Policy Option Simulation in Socio-ecological Systems

Mehryar, Sara; Sliuzas, Richard; Schwarz, Nina; Maarseveen, M.F.A.M. van

doi:10.1007/978-3-030-34127-5_30

Cited by 1 publication

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“…Moreover, by summarizing and displaying the effects of interventions, ABM has the potential to become a powerful tool to support decision-making (similar to other GIS-based planning support systems 36 , 37 ) and participation processes. 38 – 40 (6) Finally, once a suitable ABM framework has been built up in a generic, modular way, it can be (re-)used or extended for many purposes, such as new interventions, other cities, and improved calibration through newly available quality data, as has been done with the transport ABM MATSim. 41 …”

Section: The Potential Of Spatial Abm For Uei Assessmentmentioning

confidence: 99%

Agent-based modeling of urban exposome interventions: prospects, model architectures, and methodological challenges

et al. 2022

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With ever more people living in cities worldwide, it becomes increasingly important to understand and improve the impact of the urban habitat on livability, health behaviors and health outcomes. However, implementing interventions that tackle the exposome in complex urban systems can be costly and have long-term, sometimes unforeseen, impacts. Hence, it is crucial to assess the health impact, cost-effectiveness, and social distributional impacts of possible urban exposome interventions before implementing them. Spatial agent-based modeling can capture complex behavior-environment interactions, exposure dynamics, and social outcomes in a spatial context. This paper discusses model architectures and methodological challenges for successfully modeling urban exposome interventions using spatial agent-based modeling. We review the potential and limitations of the method; model components required to capture active and passive exposure and intervention effects; human-environment interactions and their integration into the macro-level health impact assessment and social costs benefit analysis; strategies for model calibration. Major challenges for a successful application of agent-based modeling to urban exposome intervention assessment are (1) the design of realistic behavioral models that can capture different types of exposure and that respond to urban interventions, (2) the mismatch between the possible granularity of exposure estimates and the evidence for corresponding exposure-response functions, (3) the scalability issues that emerge when aiming to estimate long-term effects such as health and social impacts based on high-resolution models of human-environment interactions, (4) as well as the data- and computational complexity of calibrating the resulting agent-based model. Although challenges exist, strategies are proposed to improve the implementation of ABM in exposome research.

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