1. Economic Policy Simulation and Optimization Peter Le Computer Systems Research Period 2 3/19/2009

2. Purpose Create feasible and simple economic (taxation and welfare) model Implement optimization algorithm effectively Help improve public policy through test runs and simulation data

3. Economic Policy Government regulation Citizen feedback Changes depending on demographics and economy

4. Simulation Government/Citizen relationship over a 12 year cycle Citizen objects consume, produce, spend, and are taxed Government welfare based on need/approval Society assessment based on citizen self- assessment, approval ratings, and government self-assessment

5. Problems to Solve Realistic economic cycle Feasible demographics Identifying ramifications of different policy change

6. Simulation Optimization Retrieve raw data and assess Multiple variables mean the best run isn’t necessarily optimal Optimization

7. Background Data on taxes and welfare – Higher taxes, more government programs – Upward trend of spending Not much previous research “Happiness” assessments

8. Development Q1 – Preliminary research – Starting the model Q2 – Finishing the model – Data handling and analysis Q3 – Optimization research – Coding the optimization stage Q4 – Final optimization program – Assessment of “best” policies

9. The Cycle Given Citizen traits: Wealth, wealthAssessment Given Government traits: Wealth, wealthAssessment, approvalRating, taxRate, welfareRate, salesTaxRate Monthly assessments to track progress

10. Government Wealth WealthAssessment Approval Rating Tax Rate Sales Tax Rate Responsiveness Population Wealth Approval WealthAssessment Work Rate Spending Rate Taxes Approval Welfare FitnessEvaluation

11. The Model Java, JGrasp Iterative Model allowing for multiple governments, citizen pools Input data → Read data → Cycle → Print data → Analyze data GNUPlot for data display Data somewhat arbitrary now but will look for more realistic data Optimization and randomization mitigates need for solid data

12. Optimization Methods  Hill Climbing  Genetic Algorithm Genetic algorithm seems likely  Run tests  Retrieve data, determine the “best” and “breed” them  Repeat

13. Genetic Algorithm Stochastic process Evolutionary process – Crossbreed pairs with best data – Converges to local maxima/minima Problems – Locality – Lots of variables

14. Data OutputModelAssessmentSorting Selection BreedingMutation Basic Genetic Algorithm

15. Genetic Algorithm Test Generation 1 Generation 6

16. Specific Issues Multivariate crossover Overcoming local maxima Varying degrees of importance # of generations

17. Results

19. Right Now Either the Citizens lose too much money or the Government does, the opposite happens to the other group in each case Some sort of equilibrium for aggregate Citizen wealth Assessment is erratic

20. Testing and Analysis Modifiability accomplished Data not particularly positive – Many variables → data is hard to read – What is “important”? – Sustainable economies Overall model finished, optimization in progress Run over data, “breed” best runs

21. Things to Work On A more fair assessment of the society  Current weights government and population importance equally  One group may fail but the assessment isn’t indicative if the other succeeds enough Optimization Test more situations