Modelling complex systems of heterogeneous agents to better design sustainability transitions policy

By Jean-François Mercure, H. Pollitt, A. Bassi, J. Viñuales, N. Edwards. 2015

192 citations recorded by the source corpus.

This article proposes a fundamental methodological shift in the modelling of policy interventions for sustainability transitions in order to account for complexity (e.g. self-reinforcing mechanisms, such as technology lock-ins, arising from multi-agent interactions) and agent heterogeneity (e.g. differences in consumer and investment behaviour arising from income stratification). We first characterise the uncertainty faced by climate policy-makers and its implications for investment decision-makers. We then identify five shortcomings in the equilibrium and optimisation-based approaches most frequently used to inform sustainability policy: (i) their normative, optimisation-based nature, (ii) their unrealistic reliance on the full-rationality of agents, (iii) their inability to account for mutual influences among agents (multi-agent interactions) and capture related self-reinforcing (positive feedback) processes, (iv) their inability to represent multiple solutions and path-dependency, and (v) their inability to properly account for agent heterogeneity. The aim of this article is to introduce an alternative modelling approach based on complexity dynamics and agent heterogeneity, and explore its use in four key areas of sustainability policy, namely (1) technology adoption and diffusion, (2) macroeconomic impacts of low-carbon policies, (3) interactions between the socio-economic system and the natural environment, and (4) the anticipation of policy outcomes. The practical relevance of the proposed methodology is subsequently discussed by reference to four specific applications relating to each of the above areas: the diffusion of transport technology, the impact of low-carbon investment on income and employment, the management of cascading uncertainties, and the cross-sectoral impact of biofuels policies. In conclusion, the article calls for a fundamental methodological shift aligning the modelling of the socio-economic system with that of the climatic system, for a combined and realistic understanding of the impact of sustainability policies.

Subfields: Economics and Econometrics, Environmental Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/d76ce4edbb177c7c6caf7b2ca4143e6f54e882fa

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Citations in the corpus

Climate risks and financial stability (2021, background): “In this regard, a growing stream of research has discussed the limits of traditional approaches for the analysis of the macroeconomic and financial impacts of climate change and climate policies (Farmer et al., 2015; Mercure et al., 2016; Dafermos and Nikolaidi, 2019; Monasterolo, 2020).”
Modelling innovation and the macroeconomics of low-carbon transitions: theory, perspectives and practical use (2019, background): “A more extensive review of the treatment of innovation throughout the history of economic thought is given in (Mercure et al., 2016a).”
Faraway, So Close: Coupled Climate and Economic Dynamics in an Agent-based Integrated Assessment Model (2018, unknown): “Agent-based models (Tesfatsion and Judd, 2006; Fagiolo and Roventini, 2012, 2017) constitute a valuable and promising alternative to IAMs (Smajgl et al., 2011; Farmer et al., 2015; Stern, 2016; Mer-cure et al., 2016; Balint et al., 2017).”
Complexity and the Economics of Climate Change: A Survey and a Look Forward (2017, unknown): “Different types of green fiscal (carbon tax, tax relief and breaks on investment in renewable energy) and targeted monetary policies (green bonds and quantitative easing) are simulated in the Eirin model (Monasterolo and Raberto, 2016), which combines system dynamics and agent-based features.”
Opportunities for knowledge co-production across the energy-food-water nexus: Making interdisciplinary approaches work for better climate decision making (2017, background): “In this complex system where cross-sector feedback loops, time delays at the macro-economic level (Hake et al., 2016; Stacey, 1992), and heterogeneous short-term thinking agents at the micro-economic level, influence non-linearity and policy uncertainty (Mercure et al., 2016; Rezai and Stagl, 2016).”
Macroeconomic impact of stranded fossil fuel assets (2018, methodology): “The improvement occurs despite the general increase of energy prices and hence costs for energy-intensive industries 28,29 .”