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Dynamic Complex Systems

Jennifer S. Groff
Jennifer S. Groff

This document discusses using system dynamics modeling to analyze educational systems. It provides examples of how system dynamics modeling has been used to study urban planning and infectious diseases. Key aspects of complex systems like feedback loops, nonlinear relationships, and unintended consequences are discussed. The document outlines tools for system dynamics modeling like causal loop diagrams, stock and flow maps, and computer simulations. It proposes applying these tools to study the relationships between factors like student-teacher ratios, achievement, funding, and alignment to testing in the educational system.

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D YNAMIC S YSTEMS M ODELING IN E DUCATIONAL S YSTEM D ESIGN SYSTEM DYNAMICS INTRO MODELING USES IN POLICY MODELING IN EDUCATION SIMPLE EXAMPLES FUTURE WORK DEVELOPING COMPLEX MODELS CONNECTIONS TO THINK SCENARIOS J ENNIFER G ROFF 2009
D YNAMIC C OMPLEXITY & U NINTENDED E FFECTS FORRESTER EXAMPLE CITY OF BOSTON URBAN PLANNING PARALLELS IN EDUCATION NCLB POLICY RESISTANCE LINEAR-THINKING TENDENCY TOWARDS ANALYSIS J ENNIFER G ROFF 2009
D YNAMIC C OMPLEXITY C HARACTERISTICS OF C OMPLEX S YSTEMS Constantly challenging Change in systems occurs at many time scales, and these different scales sometimes interact. Tightly coupled The actors in a system interact strongly with one another and with the natural world; everything is connected to everything else. Governed by feedback Our actions feed back on themselves, giving rise to a new situation as a result of our actions. Nonlinear Effect is rarely proportional to cause, and what happens locally in a system often does not apply in distant regions; it arises as multiple factors interact in decision-making. History-dependent Taking one road often precludes taking others and determines where you end up; many actions are irreversible. Self-organizing The dynamics of systems arise spontaneously from their internal structure, generating patterns in space and time creating path dependence. Adaptive The capabilities and decision rules of the agents in complex systems change over time. Adaption also occurs as people learn from experience, especially as they learn new ways to achieve their goals in the face of obstacles. Learning is not always bene鍖cial, however. Characterized by trade-offs Time delays in feedback channels mean the long-run response of a system to an intervention is often different from its short-run response. High leverage policies often generate transitory improvement before the problem grows worse. Counterintuitive Cause and effect are distant in time and space while we tend to look for causes near the events we seek to explain. Policy resistant The complexity of the systems in which we are embedded overwhelms our ability to understand them, resulting in many seemingly obvious solutions to problems that fail or actually worsen the problem. J ENNIFER G ROFF 2009
