The Epistemologies of Non-Forecasting Simulations, Part II

Climate, Chaos, Computing Style, and the Contextual Plasticity of Error

Datos Bibliográficos

ID14670726
AutoresLambert Williams (Max Planck Society), William Thomas (0000-0001-6969-3173, American Institute of Physics)
Año2009
Volumen22
Número2
Páginas271-310
Fecha de publicación2009-06-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaScience in Context (JOURNAL)
Identificadores de la revistaISSN: 0269-8897 • E-ISSN: 1474-0664
EditorialCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s0269889709002221
OpenAlexW2132965606
IdiomaEN
Citas recibidas1
Referencias citadas51

Argument 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

Argumentative · Epistemology · Physics · Sociology · Complex Systems and Decision Making · Computer Science · Data Visualization and Analytics · History · Philosophy · Philosophy and History of Science