Actors, decision-making, and institutions in quantitative system modelling

Enrica De Cian (), Shouro Dasgupta, Andries F. Hof, Mariësse A.E. van Sluisveld, Jonathan Köhler, Benjamin Pfluger and Detlef P. van Vuuren

Technological Forecasting and Social Change, 2020, vol. 151, issue C

Abstract: Increasing realism in quantitative system modelling with respect to the representation of actors, decision-making, and institutions is critical to better understand the transition towards a low-carbon sustainable society. Yet, studies using quantitative system models, which have become a key analytical tool to support sustainability and decarbonization policies, focus on outcomes, therefore overlooking the dynamics of the drivers of change. We explore opportunities that arise from a deeper engagement of quantitative systems modelling with social science. We argue that several opportunities for enriching the realism in model-based scenario analysis can arise through model refinements oriented towards a more detailed approach in terms of actor heterogeneity, as well as through integration across different analytical and disciplinary approaches. Several opportunities that do not require major changes in model structure are ready to be seized. Promising ones include combining different types of models and enriching model-based scenarios with evidence from applied economics and transition studies.

Keywords: Modelling; Transition pathways; Scenarios; Actors; Institutions; Governance; ABM; IAM (search for similar items in EconPapers)
JEL-codes: C63 O10 P16 Q00 Q4 Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Related works:
Working Paper: Actors, Decision-making, and Institutions in Quantitative System Modelling (2017)
Working Paper: Actors, Decision-making, and Institutions in Quantitative System Modelling (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:151:y:2020:i:c:s0040162518315907

DOI: 10.1016/j.techfore.2018.10.004

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