Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-25T20:13:15.611Z Has data issue: false hasContentIssue false
  • English
  • Français

The argument from surprise

Published online by Cambridge University Press:  01 January 2020

Adrian Currie
Adrian Currie*
Affiliation:
CSER & CRASSH, Cambridge, UK
*
E-mail: ac2075@cam.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

I develop an account of productive surprise as an epistemic virtue of scientific investigations which does not turn on psychology alone. On my account, a scientific investigation is potentially productively surprising when (1) results can conflict with epistemic expectations, (2) those expectations pertain to a wide set of subjects. I argue that there are two sources of such surprise in science. One source, often identified with experiments, involves bringing our theoretical ideas in contact with new empirical observations. Another, often identified with simulations, involves articulating and bringing together different parts of our knowledge. Both experiments and simulations, then, can surprise.

Keywords

Simulationmodelsurpriseexperiment
Type
Articles
Information
Canadian Journal of Philosophy , Volume 48 , Issue 5 , 2018 , pp. 639 - 661
Copyright
Copyright © The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2018

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

Boumans, M. 2012.Mathematics as Quasi-Matter to Build Models as Instruments. in Probabilities, Laws, and Structures, 307–318. Dordrecht: Springer. 10.1007/978-94-007-3030-4CrossRefGoogle Scholar
Chalmers, A. 1999. What is this Thing Called Science? 3rd ed. Queensland: University of Queensland Press.Google Scholar
Currie, A. forthcoming. Rock, Bone & Ruin: an Optimist’s Guide to the Historical Sciences. MIT Press.Google Scholar
Currie, A., and Levy, A. under review. Why Experiments Matter.Google Scholar
Davis, D. 1964. “The Giant Panda: A Morphological Study of Evolutionary Mechanisms.” Fieldiana ZoolMem 3: 1–339.Google Scholar
Epstein, B., and Forber, P.. 2013. “The Perils of Tweaking: How to Use Macrodata to Set Parameters in Complex Simulation Models.” Synthese 190(2): 203–218. 10.1007/s11229-012-0142-7CrossRefGoogle Scholar
Franklin, L. R. 2005. “Exploratory Experiments.” Philosophy of Science 72: 888–899. 10.1086/508117CrossRefGoogle Scholar
Frigg, R. 2010. “Models and Fiction.” Synthese 172(2): 251–268. 10.1007/s11229-009-9505-0CrossRefGoogle Scholar
Godfrey-Smith, P. 2009. “Models and Fictions in Science.” Philosophical Studies 143(1): 101–116. 10.1007/s11098-008-9313-2CrossRefGoogle Scholar
Greco, John, and Turri, John. 2015. In The Stanford Encyclopedia of Philosophy (Summer 2015 Edition), edited by Zalta, Edward N..Google Scholar
Guala, F. 2002. “Models, Simulations, and Experiments.” In Model-Based Reasoning, 59–74. New York: Springer. 10.1007/978-1-4615-0605-8CrossRefGoogle Scholar
Guala, F. 2005. The Methodology of Experimental Economics. Cambridge: Cambridge University Press. 10.1017/CBO9780511614651CrossRefGoogle Scholar
Klein, N., ed. 2011. Biology of the Sauropod Dinosaurs: Understanding the Life of Giants. Bloomington: Indiana University Press.Google Scholar
Lacovara, K. J., Lamanna, M. C., Ibiricu, L. M., Poole, J. C., Schroeter, E. R., Ullmann, P. V., Aaaa, A., and Novas, F. E.. 2014. “ A Gigantic, Exceptionally Complete Titanosaurian Sauropod Dinosaur from Southern Patagonia.” Scientific Reports, 4. Argentina.Google ScholarPubMed
Levy, A. 2012. “Models, Fictions, and Realism: Two Packages.” Philosophy of Science 79(5): 738–748. 10.1086/667992CrossRefGoogle Scholar
