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

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

3. Economic Modeling n Economic Forecasting Techniques –Time Series Data –Neural Networks

4. Time Series Data n Time Series Components –Trend ( T ) –Cyclical ( C ) –Seasonal ( S ) –Irregular ( I )

5. Curve Fitting n Linear Trend Equation

6. Curve Fitting n Exponential Trend Equation n Polynomial Trend Equation

7. Smoothing Techniques n Moving Average –each point is average of N points n Exponential Smoothing

8. Time Series Forecasting n Box-Jenkins Method

9. Economic Forecasting n Step-wise Auto-regressive method n Neural Networks

10. System Dynamics n Modeling Dynamic Systems –Information feedback loops

11. System Dynamics –Levels –Flow Rates –Decision Functions

12. System Dynamics n Levels and Rates n Laundry List Levels Rates Inflows Outflows PopulationBirth RateDeath Rate MoneyIncomeExpenses FrustrationStressAffection LoveAffectionFrustration Tumor CellsInfectionTreatment Inventory on StockShipmentsSales KnowledgeLearningForgetting Birth Rate: Population Material Standard of Living Food Quality Food Quantity Education Contraceptives Religious Beliefs

13. Structure Diagram

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

15. Structure Diagram of Forrester’s World Model

16. Shortcomings of the World Model n Levels and Rates n Laundry List

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

18. Fuzzy Inductive Reasoning Mixed Quantitative/Qualitative Modeling Quantitative Subsystem Recode FIR Model Regenerate Quantitative Subsystem Recode FIR Model Regenerate

19. Fuzzification

20. Inductive Modeling

21. Inductive Simulation

22. Modeling the Error n Making predictions is easy! n Knowing how good the predictions are: That is the real problem! n A modeling/simulation methodology that doesn’t assess its own error is worthless! n 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 n Naïve Model n Enhanced Macroeconomic Model

24. Naïve Model

25. Population Dynamics Macroeconomy Food Demand Food Supply

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

27. Population Dynamics Macroeconomy Food Demand Food Supply

28. Macroeconomy Population Dynamics Macroeconomy Food Demand Food Supply

29. Macroeconomy Population Dynamics Macroeconomy Food Demand Food Supply

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

31. Enhanced Macroeconomic Model

32. Population Layer

34. Economy Layer

35. Food Demand/Supply Layer

36. Results n Annual / Quarterly Data n Layer One - Population Layer n Layer two - Economy Layer n Layer three - Food Demand Layer n Layer Four - Food Supply Layer n Optimization

37. Population Dynamics Macroeconomy Food Demand Food Supply

38. Population Dynamics Macroeconomy Food Demand Food Supply

39. Economy Layer Population Dynamics Macroeconomy Food Demand Food Supply

40. Food Supply Layer Population Dynamics Macroeconomy Food Demand Food Supply

41. Food Demand Layer Population Dynamics Macroeconomy Food Demand Food Supply

42. Optimization

43. Optimization

44. Conclusion and Future Work n Mixed SD/FIR offers the best of both worlds. n Application to any U.S. industry with change of demand and supply layers alone. n Application to any new country or region with new data for layers 1 and 2. n 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 n Fuzzy Inductive Reasoning is highly robust when used correctly. n Fuzzy Inductive Reasoning offers a self-assessment feature, which is easily the most important characteristic of the methodology. n Optimization with data collected at more frequent intervals.