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

This study proposes a mixed modeling approach combining system dynamics, fuzzy inductive reasoning, and optimization techniques for predicting future macroeconomic behavior. The methodology is applied to food demand modeling and offers robust and self-assessment features.

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

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  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. Exponential Trend Equation Polynomial Trend Equation Curve Fitting

  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. Levels and Rates Laundry List Levels Rates Inflows Outflows Population Birth Rate Death Rate Money Income Expenses Frustration Stress Affection Love Affection Frustration Tumor Cells Infection Treatment Inventory on Stock Shipments Sales Knowledge Learning Forgetting • Population • Material Standard of Living • Food Quality • Food Quantity • Education • Contraceptives • Religious Beliefs Birth Rate: System Dynamics

  13. Structure Diagram

  14. Forrester’s World Model • Population • Capital Investment • Unrecoverable Natural Resources • Fraction of Capital Invested in the Agricultural Sector • Pollution

  15. Structure Diagram of Forrester’s 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. Quantitative Subsystem Quantitative Subsystem FIR Model FIR Model Recode Recode Regenerate Regenerate 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 doesn’t 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. Food Supply Food Demand Macroeconomy Population Dynamics Population Dynamics

  26. Food Supply Food Demand Macroeconomy Population Dynamics Population Dynamics • Predicting Growth Functions k(n+1) = FIR [ k(n), P(n), k(n-1), P(n-1), … ]

  27. Food Supply Food Demand Macroeconomy Population Dynamics Population Dynamics

  28. Food Supply Food Demand Macroeconomy Population Dynamics Macroeconomy

  29. Food Supply Food Demand Macroeconomy Population Dynamics Macroeconomy

  30. Food Supply Food Demand Macroeconomy Population Dynamics 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. Food Supply Food Demand Macroeconomy Population Dynamics Population Dynamics

  38. Food Supply Food Demand Macroeconomy Population Dynamics Population Dynamics

  39. Food Supply Food Demand Macroeconomy Population Dynamics Economy Layer

  40. Food Supply Food Demand Macroeconomy Population Dynamics 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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