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Title: Modeling the Population Health Dynamics of


1
Modeling the Population Health Dynamics of
  • Diabetes Obesity

Bobby Milstein Syndemics Prevention
NetworkCenters for Disease Control and
PreventionAtlanta, Georgia bmilstein_at_cdc.gov http
//www.cdc.gov/syndemics
Texas Public Health Association Galveston,
TX February 26, 2007
2
Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks
JS, Koplan JP. The continuing epidemics of
obesity and diabetes in the United States.
Journal of the American Medical Association
2001286(10)1195-200. Kaufman FR. Diabesity the
obesity-diabetes epidemic that threatens
America--and what we must do to stop it. New
York, NY Bantam Books, 2005.
3
Imperatives for Protecting Health
Typical Current StateStatic view of problems
that are studied in isolation
Proposed Future StateDynamic systems and
syndemic approaches
"Currently, application of complex systems
theories or syndemic science to health protection
challenges is in its infancy. -- Julie
Gerberding
Gerberding JL. Protecting health the new
research imperative. Journal of the American
Medical Association 2005294(11)1403-1406.
4
How Many Triangles Do You See?
Wickelgren I. How the brain 'sees' borders.
Science 1992256(5063)1520-1521.
5
Boundary Critique
When it comes to the problem of boundary
judgments, experts have no natural advantage of
competence over lay people.
-- Werner Ulrich
Ulrich W. Boundary critique. In Daellenbach HG,
Flood RL, editors. The Informed Student Guide to
Management Science. London Thomson 2002. p.
41-42. lthttp//www.geocities.com/csh_home/download
s/ulrich_2002a.pdfgt. Ulrich W. Reflective
practice in the civil society the contribution
of critically systemic thinking. Reflective
Practice 20001(2)247-268. http//www.geocities.c
om/csh_home/downloads/ulrich_2000a.pdf
6
Transforming the Future of Diabetes
"Every new insight into Type 2 diabetes...makes
clear that it can be avoided--and that the
earlier you intervene the better. The real
question is whether we as a society are up to the
challenge... Comprehensive prevention programs
aren't cheap, but the cost of doing nothing is
far greater..."
Gorman C. Why so many of us are getting diabetes
never have doctors known so much about how to
prevent or control this disease, yet the epidemic
keeps on raging. how you can protect yourself.
Time 2003 December 8. Accessed at
http//www.time.com/time/covers/1101031208/story.h
tml.
7
Questions Addressed by System Dynamics Modeling
Learning to Re-Direct the Course of Change
Prevalence of Diagnosed Diabetes, US
40
Where?
30
What?
Million people
20
How?
  • Markov Model Constants
  • Incidence rates (/yr)
  • Death rates (/yr)
  • Diagnosed fractions
  • (Based on year 2000 data, per demographic segment)

10
Who?
Why?
0
1980
1990
2000
2010
2020
2030
2040
2050
Honeycutt A, Boyle J, Broglio K, Thompson T,
Hoerger T, Geiss L, Narayan K. A dynamic markov
model for forecasting diabetes prevalence in the
United States through 2050. Health Care
Management Science 20036155-164. Jones AP,
Homer JB, Murphy DL, Essien JDK, Milstein B,
Seville DA. Understanding diabetes population
dynamics through simulation modeling and
experimentation. American Journal of Public
Health 200696(3)488-494.
8
Tools for Policy Analysis
Events
Time Series Models Describe trends
  • Increasing
  • Depth of causal theory
  • Data and sensitivity testing requirements
  • Robustness for longer-term projection
  • Value for developing policy insights

Multivariate Stat Models Identify historical
trend drivers and correlates
Patterns
Dynamic Simulation Models Anticipate new
trends, learn about policy consequences, and set
justifiable goals
Structure
9
A Model Is
  • An inexact representation of the real thing

It helps us understand, explain, anticipate, and
make decisions
All models are wrong, some are useful. --
George Box
10
System Dynamics Simulation Modeling Was Developed
to Address Problems Marked by Dynamic Complexity
  • Origins
  • Jay Forrester, MIT (from late 1950s)
  • Public policy applications starting late 1960s
  • Good at Capturing
  • Differences between short- and long-term
    consequences of an action
  • Time delays (e.g., transitions, detection,
    response)
  • Accumulations (e.g., prevalence, capacity)
  • Behavioral feedback (e.g., actions trigger
    reactions)
  • Nonlinear causal relationships (e.g., effect of
    X on Y is not constant-sloped)
  • Differences or inconsistencies in goals/values
    among stakeholders

