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Article ID: CBB000932756

The Epistemologies of Non-Forecasting Simulations, Part II: Climate, Chaos, Computing Style, and the Contextual Plasticity of Error (2009)

Williams, Lambert (Author)
Thomas, William (Author)

Science in Context
Volume: 22
Pages: 271--310

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Meteorology Models and modeling in science Computer science Chaos theory; chaotic behavior 20th century, late Lorenz, Edward Shaw, Robert Massachusetts Institute of Technology, MIT Princeton University University of California, Santa Cruz

We continue our analysis of modeling practices that focus more on qualitative understanding of system behavior than the attempt to provide sharp forecasts. The argument here is built around three episodes: the ambitious work of the Princeton Meteorological Project; the seemingly simple models of convection in weather systems by Edward Lorenz at MIT; and then finally analysis of the dripping faucet by Robert Shaw and the Dynamical Systems Collective at UC Santa Cruz. Using the Princeton Meteorological Project as an argumentative foil for the later chaos work of Lorenz and Shaw, we first show how the epistemological interest of modeling came to shift from issuing predictions to probing the very meaning and limits of prediction. The second step of our argument shows that what may be seen in one context of use as a modeling technology that is error ridden, imprecise, or inadequate, may be parsed completely differently in another context. This argument about technology and practice, we argue, feeds through to epistemological conceptions of error. Far from being something that can be defined in the absolute, the notion of error is shown to be contextually plastic.

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Description Three case studies: the Princeton Meteorological Project; Edward Lorenz's models of convection in weather systems at MIT; and Robert Shaw's analysis of the dripping faucet. See also JournalArticle; William Thomas; Lambert Williams; The Epistemologies of Non... (2009) [932755].

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Article Thomas, William; Williams, Lambert (2009) The Epistemologies of Non-Forecasting Simulations, Part I: Industrial Dynamics and Management Pedagogy at MIT. Science in Context (p. 245). unapi

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