Economic Policy Simulation and Optimization

Peter LeComputer Systems Research

Period 23/19/2009

Purpose

Create feasible and simple economic (taxation and welfare) model

Implement optimization algorithm effectively

Help improve public policy through test runs and simulation data

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– Higher taxes, more government

programs– Upward trend of spending

Not much previous research “Happiness” assessments

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

The Cycle

Given Citizen traits: Wealth, wealthAssessment

Given Government traits: Wealth, wealthAssessment, approvalRating, taxRate, welfareRate, salesTaxRate

Monthly assessments to track progress

GovernmentWealth

WealthAssessmentApproval Rating

Tax RateSales Tax Rate

Responsiveness

PopulationWealth

ApprovalWealthAssessment

Work RateSpending 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

Genetic Algorithm

• Stochastic process• Evolutionary process

– Crossbreed pairs with best data– Converges to local maxima/minima

• Problems– Locality– Lots of variables

Data OutputModel Assessment Sorting

SelectionBreedingMutation

Basic Genetic Algorithm

Genetic Algorithm Test

Generation 1

Generation 6

Specific Issues

• Multivariate crossover• Overcoming local maxima• Varying degrees of importance• # of generations

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 for aggregate Citizen wealth

Assessment is erratic

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

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