[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v280y2014icp117-128.html
   My bibliography  Save this article

Merging validation and evaluation of ecological models to ‘evaludation’: A review of terminology and a practical approach

Author

Listed:
  • Augusiak, Jacqueline
  • Van den Brink, Paul J.
  • Grimm, Volker
Abstract
Confusion about model validation is one of the main challenges in using ecological models for decision support, such as the regulation of pesticides. Decision makers need to know whether a model is a sufficiently good representation of its real counterpart and what criteria can be used to answer this question. Unclear terminology is one of the main obstacles to a good understanding of what model validation is, how it works, and what it can deliver. Therefore, we performed a literature review and derived a standard set of terms. ‘Validation’ was identified as a catch-all term, which is thus useless for any practical purpose. We introduce the term ‘evaludation’, a fusion of ‘evaluation’ and ‘validation’, to describe the entire process of assessing a model's quality and reliability. Considering the iterative nature of model development, the modelling cycle, we identified six essential elements of evaludation: (i) ‘data evaluation’ for scrutinising the quality of numerical and qualitative data used for model development and testing; (ii) ‘conceptual model evaluation’ for examining the simplifying assumptions underlying a model's design; (iii) ‘implementation verification’ for testing the model's implementation in equations and as a computer programme; (iv) ‘model output verification’ for comparing model output to data and patterns that guided model design and were possibly used for calibration; (v) ‘model analysis’ for exploring the model's sensitivity to changes in parameters and process formulations to make sure that the mechanistic basis of main behaviours of the model has been well understood; and (vi) ‘model output corroboration’ for comparing model output to new data and patterns that were not used for model development and parameterisation. Currently, most decision makers require ‘validating’ a model by testing its predictions with new experiments or data. Despite being desirable, this is neither sufficient nor necessary for a model to be useful for decision support. We believe that the proposed set of terms and its relation to the modelling cycle can help to make quality assessments and reality checks of ecological models more comprehensive and transparent.

