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|Title:||The requirements analysis for the Meta-Model Sustainability Impact Assessment Tools (SIAT) to ensure a High Use and Acceptability among Policy Decision Makers - A Critical Review|
|Authors:||SIEBER STEFAN; MÜLLER K.; TSCHERNING Karen; HELMING Katharina; POHLE D.; FRICKE K.; VERWEIJ P.|
|Citation:||Proceedings of the Interfacing Modelling and Simulation with Mathematical and Computational Sciences p. 2908-2914|
|Publisher:||The Modelling and Simulation Society of Australia and New Zealand Inc. and the International Association for Mathematics and Computers in Simulation|
|Type:||Articles in periodicals and books|
|Abstract:||This paper focuses a critical analysis of the science-policy interface in the frame of the SENSOR project. The meta-model Sustainability Impact Assessment Tools (SIAT) is the central tool of the European research project, which was developed over four years. We illustrate the methodology and procedure of SIAT that is tailored to simulate land use policies. SIAT allows conducting ex-ante sustainability impact assessment towards the target year 2025 at the level of 570 European regions. A critical analysis at the policy-science interface discusses the procedure of the SIAT development process and reveals the mean of prototyping as basis for the requirement analysis. We summarise the major problems we faced at intuitional level that influenced the quality of the requirement analysis. Finally we conclude on the institutional reasons for asymmetric information that (i) hinders efficient stakeholder involvement and (ii) causes shortcoming to mirror precise end-user requirements in the architectural model design: Quantifying utility level on realistic needs is not precisely applicable due to (a) high opportunity costs to survey and harmonise individual requirements, (b) uncertain forecasts on costs estimates, (c) asymmetric information related to high transactions costs for communication and strategic behaviour of policy makers, researcher and IT developer, (d) requested but unfeasible technical implementation possibilities, (e) predefined and thus limited ¿room of manoeuvre¿ and constraints laid down in research proposals and resulting contracts. We conclude that the reality always differs from theoretical optimum, although actual decisions on model design should follow ideal principles as much as the information is available.|
|JRC Institute:||Growth and Innovation|
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