[Page numbers followed by b, f, or t refer to boxed text, figures, or tables, respectively.]
A
ABMs. See Agent-based models
Add Health. See National Longitudinal Study of Adolescent Health
Addiction
individual differences in risk of, 37
modeling of, 46, 122, 131, 153
progression of smoking behaviors to, 48
Agent-based models
applications, 2–3, 20, 25, 161, 164–166, 199–200, 217–218
best practices in development of, 9, 98, 180–187
case examples, 101–105, 168–172, 199–202, 229–237
Center for Tobacco Products objectives for, xvi, 3–4, 25–26
communicating results from, 9–10, 90f, 92f, 99–100, 105, 106, 111, 113, 186–187
complex adaptive systems and, 195, 196, 198–199, 205
definition of agent in, 2, 195
design of experiments for, 66, 67, 179t, 185
as deterministic nonlinear models, 208–209
drawing conclusions from, 186
error-checking in implementation of, 182–183
evaluation of, 9, 98. See also Evaluation framework for ABMs
goals for the Institute of Medicine review of, 1–2, 2b, 20b, 21, 27–28
investigating surprising results of, 185
microsimulations and, 6–7, 77–79
misperceptions about, 188
model testing, 92f, 97, 111. See also Validation issues in modeling
for policy design and evaluation, 27, 87–88, 166–168, 169, 207–209
potential agent types in, 152–153
purpose of, xvi, 25, 161, 162–163, 205
rationale for use of, 4–5, 52–55
recommendations for development of, 6, 8, 10, 72, 80, 99–100
sensitivity analysis in, 6, 8, 71, 83, 92f, 179t, 184–185, 227–228, 241
stakeholder involvement in development of, 155–156
testing and calibration, 92f, 97, 111, 184
validation issues, 123–124, 130–133, 205–207. See also Validation issues in modeling
See also Computational models; Design of ABMs; Evaluation framework for ABMs; Individual-level models; Microsimulation; Structural models
Aggregate models, 4, 21, 26b, 52–53, 72, 76–77. See also Macro-level models; Population models
American Time Use Survey, 151
At-risk populations, 37. See also Lesbian, gay, bisexual, and transgender persons; Race; Socioeconomic status
B
BENESCO model, 26b
Biological systems as agents in ABMs, 153
Blowback, 64. See also Policy resistance; Unintended consequences
Bounded confidence opinion dynamics model, 125, 127–128. See also Deffuant–Weisbuch model
C
Calibration, 136, 177, 179t, 182, 184, 205, 206, 223, 224–227, 228–229, 239–240. See also Validation issues in modeling
Campaign Tobacco Free Kids, 35–36
Cancer Intervention and Surveillance Modeling Network, xv, 26b, 155–156
CANSAVE, 26b
Center for Tobacco Products
data collection for ABM, 15, 154–155
goals for ABM development, xiv, 3–4, 25–26
objectives for using ABM, 1–2, 2b, 19–20, 27–28
recommendations for, 6, 8, 10, 15, 16, 72, 80, 99–100, 139, 154, 155
use of modeling by, xv–xvi, 1–2, 20, 24
Centers for Disease Control and Prevention, 35–36, 43
Cessation of tobacco use
data sources, 144
identifying social interaction effects on, 70
individual differences in, 37
modeling efforts to date, 26b, 52–53
modeling of influences on individual behavior, 46–47
patterns of, 50
physiologic processes in, 50–51
rationale for agent-based models of, 54
social context of, 51
CISNET. See Cancer Intervention and Surveillance Modeling Network
Cognitive–behavioral modeling, 46
Committee on the Assessment of Agent-Based Models to Inform Tobacco Product Regulation, xvi, 1–2, 2b, 19–20, 20b, 21–22
Communication
as ABM evaluation category, 9–10, 99, 111–112, 113
of ABM findings, 9–10, 90f, 92f, 99–100, 105, 106, 111, 113, 186–187
Compartmental models. See Aggregate models; Population models
Complex adaptive systems, 31–32, 213–214, 239
ABMs and, 195, 196, 198–199, 205
epidemiologic example, 196–199
specification of agent behavior in, 196, 199
Computable general equilibrium, 219–220
Computational models, 1, 19, 25, 73–75.
