extraneous to the received causal-mechanistic view. Ensemble explanations resemble quite closely the explanations of the (...)
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    Antimodularity: Pragmatic Consequences of Computational Complexity on Scientific Explanation.Luca Rivelli - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 97-122.
    This work is concerned with hierarchical modular descriptions, their algorithmic production, and their importance for certain types of scientific explanations of the structure and dynamical behavior of complex systems. Networks are taken into consideration as paradigmatic representations of complex systems. It turns out that algorithmic detection of hierarchical modularity in networks is a task plagued in certain cases by theoretical intractability and in most cases by the still high computational complexity of most approximated methods. A new notion, antimodularity, is then (...)
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