M ODELING T OOLS FOR S YSTEM D YNAMICS Behavior-Over-Time Graphs - Displays data of change in the system in a line graph format Causal Loop Diagrams - Mapping of feedback loops and how they may interact with one another Stock/Flow Maps - "Stocks" are the accumulation of something in the system, such as money, people, etc. "Flows" are the rates of change of those stocks, such as savings or spending rate. Feedback loops within a system are what control these 鍖ows. Through these three components, one can depict the dynamics of a given system. Computer Simulation Models - Once a system is diagrammed, its accuracy can best be tested through constructing a computer simulation of that model. While no one person could simultaneously calculate the interdependent relationships of system of time that produces the troublesome behavior, a computer model can. Numerous tools have been developed to help achieve this, including StarLogo, and NetLogo.
E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
Student : Teacher Ratio J ENNIFER G ROFF 2009
Student : Teacher Ratio J ENNIFER G ROFF 2009
Student Achievement Student : Teacher Ratio J ENNIFER G ROFF 2009
Student Achievement Student : Teacher Ratio J ENNIFER G ROFF 2009
Student Achievement Student : Teacher Ratio J ENNIFER G ROFF 2009
Student Achievement Student : Teacher Ratio NCLB Funding J ENNIFER G ROFF 2009
Student Achievement Student : Teacher Ratio NCLB Funding J ENNIFER G ROFF 2009
Student Achievement Student : Teacher Ratio NCLB Funding J ENNIFER G ROFF 2009
Student Achievement Student : Teacher Ratio NCLB Funding J ENNIFER G ROFF 2009
Student Achievement Student : Teacher R Ratio NCLB Funding J ENNIFER G ROFF 2009
Student Achievement Student : Teacher R Ratio NCLB Funding State Funding J ENNIFER G ROFF 2009
Student Achievement Student : Teacher R Ratio NCLB Funding State Funding J ENNIFER G ROFF 2009
Content Alignment Rate 8 B R exposure to test content 8 class time available J ENNIFER G ROFF 2009
Content Alignment Rate Subjects Taught/ Tested 8 B R exposure to test content 8 class time available J ENNIFER G ROFF 2009
Content Alignment Rate Subjects Taught/ Tested 8 B R exposure to test content 8 class time available J ENNIFER G ROFF 2009
Content Alignment Rate Subjects Taught/ Tested 8 Subject Not Taught/ Tested B R exposure to test content 8 class time available J ENNIFER G ROFF 2009
Content Alignment Rate Subjects Taught/ Tested 8 Subject Not Taught/ Tested B Students R Proficient exposure to test content 8 class time available J ENNIFER G ROFF 2009
Content Alignment Rate Subjects Taught/ Tested 8 Subject Not Taught/ Tested B Students R Proficient exposure to test content 8 class time available Students Not Proficient J ENNIFER G ROFF 2009
H IERARCHICAL L EVELS OF E DUCATIONAL S YSTEM P OLICY A NALYSIS Federal State District School Classroom Student Teacher J ENNIFER G ROFF 2009
J ENNIFER G ROFF jennifer_groff@mail.har vard.edu 2009