See also Agent-based models
Conceptual frameworks, 46
for model evaluation, 92f, 112
for model formation, 95, 97, 105
in SnapDragon model, 124
Conclusions on
ABM and microsimulation, 7, 78–79
current data collection, 15, 154
quantifying uncertainty in models, 8, 83
SnapDragon model, 13–14, 137–139
Constraint interactions, 68
Continuous Opinions and Discrete Actions, 128–129
Correlated effects, 69
CTP. See Center for Tobacco Products
Current Population Survey, 134, 144, 151
D
Data
existing sources and tools for tobacco, 143–149, 145–146b
to inform ABM, 7, 14–15, 53–54, 79, 80–82, 89, 94, 97, 101–104, 133–135, 143, 150–155, 170–171, 184, 200–201
recommendations for, 15, 154–155
SnapDragon use of, 123–124, 133–137
strategies for improving, 14–15, 149–153, 154
types of, for tobacco research and modeling, 143
Data needs for tobacco control ABM, 149–153
Deeming of tobacco products, 24, 35, 42
Deffuant–Weisbuch model, 127–128. See also Bounded confidence opinion dynamics model
Descriptive output validation, 205
Design of ABMs
agent perspective in, 180
conceptual stage, 93, 110, 179–180
documentation, 184
environmental specifications, 177–178
evaluation of, 87–88. See also Evaluation framework for ABMs
factors to be considered in modeling individual behavior in, 45–48
level of detail, 7–8, 67–68, 78–79, 153, 183, 210–211, 214, 230, 237, 239–241
parameter estimation, 67–68, 94, 102, 132, 136, 138, 206–207, 208, 210–211
for policy development, 27, 54–55, 68, 87, 166–168, 187–188
recommendations for expertise in, 72–73, 155
repurposing, 188
role of model consumers in, 8, 54–55, 75, 83, 88, 95, 103, 155, 187–188
specifying agent characteristics in, 173–175, 195–196
specifying individual behavior in, 80–82, 175–176
time specifications, 176–177, 208
understanding of human behavior for, 5–6, 71–72, 83
Drug Policy Modelling Program, 105, 155–156
Dynamic Stochastic General Equilibrium modeling, 206–207
E
E-cigarettes, 23, 24, 35, 36, 42. See also Deeming of tobacco products; Smokeless tobacco products
Ecological momentary sampling and assessment, 151, 170
Ecological perspective, 47–48, 47f
Economic implications of tobacco use, 23, 24, 34f, 45
Economic models of tobacco use behaviors, 47
Emergent behaviors, 130, 135, 196, 200, 206, 209
Energy Information Administration, 220, 224–225
Energy modeling
assessment, 224
calibration and uncertainty, 223, 224–228, 238–239
complexity and validity, 228–229
lessons from, for ABM design, 239–240, 242
Energy Modeling Forum, 155–156
Equilibrium modeling, 73–76, 219–220
multiple equilibria, 74
Ethnographic research, 82, 103–104, 151, 200
European Smoking Prevention Framework, 147
Evaluation framework for ABMs, 28
activities, 93–95, 110–112, 124
conceptual basis, 91
descriptive grammar, 88–90, 89–90t, 106–108t
evaluation questions, 91, 108–114
high-priority questions, 97–98, 108–114
implementation review, 110–111
model suitability, 9–10, 94, 98–99
outputs specification, 95, 112–113
policy outcomes, 10, 99, 112–113
recommendation for, 10, 99–100
technical best practices, 9–10, 98–99
Expectations interactions, 68–69
Expertise, 15–16, 72–73, 93, 109, 155. See also Interdisciplinary modeling team; Modeling, generally; Subject-matter experts
F
Family Smoking Prevention and Tobacco Control Act, 3, 24, 32–35, 38, 42, 44, 148
main provisions, 39b
Federal agencies in tobacco regulation, 43–44. See also U.S. Food and Drug Administration
Feedback mechanisms
aggregate modeling, 77
complexity of, in tobacco landscape, 32, 34f
