System Dynamics and Agent Based Computer Modeling

Transcription

1 System Dynamics and Agent Based Computer Modeling Peter S. Hovmand, PhD Founding Director Brown School Social System Design Lab Washington University in St. Louis TIDIHR 2013 Washington University in St. Louis 1

2 Jackson Pollock, Number 1, 1950 (Lavender Mist),1950, National Gallery of Art, Ailsa Mellon Bruce Fund,

3 Mental models and systems thinking in a dynamically complex world Perception Reality Mental Model Action Doyle, J. K., & Ford, D. N. (1998). Mental models concepts for system dynamics research. System Dynamics Review, 14, 329. JohnsonLaird, P. (1983). Mental models: Towards a cognitive science of language, inference and consciousness. Cambridge, MA: Harvard University Press. Axelrod, R. (Ed.). (1976). Structure of decision: The cognitive maps of political elites. Princeton, NJ: Princeton: Princeton University Press. 3

4 Deep system insights Surface system insights Different types of models for different types of system insights Depth Informal Modeling Formal There is a system The components of a system How the components are related through feedback How people might think about a system Where one could intervene What is transformation What is the generic structure What are the implications of accumulations and nonlinear relationships What systems can generate the dynamic behavior Where are the leverage points When do boundary conditions determine behavior Why do things happen System pictures or diagrams Graphical models or maps Simulation models Hovmand November 16,

5 Target pop reached Three general kinds of hypotheses about what might the success and failure of scaleup and sustainability 100 % 0 % 0 Time (years) 10 H1 H2 H3 Social networks Rules of actors Feedback mechanisms Adoption Rate Potential Adopters Adopters Marketing Hovmand November 8,

6 Target pop reached In systems science, we generally use social network analysis (SNA) to test H1, agent based modeling (ABM) to test H2, and system dynamics (SD) to test H3 100 % 0 % 0 Time (years) 10 H1 H2 H3 Social networks Rules of actors Feedback mechanisms Adoption Rate Potential Adopters Adopters Marketing Hovmand November 8,

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8 Two approaches to computer simulation modeling of complex adaptive systems System Dynamics Modeling Top Down Vaccine dosages Administering dosages Dosages per child Families Unvaccinated Vaccinated children children Vaccinating Remaining time Children per staff Min time to per week Number of staff vaccinate needed Children per family Trained staff Training Staff staff turnover Avg time to hire and train staff Turnover fraction Trainees per Initial trained staff <Children per trainer per family> week Initial demand prop Potential families Creating demand Families demanding vaccine Vaccinating families Vaccinated families Contact rate Conversion rate Agent Based Modeling Bottom Up 8

9 System Dynamics Model of Childhood Immunization Program Initial dosages Vaccine dosages Administering dosages <Time> Remaining time Number of staff needed Unvaccinated children Children per staff per week Vaccinating Dosages per child Vaccinated children Min time to vaccinate Potential families Families Creating demand Conversion rate Contact rate Avg time to hire and train staff Training staff Trainees per trainer per week Trained staff Initial trained staff Staff turnover Children per family Turnover fraction Initial demand prop <Children per family> Families demanding vaccine Vaccinated families Vaccinating families P connected to vaccinated family

10 Agent Based Model of Childhood Immunization Program

11 Parameter values used for system dynamics and agent based models Model parameters ABM SD N families 250 to to 1000 N children per family 4 4 Average node degree 5 to 40 * Contact rate Conversion rate * * 5 to 40 1 b Initial demand Initial workers 1 1 Worker productivity (children per week per worker) 8 8 Recruiting and training time (weeks) Turnover fraction (1/week) Trainees per trainer (1/week)

12 Average time required to complete agent based simulation by number of families and average ties (note, equivalent SD models takes 0.01 seconds for all cases)

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14 Case 1 Case 2

15 Case 1: Comparison of SD and ABM model results for cases where N families = 250 SD model results ABM model results

16 Case 2: Comparison of SD and ABM model results for cases where N families = 1000 SD model results ABM model results

17 Closing comments Both agent based modeling and system dynamics models: Can be used at all stages of research from theory development to retrospective analysis Make theories explicit Verify logical connection between theories and hypotheses visàvis computer simulation Agent based modeling and system dynamics modeling are complementary approaches System dynamics models tend to by much faster to simulate allowing more sensitivity analysis and broader boundaries Agent based models are better at capturing network effects and heterogeneity of actors Best modeling practices increasingly relies on both approaches