|William Bechtel||HSS email@example.com|
|Naomi Oreskes||HSS firstname.lastname@example.org|
|Wendy Parker||HSS email@example.com|
Even a cursory look at contemporary research papers in the physical, biological, and social sciences reveals the ubiquity of talk of models and reliance on them for explanation and prediction. Reference to models is far more frequent than reference to laws or theories, fostered of late by the advent of inexpensive and accessible computational modeling on computers. The term model is used in a wide variety of senses from physical and scale-models to computational models. Our focus will be primarily on computational models and the role they are playing in a variety of sciences including biology, cognitive science, earth science, and climate science. We will examine a number of issues such as how they represent phenomena, how they are used experimentally, how they are evaluated, and how they figure in policy debates. In addition to analyzing how scientists use models, we expect students to acquire some first-hand experience with the modeling process, if only through the running of toy models, so as to appreciate what goes into a model and what sorts of information a model can provide.
For credit in the seminar, it is necessary to prepare for, attend, and participate in all seminar meetings and the Models and Prediction Workshop (May 26-28). Preparation for a seminar meeting involves doing all the assigned reading for the session and preparation of a question or comment on the reading. These questions or comments must be submitted to firstname.lastname@example.org by 4PM on the Monday preceeding the seminar. In addition, you are required to prepare a poster and present it at the Models and Prediction Workshop and to submit a paper based on the poster (maximum of 1500 words; figures and tables may be extra) by June 12. The poster and paper must be based in part on a actual model that you have run (links to simulation software that you may use to do the modeling can be found by clicking here).
There is an email list for this seminar: email@example.com. It is required that you subscribe to this list. Do it IMMEDIATELY. You can always unsubscribe later if you drop the seminar. The purpose of the list is twofold--to enable us to communicate information about upcoming seminar sessions and to allow members of the seminar to raise questions or engage in discussion outside of the seminar. Initially the list will be unmoderated, which will enable all (but only) subscribers to send email to the list. If this is abused, we will need to move to a moderated list.
To subscribe, you simply need to send an email message to the following address: firstname.lastname@example.org. After you send the subscribe request, you will receive a reply from email@example.com that will ask you to confirm your request. Follow the directions in this message to confirm you subscription. If you later want to remove yourself from this list, send email to firstname.lastname@example.org.
Note: This schedule of reading assignments is tentative and subject to revision. As often as possible, we will post electronic copies of the readings but in some cases you will need to get copies from the Science Studies Seminar Room. Some of the links will only work from url's within ucsd.edu or when connected to UCSD via a VPN connection.
Giere, R. (2004). How Models Are Used to Represent Reality, Philosophy of Science 71, Supplement, S742-752.
Bailer-Jones, Daniela M. (2002), Scientists’ Thoughts on Scientific Models, Perspectives on Science, 10, 275-301.
Craik, K. (1943) The Nature of Explanation. Cambridge: Cambridge University Press, chapters 5-6.
Hesse, Mary (1966). Models and Analogies in Science. Notre Dame: Notre Dame University Press
Edwards, P. (2001) Representing the global atmosphere: Computer models, data, and knowledge about climate change. In C. Miller and P. Edwards (eds.) Changing the atmosphere: Expert knowledge and environmental governance ( pp.31-65) Cambridge: MIT Press.
Shackley, S. (2001) Epistemic lifestyles in climate change modeling. In C. Miller and P. Edwards (eds.) Changing the gtmosphere: Expert knowledge and environmental governance ( pp.107-134). Cambridge: MIT Press.
Bibliography for Edwards and Shackley readings
Star, S.L. and J. Griesemer (1989) Institutional Ecology, 'Translations,' and Boundary Objects: Amateurs and Professionals in Berkeley's Museum of Vertebrate Zoology, 1907-1939. Social Studies of Science 19: 387-420.
Allen, M. (1999) Do-it-yourself climate prediction, Nature, 401, 642.
Stainforth, D.A. et al. (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases, Nature, 433, 403-406.
Provine, William B. (1972). The origins of theoretical population genetics. Chicago: University of Chicago Press, chapter 5.
Plutynski, Anya (2004). Explanation in classical population genetics, Philosophy of Science, 71, 1201-1214.
Wimsatt, W. C., 1987. False models as means to truer theories. In: M. Nitecki and A. Hoffman, eds., Neutral Models in Biology. London : Oxford University Press, pp. 23-55.
Kingland, Sharon E., (1995) Modeling nature: episodes in the history of population ecology, Chicago: University of Chicago Press, 2nd ed, chapter 5.
Levins, R. (1966). The Strategy of Model Building in Population Biology. American Scientist 54: 421431.
Odenbaugh, Jay (in press). The Strategy of “The Strategy of Model Building in Population Biology Biology and Philosophy.
