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Proposing an information criterion for individual-based models developed in a pattern-oriented modelling framework

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  • Piou, Cyril
  • Berger, Uta
  • Grimm, Volker

Abstract

Individual-based models (IBMs) have been improved in quality and reliability in recent years with an approach called pattern-oriented modelling (POM). POM proposes guidelines to develop models reproducing multiple patterns observed on the field and to test systematically how well the IBMs reproduce them. POM studies used generally traditional methods of goodness of fit such as the sum of squares evaluation or ad hoc comparisons of fitting errors and variations. Model selection, however, can be a rigorous statistical approach based on information theory and information criteria such as the Akaike's information criterion (AIC) or the deviance information criterion (DIC). So far, it has not been tried to link POM to these rigorous techniques. The main problems to achieve that are: (a) the difficulty to have likelihood functions for IBMs’ parameters and (b) the possibility to obtain posterior distributions of IBMs’ parameters given the patterns to reproduce. In a first part, this paper answers problem (a) by proposing and explaining how to calculate a deviance measure (POMDEV) for models developed in a context of POM. And while answering the second problem, a second part of the paper proposes an information criterion for model selection in a POM context (the pattern-oriented modelling information criterion: POMIC). This criterion does not yet have the same theoretical foundation as, e.g., AIC, but uses formal analogies to the DIC. In a third part POMIC is tested with a modelling exercise. This exercise shows the potential of POMIC to use multiple patterns for selecting among multiple potential submodels and eventually select the most parsimonious and well fitting model version. We conclude that POMIC, although being a heuristically derived approach, can greatly improve the POM framework.

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  • Piou, Cyril & Berger, Uta & Grimm, Volker, 2009. "Proposing an information criterion for individual-based models developed in a pattern-oriented modelling framework," Ecological Modelling, Elsevier, vol. 220(17), pages 1957-1967.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:17:p:1957-1967
    DOI: 10.1016/j.ecolmodel.2009.05.003
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    References listed on IDEAS

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    1. Kramer-Schadt, Stephanie & Revilla, Eloy & Wiegand, Thorsten & Grimm, Volker, 2007. "Patterns for parameters in simulation models," Ecological Modelling, Elsevier, vol. 204(3), pages 553-556.
    2. Jachner, Stefanie & Gerald van den Boogaart, K. & Petzoldt, Thomas, 2007. "Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV)," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 22(i08).
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    4. Piou, Cyril & Berger, Uta & Hildenbrandt, Hanno & Grimm, Volker & Diele, Karen & D’Lima, Coralie, 2007. "Simulating cryptic movements of a mangrove crab: Recovery phenomena after small scale fishery," Ecological Modelling, Elsevier, vol. 205(1), pages 110-122.
    5. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    6. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. Bauduin, Sarah & McIntire, Eliot & St-Laurent, Martin-Hugues & Cumming, Steve, 2016. "Overcoming challenges of sparse telemetry data to estimate caribou movement," Ecological Modelling, Elsevier, vol. 335(C), pages 24-34.
    2. Grimm, Volker & Berger, Uta, 2016. "Robustness analysis: Deconstructing computational models for ecological theory and applications," Ecological Modelling, Elsevier, vol. 326(C), pages 162-167.
    3. Jakoby, Oliver & Grimm, Volker & Frank, Karin, 2014. "Pattern-oriented parameterization of general models for ecological application: Towards realistic evaluations of management approaches," Ecological Modelling, Elsevier, vol. 275(C), pages 78-88.
    4. Peters, Ronny & Lin, Yue & Berger, Uta, 2016. "Machine learning meets individual-based modelling: Self-organising feature maps for the analysis of below-ground competition among plants," Ecological Modelling, Elsevier, vol. 326(C), pages 142-151.
    5. Byer, Nathan W. & Reid, Brendan N., 2022. "The emergence of imperfect philopatry and fidelity in spatially and temporally heterogeneous environments," Ecological Modelling, Elsevier, vol. 468(C).
    6. Grimm, Volker & Berger, Uta, 2016. "Structural realism, emergence, and predictions in next-generation ecological modelling: Synthesis from a special issue," Ecological Modelling, Elsevier, vol. 326(C), pages 177-187.
    7. Juste Raimbault & Julien Perret, 2019. "Generating urban morphologies at large scales," Post-Print halshs-02265415, HAL.
    8. Piou, Cyril & Prévost, Etienne, 2012. "A demo-genetic individual-based model for Atlantic salmon populations: Model structure, parameterization and sensitivity," Ecological Modelling, Elsevier, vol. 231(C), pages 37-52.
    9. Duboz, Raphaël & Versmisse, David & Travers, Morgane & Ramat, Eric & Shin, Yunne-Jai, 2010. "Application of an evolutionary algorithm to the inverse parameter estimation of an individual-based model," Ecological Modelling, Elsevier, vol. 221(5), pages 840-849.
    10. Jager, Henriette I. & DeAngelis, Donald L., 2018. "The confluences of ideas leading to, and the flow of ideas emerging from, individual-based modeling of riverine fishes," Ecological Modelling, Elsevier, vol. 384(C), pages 341-352.

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