Mixed Structural and Behavioral Models for Predicting the Future Behavior of some Aspects of the Mac - PowerPoint PPT Presentation

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Mixed Structural and Behavioral Models for Predicting the Future Behavior of some Aspects of the Mac

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FIR Model. Regenerate. Fuzzification. Inductive Modeling. Inductive Simulation. Modeling the Error ... Mixed SD/FIR offers the best of both worlds. ... – PowerPoint PPT presentation

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Title: Mixed Structural and Behavioral Models for Predicting the Future Behavior of some Aspects of the Mac


1
Mixed Structural and Behavioral Models for
Predicting the Future Behavior of some Aspects of
the Macroeconomy
  • Mukund Moorthy
  • 2nd February 1999

2
Contents
  • Economic Modeling
  • System Dynamics
  • Fuzzy Inductive Reasoning
  • Proposed Macroeconomic Model
  • Food Demand Modeling
  • Conclusion

3
Economic Modeling
  • Economic Forecasting Techniques
  • Time Series Data
  • Neural Networks

4
Time Series Data
  • Time Series Components
  • Trend ( T )
  • Cyclical ( C )
  • Seasonal ( S )
  • Irregular ( I )

5
Curve Fitting
  • Linear Trend Equation

6
Curve Fitting
  • Exponential Trend Equation
  • Polynomial Trend Equation

7
Smoothing Techniques
  • Moving Average
  • each point is average of N points
  • Exponential Smoothing

8
Time Series Forecasting
  • Box-Jenkins Method

9
Economic Forecasting
  • Step-wise Auto-regressive method
  • Neural Networks

10
System Dynamics
  • Modeling Dynamic Systems
  • Information feedback loops

11
System Dynamics
  • Levels
  • Flow Rates
  • Decision Functions

12
System Dynamics
  • Levels and Rates
  • Laundry List

13
Structure Diagram
14
Forresters World Model
  • Population
  • Capital Investment
  • Unrecoverable Natural Resources
  • Fraction of Capital Invested in the Agricultural
    Sector
  • Pollution

15
Structure Diagram of Forresters World Model
16
Shortcomings of the World Model
  • Levels and Rates
  • Laundry List

17
Fuzzy Inductive Reasoning
  • Discretization of quantitative information
    (Fuzzy Recoding)
  • Reasoning about discrete categories (Qualitative
    Modeling)
  • Inferring consequences about categories
    (Qualitative Simulation)
  • Interpolation between neighboring categories
    using fuzzy logic (Fuzzy Regeneration)

18
Fuzzy Inductive Reasoning
Mixed Quantitative/Qualitative Modeling
19
Fuzzification
20
Inductive Modeling
21
Inductive Simulation
22
Modeling the Error
  • Making predictions is easy!
  • Knowing how good the predictions are That is the
    real problem!
  • A modeling/simulation methodology that doesnt
    assess its own error is worthless!
  • Modeling the error can only be done in a
    statistical sense because otherwise, the error
    could be subtracted from the prediction leading
    to a prediction without the error.

23
Food Demand Model
  • Naïve Model
  • Enhanced Macroeconomic Model

24
Naïve Model
25
Population Dynamics
26
Population Dynamics
  • Predicting Growth Functions

k(n1) FIR k(n), P(n), k(n-1), P(n-1),
27
Population Dynamics
28
Macroeconomy
29
Macroeconomy
30
Food Demand/Supply
31
Enhanced Macroeconomic Model
32
Population Layer
33
Population Layer
34
Economy Layer
35
Food Demand/Supply Layer
36
Results
  • Annual / Quarterly Data
  • Layer One - Population Layer
  • Layer two - Economy Layer
  • Layer three - Food Demand Layer
  • Layer Four - Food Supply Layer
  • Optimization

37
Population Dynamics
38
Population Dynamics
39
Economy Layer
40
Food Supply Layer
41
Food Demand Layer
Food Supply
Food Demand
Macroeconomy
Population Dynamics
42
Optimization
43
Optimization
44
Conclusion and Future Work
  • Mixed SD/FIR offers the best of both worlds.
  • Application to any U.S. industry with change of
    demand and supply layers alone.
  • Application to any new country or region with new
    data for layers 1 and 2.
  • Fuzzy Inductive Reasoning features a model
    synthesis capability rather than a model learning
    approach. It is therefore quite fast in setting
    up the model.

45
Conclusion and Future Work
  • Fuzzy Inductive Reasoning is highly robust when
    used correctly.
  • Fuzzy Inductive Reasoning offers a
    self-assessment feature, which is easily the most
    important characteristic of the methodology.
  • Optimization with data collected at more frequent
    intervals.
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