Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-10T05:12:59.526Z Has data issue: false hasContentIssue false

Scientific Models and Decision Making

Published online by Cambridge University Press:  16 January 2024

Eric Winsberg
Affiliation:
University of Cambridge and University of South Florida
Stephanie Harvard
Affiliation:
University of British Columbia, Vancouver

Summary

This Element introduces the philosophical literature on models, with an emphasis on normative considerations relevant to models for decision-making. Chapter 1 gives an overview of core questions in the philosophy of modeling. Chapter 2 examines the concept of model adequacy for purpose, using three examples of models from the atmospheric sciences to describe how this sort of adequacy is determined in practice. Chapter 3 explores the significance of using models that are not adequate for purpose, including the purpose of informing public decisions. Chapter 4 provides a basic framework for values in modelling, using a case study to highlight the ethical challenges in building models for decision making. It concludes by establishing the need for strategies to manage value judgments in modelling, including the potential for public participation in the process.
Get access
Type
Element
Information
Online ISBN: 9781009029346
Publisher: Cambridge University Press
Print publication: 08 February 2024

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abramowitz, Gab, Herger, Nadja, Gutmann, Ethan, et al. 2019. ‘ESD Reviews: Model Dependence in Multi-Model Climate Ensembles: Weighting, Sub-Selection and Out-of-Sample Testing’. Earth System Dynamics 10 (1): 91105.CrossRefGoogle Scholar
Alexandrova, Anna. 2017. A Philosophy for the Science of Well-Being. Oxford: Oxford University Press.CrossRefGoogle Scholar
Alexandrova, Anna, and Fabian, Mark. 2021. ‘Democratising Measurement: Or Why Thick Concepts Call for Coproduction’. European Journal for Philosophy of Science 12 (1): 123. https://doi.org/10.1007/s13194-021-00437-7.Google Scholar
Alvich, Jason. n.d.-a. ‘Earth System (ESM4)’. Accessed 18 November 2022. www.gfdl.noaa.gov/earth-system-esm4/.Google Scholar
Alvich, Jason. n.d.-b. ‘Model Development’. Accessed 18 November 2022. www.gfdl.noaa.gov/model-development/.Google Scholar
Ankeny, Rachel, and Leonelli, Sabina. 2021. Model Organisms. Cambridge: Cambridge University Press.Google Scholar
Bailer-Jones, Daniela M. 2002. ‘Scientists’ Thoughts on Scientific Models’. Perspectives on Science 10 (3): 275301. https://doi.org/10.1162/106361402321899069.CrossRefGoogle Scholar
Bailer-Jones, Daniela M. 2009. Scientific Models in Philosophy of Science. Pittsburgh, PA: University of Pittsburgh Press.CrossRefGoogle Scholar
Bailer-Jones, Daniela M., and Bailer-Jones, Coryn A. L.. 2002. ‘Modeling Data: Analogies in Neural Networks, Simulated Annealing and Genetic Algorithms’. Magnani and Nersessian 2002: 147–65. https://doi.org/10.1007/978-1-4615-0605-8_9.CrossRefGoogle Scholar
Basshuysen, Philippe van, White, Lucie, Khosrowi, Donal, and Frisch, Mathias. 2021. ‘Three Ways in Which Pandemic Models May Perform a Pandemic’. Erasmus Journal for Philosophy and Economics 14 (1): 110–27. https://doi.org/10.23941/ejpe.v14i1.582.Google Scholar
Bergstrom, Carl T. @CT_Bergstrom. 19 April 2020. ‘I Believe That If #SARS-CoV-2 Is Allowed to Spread …’. Twitter Page. https://twitter.com/CT_Bergstrom/status/1252075528711860224.Google Scholar
Bergstrom, Carl, and Dean, Natalie. 2020. ‘What the Proponets of “Natural” Herd Immunity Don’t Say: Try to Reach It without a Vaccine, and Millions will Die’. New York Times, 1 May 2020. www.nytimes.com/2020/05/01/opinion/sunday/coronavirus-herd-immunity.html.Google Scholar
