The Systems Approach

1

• The Systems Approach
• 1. Set goals and objectives
• 2. Abstract Modeling
• -identify system elements
• -define system inputs and outputs
• -describe system interactions
• -identify possible solutions
• -feasibility study
• -compute expected outputs for
• various strategies
• 3. Decision
• 4. Implementation Design
• Implementation

2
System Identification

• System Environment

Environmental Inputs (Exogenous variables)
Desired Outputs
Undesired Outputs (Externalities)
System Design Parameters
Controllable Inputs
3

• The Scientific Method
• 1. Observe
• Develop a hypothesis (Induction)
• -Propose causal association or process
• Test hypothesis (Deduction)
• -Test by evaluating consequences
• 4. Test theories or laws (prediction and
• control)
• Induction developing generalities from
  specifics
• All the furry creatures Ive seen are mammals.
  Thus, all furry creatures are mammals.
• Deduction predicting specifics from
  generalities
• All furry creatures are mammals. This
• new creature is furry. Therefore, this new
  creature is a mammal.

4

• The 8 Steps of Management
• 1. Set Goals
• 2. Examine the Resource
• 3. Analyze the Situation and set Objectives
• 4. Prescribe a treatment (and evaluation plan)
• 5. Organize Personnel and Resources
• 6. Implement
• Evaluate the Results
• 8. Maintain

5

• Goals - broad statement of desired outcome of
  management
• Healthy ecosystem
• Good fishing
• Productive forest
• Safe water
• Objectives specific targets to reach goal.
  Objectives are SMART
• Specific
• Measurable
• Achievable (sometimes Action-oriented)
• Related to goal (sometimes Realistic,
  Results-focused)
• Time-Bound
• Example To reduce mercury concentration in
  drinking water supply for Lansing from 10 ppb to
  5 ppb by 2010
• Another key is that objectives do not include
  how to get to the specified targets. That is
  part of the management prescription

6
Differential Equations Difference Equations
Catastrophe models
Topological
Dynamic
Optimization
Matrix
Model Families
Leslie matrix Markov models
Linear Programming Dynamic Program. Non-linear
searches
Statistical
Stochastic
Linear models Non-linear models Distribution
models
Univariate inferential Multivariate
inferential Exploratory Estimation (sample
design) Regression
Adapted from Economic-Ecological Modeling.
Edited by L. C. Braat and W. F. J. Van Lierop.
Page 40.