Studying at York
Models & modelling in science & technology - PHI00093M
- Department: Philosophy
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2022-23
- See module specification for other years: 2023-24
Module summary
The module will introduce some of the central issues in contemporary philosophical discussions of scientific models, including who is a modeller, the nature and varieties of scientific models and modelling practices, model idealisations, model-based reasoning, model explanations, varieties of model-based knowledge, models and the scientific imagination, models and trust. A variety of models and modelling practices will be considered, with a special focus on paradigmatic cases from computational cognitive science and artificial intelligence.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Spring Term 2022-23 |
Module aims
To explore some central ongoing debates about scientific models and the practice of modelling based on philosophical analysis and paradigmatic 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
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understand and explain a range of key problems, issues, and debates in philosophy of science concerning scientific models and the modelling practice and express this understanding in clear, precise, and accessible terms;
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develop and articulate ranges of alternative solutions to specific problems and issues in an open-minded way, drawing on module materials;
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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;
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make a judgement about what is the best view on a particular problem and argue in defence of this judgement;
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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 of some basic philosophy of science will be helpful. A number of recent monographs and volumes on scientific models have been published over the past couple of years.
Frigg and Nguyen’s Modelling Nature: An Opinionated Introduction to Scientific Representation (Springer 2020) offers a comprehensive survey of contemporary philosophical theories of models as scientific representation.
The Scientific Imagination, edited by Arnon Levy and Peter Godfrey-Smith (Oxford University Press 2020), includes a number of excellent contributions on the role of models, metaphors and thought experiments in science, the connection between the imagination and scientific practices such as modelling and thought experimenting, and the correct way to understand scientific fictions.
The volume on Models and Idealizations in Science: Artifactual and Fictional Approaches, edited by Alejandro Cassini and Juan Redmond (Springer 2021), explores two recent approaches to scientific modelling, artefactualism and fictionalism, to investigate the nature and functions of models and how they are idealised and deidealised.
A number of useful encyclopaedia entries on models and related issues in philosophy of science are available in the excellent Routledge Encyclopaedia of Philosophy, available online.
The Stanford Encyclopedia of Philosophy, which is also available online, contains a number of extremely detailed articles on a variety of topics, including Frigg and Hartmann’s “Models in Science” (which I highly recommend).
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Special assessment rules
None
Additional assessment information
The formative assessment 'What puzzles me most is ...' is due on Monday, Week 7 of the Spring Term.
The summative 4,000-word essay is due on Monday, Week 2 of the Summer Term.
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Module feedback
Students will receive written feedback on summative assessment (and any reassessment) 4 weeks after submission.
Students will receive written feedback on formative assessment before the end of the term in which teaching occurs.
Students can receive additional oral feedback on formative and summative work by visiting tutor office hours or by meeting at other times by appointment.
Indicative reading
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Michael Weisberg (2007) “Who is a modeller?” The British Journal for the Philosophy of Science: 207-233
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Peter Godfrey-Smith (2006) “The Strategy of Model-Based Science,” Biology and Philosophy.
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Fiora Salis and Roman Frigg (2020) “Capturing the Scientific Imagination,” in The Scientific Imagination, Arnon Levy and Peter Godfrey-Smith (eds), Oxford University Press.
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Natalia Carrillo and Tarja Knuuttila (2021) “An artefactual perspective on idealization: Galvanic cells and electric circuits in nerve signal research.” In: Alejandro Cassini y Juan Redmond (ed.) Models and Idealizations in Science: Fictional and Artefactual Approaches (Cham: Springer).
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Charles M. Wharton and Jordan Grafman (1998) “Cognitive and AI models of reasoning,” Trends in Cognitive Sciences 2 2): 54-59.
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Andrea Ferrario, Michele Loi & Eleonora Viganò (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.
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Mariarosaria Taddeo (2010) Modelling Trust in Artificial Agents, A First Step Toward the Analysis of e-Trust, Minds and Machines 20 (2):243-257.
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Yan Teng (2021) “Towards trustworthy blockchains: normative reflections on blockchain-enabled virtual institutions.” Ethics and Information Technology 23 (3):385-397.