Sterman JD. Business dynamics systems thinking
and modeling for a complex world. Boston, MA
Irwin McGraw-Hill, 2000. Homer JB, Hirsch GB.
System dynamics modeling for public health
background and opportunities. American Journal
of Public Health 200696(3)452-458
11
Understanding Dynamic Complexity From a Very
Particular Distance
System dynamics studies problems from a very
particular distance', not so close as to be
concerned with the action of a single individual,
but not so far away as to be ignorant of the
internal pressures in the system. -- George
Richardson
Forrester JW. Counterintuitive behavior of social
systems. Technology Review 197173(3)53-68. Meado
ws DH. Leverage points places to intervene in a
system. Sustainability Institute, 1999.
Available at lthttp//www.sustainabilityinstitute.
org/pubs/Leverage_Points.pdfgt. Richardson GP.
Feedback thought in social science and systems
theory. Philadelphia, PA University of
Pennsylvania Press, 1991. Sterman JD. Business
dynamics systems thinking and modeling for a
complex world. Boston, MA Irwin McGraw-Hill,
2000.
12
Simulations for Learning in Dynamic Systems
Multi-stakeholder Dialogue
Morecroft JDW, Sterman J. Modeling for learning
organizations. Portland, OR Productivity Press,
2000. Sterman JD. Business dynamics systems
thinking and modeling for a complex world.
Boston, MA Irwin McGraw-Hill, 2000. Homer JB.
Why we iterate Scientific modeling in theory and
practice. System Dynamics Review 1996 12(1)1-19.
13
CDC Diabetes System Modeling ProjectDiscovering
Dynamics Through Action Labs
Jones AP, Homer JB, Murphy DL, Essien JDK,
Milstein B, Seville DA. Understanding diabetes
population dynamics through simulation modeling
and experimentation. American Journal of Public
Health 200696(3)488-494.
14
CDC Diabetes System Modeling ProjectContributors
  • State Diabetes Programs
  • Minnesota Heather Devlin, Jay Desai
  • California Gary He, Karen Black, Toshi Hayashi
  • VermontRobin Edelman, Jason Roberts, Ellen
    Thompson
  • CDC
  • Dara Murphy, Bobby Milstein, Chris Benjamin,
    Wayne Millington, Parul Nanavati, Sharon Daves,
    Frank Vinicor, Mark Rivera
  • Contractor Team
  • Drew Jones
  • Jack Homer
  • Joyce Essien
  • Doc Klein
  • Don Seville

15
Project Background
  • Diabetes programs face tough challenges and
    questions
  • Pressure for results on disease burden, not just
    behavioral change
  • The Diabetes Prevention Program indicates primary
    prevention is possible, but may be difficult and
    costly
  • What is achievable on a population level?
  • How should funds be allocated?
  • Standard epidemiological models rarely address
    such policy questions
  • In Fall 2003, CDC initiates System Dynamics
    modeling project
  • In Spring 2005, some states join as collaborators
    in further developing and using the SD model

