Ethics and Epistemology of Digital Methods in Science - PHI00105M

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Module summary

Digital methods, including computer simulations and artificial intelligence run on big data, are "a significant and permanent addition to the methods of science" (Paul Humphreys, Extending ourselves, 2004, p. 64). Modern physics, chemistry, biology, climate science, astronomy, economics, and engineering would now be impossible without these methods. Examples include: computer models of the evolution of black holes and galaxies, global climate models, video representations of cellular processes, reconstructions of what happens in the Large Hadron Collider when particles are smashed together, and economic models in which humans are represented as perfectly rational. This module will introduce some of the central issues in contemporary philosophical discussions of scientific digital methods, organized into metaphysical issues (e.g., "What are they?"), epistemological issues ("e.g., How do we gain knowledge through them?"), semantic issues (e.g., "How and when do they refer to reality?"), ethical issues (e.g., "How should ethical values play into their construction and use?"), and aesthetic issues ("What makes some uses of digital methods beautiful and some ugly?"). To enable deep and rigorous philosophical discussion, background knowledge will be introduced from philosophy of science on the nature and varieties of scientific models and modelling practices, model abstractions and idealisations, explanation, scientific imagination, trust, consensus, objectivity, and feminist/decolonial philosophy of science.

Module will run

Occurrence	Teaching period
A	Semester 2 2025-26

Module aims

To explore some central ongoing debates about digital methods in science based on philosophical analysis and salient case studies.

To develop some key skills, including:

• to work your way to an understanding of challenging philosophical puzzles, views, and arguments in an autonomous way, showing critical awareness and command of the material;
• to discuss complex and difficult conceptual problems with others, working together to develop understanding and critique and evaluate theories;
• to evaluate views and arguments methodically and in detail;
• to develop your own view on a question - based on and informed by a strong understanding of contributions to the debate - and then assemble a detailed reasoned case for that view;
• to undertake independent research reading;
• to find your way through a range of connected debates, making connections between them and developing those connections to gain a deeper understanding of the debates and create better arguments.

Module learning outcomes

By the end of this module students should be able to:

• understand and explain a range of key problems, issues, and debates in philosophy of science concerning the use of digital methods in science, and express this understanding in clear, precise, and accessible terms;
• develop and articulate ranges of alternative solutions to specific problems and issues in an open-minded way, drawing on module materials;
• develop and articulate arguments for the alternative solutions considered in relation to specific problems and issues, drawing on module materials, identifying some points of weakness and some potential points for development;
• make a judgement about what is the best view on a particular problem and argue in defense of this judgement;
• apply simple strategies for improving your work, based on critical reflection and feedback.

Module content

There are no formal prerequisites for taking the module, but knowledge of at least some science (social or natural) and some philosophy of science will be helpful. A number of clear and important monographs have been published over the past couple of years, including Frigg and Nguyen’s Modelling Nature: An Opinionated Introduction to Scientific Representation (Springer 2020), The Scientific Imagination, edited by Arnon Levy and Peter Godfrey-Smith (Oxford University Press 2020), Calculated Surprises by Johannes Lenhard (Oxford University Press 2019), and Interdisciplinarity in the Making: Models and Methods in Frontier Science by Nancy Nersessian (MIT Press, 2022).

A number of online encyclopaedia entries on issues in philosophy of science are also available in the Routledge Encyclopaedia of Philosophy, and the Stanford Encyclopedia of Philosophy, including Frigg and Hartmann’s “Models in Science” and Eric Winsberg's "Computer Simulations in Science".

Indicative assessment

Task	% of module mark
Essay/coursework	100.0

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Essay/coursework	100.0

Module feedback

All formative and summative feedback will be returned in accordance with University and Departmental policy.

