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Papers by Luca Rivelli
Metaphilosophy (Online Early View), 2024
Some scholars claim that epistemology of science and machine learning are actually overlapping di... more Some scholars claim that epistemology of science and machine learning are actually overlapping disciplines studying induction, respectively affected by Hume's problem of induction and its formal machine-learning counterpart, the “no-free-lunch” (NFL) theorems, to which even advanced AI systems such as LLMs are not immune. Extending Kevin Korb's view, this paper envisions a hierarchy of disciplines where the lowermost is a basic science, and, recursively, the metascience at each level inductively learns which methods work best at the immediately lower level. Due to Hume's dictum and NFL theorems, no exact metanorms for the good performance of each object science can be obtained after just a finite number of levels up the hierarchy, and the progressive abstractness of each metadiscipline and consequent ill-definability of its methods and objects makes science—as defined by a minimal standard of scientificity—cease to exist above a certain metalevel, allowing for a still rational style of inquiry into science that can be called “philosophical.” Philosophical levels, transitively reflecting on science, peculiarly manifest a non–empirically learned urge to self-reflection constituting the properly normative aspect of philosophy of science.
Perspectives on Science, 2019
I analyze a well-known argument by Stuart Kauffman about complex systems and evolution to show it... more I analyze a well-known argument by Stuart Kauffman about complex systems and evolution to show it contains a hierarchy of non-mechanistic, non-causal explanations—which I would call, following Kauffman, “ensemble explanations”—quite closely resembling the explanations of the structural kind proposed in Huneman (2017), but lacking their absolute mathematical certainty, being based on results of non-exhaustive computer simulations. In Kauffman’s core argument ensemble explanations form an explanatory chain along a hierarchy of levels, where each explanans at one level gets itself recursively explained at the lower level. Explanations at adjacent levels turn out to be related not by mereological containment as in a multilevel mechanistic explanation, but by an analog to the relationship between two specifications at different levels of a specification/implementation hierarchy as understood by computer science. A mechanistic explanation grounds the whole hierarchy enabling the explanatory chain. Interestingly, the preliminary production of ensemble explanations enables the multilevel mechanistic explanations of systems manifesting what Bedau (1997) defines as weak emergence.
A talk relating our work in digital history and philosophy of science to the broader landscape of... more A talk relating our work in digital history and philosophy of science to the broader landscape of experimental/empirical philosophy.
The British Journal for the Philosophy of Science, 2021
Empirical philosophers of science aim to base their philosophical theories on observations of sci... more 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 (DH), which allow us to analyze large volumes of scientific literature. Here we advocate for the use of these methods by addressing a number of large-scale, justificatory concerns-specifically, about the epistemic value of journal articles as evidence for what happens elsewhere in science, and about the ability of DH tools to extract this evidence. Far from ignoring the gap between scientific literature and the rest of scientific practice, effective use of DH tools requires critical reflection about these relationships.
Ce travail concerne principalement la notion de modularite hierarchique et son utilisation pour e... more Ce travail concerne principalement la notion de modularite hierarchique et son utilisation pour expliquer la structure et le comportement dynamique des systemes complexes au moyen de modeles modulaires hierarchiques, ainsi qu'un concept de ma proposition, l’antimodularite, relie a la possibilite de la detection algorithmique de la modularite hierarchique. Plus precisement, je mets en evidence la portee pragmatique de la modularite hierarchique sur la possibilite de l’explication scientifique des systemes complexes, c’est-a-dire, systemes qui, selon une description de base choisie par l’observateur, peuvent etre consideres comme composes de parties elementaires discretes interdependantes. Je souligne que la modularite hierarchique est essentielle meme au cours de l’experimentation visee a decouvrir la structure de ces systemes. Mais la detection algorithmique de la modularite hierarchique se revele etre une tâche affectee par la demontree intraitabilite computationnelle de la rec...
On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence
Universa recensioni di filosofia, vol. 1(1), 2012
Universa recensioni di filosofia, vol. 2(2), 2013
Universa recensioni di filosofia, vol. 1(2), 2012
Universa recensioni di filosofia vol. 1(2), 2012
Perspectives on Science, 27(1), 88–116, 2019
I analyze a well-known argument by Stuart Kauffman about complex systems and evolution to show it... more I analyze a well-known argument by Stuart Kauffman about complex systems and evolution to show it contains a hierarchy of non-mechanistic, non-causal explanations—which I would call, following Kauffman, “ensemble explanations”—quite closely resembling the explanations of the structural kind proposed in Huneman (2017), but lacking their absolute mathematical certainty, being based on results of non-exhaustive computer simulations. In Kauffman’s core argument ensemble explanations form an explanatory chain along a hierarchy of levels, where each explanans at one level gets itself recursively explained at the lower level. Explanations at adjacent levels turn out to be related not by mereological containment as in a multilevel mechanistic explanation, but by an analog to the relationship between two specifications at different levels of a specification/implementation hierarchy as understood by computer science. A mechanistic explanation grounds the whole hierarchy enabling the explanatory chain. Interestingly, the preliminary production of ensemble explanations enables the multilevel mechanistic explanations of systems manifesting what Bedau (1997) defines "weak emergence".
