Assessing the Use of Agent-Based Models for Tobacco Regulation (2015) / Chapter Skim
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Appendix A: Considerations and Best Practices in Agent-Based Modeling to Inform Policy--Ross A. Hammond
Appendix A: Considerations and Best Practices in Agent-Based Modeling to Inform Policy--Ross A. Hammond
Pages 161-194
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is a powerful tool that is being used to inform policy or decisions in many fields of practical importance. Recent examples include land-use and agricultural policy (Berger et al., 2007; Berger and Troost, 2014; Brady et al., 2012; Guzy et al., 2008; Happe et al., 2008; Happe et al., 2006; Heckbert, 2011)
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The particular advantages of ABM come from its flexibility, which can help model designers and users to manage three particular challenges that complexity poses for researchers and policy makers alike: heterogeneity, spatial structure, and adaptation. Heterogeneity Real-world complex systems are often characterized by substantial heterogeneity among individuals.
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By directly incorporating sophisticated spatial elements, ABM can effectively model dynamics that result from exposures across space and time (such as advertising or air pollution exposure) , patterns of contact between individuals (central to epidemic spread or social influence through networks)
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. Policy Resistance Heterogeneity, spatial structure, and adaptation all complicate analysis, and many analytical approaches struggle to address one or more of these features.
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and models designed to engage with or inform policy and to address policy resistance (Berger et al., 2007; Brown et al., 2005a; Brown et al., 2005b; Dawid and Fagiolo, 2008; Farmer, 2000; Guzy et al., 2008; Happe et al., 2008; Happe et al., 2006; Heckbert, 2011; LeBaron and Winkler, 2008; Magliocca et al., 2014; Schlüter and Pahl-Wostl, 2007; Sun et al., 2014)
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Explicit models are more easily tested, both for internal consistency and for external fidelity. Models can also be especially useful tools when fielding real-world experiments to inform policy choice is difficult, overly expensive, time-consuming, unethical, or impractical.
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ABMs that inform policy fall into three distinct categories: prospective policy models, retrospective policy models, and indirect policy models. Prospective policy models (also sometimes called ex ante models)
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ABM offers extensive capabilities for understanding etiology, bidirectional relationships between system structure and individual behavior over time, and the operation of pathways that cross levels of scale. This type of model generally does not contain any explicit representation of policies or interventions and thus does not directly simulate the potential impacts of policy choices.
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. One early MIDAS model that provides clear illustration of the prospective use of ABM to inform policy design can be found in work on smallpox preparedness (Burke et al., 2006; Epstein, 2004; Longini Jr et al., 2007)
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Earlier work in MIDAS helped to make possible the development and deployment of sophisticated models that were needed to inform policy response at both national and regional levels during the crisis. As in the case of smallpox, a primary use of ABM was for prospective consideration of varying mixtures of policy options across various contexts, with a clearly defined objective of effectively containing the epidemic.
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The goal is to provide an in silico policy laboratory to understand the potential effects (intended or unintended) of retailer-based policy options such as zoning, licensing, and type-specific retailer density reduction across a variety of contexts and over both the short term and the longer term.
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Another group of recent ABM papers focuses on the role of social networks and social influence in obesity, elucidating potential dynamic mechanisms through which social influence occurs (and which may potentially be harnessed for interventions) (Bahr et al., 2009; Hammond and Ornstein, 2014; Zhang et al., 2014)
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-- and understanding the number of design choices implicit in these building blocks helps to motivate some of the best practices discussed below (see section 4.3)
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174 FIGURE A-1 PARTE framework.
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By representing each individual actor as a separate software object, ABM allows enormous flexibility to capture heterogeneity across agents in their properties (see section 1.2)
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Rules are the central drivers of model dynamics, defining how agents choose an action, update properties, and interact with each other and their environment. Rules in an ABM can: • Take as an input the current or past value of Properties (an agent's own, those of others, or those of the environment)
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to highly complex ones (often empirically informed) such as a GIS shape file or a network structure.
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, the process of constructing and using an ABM generally follows six key steps. These steps are not unique to ABM -- they are shared with many other forms of computational modeling -- but several steps raise particular considerations for ABM.
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Some are specific to policy-aimed modeling; others are general best practices for good ABM (or even for modeling in general) but may have special relevance or importance when the aim is to inform policy.
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A clear statement of question also helps model designers to ensure that the method chosen is appropriate and well suited -- ABM may not always be the best choice (for guidance on when to choose ABM, see section 1.2 above and also Axelrod, 1997a; Axelrod, 2004, 2006a; Axelrod and Tesfatsion, 2006; Heckbert et al., 2010)
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3. Model Specification This step involves operationalizing the model "ingredients" in an implementation-ready way, moving from a conceptual design to a specific and explicit sketch of the model.
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Because software for developing ABM is not standardized and is often open-source and continually evolving, it is important to check whether any design choices or algorithms are "hard-coded" by default. The implementation stage can also lead to tension between the conceptual design and goals of the model (steps 1–3)
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represents concepts and meets design goals appropriately. Boundary-adequacy tests and extreme-event tests can help to uncover flaws in the model specification that result in dynamics that, for example, violate face validity or clash with conceptual design and require revisiting step 3 in the process.
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and sometimes specific implementation choices. BP9: Conduct thorough and appropriate sensitivity analysis.
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. Although increasing computational power makes conducting thorough sensitivity analysis easier, the importance of this step can act as a practical limit on model complexity and helps to motivate BP3.
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ABM in particular can often lend itself to very visual depictions of model dynamics, and designing and executing effective visualization can often be a time-consuming process (and may involve addi tional computer programming)
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With these in mind, a few guidelines arise for decisionmakers who wish to use modeling as an input into the decision process. Early engagement with the modeling effort can be helpful in communicating the goal or question of interest to model designers and in ensuring that the fit between the desired use of the model and the method and design of the model is appropriate.
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5. Conclusion This paper has reviewed the many potential uses of ABM to inform policy or decision making, the features of the technique that make it com
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1999. Coordination in transient social networks: An agent based computational model of the timing of retirement.
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2004. Modelling disease outbreaks in realistic urban social networks.
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2014. A model of social influence on body mass index.
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American Political Science Review 86(4)
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2014. Leveraging social influence to address overweight and obesity using agent-based models: The role of adolescent social networks.
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