16
Diabetes Burden is Driven by Population Flows
Developing
d
Inflow
Outflow
17
Diabetes Burden is Driven by Population Flows
Developing
d
Inflow
Outflow
18
Using Available Data to Calibrate the Model
Information Sources Data
U.S. Census Population growth and death rates Fractions elderly, black, hispanic Health insurance coverage
National Health Interview Survey Diabetes prevalence Diabetes detection
National Health and Nutrition Examination Survey Prediabetes prevalence Obesity prevalence
Behavioral Risk Factor Surveillance System Eye exam and foot exam Taking diabetes medications Unhealthy days (HRQOL)
Professional Literature Effects of risk factors and mgmt on onset, complications, and costs Direct and indirect costs of diabetes
19
Diabetes System Modeling ProjectConfirming Fit
to Historical Trends (2 examples out of 10)
Diagnosed Diabetes of Adults
Obese of Adults
40
8
Obese of adults
Diagnosed diabetes of adults
30
6
20
4
Data (NHIS)
Data (NHANES)
10
2
0
0
1980
1985
1990
1995
2000
2005
2010
1980
1985
1990
1995
2000
2005
2010
20
The growth of diabetes prevalence since 1980 has
been driven by growth in obesity prevalence
Obese Fraction and Diabetes per Thousand
130
0.7
Diabetes Prevalence
85
0.35
Obesity Prevalence
40
0
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
21
Although we expect obesity to increase little
after 2006, diabetes keeps growing robustly for
another 20-25 years
Obese Fraction and Diabetes per Thousand
Onset6.3 per thou
130
0.7
Estimated 2006 values
Diabetes Prevalence
Prevalence92 AND RISING
Prevalence92 / thou
85
0.35
Obesity Prevalence
Death3.8 per thou
40
0
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
With high (even if flat) onset, prevalence tub
keeps filling until deaths (4-5/yr)onset
Diabetes prevalence keeps growing after obesity
stops
WHY?
22
Unhealthy days impact of prevalence growth, as
affected by diabetes management Past and one
possible future
Unhealthy Days per Thou and Frac Managed
Obese Fraction and Diabetes per Thousand
500
Managed fraction
130
0.65
0.7
Diabetes Prevalence
375
85
0.325
0.35
Obesity Prevalence
Unhealthy Days from Diabetes
40
250
0
0
1980
1990
2000
2010
2020
2030
2040
2050
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
Diabetes prevalence keeps growing after obesity
stops
If disease management gains end, the burden grows
23
A Sequence of What-if Simulations
Start with the base case or status quo no
improvements in diabetes management or
prediabetes management after 2006
People with Diabetes per Thousand Adults
Monthly Unhealthy Days from Diabetes per Thou
150
500
Base
450
125
Base
400
100
350
75
300
50
250
1980
1990
2000
2010
2020
2030
2040
2050
1980
1990
2000
2010
2020
2030
2040
2050
24
Further Increases in Diabetes Management
Increase fraction of diagnosed diabetes getting
managed from 58 to 80 by 2015. (No change in
the mix of conventional and intensive.) What do
you think will happen?
People with Diabetes per Thousand Adults
Monthly Unhealthy Days from Diabetes per Thou
150
500
Base
Diab mgt
450
125
Base
400
Diab mgt
100
350
75
300
50
250
1980
1990
2000
2010
2020
2030
2040
2050
1980
1990
2000
2010
2020
2030
2040
2050
Keeping the burden at bay for nine years longer
More people living with diabetes
25
A Huge Push for Prediabetes Management
Increase fraction of prediabetics getting managed
from 6 to 32 by 2015. (Half of those under
intensive mgmt by 2015.) No increase in diabetes
mgmt. What do you think will happen?
People with Diabetes per Thousand Adults
Monthly Unhealthy Days from Diabetes per Thou
150
500
Base
Base
450
125
PreD mgmt
400
PreD mgmt
100
350
75
300
50
250
1980
1990
2000
2010
2020
2030
2040
2050
1980
1990
2000
2010
2020
2030
2040
2050
The improvement is relatively modestthe growth
is not stopped
26
Two Scenarios in which Obesity is Reduced
What if it were possiblein addition to the
prediabetes mgmt interventionto gradually lower
the fraction obese from 34 (2006) to the 1994
value of 25 by 2030? Or, to the 1984 value of
18?
Obese Fraction of Adult Population
0.4
Base
0.3
Obesity 25
Obesity 18
0.2
0.1
0
1980
1990
2000
2010
2020
2030
2040
2050
27
Managing Prediabetes AND Reducing Obesity
What do you think will happen if, in addition to
PreD mgmt, obesity is reduced moderately by 2030?
What if it is reduced even more?
People with Diabetes per Thousand Adults
Monthly Unhealthy Days from Diabetes per Thou
150
500
Base
450
Base
PreD mgmt
125
PreD mgmt
400
PreD Ob 25
PreD Ob 25
100
350
PreD Ob 18
75
PreD Ob 18
300
50
250
1980
1990
2000
2010
2020
2030
2040
2050
1980
1990
2000
2010
2020
2030
2040
2050
The more you reduce obesity, the sooner you stop
the growth in diabetesand the more you bring it
down
Same with the burden of diabetes
28
Intervening Effectively Upstream AND Downstream
With pure upstream intervention, burden still
grows for many years before turning around. What
do you think will happen if we add the prior
diabetes mgmt intervention on top of the
PreDOb25 one?
People with Diabetes per Thousand Adults
Monthly Unhealthy Days from Diabetes per Thou
150
500
Base
450
Base
125
PreD mgmt
PreD mgmt
400
All 3
100
Pred Ob 25
PreD Ob 25
350
All 3 -- PreD Ob 25 Diab mgmt
75
300
50
250
1980
1990
2000
2010
2020
2030
2040
2050
1980
1990
2000
2010
2020
2030
2040
2050
With a combination of effective upstream and
downstream interventions we could hold the burden
of diabetes nearly flat through 2050!
29
Cover of "The Economist", Dec. 13-19, 2003.
30
CDC Obesity Dynamics Modeling Project Contributors
  • System Dynamics Consultants
  • Jack Homer
  • Gary Hirsch
  • Project Coordinator
  • Bobby Milstein
  • Core Design Team
  • Dave Buchner
  • Andy Dannenberg
  • Bill Dietz
  • Deb Galuska
  • Larry Grummer-Strawn
  • Anne Hadidx
  • Robin Hamre
  • Laura Kettel-Khan
  • Elizabeth Majestic
  • Jude McDivitt
  • Cynthia Ogden
  • Michael Schooley
  • Time Series Analysts
  • Danika Parchment
  • Cynthia Ogden
  • Margaret Carroll
  • Hatice Zahran
  • Workshop Participants
  • Atlanta, GA May 17-18 (N47)
  • Lansing, MI July 26-27 (N55)

