Assessing the Use of Agent-Based Models for Tobacco Regulation (2015) / Chapter Skim
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3 Building Effective Models to Guide Policy Decision Making
3 Building Effective Models to Guide Policy Decision Making
Pages 63-86
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understanding the conditions under which models -- specifically i ndividual-level models -- are appropriate and useful in aiding policy decisions; (2) elucidating the empirical and theoretical challenges of specifying model inputs and interpreting model outputs appropriately; and (3)
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The committee suggests methodological strategies for specifying individuals' behaviors within micro-level models and for assessing how uncertainty in model inputs translates into uncertainty in model outputs. THE CHALLENGE OF ANTICIPATING AND UNDERSTANDING POLICY EFFECTS Policies can backfire when they fail to account for how people change their behavior in response to an intervention.
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By removing the possibility of selection bias, RCTs provide a more rigorous test of treatment effects than do observational studies. Information gleaned from RCTs alone is often insufficient for guiding policy decision making.
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. Structural Models Structural models use a set of equations or rules -- expressed analytically or computationally in programming code -- to describe different possible worlds.
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. One challenge in using structural models effectively is specifying them in a way that is empirically defensible and that allows for a clear and rigorous quantification of the assumptions embedded in the model (NRC, 2014)
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This section reviews the theoretical and empirical literature on mechanisms governing contingent behavior and suggests some ways in which these insights might be fruitfully applied in the domain of tobacco regulation. Note that here the focus is on mechanisms that occur "above the skin" (for example, environmental or societal factors)
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Moreover, although the cases outlined above represent d ifferent instances of endogenous effects, people who share the same social context may display similar behavior even in the absence of these social interactions. For example, similar behavior may arise from contextual effects, which refer to the way in which people's behavior is shaped by a shared social environment, such as neighborhood composition or school quality.
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Identifying true "social contagion" effects requires separating out the effects of homophily (people's tendency to select others who resemble them on observed or unobserved attributes) and shared social environment from the effects of social influence (Aral et al., 2009; Shalizi and Thomas, 2011)
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Later in this chapter, strategies for specifying models of behavior that try to account for these motivational factors are discussed. Conclusion 3-1: The committee concludes that a deep understanding of human behavior, decision making, and incentive structures is important for agent-based models and other models that are used to under tands how interdependent behaviors shape the outcomes of a given policy.
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Conclusion 3-1 is most relevant when models will be used to inform policy decisions. Models that do not include an understanding of human behavior, decision making, and incentive structures can be informative for some purposes.
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t OVERVIEW OF TYPES OF STRUCTURAL MODELS FOR INFORMING POLICY DECISIONS Thus far, the committee has discussed the role that models can play in guiding policy decisions, and it has reviewed some of the behavioral mechanisms that can lead to feedback between individuals' behavior and the social and regulatory environments. This section provides a high-level overview of the types of models that are used to capture this type of feedback behavior.
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This is not necessarily bad. Analytical models have several advantages over computational models.
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Analytical models allow researchers and policy makers to take both factors -- which equilibria are possible and which are most likely to be sustained -- into account. This is much harder to do with simulation models, which may not identify highly unstable equilibrium solutions.
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For tobacco control policy, not much is known about the time scales over which equilibria may be reached. An example of when it might make sense to examine only equilibrium conditions is a model of the effect of price on smoking prevalence, which falls rapidly following a price increase.
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. Microsimulation and Agent-Based Models Within the domain of individual models, some scholars distinguish semantically between two types of models: microsimulation and agentbased models.
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For instance, micro imulation s models typically keep the agents' environments simple and abstract, as these models are anchored in even simpler analytical models for which the dynamics are well understood. ABMs are sometimes grounded in analytical models, but this is not standard practice.
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It is important to note, however, that no matter what level is chosen, models provide only an imperfect representation of the real world, as computational models in general are not reality mirrors, nor are they intended for this purpose. ABMs can represent anything from low-dimensional, abstract worlds where agents are defined by just one or two attributes and interact in a highly stylized environment based on simple rules, to high-dimensional, highly detailed worlds where agents have many attributes, the environment contains a great deal of information, and agents engage in multiple behaviors (Bruch and Atwell, 2013)
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, discrete choice statistical models provide a useful framework for developing an empirically grounded representation of agents' choice behavior (Ben-Akiva and Lerman, 1985; McFadden, 1974)
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. For an example of contemporary discrete choice models that incorporate decision makers' cognitive strategies to reduce the demands of evaluating potential options, see Gilbride and Allenby (2004)
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For example, if experiments reveal a systematic bias in how people perceive their environment, an adjustment could be made to the inputs of a statistical model of behavior. MODEL UNCERTAINTY AND POLICY DECISION MAKING As should be obvious from the discussion thus far, models cannot predict the future with certainty.
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Moreover, models must provide some account of how uncertainty in model inputs translates into uncertainty in model outputs. To use these models effectively, policy makers will likely need to develop a rule for translating these uncertain predictions into a policy decision.
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Paper commissioned by the Com mittee on the Assessment of Agent-Based Models to Inform Tobacco Product Regulation (see Appendix B)
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Paper commissioned by the Committee on the Assessment of Agent-Based Models to Inform Tobacco Product Regulation (see Appendix A)
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Paper commissioned by the Committee on the Assessment of Agent Based Models to Inform Tobacco Product Regulation (see Appendix C)
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