Suggested Citation

  • Augusiak, Jacqueline & Van den Brink, Paul J. & Grimm, Volker, 2014. "Merging validation and evaluation of ecological models to ‘evaludation’: A review of terminology and a practical approach," Ecological Modelling, Elsevier, vol. 280(C), pages 117-128.
    • Handle: RePEc:eee:ecomod:v:280:y:2014:i:c:p:117-128
    DOI: 10.1016/j.ecolmodel.2013.11.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380013005450
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2013.11.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Oriade, Caleb A. & Dillon, Carl R., 1997. "Developments in biophysical and bioeconomic simulation of agricultural systems: a review," Agricultural Economics, Blackwell, vol. 17(1), pages 45-58, October.
    2. Saul I. Gass, 1983. "Feature Article—Decision-Aiding Models: Validation, Assessment, and Related Issues for Policy Analysis," Operations Research, INFORMS, vol. 31(4), pages 603-631, August.
    3. Caleb A. Oriade & Carl R. Dillon, 1997. "Developments in biophysical and bioeconomic simulation of agricultural systems: a review," Agricultural Economics, International Association of Agricultural Economists, vol. 17(1), pages 45-58, October.
    4. Latombe, Guillaume & Parrott, Lael & Fortin, Daniel, 2011. "Levels of emergence in individual based models: Coping with scarcity of data and pattern redundancy," Ecological Modelling, Elsevier, vol. 222(9), pages 1557-1568.
    5. Landry, Maurice & Malouin, Jean-Louis & Oral, Muhittin, 1983. "Model validation in operations research," European Journal of Operational Research, Elsevier, vol. 14(3), pages 207-220, November.
    6. Aumann, Craig A., 2007. "A methodology for developing simulation models of complex systems," Ecological Modelling, Elsevier, vol. 202(3), pages 385-396.
    7. James S. Hodges, 1991. "Six (Or So) Things You Can Do with a Bad Model," Operations Research, INFORMS, vol. 39(3), pages 355-365, June.
    8. Boesten, J. J. T. I., 2000. "Modeller subjectivity in estimating pesticide parameters for leaching models using the same laboratory data set," Agricultural Water Management, Elsevier, vol. 44(1-3), pages 389-409, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dirksmeyer, W., 2008. "Ist eine Reduzierung des Pflanzenschutzmitteleinsatzes im Freilandgemüsebau möglich? Ergebnisse eines bioökonomischen Simulationsmodells," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 43, March.
    2. Shibia, Mumina Guyo, 2010. "Evaluation of Economic Losses in Rearing Replacement Heifers in Pastoral and Peri-Urban Camel Herds of Isiolo District, Kenya," Research Theses 134493, Collaborative Masters Program in Agricultural and Applied Economics.
    3. Stéphane Blancard & Jean-Philippe Boussemart & Walter Briec & Kristiaan Kerstens, 2006. "Short- and Long-Run Credit Constraints in French Agriculture: A Directional Distance Function Framework Using Expenditure-Constrained Profit Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(2), pages 351-364.
    4. Lane, David & Husemann, Elke & Holland, Darren & Khaled, Abdul, 2019. "Understanding foodborne transmission mechanisms for Norovirus: A study for the UK's Food Standards Agency," European Journal of Operational Research, Elsevier, vol. 275(2), pages 721-736.
    5. Frederic H. Murphy, 2005. "ASP, The Art and Science of Practice: Elements of a Theory of the Practice of Operations Research: Expertise in Practice," Interfaces, INFORMS, vol. 35(4), pages 313-322, August.
    6. Troost, Christian & Huber, Robert & Bell, Andrew R. & van Delden, Hedwig & Filatova, Tatiana & Le, Quang Bao & Lippe, Melvin & Niamir, Leila & Polhill, J. Gareth & Sun, Zhanli & Berger, Thomas, 2023. "How to keep it adequate: A protocol for ensuring validity in agent-based simulation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 159, pages 1-21.
    7. Wenbo Zhang & Wilbert Wilhelm, 2011. "OR/MS decision support models for the specialty crops industry: a literature review," Annals of Operations Research, Springer, vol. 190(1), pages 131-148, October.
    8. Feola, Giuseppe & Binder, Claudia R., 2010. "Towards an improved understanding of farmers' behaviour: The integrative agent-centred (IAC) framework," Ecological Economics, Elsevier, vol. 69(12), pages 2323-2333, October.
    9. David C. Lane & Özge Pala & Yaman Barlas & David C. Lane, 2015. "Validity is a Matter of Confidence—But Not Just in System Dynamics," Systems Research and Behavioral Science, Wiley Blackwell, vol. 32(4), pages 450-458, July.
    10. Zachary A. Collier & James H. Lambert, 2019. "Principles and methods of model validation for model risk reduction," Environment Systems and Decisions, Springer, vol. 39(2), pages 146-153, June.
    11. Coleno, F. C. & Duru, M., 1999. "A model to find and test decision rules for turnout date and grazing area allocation for a dairy cow system in spring," Agricultural Systems, Elsevier, vol. 61(3), pages 151-164, September.
    12. Arnold Reisman & Muhittin Oral, 2005. "Soft Systems Methodology: A Context Within a 50-Year Retrospective of OR/MS," Interfaces, INFORMS, vol. 35(2), pages 164-178, April.
    13. Morel, Kevin & San Cristobal, Magali & Léger, François Gilbert, 2018. "Simulating incomes of radical organic farms with MERLIN: A grounded modeling approach for French microfarms," Agricultural Systems, Elsevier, vol. 161(C), pages 89-101.
    14. Hester, Susan M. & Cacho, Oscar, 2003. "Modelling apple orchard systems," Agricultural Systems, Elsevier, vol. 77(2), pages 137-154, August.
    15. Dirksmeyer, Walter, 2007. "Ist Eine Reduzierung Des Pflanzenschutzmitteleinsatzes Im Freilandgemüsebau Möglich? Ergebnisse Eines Bioökonomischen Simulationsmodells," 47th Annual Conference, Weihenstephan, Germany, September 26-28, 2007 7592, German Association of Agricultural Economists (GEWISOLA).
    16. Eike Nohdurft & Elisa Long & Stefan Spinler, 2017. "Was Angelina Jolie Right? Optimizing Cancer Prevention Strategies Among BRCA Mutation Carriers," Decision Analysis, INFORMS, vol. 14(3), pages 139-169, September.
    17. Rau, Philipp & Spinler, Stefan, 2017. "Alliance formation in a cooperative container shipping game: Performance of a real options investment approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 155-175.
    18. Tsoukias, Alexis, 2008. "From decision theory to decision aiding methodology," European Journal of Operational Research, Elsevier, vol. 187(1), pages 138-161, May.
    19. Haehl, Christian & Spinler, Stefan, 2018. "Capacity expansion under regulatory uncertainty:A real options-based study in international container shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 113(C), pages 75-93.
    20. Gottesburen, B. & Aden, K. & Barlund, I. & Brown, C. & Dust, M. & Gorlitz, G. & Jarvis, N. & Rekolainen, S. & Schafer, H., 2000. "Comparison of pesticide leaching models: results using the Weiherbach data set," Agricultural Water Management, Elsevier, vol. 44(1-3), pages 153-181, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:280:y:2014:i:c:p:117-128. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.