Biggs, Adam T., and Littlejohn, Lanny F.. 2021. ‘Revisiting the Initial COVID-19 Pandemic Projections’. The Lancet Microbe 2 (3): e91–2. https://doi.org/10.1016/S2666-5247(21)00029-X.CrossRefGoogle ScholarPubMed
Black, Max. 1962. Models and Metaphors: Studies in Language and Philosophy. Ithaca, NY: Cornell University Press.CrossRefGoogle Scholar
Bokulich, Alisa. 2011. ‘How Scientific Models Can Explain’. Synthese 180 (1): 3345. https://doi.org/10.1007/s11229-009-9565-1.CrossRefGoogle Scholar
Bokulich, Alisa, and Parker, Wendy. 2021. ‘Data Models, Representation and Adequacy-for-Purpose’. European Journal for Philosophy of Science 11 (1): 126.CrossRefGoogle ScholarPubMed
Briggs, Andrew, Sculpher, Mark, and Claxton, Karl. 2006. Decision Modelling for Health Economic Evaluation. Oxford: Oxford University Press.CrossRefGoogle Scholar
Britton, Tom. 2010. ‘Stochastic Epidemic Models: A Survey’. Mathematical Biosciences 225 (1): 2435. https://doi.org/10.1016/j.mbs.2010.01.006.CrossRefGoogle ScholarPubMed
Britton, Tom, Ball, Frank, and Trapman, Pieter. 2020. ‘A Mathematical Model Reveals the Influence of Population Heterogeneity on Herd Immunity to SARS-CoV-2’. Science 369 (6505): 846–9. https://doi.org/10.1126/science.abc6810.CrossRefGoogle ScholarPubMed
Broadbent, Alex. 2020. ‘Lockdown Is Wrong for Africa’. Mail & Guardian, 8 April 2020. https://mg.co.za/article/2020-04-08-is-lockdown-wrong-for-africa/.Google Scholar
Broadbent, Alex, and Streicher, Pieter. 2022. ‘Can You Lock Down in a Slum? And Who Would Benefit If You Tried? Difficult Questions about Epidemiology’s Commitment to Global Health Inequalities during Covid-19’. Global Epidemiology 4 (December): 100074. https://doi.org/10.1016/j.gloepi.2022.100074.CrossRefGoogle Scholar
Bunka, Mary, Ghanbarian, Shahzad, Riches, Linda, et al. 2022. ‘Collaborating with Patient Partners to Model Clinical Care Pathways in Major Depressive Disorder: The Benefits of Mixing Evidence and Lived Experience’. Pharmacoeconomics 40 (10): 971–7. https://doi.org/10.1007/s40273-022-01175-1.CrossRefGoogle ScholarPubMed
Cartwright, Nancy . 1983. How the Laws of Physics Lie. Vol. 34. Oxford: Clarendon Press.CrossRefGoogle Scholar
Cartwright, Nancy 1989. Nature’s Capacities and Their Measurement. Oxford: Oxford University Press.Google Scholar
Cartwright, Nancy 2019. Nature, the Artful Modeler: Lectures on Laws, Science, How Nature Arranges the World and How We Can Arrange It Better. Vol. 23. Chicago, IL: Open Court Publishing.Google Scholar
Chikina, Maria, and Pegden, Wesley. 2020. ‘Modeling Strict Age-Targeted Mitigation Strategies for COVID-19’. PLoS ONE 15 (7): e0236237. https://doi.org/10.1371/journal.pone.0236237.CrossRefGoogle ScholarPubMed
Choi, Yeon-Woo, Tuel, Alexandre, and Eltahir, Elfatih A. B.. 2021. ‘On the Environmental Determinants of COVID-19 Seasonality’. GeoHealth 5 (6): e2021GH000413. https://doi.org/10.1029/2021GH000413.CrossRefGoogle ScholarPubMed
Covid-19 Forecasting Team. 2022. ‘Variation in the COVID-19 Infection–Fatality Ratio by Age, Time, and Geography during the Pre-Vaccine Era: A Systematic Analysis’. The Lancet 399 (10334): 1469–88. https://doi.org/10.1016/S0140-6736(21)02867-1.Google Scholar
Douglas, Heather. 2000. ‘Inductive Risk and Values in Science’. Philosophy of Science 67 (4): 559–79.CrossRefGoogle Scholar
Douglas, Heather 2009. Science, Policy, and the Value-Free Ideal. Pittsburgh, PA: University of Pittsburgh Press. https://bit.ly/45lqr7P.CrossRefGoogle Scholar
Downes, Stephen M. 1992. ‘The Importance of Models in Theorizing: A Deflationary Semantic View’. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992 (1): 142–53. https://doi.org/10.1086/psaprocbienmeetp.1992.1.192750.Google Scholar