individual–social behavior, 5–6, 68–73, 74, 77
policy effects, 64
in SnapDragon model, 12, 13, 130–131, 138
Findings on
SnapDragon model, 13–14, 137–139
value of ABMs for tobacco control policy, 5, 54
Food, Drug, and Cosmetic Act, 35, 42
Fundamental evaluation categories, 9, 98
Funding as ABM resource, 93
G
Genetic factors in tobacco use, 47
Geographic context, 163
Grammar, ABM, 88–90, 89–90t, 106–108t
Graphic warning labels, 32–35. See also Warning labels
H
Health belief model, 46
Health departments as agents in ABMs, 152
Health risks, smoking-associated, 3, 23, 31, 50–51, 129
High-dimensional modeling, 7–8, 79–80
Human resources, 15–16, 72–73, 93, 109, 155. See also Interdisciplinary modeling team; Subject-matter experts
I
Identification, 69–70, 207–209
Indian Country, 45
Indian Health Service, 43
Individual behavior
current conceptualizations of, in tobacco use, 45–51
data needs for modeling of tobacco use, 14–15, 149–153
importance of, in modeling tobacco use and control, 4–5, 52–54
representations in ABMs, 5–6, 8, 71–72, 83, 196
strategies for specifying, in ABMs, 80–82
See also Social context of tobacco use
Individual-level data, 133, 134, 143
Individual-level models, 7, 63, 77–78. See also Agent-based models; Micro-level models; Microsimulation
Infectious disease modeling, 169–170, 196–199, 201–202, 205, 209–210
Initiation of tobacco use
data sources, 144
e-cigarettes, 23
individual differences in, 51
modeling efforts to date, 26b, 52–53
motivation of individuals for, 37
patterns and trends, 3, 48, 132
progression to regular use, 48
rationale for agent-based models of, 52–54
Interdisciplinary modeling team
information needs of, 93
policy development and, 99, 106
role of, in model development, 17, 97, 157
Ising spin models, 125
K
L
Legacy, 36
Legacy Tobacco Documents Library, 149
Lesbian, gay, bisexual, and transgender persons, 37, 41
Low-dimensional modeling, 7–8, 79–80
M
Macro-level models, 66. See also Aggregate models; Population models
Marketing. See Tobacco marketing
Marschak’s stability requirement, 204
Mental illness, smoking and, 37
Micro-level models, 63–64. See also Agent-based models; Individual-level models; Microsimulation
Microsimulation, 6–7, 26b, 66, 77–79
MIDAS. See Models of Infectious Disease Agent Study
Misperceptions about ABMs, 188
Model suitability, 9–10, 98–99
in SnapDragon evaluation, 129–133
Modeling, generally
current and past approaches to in tobacco control, xv–xvi, 26b, 52–53
expertise for, 15–16, 72–73, 155
individual versus aggregate level specification, 76–77
quantifying uncertainty in, 8, 83
Models of Infectious Disease Agent Study, 155–156, 165, 169–170, 187
N
National Energy Modeling System, 220–221, 222f
National Longitudinal Study of Adolescent Health (Add Health), 81, 144, 147, 149
National Youth Tobacco Survey, 144, 145, 148
New tobacco products, regulation of, 40, 42. See also E-cigarettes
O
Obesity, models of, 3, 25, 166, 172
Opinion dynamics, 11, 12, 14, 121–122, 124–129, 130–133, 138. See also Social Network Analysis for Policy on Directed Graph Networks (SnapDragon)
P
Packaging, the U.S. Food and Drug Administration authority for, 40–41. See also Warning labels
Parameters in models, 1, 67–68, 74, 94, 178, 179t
PARTE framework, 94, 173–178, 174f
PATH. See Population Assessment of Tobacco and Health
Peer influences, 49, 69–70, 81–82 data sources, 147, 149
Peer review of ABM, 10, 93, 100, 112
Physical space in ABM, 88, 89t
Pipe tobacco, 42
Plug-in electric hybrid vehicles, 101–103, 106–108t
