MUSE: An open-source agent-based integrated assessment modelling framework

Giarola, Sara; Sachs, Julia; d’Avezac, Mayeul; Kell, Alexander J. M.; Hawkes, Adam

doi:10.1016/j.esr.2022.100964

Cited by 14 publications

(11 citation statements)

References 71 publications

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“…Notably, there have been nascent efforts in improving the representations of heterogeneous actors (e.g., urban vs. rural groups) and political economy considerations (e.g., regional variations in institutional quality and policy ambition) in detailed-process IAMs, which involves working closely with social scientists ( 18 , 25 – 29 ). These exogenous socioeconomic assumptions provide an avenue to couple with other models, e.g., integrating the demand projections from ABMs that considers heterogeneous consumer behaviors ( 30 ).…”

Section: Representations Of Institutions In Three Sustainability Modelsmentioning

confidence: 99%

Simulating institutional heterogeneity in sustainability science

Davidson,

Filatova,

Peng

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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Sustainability outcomes are influenced by the laws and configurations of natural and engineered systems as well as activities in socio-economic systems. An important subset of human activity is the creation and implementation of institutions, formal and informal rules shaping a wide range of human behavior. Understanding these rules and codifying them in computational models can provide important missing insights into why systems function the way they do (static) as well as the pace and structure of transitions required to improve sustainability (dynamic). Here, we conduct a comparative synthesis of three modeling approaches— integrated assessment modeling, engineering–economic optimization, and agent-based modeling—with underexplored potential to represent institutions. We first perform modeling experiments on climate mitigation systems that represent specific aspects of heterogeneous institutions, including formal policies and institutional coordination, and informal attitudes and norms. We find measurable but uneven aggregate impacts, while more politically meaningful distributional impacts are large across various actors. Our results show that omitting institutions can influence the costs of climate mitigation and miss opportunities to leverage institutional forces to speed up emissions reduction. These experiments allow us to explore the capacity of each modeling approach to represent insitutions and to lay out a vision for the next frontier of endogenizing institutional change in sustainability science models. To bridge the gap between modeling, theories, and empirical evidence on social institutions, this research agenda calls for joint efforts between sustainability modelers who wish to explore and incorporate institutional detail, and social scientists studying the socio-political and economic foundations for sustainability transitions.

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Section: Representations Of Institutions In Three Sustainability Modelsmentioning

confidence: 99%

Simulating institutional heterogeneity in sustainability science

Davidson,

Filatova,

Peng

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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“…Finally, MUSE 57 is an agent-based, energy system model that provides a detailed account of the energy sector to calculate least-cost GHG emissions reduction pathways-or the costs of alternative climate policies. It is bottom-up, in that it assumes short-term microeconomic equilibrium on the energy system by iterating market clearance across all sector modules and interchanging price and quantity of each energy commodity in each region but it is also agent-based, in that it tries to determine a mitigation pathway by providing an as-realistic-as-possible description of the investment and operational decision-making in each geographical region within a sector.…”

Section: Models Includedmentioning

confidence: 99%

A multimodel analysis of post-Glasgow climate targets and feasibility challenges

et al. 2023

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“…A representative diagram of the model is shown in Figure 2. While a detailed description of the MUSE modeling environment is presented in [27], in this paper, an overview of the aspects of the model that emerged as relevant during the training is presented:…”

Section: Musementioning

confidence: 99%

“…The demo case was a one-region 3-sector MUSE model. This was a simplified version of the case described in [27]; differently from the article, no limits were applied to emissions, the gas sector included one technology, and some techno-economic assumptions varied. The relevant settings of the demo case are given in Table 2.…”

Section: The Muse Open University Course: Lectures and Hands-onmentioning

confidence: 99%

Sustainability Education: Capacity Building Using the MUSE Model

et al. 2023

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Education for sustainable development has among its pillars, capacity building, which equips future generations with the set of skills needed to face the challenge of the transformation of society for sustainable development. This paper presents a training course for a novel model of long-term energy planning (the ModUlar energy system Simulation Environment, MUSE), as an example of capacity building activities for sustainable development. The activities were part of the Joint Summer School on Modelling Tools for Sustainable development, held in Trieste (Italy) in 2022. This summer school was one of the first successful implementations of education and training courses in a super-hybrid mode in the post-COVID era. Describing the training activities for MUSE open-source, this paper addresses one of the challenges that education for sustainable development is expected to increasingly face in the future: the training of future professionals in the use of novel toolkits and the implementation of truly trans-disciplinary approaches.This paper discusses the pre-school online training course for MUSE, the summer school contents, and some student modeling outcomes. While doing so, it shows the importance of leveraging the abstract contents of a course with practical exercises when learning a new tool. Reflecting upon the students’ experience, this paper draws conclusions that can be used to improve future editions of the same course and be extended to the design of training courses for other tools.

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MUSE: An open-source agent-based integrated assessment modelling framework

Cited by 14 publications

References 71 publications

Simulating institutional heterogeneity in sustainability science

Simulating institutional heterogeneity in sustainability science

A multimodel analysis of post-Glasgow climate targets and feasibility challenges

Sustainability Education: Capacity Building Using the MUSE Model

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