Phd Thesis by Luca Rivelli
The thesis is concerned with the notion of hierarchical modularity in complex systems, its algori... more The thesis is concerned with the notion of hierarchical modularity in complex systems, its algorithmic detection and its use in explaining structure and dynamical behavior of such systems by means of hierarchical modular models. It proposes a new notion, antimodularity, in order to capture the possible occurrence of difficulties in scientific explanation due to the excessive computational complexity of algorithms used to find modular structure in big scientific datasets, this way highlighting a probable impending major shift of paradigm in certain special sciences.
Books by Luca Rivelli
In D. Berkich & M. V. D’Alfonso (Eds.). On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence: Themes from IACAP 2016. Springer International Publishing. ISBN 978-3-030-01799-6, 2019
This work is concerned with hierarchical modular descriptions, their algorithmic production, and ... more 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 (NP-hardness) and in most cases by the still high computational complexity of most approximated methods. A new notion, antimodularity, is then proposed, which consists in the impossibility to algorithmically obtain a modular description fitting the explanatory purposes of the observer for reasons tied to the computational cost of typical algorithmic methods of modularity detection, in relation to the excessive size of the system under assessment and to the required precision. It turns out that occurrence of antimodularity hinders both mechanistic and functional explanation, by damaging their intelligibility. Another newly proposed more general notion, explanatory emergence, subsumes antimodularity under any case in which a system resists intelligible explanations because of the excessive computational cost of algorithmic methods required to obtain the relevant explanatory descriptions from the raw data. The possible consequences, and the likelihood, of incurring in antimodularity or explanatory emergence in the actual scientific practice are finally assessed, concluding that this eventuality is possible, at least in disciplines which are based on the algorithmic analysis of big data. The present work aims to be an example of how certain notions of theoretical computer science can be fruitfully imported into philosophy of science.
Metaphilosophy (Online Early View), 2024
Some scholars claim that epistemology of science and machine learning are actually overlapping di... more Some scholars claim that epistemology of science and machine learning are actually overlapping disciplines studying induction, respectively affected by Hume's problem of induction and its formal machine-learning counterpart, the “no-free-lunch” (NFL) theorems, to which even advanced AI systems such as LLMs are not immune. Extending Kevin Korb's view, this paper envisions a hierarchy of disciplines where the lowermost is a basic science, and, recursively, the metascience at each level inductively learns which methods work best at the immediately lower level. Due to Hume's dictum and NFL theorems, no exact metanorms for the good performance of each object science can be obtained after just a finite number of levels up the hierarchy, and the progressive abstractness of each metadiscipline and consequent ill-definability of its methods and objects makes science—as defined by a minimal standard of scientificity—cease to exist above a certain metalevel, allowing for a still rational style of inquiry into science that can be called “philosophical.” Philosophical levels, transitively reflecting on science, peculiarly manifest a non–empirically learned urge to self-reflection constituting the properly normative aspect of philosophy of science.
Perspectives on Science, 2019
I analyze a well-known argument by Stuart Kauffman about complex systems and evolution to show it... more I analyze a well-known argument by Stuart Kauffman about complex systems and evolution to show it contains a hierarchy of non-mechanistic, non-causal explanations—which I would call, following Kauffman, “ensemble explanations”—quite closely resembling the explanations of the structural kind proposed in Huneman (2017), but lacking their absolute mathematical certainty, being based on results of non-exhaustive computer simulations. In Kauffman’s core argument ensemble explanations form an explanatory chain along a hierarchy of levels, where each explanans at one level gets itself recursively explained at the lower level. Explanations at adjacent levels turn out to be related not by mereological containment as in a multilevel mechanistic explanation, but by an analog to the relationship between two specifications at different levels of a specification/implementation hierarchy as understood by computer science. A mechanistic explanation grounds the whole hierarchy enabling the explanatory chain. Interestingly, the preliminary production of ensemble explanations enables the multilevel mechanistic explanations of systems manifesting what Bedau (1997) defines as weak emergence.
A talk relating our work in digital history and philosophy of science to the broader landscape of... more A talk relating our work in digital history and philosophy of science to the broader landscape of experimental/empirical philosophy.