Policy design and implementation
ABM design for, 54–55, 79–80, 87, 166–168, 187–188
blowback, 64. See also Policy resistance case example of ABM development for, 101–103, 168–172
challenge of anticipating effects of, 64–65
challenges using ABMs for, 207–209
computational modeling for, 1, 19, 25, 53, 73–75
data on policy interaction effects, 148–149
evaluation of ABM effectiveness in, 1–2, 3–4, 10, 19, 21, 99, 112–113
historical use of tobacco modeling for, 26b
identifying social interaction effects for, 68–71
indirect policy models, 168, 172
prospective models, 167–168, 169–171, 181
recommendations for model development to support, 16, 56
retrospective models, 168, 171, 181
structural modeling for, 73–79, 204–205
use of ABMs for, 1, 2–3, 4, 16–17, 20, 53–54, 68, 77–78
use of multiple models and methods in, 187
Policy realism in ABM, 90t
Policy testing, 92f, 94–95, 111
Population Assessment of Tobacco and Health, 147–148
Population models, xvi, 4, 19, 25, 26f, 52.
See also Aggregate models
Predictive output validation, 205–206
Preference interactions, 69
Prevalence of tobacco use
among specific populations, 37, 49
estimates by young people of, 49
modeling efforts to date, 26b
price sensitivity, 76
Prospective policy model, 25, 167–168, 169–171, 181
Public education, the U.S. Food and Drug Administration authority for, 41
Public health, ABM use in, 165–166, 169–171, 172
Public health standard for review of tobacco products, 24
Q
Qualitative data, 82, 133–134, 143, 151
Quantitative aggregated data, 80–82, 133, 134–135, 143, 152
R
Race, 37, 51, 134, 148, 152, 162
Randomized controlled trials, 65–66
“Real Cost, The,” 41
Recommendations on
high- and low-dimensional modeling, 8, 80
use and development of models, 16, 156
use of findings from models, 8, 83
Reinforcement of smoking behaviors, 51
Relapse of tobacco use, 5, 50–51, 53, 132
Resources
for development and use of models, 15–16, 72–73, 155
as evaluation criteria for ABMs, 9, 91–93, 98, 109–110
Retrospective policy models, 168, 171, 181
S
Sales, tobacco. See Tobacco distribution and sales
SAMMEC model, 26b
Sandia National Laboratories, xvi, 3–4, 25–27, 119, 120–121
Schelling segregation model, 72, 200
Sensitivity analysis, 6, 8, 71, 82–83, 92f, 179t, 184–185, 227–228, 241
SIENA, 147
SIS process, 196–199, 203, 209–210
Smallpox epidemic, modeling of, 169–170, 201–202, 205
Smokeless tobacco products, 3, 23, 24, 38, 40–41, 44–45. See also Deeming of tobacco products; E-cigarettes
SnapDragon. See Social Network Analysis for Policy on Directed Graph Networks
Social cognitive theory, 46
Social context of tobacco use
cessation and, 51
data sources, 144–147, 149–150
ecological perspective, 47–48, 47f
rationale for ABMs to study, 4–5, 53–54, 164
significance of, among youth, 49–50
Social dynamics, 81, 89t, 126, 162
Social interactions, modeling of, 5, 52–54, 68–70, 81–82
Social multipliers, 64
Social Network Analysis for Policy on Directed Graph Networks (SnapDragon)
calibration and verification, 136–137
conclusions and findings from review of, 12–14, 137–139
current state of, xvi, 10–11, 120–121
descriptive grammar applied to, 106–108t
health risk modeling in, 129–130
imitation, smoking adoption, 12–13, 131
lack of feedback mechanism from behavior to opinion in, 12, 13, 130–131, 138
media influence in, 120, 123, 128
opinion dynamics, 121–122, 124–129, 130–133, 138
product switching in, 122–123, 136
recommendations on use of, 14, 139
structure and dynamics, 11–12, 121–124