Wouter, Edeling, Arabnejad, Hamid, Sinclair, Robbie, et al. 2021. ‘The Impact of Uncertainty on Predictions of the CovidSim Epidemiological Code’. Nature Computational Science 1 (2): 128–35. https://doi.org/10.1038/s43588-021-00028-9.Google Scholar
Elliott, Kevin Christopher. 2017. A Tapestry of Values: An Introduction to Values in Science. New York: Oxford University Press.CrossRefGoogle Scholar
Elliott, Kevin C., and McKaughan, Daniel J.. 2014. ‘Nonepistemic Values and the Multiple Goals of Science’. Philosophy of Science 81 (1): 121.CrossRefGoogle Scholar
Elliott, Kevin Christopher, and Richards, Ted. 2017. Exploring Inductive Risk: Case Studies of Values in Science. New York: Oxford University Press.Google Scholar
Ferguson, Neil M., Cummings, Derek A. T., Cauchemez, Simon, et al. 2005. ‘Strategies for Containing an Emerging Influenza Pandemic in Southeast Asia’. Nature 437 (7056): 209–14.CrossRefGoogle ScholarPubMed
Ferguson, Neil M., Cummings, Derek A. T., Fraser, Christophe, et al. 2006. ‘Strategies for Mitigating an Influenza Pandemic’. Nature 442 (7101): 448–52.CrossRefGoogle ScholarPubMed
Ferguson, Neil, Laydon, Daniel, Nedjati Gilani, Gemma, et al. 2020. ‘Report 9: Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID19 Mortality and Healthcare Demand’. Imperial College London. https://doi.org/10.25561/77482.CrossRefGoogle Scholar
FitzGerald, J. Mark, Arnetorp, Sofie, Smare, Caitlin, et al. 2020. ‘The Cost-Effectiveness of As-Needed Budesonide/Formoterol versus Low-Dose Inhaled Corticosteroid Maintenance Therapy in Patients with Mild Asthma in the UK’. Respiratory Medicine 171: 106079. http://doi.org/10.1016/j.rmed.2020.106079.CrossRefGoogle ScholarPubMed
Frigg, Roman, and Hartmann, Stephan. 2012. ‘Models in Science’. In Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta. http://plato.stanford.edu/archives/fall2012/entries/models-science.Google Scholar
Frigg, Roman, and Nguyen, James. 2021. ‘Seven Myths about the Fiction View of Models’. In Models and Idealizations in Science, edited by Alejandro Cassini and Juan Redmond, 133–57. Cham: Springer.Google Scholar
Frisch, Mathias. 2018. ‘Modeling Climate Policies: The Social Cost of Carbon and Uncertainties in Climate Predictions’. In Climate Modelling: Philosophical and Conceptual Issues, edited by Lloyd, Elisabeth A. and Winsberg, Eric, 413–48. Cham: Springer. https://doi.org/10.1007/978-3-319-65058-6_14.Google Scholar
Giere, Ronald N. 1988. Explaining Science: A Cognitive Approach. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Giere, Ronald N. 1999. Science without Laws. Chicago, IL: University of Chicago Press.Google Scholar
Giere, Ronald N. 2006. Scientific Perspectivism. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Giere, Ronald N, Bickle, John, and Mauldin, Robert F. 1979. Understanding Scientific Reasoning. Belmont, CA : Thomson/Wadsworth.Google Scholar
Godfrey-Smith, Peter. 2009. ‘Abstractions, Idealizations, and Evolutionary Biology. In Mapping the Future of Biology: Evolving Concepts and Theories, edited by Barberousse, Anouk, Morange, Michel, and Pradeu, Thomas. Boston Studies in the Philosophy of Science 266. Dordrecht: Springer Netherlands, 47–55. https://doi.org/10.1007/978-1-4020-9636-5_4.Google Scholar
Gray, Steven, Paolisso, Michael, Jordan, Rebecca, and Gray, Stefan, eds. 2016. Environmental Modeling with Stakeholders: Theory, Methods, and Applications. Cham: Springer.Google Scholar
Handel, Andreas, Longini, Ira M, and Antia, Rustom. 2007. ‘What Is the Best Control Strategy for Multiple Infectious Disease Outbreaks?’. Proceedings of the Royal Society B: Biological Sciences 274 (1611): 833–7. https://doi.org/10.1098/rspb.2006.0015.Google ScholarPubMed
