Governance of Transitions. A Simulation Experiment on Urban Transportation

Weyer, Johannes; Adelt, Fabian; Hoffmann, Sebastian

doi:10.1007/978-3-030-30298-6_9

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…According to analytical sociology, SimCo tries to explain system dynamics as the emergent result of the interactions of heterogeneous agents, making autonomous decisions. SimCo has been used for various experiments on risk management and system transformation, mostly in road transportation (Philipp and Adelt 2018;Weyer et al 2019). Several what-if scenarios have been investigated analyzing the effects of external interventions on individual behavior, especially on mode and route choice (Adelt and Hoffmann 2017).…”

Section: The Simulation Framework Simcomentioning

confidence: 99%

Modeling sustainable mobility: Impact assessment of policy measures

Weyer

Adelt

Philipp

2023

TATuP

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Sociologically based models of complex systems can help estimate the impact of policy measures on individuals and explain the resulting system dynamics. Using the example of the Ruhr region and the mobility of the people living there, the article demonstrates the concept of agent-based modeling, which draws on assumptions from analytical sociology and distinguishes between different types of actors. Simulation experiments conducted as part of the InnaMoRuhr project show significant differences in the behavior of these types, especially in their response to policy interventions. Policymakers should take this into account when planning and designing measures aimed at sustainable transformation.

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Section: The Simulation Framework Simcomentioning

confidence: 99%

Modeling sustainable mobility: Impact assessment of policy measures

Weyer

Adelt

Philipp

2023

TATuP

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Section: The Simulation Framework Simcomentioning

confidence: 99%

Modeling for policy: Challenges for technology assessment from new prognostic methods

Kaminski

Gramelsberger

Scheer

2023

TATuP

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Modeling for policy has become an integral part of policy making and technology assessment. This became particularly evident to the general public when, during the COVID-19 pandemic, forecasts of infection dynamics based on computer simulations were used to evaluate and justify policy containment measures. Computer models are also playing an increasing role in technology assessment (TA). Computer simulations are used to explore possible futures related to specific technologies, for example, in the area of energy systems analysis. Artificial intelligence (AI) models are also becoming increasingly important. The results is a mix of methods where computer simulations and machine learning converge, posing particular challenges and opening up new research questions. This Special topic brings together case studies from different fields to explore the current state of computational models in general and AI methods in particular for policy and TA.

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Safe Transitions in Complex Systems

Weyer

2024

SpringerBriefs in Applied Sciences and Technology

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Complex systems, including energy and transportation systems, constitute a crucial part of modern societies’ critical infrastructure. It is imperative to ensure their stability even during periods of crisis or fundamental transformation, such as sustainability transformation. It is difficult to anticipate how individuals will respond to policy interventions aimed at preserving stability, for example, by banning cars from congested roads, or to policy interventions aimed at fundamentally altering the system, for instance, by promoting renewable energies. A conflict of interest may occur at both an individual and institutional level if sustainability measures, such as increasing the number of electric vehicles or photovoltaic systems, jeopardize the stability of the system, for example, by increasing grid volatility. Furthermore, research into complex systems has demonstrated that they tend to develop nonlinearly rather than linearly, making them difficult to predict. Agent-based modeling (ABM) has emerged as a valuable method to comprehend the dynamics of complex socio-technical systems. Moreover, ABM enables us to anticipate future outcomes and evaluate the effectiveness of different policy measures aimed at enhancing safety or promoting sustainability (or both). The chapter briefly introduces the ABM concept and the SimCo simulation framework, developed at TU Dortmund University. SimCo is grounded in analytical sociology, focusing on people’s everyday practices, bounded-rational decision-making and on governance concerns. Additionally, this chapter will present the outcomes of several simulation experiments to address the question of how to achieve safe transformations of complex systems.

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Governance of Transitions. A Simulation Experiment on Urban Transportation

Cited by 4 publications

References 7 publications

Modeling sustainable mobility: Impact assessment of policy measures

Modeling sustainable mobility: Impact assessment of policy measures

Modeling for policy: Challenges for technology assessment from new prognostic methods

Safe Transitions in Complex Systems

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