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Dynamic Complex Systems

  • 1. D YNAMIC S YSTEMS M ODELING IN E DUCATIONAL S YSTEM D ESIGN SYSTEM DYNAMICS INTRO MODELING USES IN POLICY MODELING IN EDUCATION SIMPLE EXAMPLES FUTURE WORK DEVELOPING COMPLEX MODELS CONNECTIONS TO THINK SCENARIOS J ENNIFER G ROFF 2009
  • 2. D YNAMIC C OMPLEXITY & U NINTENDED E FFECTS FORRESTER EXAMPLE CITY OF BOSTON URBAN PLANNING PARALLELS IN EDUCATION NCLB POLICY RESISTANCE LINEAR-THINKING TENDENCY TOWARDS ANALYSIS J ENNIFER G ROFF 2009
  • 3. D YNAMIC C OMPLEXITY C HARACTERISTICS OF C OMPLEX S YSTEMS Constantly challenging Change in systems occurs at many time scales, and these different scales sometimes interact. Tightly coupled The actors in a system interact strongly with one another and with the natural world; everything is connected to everything else. Governed by feedback Our actions feed back on themselves, giving rise to a new situation as a result of our actions. Nonlinear Effect is rarely proportional to cause, and what happens locally in a system often does not apply in distant regions; it arises as multiple factors interact in decision-making. History-dependent Taking one road often precludes taking others and determines where you end up; many actions are irreversible. Self-organizing The dynamics of systems arise spontaneously from their internal structure, generating patterns in space and time creating path dependence. Adaptive The capabilities and decision rules of the agents in complex systems change over time. Adaption also occurs as people learn from experience, especially as they learn new ways to achieve their goals in the face of obstacles. Learning is not always bene鍖cial, however. Characterized by trade-offs Time delays in feedback channels mean the long-run response of a system to an intervention is often different from its short-run response. High leverage policies often generate transitory improvement before the problem grows worse. Counterintuitive Cause and effect are distant in time and space while we tend to look for causes near the events we seek to explain. Policy resistant The complexity of the systems in which we are embedded overwhelms our ability to understand them, resulting in many seemingly obvious solutions to problems that fail or actually worsen the problem. J ENNIFER G ROFF 2009
  • 4. M ODELING T OOLS FOR S YSTEM D YNAMICS Behavior-Over-Time Graphs - Displays data of change in the system in a line graph format Causal Loop Diagrams - Mapping of feedback loops and how they may interact with one another Stock/Flow Maps - "Stocks" are the accumulation of something in the system, such as money, people, etc. "Flows" are the rates of change of those stocks, such as savings or spending rate. Feedback loops within a system are what control these 鍖ows. Through these three components, one can depict the dynamics of a given system. Computer Simulation Models - Once a system is diagrammed, its accuracy can best be tested through constructing a computer simulation of that model. While no one person could simultaneously calculate the interdependent relationships of system of time that produces the troublesome behavior, a computer model can. Numerous tools have been developed to help achieve this, including StarLogo, and NetLogo.
  • 5. E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
  • 6. E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
  • 7. E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
  • 8. E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
  • 9. E XAMPLE FROM S CIENCE I NFECTIOUS A CTIVITY J ENNIFER G ROFF 2009
  • 10. Student : Teacher Ratio J ENNIFER G ROFF 2009
  • 11. Student : Teacher Ratio J ENNIFER G ROFF 2009
  • 12. Student Achievement Student : Teacher Ratio J ENNIFER G ROFF 2009
  • 13. Student Achievement Student : Teacher Ratio J ENNIFER G ROFF 2009
  • 14. Student Achievement Student : Teacher Ratio J ENNIFER G ROFF 2009
  • 15. Student Achievement Student : Teacher Ratio NCLB Funding J ENNIFER G ROFF 2009
  • 16. Student Achievement Student : Teacher Ratio NCLB Funding J ENNIFER G ROFF 2009
  • 17. Student Achievement Student : Teacher Ratio NCLB Funding J ENNIFER G ROFF 2009
  • 18. Student Achievement Student : Teacher Ratio NCLB Funding J ENNIFER G ROFF 2009
  • 19. Student Achievement Student : Teacher R Ratio NCLB Funding J ENNIFER G ROFF 2009
  • 20. Student Achievement Student : Teacher R Ratio NCLB Funding State Funding J ENNIFER G ROFF 2009
  • 21. Student Achievement Student : Teacher R Ratio NCLB Funding State Funding J ENNIFER G ROFF 2009
  • 22. Content Alignment Rate 8 B R exposure to test content 8 class time available J ENNIFER G ROFF 2009
  • 23. Content Alignment Rate Subjects Taught/ Tested 8 B R exposure to test content 8 class time available J ENNIFER G ROFF 2009
  • 24. Content Alignment Rate Subjects Taught/ Tested 8 B R exposure to test content 8 class time available J ENNIFER G ROFF 2009
  • 25. Content Alignment Rate Subjects Taught/ Tested 8 Subject Not Taught/ Tested B R exposure to test content 8 class time available J ENNIFER G ROFF 2009
  • 26. Content Alignment Rate Subjects Taught/ Tested 8 Subject Not Taught/ Tested B Students R Proficient exposure to test content 8 class time available J ENNIFER G ROFF 2009
  • 27. Content Alignment Rate Subjects Taught/ Tested 8 Subject Not Taught/ Tested B Students R Proficient exposure to test content 8 class time available Students Not Proficient J ENNIFER G ROFF 2009
  • 28. H IERARCHICAL L EVELS OF E DUCATIONAL S YSTEM P OLICY A NALYSIS Federal State District School Classroom Student Teacher J ENNIFER G ROFF 2009
  • 29. J ENNIFER G ROFF jennifer_groff@mail.har vard.edu 2009