Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections
Effective enforcement of laws and regulations hinges heavily on robust inspection policies. While data-driven approaches to testing the effectiveness of these policies are gaining popularity, they suffer significant drawbacks, particularly a lack of explainability and generalizability. This paper pr...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Data & Policy |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2632324924000348/type/journal_article |
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| author | Eunice Koid Haiko van der Voort Martijn Warnier |
| author_facet | Eunice Koid Haiko van der Voort Martijn Warnier |
| author_sort | Eunice Koid |
| collection | DOAJ |
| description | Effective enforcement of laws and regulations hinges heavily on robust inspection policies. While data-driven approaches to testing the effectiveness of these policies are gaining popularity, they suffer significant drawbacks, particularly a lack of explainability and generalizability. This paper proposes an approach to crafting inspection policies that combines data-driven insights with behavioral theories to create an agent-based simulation model that we call a theory-infused phenomenological agent-based model (TIP-ABM). Moreover, this approach outlines a systematic process for combining theories and data to construct a phenomenological ABM, beginning with defining macro-level empirical phenomena. Illustrated through a case study of the Dutch inland shipping sector, the proposed methodology enhances explainability by illuminating inspectors’ tacit knowledge while iterating between statistical data and underlying theories. The broader generalizability of the proposed approach beyond the inland shipping context requires further research. |
| format | Article |
| id | doaj-art-ab7bd2ea982c4486b1f2070b1e36f0ae |
| institution | Kabale University |
| issn | 2632-3249 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data & Policy |
| spelling | doaj-art-ab7bd2ea982c4486b1f2070b1e36f0ae2025-01-16T21:51:05ZengCambridge University PressData & Policy2632-32492025-01-01710.1017/dap.2024.34Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspectionsEunice Koid0Haiko van der Voort1https://orcid.org/0000-0002-2795-9444Martijn Warnier2Multi-Actor Systems Department, Delft University of Technology, Delft, The NetherlandsMulti-Actor Systems Department, Delft University of Technology, Delft, The NetherlandsMulti-Actor Systems Department, Delft University of Technology, Delft, The NetherlandsEffective enforcement of laws and regulations hinges heavily on robust inspection policies. While data-driven approaches to testing the effectiveness of these policies are gaining popularity, they suffer significant drawbacks, particularly a lack of explainability and generalizability. This paper proposes an approach to crafting inspection policies that combines data-driven insights with behavioral theories to create an agent-based simulation model that we call a theory-infused phenomenological agent-based model (TIP-ABM). Moreover, this approach outlines a systematic process for combining theories and data to construct a phenomenological ABM, beginning with defining macro-level empirical phenomena. Illustrated through a case study of the Dutch inland shipping sector, the proposed methodology enhances explainability by illuminating inspectors’ tacit knowledge while iterating between statistical data and underlying theories. The broader generalizability of the proposed approach beyond the inland shipping context requires further research.https://www.cambridge.org/core/product/identifier/S2632324924000348/type/journal_articleagent-based modelingdata-driven modelinginspectionsrisk-based regulation |
| spellingShingle | Eunice Koid Haiko van der Voort Martijn Warnier Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections Data & Policy agent-based modeling data-driven modeling inspections risk-based regulation |
| title | Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections |
| title_full | Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections |
| title_fullStr | Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections |
| title_full_unstemmed | Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections |
| title_short | Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections |
| title_sort | agent based modeling for data driven enforcement combining empirical data with behavioral theory for scenario based analysis of inspections |
| topic | agent-based modeling data-driven modeling inspections risk-based regulation |
| url | https://www.cambridge.org/core/product/identifier/S2632324924000348/type/journal_article |
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