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Incorporating output variance in local sensitivity analysis for stochastic models

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  • Bar Massada, Avi
  • Carmel, Yohay

Abstract

The output of stochastic models is a distribution of values, rather than a single value such as in deterministic models. Local sensitivity analyses of such models typically ignore the higher moments of the output distribution and instead use the distribution mean to represent model output. This might be simplistic, since the shape of the distribution might also be sensitive to changes in model parameters. Here, we construct a simple sensitivity index that captures also the shape of the output distribution, by incorporating its variance in addition to its mean. To evaluate its performance, we reconstructed an existing stochastic individual-based model for mosquitofish (Gambusia holbrooki) population. We compared the performance of the new sensitivity index to the standard sensitivity index (∂Y/∂P) that was calculated using the mean of the output distribution, by ranking model parameters according to their impact on the output. Sensitivity analyses using both methods identified different parameters as the most influential on model output, and rankings were inconsistent between methods regardless of the number of simulations used for generating the output distributions. It is shown that the new index indeed captured better the effect of parameters on model output since it accounted for the variance of the output distribution.

Suggested Citation

  • Bar Massada, Avi & Carmel, Yohay, 2008. "Incorporating output variance in local sensitivity analysis for stochastic models," Ecological Modelling, Elsevier, vol. 213(3), pages 463-467.
    • Handle: RePEc:eee:ecomod:v:213:y:2008:i:3:p:463-467
    DOI: 10.1016/j.ecolmodel.2008.01.021
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    References listed on IDEAS

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    1. Lawrie, Jock & Hearne, John, 2007. "Reducing model complexity via output sensitivity," Ecological Modelling, Elsevier, vol. 207(2), pages 137-144.
    2. Tibor F. Liska, 2007. "The Liska model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 29(3), pages 363-381, December.
    3. Cariboni, J. & Gatelli, D. & Liska, R. & Saltelli, A., 2007. "The role of sensitivity analysis in ecological modelling," Ecological Modelling, Elsevier, vol. 203(1), pages 167-182.
    4. Chu, Peter C. & Ivanov, Leonid M. & Margolina, Tetyana M., 2007. "On non-linear sensitivity of marine biological models to parameter variations," Ecological Modelling, Elsevier, vol. 206(3), pages 369-382.
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    1. Melbourne-Thomas, J. & Johnson, C.R. & Fulton, E.A., 2011. "Characterizing sensitivity and uncertainty in a multiscale model of a complex coral reef system," Ecological Modelling, Elsevier, vol. 222(18), pages 3320-3334.
    2. Engel, Markus & Körner, Michael & Berger, Uta, 2018. "Plastic tree crowns contribute to small-scale heterogeneity in virgin beech forests—An individual-based modeling approach," Ecological Modelling, Elsevier, vol. 376(C), pages 28-39.
    3. Verwaeren, Jan & Van der Weeën, Pieter & De Baets, Bernard, 2015. "A search grid for parameter optimization as a byproduct of model sensitivity analysis," Applied Mathematics and Computation, Elsevier, vol. 261(C), pages 8-27.
    4. Belsare, Aniruddha V. & Gompper, Matthew E., 2015. "A model-based approach for investigation and mitigation of disease spillover risks to wildlife: Dogs, foxes and canine distemper in central India," Ecological Modelling, Elsevier, vol. 296(C), pages 102-112.
    5. Piacenza, Susan E. & Richards, Paul M. & Heppell, Selina S., 2017. "An agent-based model to evaluate recovery times and monitoring strategies to increase accuracy of sea turtle population assessments," Ecological Modelling, Elsevier, vol. 358(C), pages 25-39.

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