Homer J, Milstein B, Dietz W, Buchner D, Majestic
D. Obesity population dynamics exploring
historical growth and plausible futures in the
U.S. 24th International Conference of the System
Dynamics Society Nijmegen, The Netherlands July
26, 2006.
31
The Rise and Future Fall of ObesityThe Why and
the How in Broad Strokes
Responses to Growth B1 Self-improvement B2
Medical response B3 Improving preventive
healthcare B4 Creating better messages B5
Creating better options in beh. settings B6
Creating better conditions in wider environ B7
Addressing related health conditions
Engines of Growth R1 Spiral of poor health and
habits R2 Parents and peer transmission R3 Media
mirrors R4 Options shape habits shape options R5
Society shapes options shape society
Drivers of Unhealthy Habits
Resources, Resistance, Benefits Supports R6
Disease care costs squeeze prevention B8
Up-front costs undercut protection efforts B9
Defending the status quo B10 Potential savings
build support R7 Broader benefits build support
Time
32
Focus of Our Simulation Model
  • Explore effects of new interventions affecting
    caloric balance (intake less expenditure)
  • U.S. policy discourse is primarily focused on
  • prevention among school-aged youth
  • medical treatment for the severely obese
  • What are the likely consequences?
  • How much impact on adult obesity?
  • How long will it take to see?
  • Should we target other subpopulations? (age,
    sex, weight category)
  • Consider two classes of interventions
  • Changes in food activity environments
  • Weight loss/maintenance services for individuals
  • Additional intervention details (composition,
    coverage, efficacy, cost) left outside
    model boundary for now
  • Available data are inadequate to quantify impacts
    and cost-effectiveness
  • Could stakeholder Delphi help?