suitability evaluation, 129–133
time path to equilibrium in, 132–133
use of social networks in, 11, 121, 123, 127–128
user classification and status in, 121, 129
Social psychology, 125
Social space in ABM, 89t
Socioeconomic status, 37, 51, 134, 152, 162
Sociophysics, 125
State and local governments
as agents in ABMs, 152
regulatory authority of, 42, 44–45
revenue from tobacco for, 36–37
Subject-matter experts, xvii, 6, 17, 72, 92f, 93, 155, 186
recommendations for, 6, 72, 155
Substance abuse, 103–105, 200–201, 209
System dynamics models, 4, 5, 21, 52, 66, 87
T
Taxes, tobacco, 36–37, 44, 45, 64, 71, 78
Time-use data, 151
Tobacco Centers of Regulatory Science, 15, 155
Tobacco control
agents and relationships in, 33f, 34f, 35–37, 43–44
challenges for the U.S. Food and Drug Administration, 34–36
challenges to anticipating policy effects, 64–66
federal agency authority of, 38–44
modeling efforts to date, 1, 25, 26b, 52–53
rationale for ABMs in study of, 1–3, 4, 52–55
SnapDragon modeling of, 122–124
state and local authority, 42, 44–45
the U.S. Food and Drug Administration authority for, 3, 19, 23–24, 38–42
Tobacco Control Act. See Family Smoking Prevention and Tobacco Control Act
Tobacco distribution and sales
data sources, 148
minimum purchase age requirements, xv, 41–42, 44
regulatory authority, 3, 41, 44
retailer density, 35, 44, 170–171
See also Tobacco industry
Tobacco industry
challenges to tobacco regulation from, 35, 36
data collection by, 149
modeling policy effect on behavior of, 71
See also Tobacco distribution and sales;
Tobacco manufacture
Tobacco manufacture
nicotine content, 35
regulatory authority, 3, 39–40
See also Tobacco industry
Tobacco Prevalence and Health Effects Model, 26b
Tobacco Products Scientific Advisory Committee, 35, 38
Tobacco-related disease and death, 3, 23, 31. See also Health risks, smoking-associated
Tobacco use
characteristics of user population, 37
data types for research and modeling of, 133, 143
economic burden, 23
factors to be considered for modeling individual behavior, 45–51
health risks, 3, 23, 31, 50–51, 129
patterns and trends, 3, 22–23, 31, 37
rationale for ABMs in study of, 4–5, 52–55
strategies for improving data collection on, 14–15, 149–154
See also Cessation of tobacco use; Initiation of tobacco use; Prevalence of tobacco use; Social context of tobacco use
Tobacco Use Supplement to Current Population Survey, 144
Tolerance, nicotine, 50
TPSAC. See Tobacco Products Scientific Advisory Committee
Transparency, 186
U
Uncertainty
communication of, 8, 27, 82–83, 186–187
managing and communicating, 186–187
Unintended consequences, 37, 64, 167, 171. See also Blowback; Policy resistance
U.S. Department of Health and Human Services, 43
U.S. Environmental Protection Agency, 43–44, 221
U.S. Food and Drug Administration challenges for, in tobacco regulation, 34–36
limits to authority of, 42
recommendations for, 8, 16, 83, 156
tobacco regulation authority, 3, 19, 23–24, 38–42
use of models by, xv–xvi, 1, 14–16, 19–20, 154–156
V
Validation issues in modeling, 123–124, 130–133, 205–207, 219, 228–229
in model evaluation, 92f, 94, 97
in SnapDragon model, 126, 135, 136
in case studies, 104
in SnapDragon Model, 136
W
Y
Youth
determinants of tobacco initiation in, 48–50
e-cigarette use, 23
progression of smoking behaviors, 48–49
projected mortality among tobacco users, 3, 23
restrictions on advertising to, 40
social context of tobacco use among, 49–50
tobacco initiation rates, 3, 22–23
tobacco prevention campaigns, 41