Economic Policy Simulation and Optimization Peter Le Computer Systems Research Period 2 1/20/2009

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

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

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

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

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

Background Data on taxes and welfare Not much previous research “Happiness” assessments

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

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

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

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

Results

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 Assessment is erratic

Testing and Analysis Modifiability accomplished Data not particularly positive Overall model finished, moving on to optimization Run over data, “breed” best runs

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  May use ideas from AI last year  Any sort of improvement on data is success Test more situations