Hartmann, Stephan. 1995. ‘Models as a Tool for Theory Construction: Some Strategies of Preliminary Physics’. In Theories and Models in Scientific Processes, edited by Herfel, William, Krajewski, Władysław, Niiniluoto, Ilkka, and Wójcicki, Ryszard, 4967. Amsterdam: Rodopi.CrossRefGoogle Scholar
Hartmann, Stephan 1998. ‘Idealization in Quantum Field Theory’. In Idealization IX: Idealization in Contemporary Physics, edited by Shanks, Niall, 99122. Amsterdam: Rodopi.CrossRefGoogle Scholar
Harvard, Stephanie, and Winsberg, Eric. 2021. ‘Causal Inference, Moral Intuition, and Modeling in a Pandemic’. Philosophy of Medicine 2 (2): 110.CrossRefGoogle Scholar
Harvard, Stephanie, and Winsberg, Eric 2022. ‘The Epistemic Risk in Representation’. Kennedy Institute of Ethics Journal 32 (1): 131.CrossRefGoogle ScholarPubMed
Harvard, Stephanie, and Winsberg, Eric 2023. ‘Patient and Public Involvement in Health Economics Modelling Raises the Need for Normative Guidance’. Pharmacoeconomics 41 (7): 733–40.CrossRefGoogle ScholarPubMed
Harvard, Stephanie, Winsberg, Eric, Symons, John, and Adibi, Amin. 2021. ‘Value Judgments in a COVID-19 Vaccination Model: A Case Study in the Need for Public Involvement in Health-Oriented Modelling’. Social Science & Medicine 286. http://doi.org/10.1016/j.socscimed.2021.114323.CrossRefGoogle Scholar
Hempel, Carl G. 1954. ‘A Logical Appraisal of Operationism’. Scientific Monthly 79 (4): 215–20.Google Scholar
Hesse, Mary. 1963. Models and Analogies in Science. London: Sheed and Ward.Google Scholar
Hesse, Mary 1967. ‘Models and Analogy in Science’. In Encyclopedia of Philosophy, edited by Edwards, Paul, 354–9. New York: Macmillan.Google Scholar
Hesse, Mary 1974. The Structure of Scientific Inference. London: Macmillan.CrossRefGoogle Scholar
Horner, Jack K., and Symons, John F.. 2020. ‘Software Engineering Standards for Epidemiological Models’. History and Philosophy of the Life Sciences 42 (4): 124.CrossRefGoogle ScholarPubMed
Hughes, Richard I. G. 1997. ‘Models and Representation’. Philosophy of Science 64 (S4): S325–36.CrossRefGoogle Scholar
Husereau, Don, Drummond, Michael, Augustovski, Federico, et al. 2022. ‘Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) Statement: Updated Reporting Guidance for Health Economic Evaluations’. Pharmacoeconomics 40 (6): 601–9. https://doi.org/10.1007/s40273-021-01112-8.CrossRefGoogle ScholarPubMed
Ioannidis, John P. A., Cripps, Sally, and Tanner, Martin A.. 2022. ‘Forecasting for COVID-19 Has Failed’. International Journal of Forecasting 38 (2): 423–38. https://doi.org/10.1016/j.ijforecast.2020.08.004.CrossRefGoogle ScholarPubMed
Jones, Martin R. 2005. ‘Idealization and Abstraction: A Framework’. In Idealization XII: Correcting the Model, edited by Jones, Martin R. and Cartwright, Nancy, 173217. Amsterdam: Rodopi.Google Scholar
Kitcher, Philip. 2010. ‘Varieties of Freedom and Their Distribution’. Social Research 77 (3): 857–72.CrossRefGoogle Scholar
Knutti, Reto. 2018. ‘Climate Model Confirmation: From Philosophy to Predicting Climate in the Real World’. In Climate Modelling: Philosophical and Conceptual Issues, edited by Lloyd, Elisabeth A. and Winsberg, Eric, 325–59. Cham: Springer.Google Scholar
Knuuttila, Tarja. 2005. ‘Models, Representation, and Mediation’. Philosophy of Science 72 (5): 1260–71. http://doi.org/doi:10.1086/508124.CrossRefGoogle Scholar
Knuuttila, Tarja. 2011. ‘Modelling and Representing: An Artefactual Approach to Model-Based Representation’. Studies in History and Philosophy of Science Part A, 42 (2): 262–71. http://doi.org/10.1016/j.shpsa.2010.11.034.CrossRefGoogle Scholar
Laymon, Ronald. 1982. ‘Scientific Realism and the Hierarchical Counterfactual Path from Data to Theory’. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982 (1): 107–21. https://doi.org/10.1086/psaprocbienmeetp.1982.1.192660.Google Scholar
