Assessing the Use of Agent-Based Models for Tobacco Regulation (2015) / Chapter Skim
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5 Review of the Social Network Analysis for Policy on Directed Graph Networks Model
5 Review of the Social Network Analysis for Policy on Directed Graph Networks Model
Pages 119-142
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From page 119... ...
, with model development beginning in May 2010.1 A major component of the statement of task provided by the FDA to this committee was to review the model, identify its strengths and weaknesses, and make recommendations for its improvement. This chapter describes this model, entitled Social Network Analysis for Policy on Directed Graph Networks (SnapDragon)
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BACKGROUND The SnapDragon model was developed for use by FDA to examine the impact of smoking control policies on certain population smoking metrics, such as prevalence as well as initiation and cessation rates. FDA first directed the model development team to use the model to explore the potential effects of various public education campaigns on the prevalence of tobacco use to help inform its public education efforts.
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, dynamic graphs, and regular graphs such as rings and lattices" (e-mail communication between the IOM staff and SNL staff, August 1, 2014; available upon request from the project public access file)
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Switching between two products is 10 "Weightscan therefore represent the closeness of the relationship (e.g., a best friend can be more highly weighted than friend) , as well as the effectiveness of a media campaign" (e-mail communication between the IOM staff and SNL staff, August 1, 2014; available upon request from the project public access file)
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For example, if Product B is a less suitable replacement for Product A, then the additional opinion boost should be scaled down" (e-mail communication between the IOM staff and SNL staff, August 1, 2014; available upon request from the project public access file)
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SnapDragon Model: Conceptual Overview As noted earlier, opinion dynamics provides the underlying conceptual framework for the SnapDragon model. By basing SnapDragon on opinion dynamics, the modelers are making the explicit assumption that the 14 E-mailcommunication between the IOM staff and SNL staff, August 1, 2014; available upon request from the project public access file.
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. Therefore, a common aim of opinion dynamics models is to identify how the opinions of individual agents are influenced by the opinions of neighboring agents and how they all converge to consensus.15 Conceptually, opinion dynamics stems from sociological and social psychology theories (Cartwright and Harary, 1956; Heider, 1946)
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1–2) wrote, "it is difficult to track and measure how opinions change under experimental conditions, as these changes depend on many social and psychological factors such as the personality of the individuals, their confidence level, their credibility, their social status, or their persuasive power." Existing opinion dynamics models tend to start either from plausible criteria on the effect of social interactions on opinion changes or from established social theories, but there has been a minimal effort to compare the predictions of the models with data on real social dynamics.
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As in many other types of opinion dynamics models, the opinions of agents in SnapDragon can, over time, be influenced by other agents in the environment, either through random connections in a well-mixed, non-networked population or else by interactions within a social network topology, with the latter being what the SnapDragon model uses. However, in bounded confidence models, agents interact with each other only when their opinions are close together -- that is, within certain tolerance bounds; if their opinions are very different from one another, they do not interact (see Equation 1)
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The SnapDragon modeling team adopted this idea of media influence, but again, as described above, instead of assuming a well-mixed population in which the media interact with all individuals in the population at the same time, they defined those interactions within the constraints imposed by a social network. Thus, the media are integrated into the social network topology.
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Although SnapDragon has been designed to evaluate a wide range of tobacco products, for ease of exposition the committee comments on how the structure of the model can accommodate known facts about smoking behavior. Models that describe smoking behavior have traditionally classified individuals by various demographic characteristics (e.g., age and gender)
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inherent in tobacco use as a result of a convergence of opinions about specific tobacco products through the interaction among individuals in the population, guided by the opinion dynamics formulation discussed in the previous section. This overarching assumption supports the use of an agent-based framework to implement the model, as individual interactions are unique to the social network structure in which they occur.
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This formulation could lead to highly unrealistic scenarios. For example, the model implies that two individuals with the same opinion about a tobacco product could exhibit different behaviors (user and nonuser)
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Estimation of time paths is important in tobacco control because the evaluation of policies usually involves the determination of discounted benefits and costs wrought by specific interventions. As the dynamics of smoking behavior carry much inertia, the full effects of tobacco control interventions may take a long time to realize, and thus the time trajectory of smoking rates becomes very relevant.
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. 19 See also communication between the IOM and SNL staff, June 25, 2014; available upon request from the project public access file.
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Such data are not used by SnapDragon to model differences seen across geographic regions or social networks. Although the commit 21 See http://www.cdc.gov/nchs/nhanes.htm (accessed March 2, 2015)
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26 See also communication between the IOM and SNL staff, January 26, 2015; available upon request from the project public access file. 27 These data were collected as part of NIH/NCI grant 3R01CA157577-02S1 (Extending a School-Based Cohort to Improve Longitudinal Modeling)
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33 E-mail communication between the IOM staff and SNL Staff, January 21, 2014, page 3; available upon request from the project public access file.
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It would be relatively straightforward for SnapDragon to use real networks as an input (e.g., Add Health or the data collected by Valente34) , which would have the advantage of including agent attributes within the social network context.
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Conclusion 5-2: The committee concludes that the modeling decision of making interacting opinions about tobacco converge to a weighted average is not supported by evidence and is unlikely to be an accurate representation of tobacco use behavior. Finding 5-2: Whereas some other models based on opinion dynamics have been able to replicate the equilibrium patterns of socially driven processes, the committee has not found applications in which the spe cific time path to equilibrium has been empirically validated.
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1998. Selected cigarette smoking initiation and quitting behaviors among high school students -- United States, 1997.
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2015. Public health implications of raising the minimum age of legal access to tobacco products.
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2006. Simulation modeling and tobacco control: Creat ing more robust public health policies.
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2013. Social networks and smoking: Exploring the effects of peer influence and smoker popularity through simulations.
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