Overview of System Dynamics Simulation Modeling

Overview of System Dynamics Simulation Modeling Bobby Milstein Syndemics Prevention NetworkCenters for Disease Control and PreventionAtlanta, Georgia bmilstein@cdc.gov Systems Thinking and Modeling Workshop Office of Disease Prevention and Health Promotion Bethesda, MD May 8, 2006

Research 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 2005;294(11):1403-1406.

AJPH Systems Issue Science Seminars and Professional Development Efforts CDC Evaluation Framework Recommends Logic Models ODPHP Modelers Meeting SD Emerges as a Promising Methodology System Change Initiatives Encounter Limitations of Logic Models and Conventional Planning/Evaluation Methods Syndemics Modeling Neighborhood Assistance Game Diabetes Action Labs* Fetal & Infant Health Goal-Setting Obesity Overthe Lifecourse* Upstream-Downstream Investments Hypertension Prevention & Control * Milestones in the Recent Use of System Dynamics Modeling at CDC 1999 2000 2001 2002 2003 2004 2005 2006 * Dedicated multi-year budget

System Dynamics Was Designed 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) • 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 2006;96(3):452-458.

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 1971;73(3):53-68. Meadows DH. Leverage points: places to intervene in a system. Sustainability Institute, 1999. Available at <http://www.sustainabilityinstitute.org/pubs/Leverage_Points.pdf>. 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.

Tools for Policy Analysis Events Time Series Models Describe trends • Increasing: • Depth of causal theory • Degrees of uncertainty • 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

How Many Triangles Do You See? Wickelgren I. How the brain 'sees' borders. Science 1992;256(5063):1520-1521.

Boundary Critique Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268. http://www.geocities.com/csh_home/downloads/ulrich_2000a.pdf

Public Work Society's Health Response Tertiary General Targeted Primary Secondary Prevention Protection Protection Prevention Prevention Demand for response Becoming safer and healthier Safer Afflicted Afflicted with Vulnerable Healthier without Complications People People Developing Becoming Becoming Complications complications vulnerable afflicted Dying from complications Adverse Living Conditions Health System Dynamics Milstein B, Homer J. The dynamics of upstream and downstream: why is so hard for the health system to work upstream, and what can be done about it? CDC Futures Health Systems Workgroup; Atlanta, GA; 2003. Milstein B, Homer J. The dynamics of upstream and downstream: why is so hard for the health system to work upstream, and what can be done about it? CDC Futures Health Systems Work Group; Atlanta, GA; December 3, 2003. Gerberding JL. CDC's futures initiative. Atlanta, GA: Public Health Training Network; April 12, 2004. Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. American Journal of Public Health 2006;96(3):452-458.

Citizen Involvement in Public Life Public Strength - Vulnerable and Afflicted People Fraction of Adversity, Social Division Vulnerability and Affliction Borne by Disadvantaged Sub-Groups (Inequity) Understanding Health as Public Work Public Work - Society's Health Response Tertiary General Targeted Primary Secondary Prevention Protection Protection Prevention Prevention Demand for response Becoming safer and healthier - Safer Afflicted Afflicted with Vulnerable Healthier without Complications People People Developing Becoming Becoming Complications complications vulnerable afflicted Dying from complications Adverse Living Conditions

Public Work Citizen Involvement - in Public Life Public Society's Health Strength Response - Tertiary General Targeted Primary Secondary Prevention Protection Protection Prevention Prevention Demand for response Becoming safer and healthier - Safer Afflicted Afflicted with Vulnerable Healthier without Complications People People Developing Becoming Becoming Complications complications vulnerable afflicted Dying from complications Adverse Living Conditions Vulnerable and Afflicted People Fraction of Adversity, Social Division Vulnerability and Affliction Borne by Disadvantaged Sub-Groups (Inequity) Testing Dynamic Hypotheses -- How can we learn about the consequences of actions in a system of this kind?-- Could the behavior of this system be analyzed using conventional epidemoiological methods (e.g., logistic or multi-level regression)?

