Economic Policy Simulation and Optimization

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Economic Policy Simulation and Optimization

• Peter Le
• Computer Systems Research
• Period 2
• 5/28/2009

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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 isnt
  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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Approval

• Government
• Wealth
• WealthAssessment
• Approval Rating
• Tax Rate
• Sales Tax Rate
• Responsiveness

Taxes
Welfare

• Population
• Wealth
• Approval
• WealthAssessment
• Work Rate
• Spending Rate

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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Why Java?

• Alternatives MASON, NetLogo
• Not pre-packaged, but easily modifiable
• Agent based approach with outside genetic
  algorithm warrants relatively complex code
• Handling input/output

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Optimization

• Methods
• Hill Climbing
• Genetic Algorithm
• Genetic algorithm
• Run tests
• Retrieve data, determine the best and breed
  them
• Repeat
• Advantage over Hill Climbing

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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 Output
Model
Assessment
Sorting
Basic Genetic Algorithm
Selection
Breeding
Mutation
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Genetic Algorithm Test
Generation 1
Generation 6
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Specific Issues

• Multivariate crossover
• Tax rate (Income and sales)
• Welfare rate and criteria (Responsiveness)
• Overcoming local maxima
• Varying degrees of importance
• of generations

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Model Results
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Run 1
Government Wealth
Civilian Aggregate Wealth
Economic Assessment
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Run 2
Government Wealth
Civilian Aggregate Wealth
Economic Assessment
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Run 9
Government Wealth
Civilian Aggregate Wealth
Economic Assessment
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Model Discussion

• Dual-curve behavior elusive
• Economic assessment generally curves downwards
  after variation lessens
• Governments gain wealth steadily
• Civilians gain or lose wealth, but converge to an
  equilibrium

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Model Discussion

• Government wealth always increases without
  deceleration
• Some sort of equilibrium for aggregate Citizen
  wealth
• Assessment is erratic
• Based on immediately previous data
• Based on ratios, large drops/gains are forgotten

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Genetic Algorithm Results

• Early trial Indication of a problem
• Trial number, generation, assessment
• 0, 0, 1.0047507115885828
• 0, 1, 1.0083489109062942
• 0, 2, 1.4181731746426252
• 0, 3, 2.0526606700631387
• 0, 4, NaN
• 0, 5, NaN
• 0, 6, NaN
• 0, 7, NaN
• 0, 8, NaN
• 0, 9, NaN

NaN Not a Number
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Test runs

• Most result in higher assessments
• Some anomalic low assessments

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Whats going on?

• Ratio based assessment
• Large debt or profit or zeros create calculation
  problems
• Government basically loses too much money
• Genetic algorithm doesnt significantly result in
  lower assessments in most runs, but clearly there
  is a problem
• Local maxima

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Discussion

• If the citizenrys gains are more drastic than
  the governments losses, the citizenry approves
• Pattern of over-spending
• People want low taxes but benefits
• High spending, low revenue ? debt
• Government spending doesnt necessarily help the
  economy
• Model doesnt change policy dynamically within a
  run

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Aftermath

• Modifiability accomplished
• Initial data not particularly positive
• Many variables ? data is hard to read
• What is important?
• Sustainable economies
• Genetic algorithm is a success, but the models
  success ultimately lies in the assessment

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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 isnt
  indicative if the other succeeds enough
• Optimization tweaking
• Test more situations different government
  structures

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Lessons Learned

• Variable data sets
• Inferring trends and cause-effect relationships
  from data
• A clear objective is essential