The British Journal for the Philosophy of Science, 2021
Empirical philosophers of science aim to base their philosophical theories on observations of sci... more 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 (DH), which allow us to analyze large volumes of scientific literature. Here we advocate for the use of these methods by addressing a number of large-scale, justificatory concerns-specifically, about the epistemic value of journal articles as evidence for what happens elsewhere in science, and about the ability of DH tools to extract this evidence. Far from ignoring the gap between scientific literature and the rest of scientific practice, effective use of DH tools requires critical reflection about these relationships.
Ce travail concerne principalement la notion de modularite hierarchique et son utilisation pour e... more Ce travail concerne principalement la notion de modularite hierarchique et son utilisation pour expliquer la structure et le comportement dynamique des systemes complexes au moyen de modeles modulaires hierarchiques, ainsi qu'un concept de ma proposition, l’antimodularite, relie a la possibilite de la detection algorithmique de la modularite hierarchique. Plus precisement, je mets en evidence la portee pragmatique de la modularite hierarchique sur la possibilite de l’explication scientifique des systemes complexes, c’est-a-dire, systemes qui, selon une description de base choisie par l’observateur, peuvent etre consideres comme composes de parties elementaires discretes interdependantes. Je souligne que la modularite hierarchique est essentielle meme au cours de l’experimentation visee a decouvrir la structure de ces systemes. Mais la detection algorithmique de la modularite hierarchique se revele etre une tâche affectee par la demontree intraitabilite computationnelle de la rec...
On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence
Universa recensioni di filosofia, vol. 1(1), 2012
Universa recensioni di filosofia, vol. 2(2), 2013
Universa recensioni di filosofia, vol. 1(2), 2012
Universa recensioni di filosofia vol. 1(2), 2012
Perspectives on Science, 27(1), 88–116, 2019
I analyze a well-known argument by Stuart Kauffman about complex systems and evolution to show it... more I analyze a well-known argument by Stuart Kauffman about complex systems and evolution to show it contains a hierarchy of non-mechanistic, non-causal explanations—which I would call, following Kauffman, “ensemble explanations”—quite closely resembling the explanations of the structural kind proposed in Huneman (2017), but lacking their absolute mathematical certainty, being based on results of non-exhaustive computer simulations. In Kauffman’s core argument ensemble explanations form an explanatory chain along a hierarchy of levels, where each explanans at one level gets itself recursively explained at the lower level. Explanations at adjacent levels turn out to be related not by mereological containment as in a multilevel mechanistic explanation, but by an analog to the relationship between two specifications at different levels of a specification/implementation hierarchy as understood by computer science. A mechanistic explanation grounds the whole hierarchy enabling the explanatory chain. Interestingly, the preliminary production of ensemble explanations enables the multilevel mechanistic explanations of systems manifesting what Bedau (1997) defines "weak emergence".
The thesis is concerned with the notion of hierarchical modularity in complex systems, its algori... more The thesis is concerned with the notion of hierarchical modularity in complex systems, its algorithmic detection and its use in explaining structure and dynamical behavior of such systems by means of hierarchical modular models. It proposes a new notion, antimodularity, in order to capture the possible occurrence of difficulties in scientific explanation due to the excessive computational complexity of algorithms used to find modular structure in big scientific datasets, this way highlighting a probable impending major shift of paradigm in certain special sciences.
In D. Berkich & M. V. D’Alfonso (Eds.). On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence: Themes from IACAP 2016. Springer International Publishing. ISBN 978-3-030-01799-6, 2019
This work is concerned with hierarchical modular descriptions, their algorithmic production, and ... more 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 (NP-hardness) and in most cases by the still high computational complexity of most approximated methods. A new notion, antimodularity, is then proposed, which consists in the impossibility to algorithmically obtain a modular description fitting the explanatory purposes of the observer for reasons tied to the computational cost of typical algorithmic methods of modularity detection, in relation to the excessive size of the system under assessment and to the required precision. It turns out that occurrence of antimodularity hinders both mechanistic and functional explanation, by damaging their intelligibility. Another newly proposed more general notion, explanatory emergence, subsumes antimodularity under any case in which a system resists intelligible explanations because of the excessive computational cost of algorithmic methods required to obtain the relevant explanatory descriptions from the raw data. The possible consequences, and the likelihood, of incurring in antimodularity or explanatory emergence in the actual scientific practice are finally assessed, concluding that this eventuality is possible, at least in disciplines which are based on the algorithmic analysis of big data. The present work aims to be an example of how certain notions of theoretical computer science can be fruitfully imported into philosophy of science.