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1 December 2011 Using Multicriteria Analysis of Simulation Models to Understand Complex Biological Systems
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Abstract

Scientists frequently use computer-simulation models to help solve complex biological problems. Typically, such models are highly integrated, they produce multiple outputs, and standard methods of model analysis are ill suited for evaluating them. We show how multicriteria optimization with Pareto optimality allows for model outputs to be compared to multiple system components simultaneously and improves three areas in which models are used for biological problems. In the study of optimal biological structures, Pareto optimality allows for the identification of multiple solutions possible for organism survival and reproduction, which thereby explains variability in optimal behavior. For model assessment, multicriteria optimization helps to illuminate and describe model deficiencies and uncertainties in model structure. In environmental management and decisionmaking, Pareto optimality enables a description of the trade-offs among multiple conflicting criteria considered in environmental management, which facilitates better-informed decisionmaking.

© 2011 by American Institute of Biological Sciences. All rights reserved. Request permission to photocopy or reproduce article content at the University of California Press's Rights and Permissions Web site at www.ucpressjournals.com/reprintinfo.asp.
Maureen C. Kennedy and E. David Ford "Using Multicriteria Analysis of Simulation Models to Understand Complex Biological Systems," BioScience 61(12), 994-1004, (1 December 2011). https://doi.org/10.1525/bio.2011.61.12.9
Published: 1 December 2011
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