Rural System's

Modeling Strategy

evolving since March 30, 1999

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Strategy may be a better word for the following presentation but that is overused. I'm trying to present an approach, and suggested procedure, a thought pattern. It has been useful; it can be again. It's worth revising. At least it may help newcomers to Rural System to become effective rapidly.

Partially based on class notes of Fred Bunnell, University of British Columbia.

Topics:

Piotr Szmptko suggested 5 types of models

  1. of a simple system (possibly combined as for type #5)
  2. of a teleological system (a system relative to a subsystem)
  3. of a functional system
  4. of a purposeful system
  5. of a multiple system

Concepts and definitions of models vary. A model is any set of statements which define or describe a system (W. Scott Overton 1974).

A model is a representation of reality.

I hold that whole models of rural communities both natural and social and those together, can be modeled. Parts are modeled and integrated, the results of one, becomming input to another (or others).

Modeling must be of subsystems, with clear limits and potential uses. Collecting all information and trying to model everything in hopes of finding something or seeing something differently is not likely to work. Because a model "runs" is not proof of its goodness. There must be a theory of how things of the system are related.

Models are general systems. They can be described as what they do or how they work (behavior or function) and as what parts they contain (structure). Relations may express action between subsystems or within large systems (the believed relationship of system parts, the rules of the system).

Modelers need to clarify when systems on different trajectories arrive at similar or same places. Equifinality is real and difficult to model, and may cause doubts in model performance.

  1. Integrating concepts and theories
  2. Organizing enterprise and research groups
  3. Stimulating ideas about structures and processes of systems
  4. Analyzing and synthesizing data
  5. Testing hypotheses
  6. Improving conceptual consistency
  7. Selecting optimum actions or alternatives
  8. Extrapolating information to related sites
  9. Developing strategies for enterprise and ecosystem development and change (mitigating change, improving competition)
  10. Predicting future conditions or states of ecosystems or enterprises

Generalized Procedures

  1. Present the concept
  2. Clarify the questions
  3. Start at the end! Start with the written final printed output and graphics! Use the models to correct and revise the "final" results and fill in the blanks. In most systems there are few blanks; estimates are usually possible, even if very far off the correct answer.
  4. State the major relations perceived
    Q = f (a,b,c...)
    R = f(Q)
    R approaching R*
  5. Clarify and quantify approximate values of variables like "a"
  6. Decide on a solution paradigm like linear programming, simulation, optimum searching techniques, etc) typically iterative solutions heuristic programming or expert systems
  7. describe "a" in time as a function of x1..
    at + 1 = f( x1x2x3x4...)
    at + 1 = sum of inputs - sum of outputs
  8. Assign tasks. Each participant says here is my "a" and these are the factors that affect it.
  9. These are the ways (plus/minus. rate) it affects "a".
  10. Get more factors (usually needed).
  11. Stop at 80% confidence level.
  12. Frequent high intensity meetings; get a first approximation working quickly.
  13. Start anywhere (make it easy to start; everything is connected).
  14. Preserve alternatives when strong objections arise. There is no one best way to model. In general, stick to easier and more easily corrected and revised alternative.
  15. A review of the needed (desired) outputs will usually reduce the alternatives.
  16. Avoid people that take themselves too seriously.
  17. Avoid mathematical jargon.
  18. Stay flexible, even main groups.
  19. Clarify programs; bugs will delay the larger process.
  20. Avoid traditional institutional categories (e.g., zoology, botany, economics).
  21. Ease and surety of making inputs to the system creation and to making changes is important. After adjustments and stabilization, there are likely to be very few variables changed in making local decisions.
  22. The time scale can be changed later.
  23. The space scale can be changed later; scales need to be tentative (prices- local, regional,national)
  24. Use IF statement within the programming. If prices change, then x occurs. This is not a prediction or forecast, merely a conditional statement.
  25. When in doubt, make an estimate...and a note. The system operation will probably show where poor estimates have been made.
  26. Seek to explain. Prediction is rarely possible due to chaos theory.
  27. Models can only be invalidated. We can only fail to invalidate a model.
  28. A fit to data provides no test of underlying assumptions. A fit to data may be a random occurrence.
  29. Write it up. People, programming languages, and hardware disappear. A model is an investment. It must be used; it must be protected and revised. If "no good", it probably can be revised, not completely discarded. (Few of us are ever that wrong.!)

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See Modeling Notes in Modern Wild Faunal System Management


Perhaps you will share ideas with me about some of the topic(s) above .

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Robert H. Giles, Jr.
August 14, 2005, December, 2007