Laymon, Ronald 1985. ‘Idealizations and the Testing of Theories by Experimentation’. In Observation, Experiment, and Hypothesis in Modern Physical Science, edited by Achinstein, Peter and Hannaway, Owen, 147–73. Cambridge, MA: MIT Press.Google Scholar
Leonelli, Sabina. 2010. ‘Packaging Small Facts for Re-Use: Databases in Model Organism Biology’. In How Well Do Facts Travel? The Dissemination of Reliable Knowledge, edited by Howlett, Peter and Morgan, Mary S., 325–48. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511762154.017.Google Scholar
Leonelli, Sabina 2016. Data-Centric Biology: A Philosophical Study. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Leonelli, Sabina 2019. ‘What Distinguishes Data from Models?’. European Journal for Philosophy of Science 9 (2): 22. https://doi.org/10.1007/s13194-018-0246-0.CrossRefGoogle ScholarPubMed
Levy, Arnon, and Currie, Adrian. 2015. ‘Model Organisms Are Not (Theoretical) Models’. British Journal for the Philosophy of Science 66 (2): 327–48. https://doi.org/10.1093/bjps/axt055.CrossRefGoogle Scholar
Longino, Helen E. 1990. Science as Social Knowledge: Values and Objectivity in Scientific Inquiry. Princeton, NJ: Princeton University Press. https://bit.ly/48Km49q.CrossRefGoogle Scholar
Lorenzo, Magnani, and Nersessian, Nancy J., eds. 2002. Model Based Reasoning: Science, Technology, Values. New York: Springer. https://doi.org/10.1007/978-1-4615-0605-8.Google Scholar
Magnani, Lorenzo, Nersessian, Nancy J., and Thagard, Paul, eds. 1999. Model-Based Reasoning in Scientific Discovery. Boston, MA: Springer US. https://doi.org/10.1007/978-1-4615-4813-3.CrossRefGoogle Scholar
Massimi, Michela. 2018a. ‘Four Kinds of Perspectival Truth’. Philosophy and Phenomenological Research 96 (2): 342–59. https://doi.org/10.1111/phpr.12300.CrossRefGoogle ScholarPubMed
Massimi, Michela 2018b. ‘Perspectival Modeling’. Philosophy of Science 85 (3): 335–59. https://doi.org/10.1086/697745.CrossRefGoogle Scholar
Mauritsen, Thorsten, Stevens, Bjorn, Roeckner, Erich, et al. 2012. ‘Tuning the Climate of a Global Model’. Journal of Advances in Modeling Earth Systems 4 (3). http://onlinelibrary.wiley.com/doi/10.1029/2012MS000154/full.CrossRefGoogle Scholar
Mayo, Deborah. 1996. Error and the Growth of Experimental Knowledge. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Mayo, Deborah 2018. Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781107286184.CrossRefGoogle Scholar
McMullin, Ernan. 1985. ‘Galilean Idealization’. Studies in History and Philosophy of Science Part A 16 (3): 247–73. https://doi.org/10.1016/0039-3681(85)90003-2.CrossRefGoogle Scholar
Morgan, Mary S., and Morrison, Margaret, eds. 1999. Models as Mediators: Perspectives on Natural and Social Science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Morrison, Margaret. 1999. ‘Models as Autonomous Agents’. In Models as Mediators: Perspectives on Natural and Social Science, edited by Morgan, Mary S. and Morrison, Margaret. Cambridge: Cambridge University Press, 38–65. https://doi.org/10.1017/CBO9780511660108.004.Google Scholar
Morrison, Margaret 2000. Unifying Scientific Theories: Physical Concepts and Mathematical Structures. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511527333.CrossRefGoogle Scholar
Morrison, Margaret 2005. ‘Approximating the Real: The Role of Idealizations in Physical Theory’. In Idealization XII: Correcting the Model, edited by Jones, Martin R. and Cartwright, Nancy, 145–72. Amsterdam: Rodopi. https://doi.org/10.1163/9789401202732_009.Google Scholar
Morrison, Margaret 2009. ‘Understanding in Physics and Biology: From the Abstract to the Concrete’. In Scientific Understanding: Philosophical Perspectives, edited by Eigner, Kai, Leonelli, Sabina, and de Regt, Henk W., 123–45. Pittsburgh, PA: University of Pittsburgh Press.Google Scholar
