Agent-Based Models

Marchi, Scott de; Page, Scott E.

doi:10.1146/annurev-polisci-080812-191558

Cited by 102 publications

(71 citation statements)

References 76 publications

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“…Axelrod and Epstein both called it a third way of doing science [272,290]: generative and bottom-up and distinct of the usual inductive or deductive approaches. In recent years an increasing number of reviews note their promise for advancing the social sciences [291,292]. And, as we already saw, ABM is increasingly used in economics.…”

Section: Agent-based Modellingmentioning

confidence: 96%

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Hoekstra

Steinbuch

Verbong³

2017

Complexity

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The energy domain is still dominated by equilibrium models that underestimate both the dangers and opportunities related to climate change. In reality, climate and energy systems contain tipping points, feedback loops, and exponential developments. This paper describes how to create realistic energy transition management models: quantitative models that can discover profitable pathways from fossil fuels to renewable energy. We review the literature regarding agent-based economics, disruptive innovation, and transition management and determine the following requirements. Actors must be detailed, heterogeneous, interacting, learning, and strategizing. Technology should be represented as a detailed and heterogeneous portfolio that can develop in a bottom-up manner, using endogenous feedback loops. Assumptions about discount rates and the social cost of carbon should be configurable. The model should contain interactions between the global, national, local, and individual level. A review of modelling techniques shows that equilibrium models are unsuitable and that system dynamics and discrete event simulation are too limited. The agent-based approach is found to be uniquely suited for the complex adaptive sociotechnical systems that must be modelled. But the choice for agent-based models does not mean a rejection of other approaches because they can be accommodated within the agent-based framework. We conclude with practical guidelines.

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Section: Agent-based Modellingmentioning

confidence: 96%

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Hoekstra

Steinbuch

Verbong³

2017

Complexity

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“…As a bottom-up method, ABM integrates micro-macro relationships while accommodating agents' heterogeneity and their adaptive behavior. It ensures that the interaction between the spatial environment and the behavior agents can integrate a variety of data inputs including aggregated, disaggregated and qualitative data [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

et al. 2020

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Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agentbased modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socioenvironmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.

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“…Other emerging analytic strategies in DII science research include the use of health impact assessments and complex systems science methods such as agent-based modeling and microsimulations. 24,32,[40][41][42] Equipped with more knowledge and methodological expertise from CTSAs, public health leaders and staff are positioned and better suited than external researchers or research firms to integrate these approaches in the evaluation of implementation processes related to public health interventions (e.g., policies, system changes, population health programming).…”

Section: Solution: Develop and Utilize DII Methods That Fit The Dynammentioning

confidence: 99%

Dissemination, Implementation, and Improvement Science Research in Population Health: Opportunities for Public Health and CTSAs

Kuo

Gase

Inkelas

2015

Clinical Translational Sci

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Importance The complex, dynamic nature of health systems requires dissemination, implementation and improvement (DII) sciences to effectively translate emerging knowledge into practice. Although they hold great promise for informing multi-sector policies and system-level changes, these methods are often not strategically used by public health. Objectives and Methods More than 120 stakeholders from Southern California, including the community, federal and local government, university, and health services were convened to identify key priorities and opportunities for public health departments and Clinical and Translational Science Awards programs (CTSAs) to advance DII sciences in population health. Main Outcomes Participants identified challenges (mismatch of practice realities with narrowly-focused research questions; lack of iterative learning) and solutions (using methods that fit the dynamic nature of the real world; aligning theories of change across sectors) for applying DII science research to public health problems. Pragmatic steps that public health and CTSAs can take to facilitate DII science research include: employing appropriate study designs; training scientists and practicing professionals in these methods; securing resources to advance this work; and supporting team science to solve complex-systems issues. Conclusions Public health and CTSAs represent a unique model of practice for advancing DII research in population health. The partnership can inform policy and program development in local communities.

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Agent-Based Models

Cited by 102 publications

References 76 publications

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

Dissemination, Implementation, and Improvement Science Research in Population Health: Opportunities for Public Health and CTSAs

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