33
Obesity Dynamics Over the Decades Dynamic
Population Weight Framework
34
Obesity Dynamics Over the Decades Dynamic
Population Weight Framework
Dynamic Population Weight Framework
Birth
Immigration
Yearly aging
Population by Age (0-99) and Sex
Flow-rates between
Moderately
Moderately
Severely
Not
BMI categories
Overweight
Obese
Obese
Overweight
Death
Overweight and
obesity prevalence
35
Obesity Prevalence Over the Decades
Dynamic Population Weight Framework
36
Obesity Dynamics Over the Decades Dynamic
Population Weight Framework
Dynamic Population Weight Framework
Immigration
Birth
Yearly aging
Population by Age (0-99) and Sex
Caloric
Flow-rates between
Moderately
Moderately
Severely
Not
Balance
BMI categories
Overweight
Obese
Obese
Overweight
Death
Overweight and
obesity prevalence
37
Obesity Dynamics Over the DecadesTwo Classes of
Interventions
Dynamic Population Weight Framework
Immigration
Birth
Yearly aging
Changes in the Physical
Population by Age (0-99) and Sex
and Social Environment
Caloric
Trends and Planned
Flow-rates between
Moderately
Moderately
Severely
Not
Balance
Interventions
BMI categories
Overweight
Obese
Obese
Overweight
Weight Loss/Maintenance
Services for Individuals
Death
Overweight and
obesity prevalence
38
Obesity Dynamics Over the DecadesMany
Environmental Factors Come Into Play
Options for Safe, Accessible
Physical Activity (Work,
Options for Affordable
School, Neighborhoods)
Social Influences on
Recommended Foods (Work,
Active/Inactive
School, Markets, Restaurants)
Options
Social Influences on
Consumption
Distance from Home to
Selection
Work, School, Errands
Dynamic Population Weight Framework
Food Price
Food
Activity
Environment
Environment
Electronic Media
Smoking
in the Home
Activity Limiting
Immigration
Conditions
Birth
Yearly aging
Changes in the Physical
Population by Age (0-99) and Sex
and Social Environment
Caloric
Trends and Planned
Flow-rates between
Moderately
Moderately
Severely
Not
Balance
Interventions
BMI categories
Overweight
Obese
Obese
Overweight
Weight Loss/Maintenance
Services for Individuals
Death
Overweight and
obesity prevalence
39
Information Sources
Topic Area Data Source
Prevalence of Overweight and Obesity Prevalence of Overweight and Obesity
BMI prevalence by sex and age (10 age ranges) National Health and Nutrition Examination Survey (1971-2002)
Translating Caloric Balances into BMI Flow-Rates Translating Caloric Balances into BMI Flow-Rates
BMI category cut-points for children and adolescents CDC Growth Charts
Median BMI within each BMI category National Health and Nutrition Examination Survey (1971-2002)
Median height National Health and Nutrition Examination Survey (1971-2002)
Average kilocalories per kilogram of weight change Forbes 1986
Estimating BMI Category Down-Flow Rates Estimating BMI Category Down-Flow Rates
In adults Self-reported 1-year weight change by sex and age NHANES (2001-2002) indicates 7-12 per year
In children BMI category changes by grade and starting BMI Arkansas pre-K through 12th grade assessment (2004-2005) indicates 15-28 per year
Population Composition Population Composition
Population by sex and age U.S. Census and Vital Statistics (1970-2000 and projected)
Death rates by sex and age U.S. Census and Vital Statistics (1970-2000 and projected)
Birth and immigration rates U.S. Census and Vital Statistics (1970-2000 and projected)
Influence of BMI on Mortality Influence of BMI on Mortality
Impact of BMI category on death rates by age Flegal, Graubard, et al. 2005.
40
Calibration of Uncertain ParametersTo Reproduce
60 BMI Prevalence Time Series(10 age ranges x 2
sexes x 3 high-BMI categories)
  • Step 1 Iteratively adjust up-rate and down-rate
    constants and initial BMI prevalences to
    reproduce steady-state BMI prevalence for the
    early 1970s
  • Step 2 Adjust 57 caloric balance time series (by
    age, sex, and BMI category, 1975-2000) to
    reproduce BMI prevalence growth for the 1980s and
    1990s

41
Reproducing Historical Trends One of 20 sex,
age Subgroups Females age 55-64
(a) Overweight fraction
(b) Obese fraction
80
50
40
60
30
Fraction of women age 55-64
Fraction of women age 55-64
40
20
20
10
0
0
1970
1975
1980
1985
1990
1995
2000
2005
1970
1975
1980
1985
1990
1995
2000
2005
NHANES
Simulated
NHANES
Simulated
(c) Severely obese fraction
25
20
15
Fraction of women age 55-64
10
5
0
1970
1975
1980
1985
1990
1995
2000
2005
NHANES
Simulated
Note S-shaped curves, with inflection in the
1990s
42
Explaining BMI Prevalence Growth Age-to-Age
Carryover Caloric ImbalanceExample Females
Age 55-64
Severely obese fractions of middle-aged women
Overweight fractions of middle-aged women
Obese fractions of middle-aged women
25
80
50
20
40
60
15
30
Fraction of women by age group
Fraction of women by age group
Fraction of women by age group
40
10
20
20
5
10
0
0
0
1970
1975
1980
1985
1990
1995
2000
2005
1970
1975
1980
1985
1990
1995
2000
2005
1970
1975
1980
1985
1990
1995
2000
2005
Age 55-64
Age 45-54
Age 55-64
Age 45-54
Age 55-64
Age 45-54
Estimated caloric imbalances for women age 55-64
20
15
Kcal per day
10
5
0
1970
1975
1980
1985
1990
1995
2000
2005
Not overwt
Mod overwt
Obese
43
Assumptions for Future Scenarios
  • Base Case
  • Caloric balances stay at 2000 values through 2050
  • Altering Food and Activity Environments
  • Reduce caloric balances to their 1970 values by
    2015
  • Focused on
  • School Youth youth ages 6-19
  • All Youth all youth ages 0-19
  • SchoolParents school youth plus their parents
  • All Adults all adults ages 20
  • All Ages all youth and adults
  • Subsidized Weight Loss Programs for Obese
    Individuals
  • Net daily caloric reduction of program is 40
    calories/day (translates to 1.8 kg weight loss
    per year)
  • Fully effective by 2010 and terminated by 2020