Nersessian, Nancy J. 2010. Creating Scientific Concepts. Cambridge, MA: MIT Press.Google Scholar
Parker, Wendy. 2018. ‘Climate Science’. In Stanford Encyclopedia of Philosophy (Fall 2023 Edition), edited by Edward N. Zalta and Uri Nodelman. https://plato.stanford.edu/entries/climate-science/.Google Scholar
Parker, Wendy S., and Winsberg, Eric. 2018. ‘Values and Evidence: How Models Make a Difference’. European Journal for Philosophy of Science 8 (1): 125–42.CrossRefGoogle Scholar
Peschard, Isabelle. 2011. ‘Making Sense of Modeling: Beyond Representation’. European Journal for Philosophy of Science 1 (3): 335–52. http://doi.org/10.1007/s13194-011-0032-8.CrossRefGoogle Scholar
Peschard, Isabelle F., and van Fraassen, Bas C.. 2014. ‘Making the Abstract Concrete: The Role of Norms and Values in Experimental Modeling’. Studies in History and Philosophy of Science Part A 46: 310.CrossRefGoogle ScholarPubMed
Primiero, Giuseppe. 2014. ‘On the Ontology of the Computing Process and the Epistemology of the Computed’. Philosophy and Technology 27 (3): 485–9.CrossRefGoogle Scholar
Redhead, Michael. 1980. ‘Models in Physics’. British Journal for the Philosophy of Science 31 (2): 145–63.CrossRefGoogle Scholar
Rucinski, Stefanea L., Binnicker, Matthew J., Thomas, Amber S., and Patel, Robin. 2020. ‘Seasonality of Coronavirus 229E, HKU1, NL63, and OC43 From 2014 to 2020’. Mayo Clinic Proceedings 95 (8): 1701–3. https://doi.org/10.1016/j.mayocp.2020.05.032.CrossRefGoogle ScholarPubMed
Saatsi, Juha. 2016. ‘Models, Idealisations, and Realism’. In Models and Inferences in Science, edited by Ippoliti, Emiliano, Sterpetti, Fabio, and Nickles, Thomas, 173–89. Cham: Springer. https://doi.org/10.1007/978-3-319-28163-6_10.Google Scholar
Schroeder, S. A. 2017. ‘Using Democratic Values in Science: An Objection and (Partial) Response’. Philosophy of Science 84 (5):1044–54.CrossRefGoogle Scholar
Shanker, Roshni, and Raghavan, Prabhat. 2021. ‘The Invisible Crisis: Refugees and COVID-19 in India’. International Journal of Refugee Law 32 (4): 680–4. https://doi.org/10.1093/ijrl/eeab011.Google Scholar
Smeenk, Chris. 2020. ‘Some Reflections on the Structure of Cosmological Knowledge’. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 71: 220–31.CrossRefGoogle Scholar
Staniszewska, Sophie, Hill, Edward M., Grant, Richard, et al. 2021. ‘Developing a Framework for Public Involvement in Mathematical and Economic Modelling: Bringing New Dynamism to Vaccination Policy Recommendations’. Patient 14 (4): 435–45. https://doi.org/10.1007/s40271-020-00476-x.Google ScholarPubMed
Sterrett, Susan G. 2006. ‘Models of Machines and Models of Phenomena’. International Studies in the Philosophy of Science 20 (1): 6980. https://doi.org/10.1080/02698590600641024.CrossRefGoogle Scholar
Sterrett, Susan G. 2021. ‘Scale Modeling’. In Routledge Handbook of Philosophy of Engineering, edited by Michelfelder, Diane and Doorn, Neelke, 394–407. New York: Routledge.Google Scholar
Stone, Dáithí A, and Knutti, Reto. 2010. ‘Weather and Climate’. In Modelling the Impact of Climate Change on Water Resources, edited by Fung, Fai, Lopez, Ana, and New, Mark, 433. Chichester: Wiley-Blackwell.CrossRefGoogle Scholar
Suppe, Frederick. 1972. ‘What’s Wrong with the Received View on the Structure of Scientific Theories?’. Philosophy of Science 39 (1): 119.CrossRefGoogle Scholar
Suppes, Patrick. 1960. ‘A Comparison of the Meaning and Uses of Models in Mathematics and the Empirical Sciences’. Synthese 12 (2–3): 287301. https://doi.org/10.1007/BF00485107.CrossRefGoogle Scholar
