@unpublished{munteanFunctionalInterventionistApproach2023,
	title = {A functional and interventionist approach to scientific progress: computational science at work},
	copyright = {All rights reserved},
	abstract = {A functional and interventionist approach to scientific progress:
1.	Abstract
We start with a plausible assumption: ‘scientific progress’ integrates philosophy and history of science. We can informally define scientific progress as a process that connects two (or more) stages of a scientific discipline D: it is said that D progresses from stage D1 to stage D2 if the latter improves compared to D1, based on a standard S. Knowing enough about D1 and about D2 is sometimes a daunting task for the historian; understanding what S is supposed to be is for the philosopher to discover. And there is the normative aspect of progress: what does it mean “to improve” a discipline, and what is S?
Most philosophers and historians would agree that scientific progress is not an internal process to science, but a complex interplay of economical, educational, professional, societal, and technological dimensions. This paper discusses historical and philosophical aspects of the latter dimension: in what sense is technological advancement a crucial component of scientific progress? If this correlation is granted, we can call it ‘techno-scientific progress.’ It assumes that the interplay between science and technology is adamant to scientific progress.
This paper adopts a dual approach to standard S: first functionalist (as “problem-solving” function) and second interventionist. In short, technologies solve problems (or “puzzles”) in science and improve our knowledge about possible, albeit not actual, interventions (mostly causal).
One technology and two episodes in science illustrate well this dual nature of progress. The technology that played a vital role in the progress of science is computer science (computational science is the application of computer science to scientific disciplines). This paper investigates in what sense recent scientific progress is correlated to advancement in computation. We take computer simulations and more recently machine learning algorithms as methodological tools used to solve problems in science and to expand our knowledge about possible interventions. To relate scientific progress to computational science and its advancement, we need to clarify a S, the standard of improvement. In the functionalist approach, S is more or less a function, typically a problem-solving function. Progress is obtained when this function is fulfilled (Laudan 1978; Shan 2019). In the epistemic approach, progress is defined as the accumulation of scientific knowledge (Bird 2008). In the present approach, we will use a specific epistemic approach in which knowledge obtained from computational methods is related to interventions.
The backbone of the present argument is to suggest that the combination of the functional and epistemic approaches to techno-scientific progress is more accurate both from a descriptive (historical) and normative point of view. It captures relevant episodes in the development of recent scientific disciplines.
Scientific progress can be conceptualized in many ways, but in a functionalist vein, one can define it as an effective and efficient way of solving new and old problems: “science progresses just in case successive theories solve more problems than their predecessors” (Laudan 1981). The functionalist approach to progress adopted here retains the role of explanation in clarifying and solving problems in science, as well as it being part of the solution.
What does it mean to solve a problem by numerical simulations? Here our approach follows the standard approach in the philosophy of science that considers numerical simulations as solutions to intractable problems (Humphreys 2009; Parker 2009; Winsberg 2010). Numerical simulations are typical problem-solving tools and from a functional point of view, we take them as a component of scientific progress.
In respect of interventionism, we define an intervention as a change in the setup of a scientific experiment or observation. According to Bain, scientific progress can be identified with the accumulation of knowledge. We argue in this paper that the advance of knowledge those computational methods bring to science is interventionist in nature: without performing actual experiments computer simulations can inform scientists about possible interventions and their results. An intervention is defined here in a more pragmatic way, as an activity that enables the scientist to change both the initial conditions under which we gain information about a system and the rules (or laws) that govern such a system.
This paper concludes with a discussion of two case studies.
First, as a clear case of interventionist progress, we offer a short history of the Lazarus project. This project simulated a binary black hole exclusively based on numerical simulation (Baker, Campanelli, and Lousto 2002). The history of this project and its results are discussed in some detail both as a functionalist and interventionist techno-scientific progress.