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.

Historical Markov Forecasting Model Data Simulation Experiments in Action Labs System Dynamics Modeling SupportsNavigational Policy Dialogues 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 2003;6:155-164. 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 2006;96(3):488-494.

Dynamic Hypothesis (Causal Structure) Plausible Futures (Policy Experiments) Deaths per Population 0.0035 0.003 Mixed Base 0.0025 Upstream 0.002 Downstream 0.0015 1980 1990 2000 2010 2020 2030 2040 2050 Time (Year) Blue: Base run; Red: Clinical mgmt up from 66% to 90%; Green: Caloric intake down 4% (99 Kcal/day); Black: Clin mgmt up to 80% & Intake down 2.5% (62 Kcal/day) Simulations for Learning in Dynamic Systems “All models are wrong. Some are useful.” 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. Sterman JD. Learning from evidence in a complex world. American Journal of Public Health 2006;96(3):505-514. Sterman JD. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review 2002;18(4):501-531.

What? Where? Prevalence of Obese Adults, United States Why? How? Who? 2020 2010 Data Source: NHANES “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. <http://www.pscs.umich.edu/pub/papers/AdvancingArtofSim.pdf>. Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin McGraw-Hill, 2000.

SYSTEMS THINKING & MODELING (understanding change) SOCIAL NAVIGATION (governing movement) • What causes population health problems? • How are efforts to protect the public’s health organized? • How and when do health systems change (or resist change)? Directing Change • Who does the work? • By what means? • According to whose values? Charting Progress • How are conditions changing? • In which directions? PUBLIC HEALTH(setting direction) What are health leaderstrying to accomplish? Questioning the Character of Public Health Work PUBLIC HEALTH WORK InnovativeHealth Ventures

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Potential Users and Uses of Health SD Simulation Models • Planners/Evaluators/Media: Chart Progress Toward Goals • Define a “status quo” future • Define alternative futures based on policy scenarios • Define types of information to be routinely collected • Track and interpret trajectories of change • Estimate how strong interventions must be to make a difference • Researchers: Better Measurement and New Knowledge • Integrate diverse data sources into a single analytic environment • Infer properties of unmeasured or poorly measured parameters • Analyze historical drivers of change • Locate areas of uncertainty to be addressed in new research • Policy Makers: Convene Multistakeholder Action Labs • Understand how a dynamically complex system functions • Discover short- and long-term consequences of alternative policies • Prepare for difficult patterns of change (e.g., worse-before-better) • Consider the cost effectiveness of alternative policies • Explore ways of combining and aligning policies for better results • Increase policy-makers’ motivation to act differently • Others…

Possible Roles for System Dynamics in Public Health SD is especially well-suited for studying… • Individual diseases and risk factorsExamining momentum and setting justifiable goals • Life course dynamics Following health trajectories across life stages • Mutually reinforcing afflictions (syndemics)Exploring interactions among related afflictions, adverse living conditions, and the public’s capacity to address them both • Capacities of the health protection system Understanding how ambitious health ventures may be configured without overwhelming/depleting capacity--perhaps even strengthening it • Value trade-offs Analyzing phenomena like the imbalance of upstream-downstream effort, growth of the uninsured, rising costs, declining quality, entrenched inequalities • Organizational managementLinking balanced scorecards to a dynamic understanding of processes • Group model building and scenario planningBringing more structure, evidence, and insight to public dialogue and judgment

Learn About Policy Consequences Test proposed policies, searching for ones that best govern change Convert the Map Into a Simulation ModelFormally quantify the hypothesis using allavailable evidence Create a Dynamic HypothesisIdentify and map the main causal forces that create the problem Choose AmongPlausible FuturesDiscuss values and consider trade-offs Run Simulation ExperimentsCompare model’s behavior to expectations and/or data to build confidence in the model Enact PoliciesBuild power and organize actors to establish chosen policies Steps for Developing Dynamic Policy Models Identify a Persistent ProblemGraph its behavior over time

Overview of System Dynamics Simulation Modeling