A Methodology to Develop Agent-Based Models for Policy Support Via Qualitative Inquiry

Vittorio Nespeca (), Tina Comes () and Frances Brazier ()
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Vittorio Nespeca: https://www.linkedin.com/in/vittorionespeca/

Journal of Artificial Societies and Social Simulation, 2023, vol. 26, issue 1, 10

Abstract: Qualitative research is a powerful means to capture human interactions and behavior. Although there are different methodologies to develop models based on qualitative research, a methodology is missing that enables to strike a balance between the comparability across cases provided by methodologies that rely on a common and context-independent framework and the flexibility to study any policy problem provided by methodologies that focus on capturing a case study without relying on a common framework. Additionally, a rigorous methodology is missing that enables the development of both theoretical and empirical models for supporting policy formulation and evaluation with respect to a specific policy problem. In this article, the authors propose a methodology targeting these gaps for ABMs in two stages. First, a novel conceptual framework centered on a particular policy problem is developed based on existing theories and qualitative insights from one or more case studies. Second, empirical or theoretical ABMs are developed based on the framework and generic models. This methodology is illustrated by an example application for disaster information management in Jakarta, resulting in an empirical descriptive agent-based model.

Keywords: Research Design; Simulation Methodology; Empirical Agent-Based Models; Information Diffusion; Information Management; Crisis Management (search for similar items in EconPapers)
Date: 2023-01-31
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2021-152-3

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