Lloyd, E. A. 2009, June. “I – Varieties of Support and Confirmation of Climate Models.” Aristotelian Society Supplementary Volume 83(1): 213–232.CrossRefGoogle Scholar
Lloyd, E. A. 2010. “Confirmation and Robustness of Climate Models.” Philosophy of Science 77(5): 971–984. 10.1086/657427CrossRefGoogle Scholar
Mitchell, S. D. 2002. “Integrative Pluralism.” Biology and Philosophy 17(1): 55–70. 10.1023/A:1012990030867CrossRefGoogle Scholar
Mitchell, S. D. 2003. Biological Complexity and Integrative Pluralism. Cambridge: Cambridge University Press. 10.1017/CBO9780511802683CrossRefGoogle Scholar
Morgan, M. 2002. “Model Experiments and Models in Experiments.” In Model-Based Reasoning: Science, Technology, Values, edited by Magnani, Lorenzo and Nersessian, Nancy, 41–58. New York: Kluwer. 10.1007/978-1-4615-0605-8CrossRefGoogle Scholar
Morgan, M. 2005. “Experiments Versus Models: New Phenomena, Inference and Surprise.” Journal of Economic Methodology 12(2): 317–329. 10.1080/13501780500086313CrossRefGoogle Scholar
Morgan, M. 2003. “Experiments Without Material Intervention: Model Experiments, Virtual Experiments and Virtually Experiments.” In The Philosophy of Scientific Experimentation, edited by Radder, Hans, 216–235. Pittsburgh, PA: University of Pittsburgh Press.CrossRefGoogle Scholar
Morgan, M. S., and Morrison, M., eds. 1999. Models as mediators: Perspectives on natural and social science. Vol. 52. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Parke, C. 2014. “Experiments, Simulations, and Epistemic Privilege.” Philosophy of Science. 81(4): 516–536. 10.1086/677956CrossRefGoogle Scholar
Parker, W. 2011. “When Climate Models Agree: The Significance of Robust Model Predictions.” Philosophy of Science 78(4): 579–600. 10.1086/661566CrossRefGoogle Scholar
Parker, W. S. 2009. “II – Confirmation and adequacy‐for‐purpose in climate modelling.” Aristotelian Society Supplementary Volume 83(1): 233–249).CrossRefGoogle Scholar
Parker, W. 2008. “Franklin, Holmes, and the Epistemology of Computer Simulation.” International Studies in the Philosophy of Science 22(2): 165–183. 10.1080/02698590802496722CrossRefGoogle Scholar
Sellers, W. I., Margetts, L., Coria, R. A., and Manning, P. L.. 2013. “March of the Titans: The Locomotor Capabilities of Sauropod Dinosaurs.” PloS one 8(10): e78733. 10.1371/journal.pone.0078733CrossRefGoogle ScholarPubMed
Turing, A. M. 1950. “Computing Machinery and Intelligence.” Mind 433–460. 10.1093/mind/LIX.236.433CrossRefGoogle Scholar
Werndl, Charlotte, and Steele, Katie. 2013. “Climate Models, Calibration, and Confirmation.” British Journal for the Philosophy of Science 64(3): 609–635.Google Scholar
Weisberg, M. 2007. “Three Kinds of Idealization.” Journal of Philosophy 104(12): 639–659. 10.5840/jphil20071041240CrossRefGoogle Scholar
Weisberg, M. 2012. Simulation and similarity: Using models to understand the world. Oxford: Oxford University Press.Google Scholar
Wimsatt, W. C. 1987. “False models as means to truer theories.” Neutral models in biology 23–55.Google Scholar
Winsberg, E. 1999. “Sanctioning Models: The Epistemology of Simulation.” Science in Context 12(02): 275–292.CrossRefGoogle Scholar
Winsberg, E. 2003. “Simulated Experiments: Methodology for a Virtual World.” Philosophy of Science 70: 105–125. 10.1086/367872CrossRefGoogle Scholar
Winsberg, E. 2009. “A Tale of Two Methods.” Synthese 169(3): 575–592. 10.1007/s11229-008-9437-0CrossRefGoogle Scholar
Winsberg, E. 2010. Science in the Age of Computer Simulation. Chicago, IL: The University of Chicago Press.CrossRefGoogle Scholar