Beven, Keith (2001). Calibration, validtation and equifinality in hydrological modeling: A continuing discussion .. . . In M. G. Anderson and P. D. Bates, Model validation: Perspectives in hydrological science ( pp. 43-55). New York: John Wiley and Son
Morton, A. (1993). Mathematical models: Questions of trustworthiness. British Journal of Philosophy of Science, 44, 659-674.
Oreskes, N., Schrader-Frechette, K., Belitz, K. (1994). Verification, validation and confirmation of numerical models in the earth sciences. Science, 263, 641-646.
Schrader-Frechette, K. S. (1989). Idealized laws, antirealism and applied science: A case in hydrogeology. Synthese, 81, 329-352
Beck, M. B. (1994). Understanding uncertain environmental systems. In Grasman, J. and van Straten, G. (Eds.). Predictability and nonlinear modeling in natural sciences and economics. Dordrecht: Kluwer, 294-311.
Guala, F. (2002) Models, simulations, and experiments, pp.59-74 in L. Magnani and N.J. Nersessian (Eds.) Model-based reasoning: Science, technology, values. Dordrecht: Kluwer.
Morgan, M. (2005) Experiments versus models: New phenomena, inference and surprise, Journal of Economic Methodology 12:2, 317-329.
Winsberg, E. (2003) Simulated Experiments: Methodology for a Virtual World, Philosophy of Science 70, 105-125.
Dowling, Deborah (1999). Experimenting on theories. Science in Context, 12, 261-273.
Bechtel, W. and Abrahamsen, A. (2005). Explanation: A Mechanistic Alternative. Studies in History and Philosophy of the Biological and Biomedical Sciences, 36, 421-441.
Schaffner, K. F. (in press). Theories, Models, and Equations in Systems Biology: Prototypes and Emergent Unification. In F. Boogerd et al. (eds.), Towards a Philosophy of Systems Biology, Amsterdam : Elsevier Publications
Craver, Carl C. (in press) When mechanistic models explain.
Hodgkin, A.L. and A.F. Huxley. (1952) Quantitative description of membrane current its application to conduction and excitation in nerve. Journal of Physiology, 117, 500-544.
Abrahamsen, A., & Bechtel, W. (2006). Phenomena and mechanisms: Putting the symbolic, connectionist, and dynamical systems debate in broader perspective. In R. Stainton (Ed.), Contemporary debates in cognitive science. Oxford: Basil Blackwell.
McClelland, James (1999). Cognitive modeling, Connectionist. In R. Wilson and F.Keil (Eds.) MIT Encyclopedia of Cognitive Science, Cambridge, MA: MIT Press.
Lewis, Rick (1999). Cognitive modeling, Symbolic. In R. Wilson and F.Keil (Eds.) MIT Encyclopedia of Cognitive Science, Cambridge, MA: MIT Press.
Newell, Allen (1980). Physical symbol systems. Cognitive Science, 4, 135-183.
Plunkett. K., and Marchman, V. (1991). U-shaped learning and frequency effects in a multi-
layered perceptron: Implications for child language acquisition. Cognition, 38: 43-102.
Watts, D. J. and Strogatz, S. H. Collective dynamics of 'small-world' networks, Nature, 393:440-442 (1998)
A.-L. Barabási, and R. Albert, Emergence of scaling in random networks, Science 286, 509–512 (1999)
Van Leeuwen, Cees, Steyvers, Mark, and Nooter, Maarten (1997). Stability and intermittency in large-scale coupled oscillator models for perceptual segmentation. Journal of Mathematical Psychology, 41, 319-344.
Raffone, Antonino and van Leeuwen, Cees (2001). Activation and coherence in memory processes: revisiting the Parallel Distributed Processing approach to retrieval. Connection Science, 13, 349-382.
Bruggeman, Frank (2005). Of molecules and cells: Emergent mechanisms. Dissertation, Vrije Universiteit Amsterdam, chapter 1.
Oreskes, Naomi and Belizt, Kenneth (2001). Philosophical issues in model assessment. In M.G. Anderson and P. D. Bates, Model validation: Perspectives in hydrological science. New York : John Wiley and Son. 23-39.
From Sarewitz, D., R.A. Pielke Jr., and R. Byerly Jr. (Eds.) (2000) Prediction: Science, Decision Making, and the Future of Nature, Washington DC: Island Press:
Sarewitz, D. et al. (2000) "Introduction: Death, Taxes, and Environmental Policy", pp.1-7.
Sarewitz, D. and Pielke Jr. (2000) "Prediction in Science and Policy", pp.11-22.
Nigg, J. (2000) "Predicting Earthquakes: Science, Pseudoscience, and Public Policy Paradox", pp.135-156.
Pilkey, O. (2000) "What You Know Can Hurt You: Predicting the Behavior of Nourished Beaches", pp.159-184.
Rayner, S. (2000) "Prediction and Other Approaches to Climate Change Policy", pp.269-296.