Tobacco consumption continues to be the leading cause of preventable disease and death in the United States. The Food and Drug Administration (FDA) regulates the manufacture, distribution, and marketing of tobacco products - specifically cigarettes, cigarette tobacco, roll-your-own tobacco, and smokeless tobacco - to protect public health and reduce tobacco use in the United States. Given the strong social component inherent to tobacco use onset, cessation, and relapse, and given the heterogeneity of those social interactions, agent-based models have the potential to be an essential tool in assessing the effects of policies to control tobacco.


Assessing the Use of Agent-Based Models for Tobacco Regulation describes the complex tobacco environment; discusses the usefulness of agent-based models to inform tobacco policy and regulation; presents an evaluation framework for policy-relevant agent-based models; examines the role and type of data needed to develop agent-based models for tobacco regulation; provides an assessment of the agent-based model developed for FDA; and offers strategies for using agent-based models to inform decision making in the future.

Table of Contents


Front Matter
Summary
1 Introduction
2 Tobacco Control Landscape
3 Building Effective Models to Guide Policy Decision Making
4 An Evaluation Framework for Policy-Relevant Agent-Based Models
5 Review of the Social Network Analysis for Policy on Directed Graph Networks Model
6 Data and Implementation Needs for Computational Modeling for Tobacco Control
Appendix A: Considerations and Best Practices in Agent-Based Modeling to Inform Policy--Ross A. Hammond
Appendix B: Agent-Based Models for Policy Analysis--Lawrence Blume
Appendix C: Assessing Agent-Based Models for Regulatory Applications: Lessons from Energy Analysis--Alan H. Sanstad
Appendix D: Committee Meeting Agendas
Appendix E: Committee Biographical Sketches
Index