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Agent-Based Modeling: an Underutilized Tool in Community Violence Research
PURPOSE OF REVIEW: Community violence is a serious public health problem, and generational investments are being made to address it. Agent-based models (ABMs) are computational tools that can help to optimize allocation of those investments, analogous to how computer simulation models, broadly, have...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9263807/ https://www.ncbi.nlm.nih.gov/pubmed/35821794 http://dx.doi.org/10.1007/s40471-022-00292-x |
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author | Goldstick, Jason E. Jay, Jonathan |
author_facet | Goldstick, Jason E. Jay, Jonathan |
author_sort | Goldstick, Jason E. |
collection | PubMed |
description | PURPOSE OF REVIEW: Community violence is a serious public health problem, and generational investments are being made to address it. Agent-based models (ABMs) are computational tools that can help to optimize allocation of those investments, analogous to how computer simulation models, broadly, have informed decision making in other fields, such as infectious disease control. In this review, we describe ABMs, explain their potential role in community violence research, discuss recent studies that have applied ABMs to community violence, and point to opportunities for further progress. RECENT FINDINGS: We identified three recent studies that applied ABMs to community violence research, which points to the paucity of this line of work. Each of these works leverages a major advantage of ABMs—their ability to study the natural evolution of a process governed by the actions of autonomous agents, and how that evolution changes under counterfactual conditions, such as different intervention strategies (e.g., violence interruption), and policy changes (e.g., alcohol outlet licensing policies). SUMMARY: ABMs continue to be an underutilized tool for the study of community violence. Their increased use could add important information to help stakeholders decide between competing intervention strategies in terms of their costs and the overall resulting changes in violence rates. In addition, ABMs have value in identifying unintended changes/diffusions resulting from interventions. Regardless of the application, ABMs can only be impactful if stakeholders believe and use the information, pointing to the importance of engaging policy makers and other stakeholders in the model formulation process when possible. |
format | Online Article Text |
id | pubmed-9263807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-92638072022-07-08 Agent-Based Modeling: an Underutilized Tool in Community Violence Research Goldstick, Jason E. Jay, Jonathan Curr Epidemiol Rep Injury Epidemiology (A Rowhani-Rahbar, Section Editor) PURPOSE OF REVIEW: Community violence is a serious public health problem, and generational investments are being made to address it. Agent-based models (ABMs) are computational tools that can help to optimize allocation of those investments, analogous to how computer simulation models, broadly, have informed decision making in other fields, such as infectious disease control. In this review, we describe ABMs, explain their potential role in community violence research, discuss recent studies that have applied ABMs to community violence, and point to opportunities for further progress. RECENT FINDINGS: We identified three recent studies that applied ABMs to community violence research, which points to the paucity of this line of work. Each of these works leverages a major advantage of ABMs—their ability to study the natural evolution of a process governed by the actions of autonomous agents, and how that evolution changes under counterfactual conditions, such as different intervention strategies (e.g., violence interruption), and policy changes (e.g., alcohol outlet licensing policies). SUMMARY: ABMs continue to be an underutilized tool for the study of community violence. Their increased use could add important information to help stakeholders decide between competing intervention strategies in terms of their costs and the overall resulting changes in violence rates. In addition, ABMs have value in identifying unintended changes/diffusions resulting from interventions. Regardless of the application, ABMs can only be impactful if stakeholders believe and use the information, pointing to the importance of engaging policy makers and other stakeholders in the model formulation process when possible. Springer International Publishing 2022-07-08 2022 /pmc/articles/PMC9263807/ /pubmed/35821794 http://dx.doi.org/10.1007/s40471-022-00292-x Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Injury Epidemiology (A Rowhani-Rahbar, Section Editor) Goldstick, Jason E. Jay, Jonathan Agent-Based Modeling: an Underutilized Tool in Community Violence Research |
title | Agent-Based Modeling: an Underutilized Tool in Community Violence Research |
title_full | Agent-Based Modeling: an Underutilized Tool in Community Violence Research |
title_fullStr | Agent-Based Modeling: an Underutilized Tool in Community Violence Research |
title_full_unstemmed | Agent-Based Modeling: an Underutilized Tool in Community Violence Research |
title_short | Agent-Based Modeling: an Underutilized Tool in Community Violence Research |
title_sort | agent-based modeling: an underutilized tool in community violence research |
topic | Injury Epidemiology (A Rowhani-Rahbar, Section Editor) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9263807/ https://www.ncbi.nlm.nih.gov/pubmed/35821794 http://dx.doi.org/10.1007/s40471-022-00292-x |
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