44
Alternative FuturesObesity in Adults (20-74)
Obese fraction of Adults (Ages 20-74)
50
40
30
Fraction of popn 20-74
20
10
0
1970
1980
1990
2000
2010
2020
2030
2040
2050
Base
SchoolYouth
AllYouth
SchoolParents
AllAdults
AllAges
AllAgesWtLoss
45
Findings
  • Inflection point in obesity probably occurred
    during the 1990s
  • Simple extrapolations based on 1990s growth
    likely exaggerate future prevalences
  • Caloric imbalance vs. 1970 only 1-2 (less than
    50 cal./day) within any given age, sex, and BMI
    category
  • Most of observed 9-13 cal./day increase in
    intake (USDA 1977-1996) has been natural
    consequence of weight gain (via metabolic
    adjustment), not its cause
  • Impacts of changing environments on adult obesity
    take decades to play out fully Carryover
    effect
  • Youth interventions have only small impact on
    overall adult obesity
  • Assumes (1) adult habits determined by adult
    environment, and (2) childhood overweight
    causes no irreversible metabolic changes
  • Weight-loss for the obese could accelerate
    progress--but, first, an effective program that
    minimizes recidivism must be found

46
Conclusions Limitations
  • This model improves our understanding of
    population dynamics of weight change and supports
    pragmatic planning/evaluation
  • No other analytical model plays out effects of
    changes in caloric balance on BMI prevalences
    over the life-course
  • Traces plausible impacts of population-level and
    individual-level interventions
  • And addresses questions of whom to target, by how
    much, and by when
  • But it has limitationssome addressable, some due
    to lack of data
  • Does not indicate exact nature of interventions
  • Does not address cost-effectiveness of
    interventions, nor political reinforcement and
    resistance
  • Does not address racial/ethnic sub-groups
  • Does not trace individual life histories
    (compartmental structure)
  • Assumes habits determined by current environment,
    not by childhood learning
  • Assumes no irreversible metabolic changes
    sustained as a result of childhood
    overweight/obesity

47
Learning In and About Dynamic Systems
In dynamically complex circumstances
simulation becomes the only reliable way to test
a hypothesis and evaluate the likely effects of
policies." -- John Sterman
  • Benefits of Simulation/Game-based Learning
  • Formal means of evaluating options
  • Experimental control of conditions
  • Compressed time
  • Complete, undistorted results
  • Actions can be stopped or reversed
  • Visceral engagement and learning
  • Tests for extreme conditions
  • Early warning of unintended effects
  • Opportunity to assemble stronger support
  • Dynamic Complexity Hinders
  • Generation of evidence (by eroding the
    conditions for experimentation)
  • Learning from evidence (by demanding new
    heuristics for interpretation)
  • Acting upon evidence (by including the behaviors
    of other powerful actors)

Sterman JD. Learning from evidence in a complex
world. American Journal of Public Health (in
press). Sterman JD. Business dynamics systems
thinking and modeling for a complex world.
Boston, MA Irwin McGraw-Hill, 2000.
48
Simulation is a third way of doing science.
Like deduction, it starts with a set of explicit
assumptions. But unlike deduction, it does not
prove theorems. Instead, a simulation generates
data that can be analyzed inductively. Unlike
typical induction, however, the simulated data
comes from a rigorously specified set of rules
rather than direct measurement of the real world.
While induction can be used to find patterns in
data, and deduction can be used to find
consequences of assumptions, simulation modeling
can be used as an aid to intuition.
Simulation ExperimentsOpen a Third Branch of
Science
The complexity of our mental models vastly
exceeds our ability to understand their
implications without simulation." -- John
Sterman
-- Robert Axelrod
Axelrod R. Advancing the art of simulation in the
social sciences. In Conte R, Hegselmann R, Terna
P, editors. Simulating Social Phenomena. New
York, NY Springer 1997. p. 21-40.
lthttp//www.pscs.umich.edu/pub/papers/AdvancingArt
ofSim.pdfgt. Sterman JD. Business Dynamics
Systems Thinking and Modeling for a Complex
World. Boston, MA Irwin McGraw-Hill, 2000.
49
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emics
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