economic policy simulation and optimization peter le computer systems research period 2 3/19/2009
TRANSCRIPT
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Economic Policy Simulation and Optimization
Peter LeComputer Systems Research
Period 23/19/2009
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Purpose
Create feasible and simple economic (taxation and welfare) model
Implement optimization algorithm effectively
Help improve public policy through test runs and simulation data
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Economic Policy
Government regulation Citizen feedback Changes depending on demographics
and economy
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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
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Problems to Solve
Realistic economic cycle Feasible demographics Identifying ramifications of different policy
change
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Simulation Optimization
Retrieve raw data and assess Multiple variables mean the best run isn’t
necessarily optimal Optimization
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Background
Data on taxes and welfare– Higher taxes, more government
programs– Upward trend of spending
Not much previous research “Happiness” assessments
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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
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The Cycle
Given Citizen traits: Wealth, wealthAssessment
Given Government traits: Wealth, wealthAssessment, approvalRating, taxRate, welfareRate, salesTaxRate
Monthly assessments to track progress
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GovernmentWealth
WealthAssessmentApproval Rating
Tax RateSales Tax Rate
Responsiveness
PopulationWealth
ApprovalWealthAssessment
Work RateSpending Rate
Taxes
Approval
Welfare
FitnessEvaluation
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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
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Optimization
Methods Hill Climbing Genetic Algorithm
Genetic algorithm seems likely Run tests Retrieve data, determine the “best” and
“breed” them Repeat
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Genetic Algorithm
• Stochastic process• Evolutionary process
– Crossbreed pairs with best data– Converges to local maxima/minima
• Problems– Locality– Lots of variables
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Data OutputModel Assessment Sorting
SelectionBreedingMutation
Basic Genetic Algorithm
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Genetic Algorithm Test
Generation 1
Generation 6
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Specific Issues
• Multivariate crossover• Overcoming local maxima• Varying degrees of importance• # of generations
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Results
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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
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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
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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