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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/61050 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/61050 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/61050 archive 351 disk0/00/00/15/83 2016-01-15 01:49:20 2021-08-27 17:10:13 2016-01-15 01:49:20 monograph show 3 Fedra K. 1049 Straten G. van AL1730 Beck M.B. 562 pub iis_mod iis_wat prog_ren Mathematical models of ecosystems are considerable simplifications of reality, and the data upon which they are based are usually scarce and uncertain. Calibration of large complex models depends upon arbitrary assumptions and choices, and frequently calibration procedures do not deal adequately with the uncertainty in the data describing the system under study. Since much of the uncertainty and arbitrariness in ecological modelling is inevitable, because of both practical as well as theoretical limitations, model-based predictions should at least reveal their dependence on, and sensitivity to, uncertainty and arbitrary assumptions. This paper proposes a method that explicitly takes in account the uncertainty associated with data for modelling. By reference to a partly qualitative and somewhat vague definition of system behaviour in terms of allowable ranges, an ensemble of acceptable parameter vectors for the model may be identified. This contrasts directly with a more conventional approach to model calibration, in which a quantitative (squared-error) criterion is minimized and through which a supposedly "unique" and "best" set of parameters can be derived. The ensemble of parameter vectors is then used for the simulation of a multitude of future systems behaviour patterns, so that the uncertainty in the initial data and assumptions is preserved, and thus the predicted future systems response can be interpreted in a probabilistic manner. 1981 published RR-81-026. Reprinted from Ecological Modelling, 13 [1981]. RP-81-026 Reprinted from Ecological Modelling; 13 [1981] 10 1241 2771 2986 public research_reprint Ecological Modelling 13 IIASA, Laxenburg, Austria 26 TRUE FALSE info:eu-repo/semantics/book