Suppes, Patrick 1962. ‘Models of Data’. In Logic, Methodology and Philosophy of Science: Proceedings of the 1960 International Congress, edited by Nagel, Ernest, Suppes, Patrick, and Tarski, Alfred, 252–61. Stanford, CA: Stanford University Press.Google Scholar
Suppes, Patrick 1969. ‘Models of Data’. In Studies in the Methodology and Foundations of Science, 2435. Dordrecht: Springer. http://link.springer.com/chapter/10.1007/978-94-017-3173-7_2.CrossRefGoogle Scholar
Suppes, Patrick 2007. ‘Statistical Concepts in Philosophy of Science’. Synthese 154 (3): 485–96. https://doi.org/10.1007/s11229-006-9122-0.CrossRefGoogle Scholar
Teller, Paul. 2018. ‘Referential and Perspectival Realism’. Spontaneous Generations: A Journal for the History and Philosophy of Science 9 (1): 151–64. https://doi.org/10.4245/sponge.v9i1.26990.Google Scholar
van Fraassen, Bas, C. 1980. The Scientific Image. New York: Oxford University Press.Google Scholar
Verity, Robert, Okell, Lucy C., Dorigatti, Ilaria, et al. 2020. ‘Estimates of the Severity of Coronavirus Disease 2019: A Model-Based Analysis’. The Lancet. Infectious Diseases 20 (6): 669–77. https://doi.org/10.1016/S1473-3099(20)30243-7.CrossRefGoogle ScholarPubMed
Voinov, Alexey, and Bousquet, Francois. 2010. ‘Modelling with Stakeholders’. Environmental Modelling & Software 25(11):1268–81.CrossRefGoogle Scholar
Weber, Max. 1949. Max Weber on the Methodology of the Social Sciences, translated and edited by Shils, Edward A. and Finch, Henry A.. Glencoe, IL: Free Press.Google Scholar
Weisberg, Michael. 2013. Simulation and Similarity: Using Models to Understand the World. New York: Oxford University Press.CrossRefGoogle Scholar
Williams, Mary. 2011. ‘My Life as a Lab Rat’. Salon, 24 November. www.salon.com/2011/11/24/my_life_as_a_lab_rat/.Google Scholar
Winsberg, Eric. 2010. Science in the Age of Computer Simulation. Chicago, IL: University of Chicago Press. https://bit.ly/3ZMZdG5.CrossRefGoogle Scholar
Winsberg, Eric 2012. ‘Values and Uncertainties in the Predictions of Global Climate Models’. Kennedy Institute of Ethics Journal 22 (2): 111–37.CrossRefGoogle ScholarPubMed
Winsberg, Eric 2018. Philosophy and Climate Science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Winsberg, Eric 2022. ‘Who Is Responsible for Global Health Inequalities after Covid-19?’. Global Epidemiology 4: 100081. https://doi.org/10.1016/j.gloepi.2022.100081.CrossRefGoogle ScholarPubMed
Winsberg, Eric, and Harvard, Stephanie. 2022. ‘Purposes and Duties in Scientific Modelling’. Journal of Epidemiology and Community Health 76 (5): 512–17.CrossRefGoogle Scholar
Xie, Richard Z., Malik, Erica deFur, Linthicum, Mark T., and Bright, Jennifer L.. 2021. ‘Putting Stakeholder Engagement at the Center of Health Economic Modeling for Health Technology Assessment in the United States’. Pharmacoeconomics 39 (6): 631–8. https://doi.org/10.1007/s40273-021-01036-3.CrossRefGoogle Scholar

Save element to Kindle

To save this element to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Scientific Models and Decision Making
  • Eric Winsberg, University of Cambridge and University of South Florida, Stephanie Harvard, University of British Columbia, Vancouver
  • Online ISBN: 9781009029346
Available formats
×

Save element to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Scientific Models and Decision Making
  • Eric Winsberg, University of Cambridge and University of South Florida, Stephanie Harvard, University of British Columbia, Vancouver
  • Online ISBN: 9781009029346
Available formats
×

Save element to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Scientific Models and Decision Making
  • Eric Winsberg, University of Cambridge and University of South Florida, Stephanie Harvard, University of British Columbia, Vancouver
  • Online ISBN: 9781009029346
Available formats
×