Second, as a case of functionalist techno-scientific progress, the DeepMind project called AlphaFold2 is a cluster of models that can predict the way a protein may fold in 3D when the input is the amino-acid sequence of the protein. It can compete with real experimental methods (X-ray crystallography or cryo-electron microscopy). The problem here (CASP=Critical Assessment of Structure Prediction) is a competition to predict spatial structures of proteins that have been solved using experimental methods, “but for which the structures have not been made public.” (Callaway 2022)
In conclusion, we showed how the concept of ‘techno-scientific progress’ integrates (recent) history and the philosophy of science with the advancement in computational science.},
	language = {1. Philosophy of science},
	author = {Muntean, Ioan},
	year = {2023},
	note = {Presented at HPS9, 2023 03},
	keywords = {Computation, Scientific Change, Scientific Progress},
}

{"_id":"GcETYyKSh4Hcki2zM","bibbaseid":"muntean-afunctionalandinterventionistapproachtoscientificprogresscomputationalscienceatwork-2023","author_short":["Muntean, I."],"bibdata":{"bibtype":"unpublished","type":"unpublished","title":"A functional and interventionist approach to scientific progress: computational science at work","copyright":"All rights reserved","abstract":"A functional and interventionist approach to scientific progress: 1. Abstract We start with a plausible assumption: ‘scientific progress’ integrates philosophy and history of science. We can informally define scientific progress as a process that connects two (or more) stages of a scientific discipline D: it is said that D progresses from stage D1 to stage D2 if the latter improves compared to D1, based on a standard S. Knowing enough about D1 and about D2 is sometimes a daunting task for the historian; understanding what S is supposed to be is for the philosopher to discover. And there is the normative aspect of progress: what does it mean “to improve” a discipline, and what is S? Most philosophers and historians would agree that scientific progress is not an internal process to science, but a complex interplay of economical, educational, professional, societal, and technological dimensions. This paper discusses historical and philosophical aspects of the latter dimension: in what sense is technological advancement a crucial component of scientific progress? If this correlation is granted, we can call it ‘techno-scientific progress.’ It assumes that the interplay between science and technology is adamant to scientific progress. This paper adopts a dual approach to standard S: first functionalist (as “problem-solving” function) and second interventionist. In short, technologies solve problems (or “puzzles”) in science and improve our knowledge about possible, albeit not actual, interventions (mostly causal). One technology and two episodes in science illustrate well this dual nature of progress. The technology that played a vital role in the progress of science is computer science (computational science is the application of computer science to scientific disciplines). This paper investigates in what sense recent scientific progress is correlated to advancement in computation. We take computer simulations and more recently machine learning algorithms as methodological tools used to solve problems in science and to expand our knowledge about possible interventions. To relate scientific progress to computational science and its advancement, we need to clarify a S, the standard of improvement. In the functionalist approach, S is more or less a function, typically a problem-solving function. Progress is obtained when this function is fulfilled (Laudan 1978; Shan 2019). In the epistemic approach, progress is defined as the accumulation of scientific knowledge (Bird 2008). In the present approach, we will use a specific epistemic approach in which knowledge obtained from computational methods is related to interventions. The backbone of the present argument is to suggest that the combination of the functional and epistemic approaches to techno-scientific progress is more accurate both from a descriptive (historical) and normative point of view. It captures relevant episodes in the development of recent scientific disciplines. Scientific progress can be conceptualized in many ways, but in a functionalist vein, one can define it as an effective and efficient way of solving new and old problems: “science progresses just in case successive theories solve more problems than their predecessors” (Laudan 1981). The functionalist approach to progress adopted here retains the role of explanation in clarifying and solving problems in science, as well as it being part of the solution. What does it mean to solve a problem by numerical simulations? Here our approach follows the standard approach in the philosophy of science that considers numerical simulations as solutions to intractable problems (Humphreys 2009; Parker 2009; Winsberg 2010). Numerical simulations are typical problem-solving tools and from a functional point of view, we take them as a component of scientific progress. In respect of interventionism, we define an intervention as a change in the setup of a scientific experiment or observation. According to Bain, scientific progress can be identified with the accumulation of knowledge. We argue in this paper that the advance of knowledge those computational methods bring to science is interventionist in nature: without performing actual experiments computer simulations can inform scientists about possible interventions and their results. An intervention is defined here in a more pragmatic way, as an activity that enables the scientist to change both the initial conditions under which we gain information about a system and the rules (or laws) that govern such a system. This paper concludes with a discussion of two case studies. First, as a clear case of interventionist progress, we offer a short history of the Lazarus project. This project simulated a binary black hole exclusively based on numerical simulation (Baker, Campanelli, and Lousto 2002). The history of this project and its results are discussed in some detail both as a functionalist and interventionist techno-scientific progress. Second, as a case of functionalist techno-scientific progress, the DeepMind project called AlphaFold2 is a cluster of models that can predict the way a protein may fold in 3D when the input is the amino-acid sequence of the protein. It can compete with real experimental methods (X-ray crystallography or cryo-electron microscopy). The problem here (CASP=Critical Assessment of Structure Prediction) is a competition to predict spatial structures of proteins that have been solved using experimental methods, “but for which the structures have not been made public.” (Callaway 2022) In conclusion, we showed how the concept of ‘techno-scientific progress’ integrates (recent) history and the philosophy of science with the advancement in computational science.","language":"1. Philosophy of science","author":[{"propositions":[],"lastnames":["Muntean"],"firstnames":["Ioan"],"suffixes":[]}],"year":"2023","note":"Presented at HPS9, 2023 03","keywords":"Computation, Scientific Change, Scientific Progress","bibtex":"@unpublished{munteanFunctionalInterventionistApproach2023,\n\ttitle = {A functional and interventionist approach to scientific progress: computational science at work},\n\tcopyright = {All rights reserved},\n\tabstract = {A functional and interventionist approach to scientific progress:\n1.