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The Context of Forest Management & Economics, Modeling Fundamentals

The Context of Forest Management & Economics, Modeling Fundamentals. Lecture 1 (03/27/2017). About the Instructor. Teaching : inspire students to be curious but critical learners who can think for themselves and nurture creative ideas

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The Context of Forest Management & Economics, Modeling Fundamentals

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  1. The Context of Forest Management & Economics, Modeling Fundamentals Lecture 1 (03/27/2017)

  2. About the Instructor • Teaching: inspire students to be curious but critical learners who can think for themselves and nurture creative ideas • Research: quantify resource tradeoffs and production possibilities to aid natural resource management decision • Training: forest engineering, operations research and forest management science

  3. Forest Management • Definition: Forest management is the science of making decisions about forests at different levels (stand-, forest-, landscape-, national-, and global levels) in order to bring the current state of the forest resource in question to a desired state, while at the same time providing the public (or the private landowner) with a balanced combination of benefits they demand.

  4. The Benefit Bundle of Forests Source: Toth, S. and T. Payn. 2006. Realizing Non-Timber Forest Benefits. Unpublished presentation.

  5. The decision making process in natural resources management Natural Science Management Science Social science • Delphi-process • Nominal Group Technique Demonstration/ visualization of alternatives & tradeoffs Decision tools to generate manage- ment alternatives Data collection Data processing Consensus Building Decision • Optimization • Simulation • Economics • Finance • Remote Sensing • Field Surveys • Permanent Plots • Questionnaires Implementation Monitoring

  6. Management Alternatives and Consensus Building

  7. Models to Solve Natural Resource Problems • Descriptive models • What’s there? – patterns • What’s happening? – processes • Spatial and temporal interactions • Measurements, monitoring • Statistical models

  8. Models to Solve Natural Resource Problems (cont.) • Predictive models • What happens if we do this vs. that? • Simulation, stochastic model, scenario analyses, etc. • Prescriptive models • What is the best course of action? • Optimization

  9. How do descriptive, predictive and prescriptive models work together? Descriptions Predictions Prescriptions • Where are the insects? • Where are the damaged trees? • Intensity of damages • Host selection behavior • Population dynamics • Stand susceptibility and risk • Projected spatial dispersal • Expected insect and host res- ponse to treatments Image source: Mark McGregor, USDA Forest Service, Bugwood.org

  10. The role of decision models

  11. Models and Model Building Fundamentals

  12. Models • Abstract representations of the real world • Lack insignificant details • Can help better understand the key relations in the system/problem • Useful for forecasting and decision making

  13. Model types • Scale models (e.g., model airplane) • Pictorial models (photographs, maps) • Flow charts: illustrate the interrelationships among components • Mathematical models

  14. f (5ac) d (12ac) b (4ac) a (5ac) c (6ac) e (9ac) f c d b a e

  15. f (5ac) f (5ac) d (12ac) d (12ac) d (12ac) B (4ac) b (4ac) b (4ac) b (4ac) a (5ac) a (5ac) a (5ac) A (5ac) c (6ac) c (6ac) E (9ac) e (9ac) e (9ac) e (9ac)

  16. f (5ac) d (12ac) b (4ac) a (5ac) c (6ac) e (9ac)

  17. A mathematical program: f (5ac) Objective function(s) d (12ac) b (4ac) a (5ac) c (6ac) e (9ac) Constraints

  18. Mathematical models • The most abstract • Concise • Can be solved by efficient algorithms using electronic computers, • Thus, very powerful.

  19. Good Modeling Practices • The quality of input data determines the quality of output data • The nature of the management problem determines the choice of the model (not the other way around) Ask: • Is the model to be used to simulate, evaluate, optimize, or describe the system or phenomenon?

  20. Good Modeling Practices (cont.) • What is the scale, resolution and extent of the problem? • What are the outputs (results) of the model? • What are these results used for? • Who will use them?

  21. Optimization Models • Deterministic vs. probabilistic optimization • Convex vs. non-convex problems • Constrained vs. unconstrained optimization • Exact vs. ad-hoc (heuristic) optimization • Static vs. sequential (dynamic) decisions • Single vs. multi-objective optimization • Single vs. multiple decision makers • Single vs. multiple players (games)

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