Indicative reading

• Alexandrova, Anna. 2008. “Making Models Count.” Philosophy of Science 75: 383–404.
• Arcangeli, Margherita. 2018. “The Hidden Links Between Real, Thought and Numerical Experiments.” Croatian Journal of Philosophy 18 (1): 3–22.
• Bailer-Jones, Daniela M. 2000. “Scientific Models as Metaphors.” In Metaphor and Analogy in the Sciences, edited by Fernand Hallyn, 181–98. Origins. Dordrecht: Springer Netherlands.
• Beisbart, Claus. 2018. “Are Computer Simulations Experiments? And If Not, How Are They Related to Each Other?” European Journal for Philosophy of Science 8 (2): 171–204.
• Black, M. 1962. Models and Metaphors. Ithaca and London: Cornell University Press.
• Boesch, Brandon. 2019. “The Means-End Account of Scientific, Representational Actions.” Synthese 196 (6): 2305–22.
• Bueno, Otávio, Steven French, and James Ladyman. 2002. “On Representing the Relationship between the Mathematical and the Empirical.” Philosophy of Science 69 (3): 497–518.
• Callender, Craig, and Jonathan Cohen. 2006. “There Is No Special Problem About Scientific Representation.” Theoria. Revista de Teoría, Historia y Fundamentos de La Ciencia.
• Camp, Elisabeth. 2020. “Imaginative Frames for Scientific Inquiry: Metaphors, Telling Facts, and Just-So Stories.” In The Scientific Imagination, edited by Arnon Levy and Peter Godfrey-Smith, 304–36. Oxford University Press.
• Dellsén, Finnur. 2020. “Beyond Explanation: Understanding as Dependency Modelling.” The British Journal for the Philosophy of Science.
• Ferrario, Andrea, Loi, Michele & Viganò, Eleonora. 2020. “In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions,” Philosophy and Technology 33 (3): 523-539.
  Frigg, Roman, and James Nguyen. 2016. “The Fiction View of Models Reloaded.” The Monist 99 (3): 225–42.
• Hesse, Mary. 1966. Models and Analogies in Science. Notre Dame: University of Notre Dame Press.
• Knuuttila, Tarja. 2011. “Modelling and Representing: An Artefactual Approach to Model-Based Representation.” Studies in History and Philosophy of Science Part A, Model-Based Representation in Scientific Practice, 42 (2): 262–71.
• Knuuttila, Tarja, and Loettgers Andrea, 2021, “Biological Control Variously Materialized: Modeling, Experimentation and Exploration in Multiple Media”. Perspectives on Science 2021; 29 (4): 468–492.
• Morgan, Mary, and Margaret Morrison. 1999. Models as Mediators. Cambridge: Cambridge University Press.
• Morrison, Margaret. 2009. “Models, Measurement and Computer Simulation: The Changing Face of Experimentation.” Philosophical Studies 143 (1): 33–57.
• Myers, Joshua. 2021. “The Epistemic Status of the Imagination.” Philosophical Studies, January.
• Nanay, Bence. forthcoming. “Against Imagination.” In Contemporary Debates in the Philosophy of Mind (2nd Edition), edited by Jonathan Cohen and Brian McLaughlin. Oxford: Blackwell.
• Parker, Wendy S. 2020. “Model Evaluation: An Adequacy-for-Purpose View.” Philosophy of Science 87 (3): 457–77.
• Taddeo, Mariarosaria. 2010. "Modelling Trust in Artificial Agents, A First Step Toward the Analysis of e-Trust." Minds and Machines 20 (2):243-257.
• Teng, Yan. 2021. “Towards trustworthy blockchains: normative reflections on blockchain-enabled virtual institutions.” Ethics and Information Technology 23 (3):385-397.
• Toon, Adam. 2012. Models as Make-Believe: Imagination, Fiction and Scientific Representation. New Directions in the Philosophy of Science. Palgrave Macmillan UK.
• Winsberg, Eric, and Stephanie Harvard. 2022. “Purposes and Duties in Scientific Modelling.” J Epidemiol Community Health 76 (5): 512–17.
• Weisberg, Michael. 2007. “Who is a modeller?” The British Journal for the Philosophy of Science: 207-233.