\tAbstract\nWe start with a plausible assumption: ‘scientific progress’ integrates philosophy and history of science. We can informally define scientific progress as a process that connects two (or more) stages of a scientific discipline D: it is said that D progresses from stage D1 to stage D2 if the latter improves compared to D1, based on a standard S. Knowing enough about D1 and about D2 is sometimes a daunting task for the historian; understanding what S is supposed to be is for the philosopher to discover. And there is the normative aspect of progress: what does it mean “to improve” a discipline, and what is S?\nMost philosophers and historians would agree that scientific progress is not an internal process to science, but a complex interplay of economical, educational, professional, societal, and technological dimensions. This paper discusses historical and philosophical aspects of the latter dimension: in what sense is technological advancement a crucial component of scientific progress? If this correlation is granted, we can call it ‘techno-scientific progress.’ It assumes that the interplay between science and technology is adamant to scientific progress.\nThis paper adopts a dual approach to standard S: first functionalist (as “problem-solving” function) and second interventionist. In short, technologies solve problems (or “puzzles”) in science and improve our knowledge about possible, albeit not actual, interventions (mostly causal).\nOne technology and two episodes in science illustrate well this dual nature of progress. The technology that played a vital role in the progress of science is computer science (computational science is the application of computer science to scientific disciplines). This paper investigates in what sense recent scientific progress is correlated to advancement in computation. We take computer simulations and more recently machine learning algorithms as methodological tools used to solve problems in science and to expand our knowledge about possible interventions. To relate scientific progress to computational science and its advancement, we need to clarify a S, the standard of improvement. In the functionalist approach, S is more or less a function, typically a problem-solving function. Progress is obtained when this function is fulfilled (Laudan 1978; Shan 2019). In the epistemic approach, progress is defined as the accumulation of scientific knowledge (Bird 2008). In the present approach, we will use a specific epistemic approach in which knowledge obtained from computational methods is related to interventions.\nThe backbone of the present argument is to suggest that the combination of the functional and epistemic approaches to techno-scientific progress is more accurate both from a descriptive (historical) and normative point of view. It captures relevant episodes in the development of recent scientific disciplines.\nScientific progress can be conceptualized in many ways, but in a functionalist vein, one can define it as an effective and efficient way of solving new and old problems: “science progresses just in case successive theories solve more problems than their predecessors” (Laudan 1981). The functionalist approach to progress adopted here retains the role of explanation in clarifying and solving problems in science, as well as it being part of the solution.\nWhat does it mean to solve a problem by numerical simulations? Here our approach follows the standard approach in the philosophy of science that considers numerical simulations as solutions to intractable problems (Humphreys 2009; Parker 2009; Winsberg 2010). Numerical simulations are typical problem-solving tools and from a functional point of view, we take them as a component of scientific progress.\nIn respect of interventionism, we define an intervention as a change in the setup of a scientific experiment or observation. According to Bain, scientific progress can be identified with the accumulation of knowledge. We argue in this paper that the advance of knowledge those computational methods bring to science is interventionist in nature: without performing actual experiments computer simulations can inform scientists about possible interventions and their results. An intervention is defined here in a more pragmatic way, as an activity that enables the scientist to change both the initial conditions under which we gain information about a system and the rules (or laws) that govern such a system.\nThis paper concludes with a discussion of two case studies.\nFirst, as a clear case of interventionist progress, we offer a short history of the Lazarus project. This project simulated a binary black hole exclusively based on numerical simulation (Baker, Campanelli, and Lousto 2002). The history of this project and its results are discussed in some detail both as a functionalist and interventionist techno-scientific progress.\nSecond, as a case of functionalist techno-scientific progress, the DeepMind project called AlphaFold2 is a cluster of models that can predict the way a protein may fold in 3D when the input is the amino-acid sequence of the protein. It can compete with real experimental methods (X-ray crystallography or cryo-electron microscopy). The problem here (CASP=Critical Assessment of Structure Prediction) is a competition to predict spatial structures of proteins that have been solved using experimental methods, “but for which the structures have not been made public.” (Callaway 2022)\nIn conclusion, we showed how the concept of ‘techno-scientific progress’ integrates (recent) history and the philosophy of science with the advancement in computational science.},\n\tlanguage = {1. 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