Problem Definition and Causal Loop Diagrams

1. Problem Definition and Causal Loop Diagrams James R. Burns July 2008

2. Assignment • Complete exercise 12 and use VENSIM to create the CLD • VENSIM cannot translate CLD’s into working simulations • Develop the CLD for your term project problem

3. Problem Definition • The wrong model for the right problem is disconcerting, but fixable • The “right” model for the wrong problem is disastrous

4. The Right Problem • The first order of the day • Requires discussion, dialogue, listening • “I feel your pain”

5. The right paradigm • Is this a dynamic problem? • Are there risk aspects to it? • Is it a resource allocation problem? • A scheduling/routing problem? • A cost minimization problem? • APPLY THE RIGHT PARADIGM

6. Dynamic problems • There is change over time • The changing character of the situation IS THE PROBLEM • The problem should be studied in aggregates • The problem does not have a significant stochastic component or complexion to it

7. Dynamical Models: Explicit, Ordinary Differential Equations

8. Start with descriptions of the following • PURPOSE • Identify who the decision-maker(s) are and involve them in the model-building process • PERSPECTIVE • PROBLEM • MODE

9. What are we doing here???? • Attempting to characterize, cope with and understand complexity • Especially DYNAMIC complexity, but also to a lesser extent detail complexity • Inventing a physics for a system or process for which there exists no physics • You get to become a Newton, a Liebnitz, a Galileo, an Einstein, a ….

10. WHY??? • How many of you have ever used a model to make a decision or take an action? • All decisions/executive actions are taken on the basis of models all the time • Because mental models frame and color our understanding of the problem—forcing us to take a particular course of action • Mental models must be driven by more formal, refined and analytical models—causal models/simulation models

11. Problem Problem SD Model Mental Model Mental Model Decision Decision Action Action

12. Uses to which these models can be put • What IF experiments—hands on experimentation • Decision making • Planning • Problem solving • Creativity • Out of the box thinking • Hypothesis testing • LEARNING

13. The Methodology once problem is identified • Find substance • Delineate CLDs, BOT charts • Submit these for outside scrutiny • Delineate SFD • Implement simulation in VENSIM • Submit for outside VALIDATION • Utilize model for policy experimentation

14. Find substance • Written material • Books • Articles • Policy and procedure manuals • People’s heads • Order of magnitude more here • Must conduct interviews, build CLD’s, show them to the interviewees to capture this

15. Delineate CLDs, BOTs • Collect info on the problem • List variables on post-it notes • Describe causality using a CLD • Describe behavior using a BOT diagram

16. Submit these for outside scrutiny • We simply must get someone qualified to assess the substance of the model

17. Delineate SFD • Translate CLD into SFD

18. Implement simulation in VENSIM • Enter into VENSIMPerform sensitivity and validation studies

19. Submit for outside validation

20. Utilize model for policy experimentation • Perform policy and WHAT IF experimentsWrite recommendations

21. Key Benefits of the ST/SD • A deeper level of learning • Far better than a mere verbal description • A clear structural representation of the problem or process • A way to extract the behavioral implications from the structure and data • A “hands on” tool on which to conduct WHAT IF

22. Places where failure can occur • You must have decision maker involvement • If you are going to have an impact on their mental models, they must be involved in the model development process from beginning to end • Solutions to the model must be reality checked to see if in-fact they can become solutions to the problem

23. Causal Loop Diagrams [CLD’s]

24. Motivation: CLD’s are excellent for… • Capturing hypotheses about the structural causes of the dynamics • Capturing the mental models of individuals or teams • Communicating the important feedbacks you believe are responsible for creating a problem

25. Notation • Variables and constants called quantities • Arrows—denoting the casual influences among the quantities • Independent quantity—the cause • Dependent quantity—the effect

26. Quantities • Use nouns of noun phrases • Assert nouns and noun phrases in their positive sense

27. Example

28. The Connector • Also called “arrow,” “edge,” • Is always directed from a quantity to a quantity • Denotes causation or influence • Could be proportional • Inversely • Directly • Could be accumulative or depletive

29. Single-sector Exponential growth Model we considered • Consider a simple population with infinite resources--food, water, air, etc. Given, mortality information in terms of birth and death rates, what is this population likely to grow to by a certain time? • Over a period of 200 years, the population is impacted by both births and deaths. These are, in turn functions of birth rate norm and death rate norm as well as population. • A population of 1.6 billion with a birth rate norm of .04 and a death rate norm of .028

30. We Listed the Quantities • Population • Births • Deaths • Birth rate norm • Death rate norm

32. Using VENSIM TO CONSTRUCT CLD’s • Use the variable – auxiliary/constant tool to establish the quantities and their locations • Use the “arrow” tool to establish the links between the quantities • Use the “Comment” tool to mark the polarities of the causal edges (links, arrows) • Use the “Comment” tool to mark the loops as reinforcing or balancing

33. Experiments with growth models • Models with only one rate and one state • Average lifetime death rates • Models in which the exiting rate is not a function of its adjacent state

34. Example: • Build a model of work flow from work undone to work completed. • This flow is controlled by a “work rate.” • Assume there are 1000 days of undone work • Assume the work rate is 20 completed days a month • Assume the units on time are months • Assume no work is completed initially.

35. Solving the problem of negative stock drainage • pass information to the outgoing rate • use the IF THEN ELSE function

36. Causation vs. Correlation

37. Inadequate cause: Confusion

38. Validation of CLD’s • Clarity • Quantity existence • Connection edge existence • Cause sufficiency • Additional cause possibility • Cause/effect reversal • Predicted effect existence • Tautology

39. Simplified Translationof CLD's into SFD's

40. Motivation • In the current “environment” there are too many connection “opportunities” that confuse and invalidate models built by naive users • The conventional translation of CLD’s into SFD’s is not easy. • We may need to distinguish between Senge-style CLD’s created for just the purpose of capturing the dynamics of the process from CLD’s intended to lead us to a SFD

41. More Motivation

47. Robust Loops • In any loop involving a pair of quantities/edges, • one quantity must be a rate • the other a state or stock, • one edge must be a flow edge • the other an information edge

48. CONSISTENCY • All of the edges directed toward a quantity are of the same type • All of the edges directed away from a quantity are of the same type

49. Rates and their edges

50. Parameters and their edges