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An Objective-Based Perspective on Assessment of Model-Supported Policy Processes

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Simulation models, being in use for a long time in natural sciences and engineering domains, are diffusing to a wider context including policy analysis studies. The differences between the nature of the domain of application, as well as the increased variety of usage partially induced by this difference naturally imply new challenges to be overcome. One of these challenges is related to the assessment of the simulation-based outcomes in terms of their reliability and relevance in the policy context being studied. The importance of this assessment is twofold. First of all, it is all about conducting a high quality policy study with effective results. However, the quality of the study does not necessarily imply acceptance of the results by the clients and/or colleagues. This problem of policy analysts increases the importance of such an assessment; an effective assessment may induce the acceptance of the conclusions drawn from the study by the clients and/or colleagues. The main objective of this paper is to introduce an objective-based assessment perspective for simulation model-supported policy studies. As a first step towards such a goal, an objective-based classification of models is introduced. Based on that, we will discuss the importance of different aspects of the assessment for each type. In doing so, we aim to provide a structured discussion that may serve as a sort of methodological guideline to be used by policy analysts, and also by clients.

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  • Gönenç Yücel & Els van Daalen, 2009. "An Objective-Based Perspective on Assessment of Model-Supported Policy Processes," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-3.
    • Handle: RePEc:jas:jasssj:2009-70-1
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    1. Petra Ahrweiler & Nigel Gilbert, 2005. "Caffè Nero: The Evaluation of Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-14.
    2. Riccardo Boero & Flaminio Squazzoni, 2005. "Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-6.
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    Cited by:

    1. Nicola Lettieri, 2016. "Computational Social Science, the Evolution of Policy Design and Rule Making in Smart Societies," Future Internet, MDPI, vol. 8(2), pages 1-17, May.
    2. Yücel, Gönenç & van Daalen, Cornelia, 2012. "A simulation-based analysis of transition pathways for the Dutch electricity system," Energy Policy, Elsevier, vol. 42(C), pages 557-568.
    3. Hofbauer, Leonhard & McDowall, Will & Pye, Steve, 2022. "Challenges and opportunities for energy system modelling to foster multi-level governance of energy transitions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    4. Li, Francis G.N. & Trutnevyte, Evelina & Strachan, Neil, 2015. "A review of socio-technical energy transition (STET) models," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 290-305.
    5. Thomas Brenner & Claudia Werker, 2009. "Policy Advice Derived from Simulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-2.

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