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Agent-based modeling as a tool for program design and evaluation.

Jennifer A Lawlor1, Sara McGirr1

  • 1Michigan State University, 316 Physics Rd., Rm 262, East Lansing, MI 48824, United States.

Evaluation and Program Planning
|September 3, 2017
PubMed
Summary
This summary is machine-generated.

This paper explores agent-based modeling (ABM), a quantitative tool, for enhancing evaluation practice within systems science. It details ABM applications in stakeholder engagement, intervention selection, and result interpretation, offering practical guidance for evaluators.

Keywords:
Agent-based modelingComplexityEvaluation practiceSystems

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Area of Science:

  • Evaluation
  • Systems Science
  • Computational Social Science

Background:

  • Systems thinking and systems science are increasingly popular in evaluation.
  • Quantitative tools for implementing systems approaches in evaluation are underexplored.

Purpose of the Study:

  • To explore the potential uses of agent-based modeling (ABM) as a quantitative tool in evaluation practice.
  • To demonstrate how ABM can assist evaluators in various stages of the evaluation process.

Main Methods:

  • Definition of agent-based modeling (ABM).
  • Identification of potential uses of ABM in evaluation activities: stakeholder engagement, intervention selection, program theory modeling, performance target setting, and result interpretation.
  • Review of demonstrative examples from published ABM efforts.

Main Results:

  • Agent-based modeling (ABM) offers versatile applications across typical evaluation activities.
  • Examples illustrate ABM's utility in diverse contexts, both within and outside evaluation.
  • Potential pitfalls and cautions for implementing ABM in evaluation are identified.

Conclusions:

  • Agent-based modeling (ABM) presents a valuable quantitative tool for enhancing evaluation practice within systems science.
  • Further formal exploration of ABM and other simulation modeling approaches in evaluation is encouraged.
  • The integration of ABM can advance the sophistication and rigor of evaluation methods.