Computable Economics: Challenge #3

8/3/2019 Computable Economics: Challenge #3

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ECO 320v: Computable Economics

Sam Reisz

Jordan FoxJoshua Carpenter


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We will be using the Wealth distribution model as a way to implement theconcept of inheritance. Our curiosity for the idea of inheritance spawns from the oft seen

passing down of money through generations. In many situations, individuals are wealthy

not because of what they themselves have done, but rather because of the passing offamilial wealth. Through the Netlogo model, we plan on giving newly spawned turtles

the ability to inherit the wealth of the previous turtles parent. This inheritance concept

would only affect the newly created cell when the parent cell has reached their maximumlife span. Inheritance would not affect the cells where a turtle has died because they ran

out of grain, due to the fact that there will be no grain to inherit. We have also added twoother dimensions to the experiment. First, the metabolism will be set to vary. This will

allow us to test and see whether the rate at which each class consumes has any effect onthe gini-coefficient. Also, we have reprogrammed the model so that the turtles are not

dependent upon their neighbors to move. Now they move in the four directions of

allowance randomly. Along with testing inheritance and varying metabolism, we willalso have two other treatment factors vary. And as well, we expect to find patterns with

these other treatment factors. The other treatment factors that we will be testing are

vision and number of grain grown.

The hypothesis:

If new turtles are able to inherit the previous wealth of their parent, then the inequality of

wealth will be the greatest when metabolism is at its lowest, vision is at its highest and

number of grain grown is at its highest.

By varying each of these factors, we will be able to see the effect that each value

of the given variable and combinations of them may have on the gini-coefficient. This

will be vital in our analysis and likely lead to additional conclusions not in ourhypothesis. We have created this theory based on the ability of new turtles to stockpile

wealth between generations. There is a global assumption that the rich consume a lesser

proportion of their expendable income than the under-classes do. Thus increasing their

savings and consequently increasing wealth. This could be a piece of the explanation forthe very large disproportion between the rich and poor.

Reporting Results

In order for our hypothesis to be tested we must first develop values for the

counterparts that can be verified by the data drawn from the experiment we perform.Therefore, new turtles must be able to inherit their parents wealth after their parent has

died, or the structural feature inherit must be set equal to True. Second, we must

specify that the largest over-all differential between classes will be when metabolism is atits lowest, vision is at its highest and number-grain-grown is at its highest. Let the

treatment factors be defined as:

c = Gini-Coefficient

v = Vision

g = Number-grain-grown

m = Metabolism

And a function of the gini-coefficient:


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= (,,)

Let the ideal values be defined as:

Max-vision = Vmax

Max-grain-grown = Gmax

Min-metabolism = mminMax-Gini-Coefficient =

Optimizing the gini-coefficient function:

| = fVmax, Gmax, mmin =

We define our null hypothesis and test it with the raw data from our experiment.

H0: fVmax, Gmax, mmin

H1: = fVmax, Gmax, mmin

Essentially, out of the 30 runs, we want to find the combination:

Gini-Coefficient Vision Grain-Grown Metabolism

15 10 1

The maximum gini-coefficient, , is that of all other gini-coefficients in the experiment.

The results were surprising. We found that the maximum gini-coefficient of ourexperiment came during the 8th run with the value, = 0.630894189. This part of the null

hypothesis holds true. Further in the data though we see that other values arent where

we expected. Vision is at its lowest, Grain-Grown is at mid-interval and Metabolism is atmid-interval.

If we expand the hypothesis and view the top five runs we find the surprisingresults. The following table gives a summary of the top and bottom five values of the

gini-coefficient and the level of the treatment factors at that period in time:

Run Top Five Gini-Coefficient Vision Grain-Grown Metabolism

#8 1 0.630894189 1 5 14

#1 2 0.622832067 1 1 1

#12 3 0.612047308 1 10 14#2 4 0.566106107 1 1 1#6 5 0.554769622 1 5 1


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Run Bottom Five Gini-Coefficient Vision Grain-Grown Metabolism

#29 30 0.154447984 11 5 1

#30 29 0.159194854 11 5 1

#20 28 0.179897900 6 10 1

#22 27 0.190644613 6 10 1#18 26 0.196794208 6 5 1

Since the maximum gini-coefficient doesnt happen at:

= fVmax, Gmax, mmin

We fail to reject the null hypothesis.

Analysis of the Findings

Treatment Factor Value Report

The treatment factors that vary:

Metabolism 1 13 25

Num-Grain-Grown 1 5 10

Vision 1 8 15

Fixed values:

Life-expectancy-min 1

Life-expectancy-max 83

Grain-growth-interval 6Percent-best-land 10

test

10/22/2009 16:36:07:568 -0400

min-pxcor max-pxcor min-pycor max-pycor

-25 25 -25 25

Run # inherit life-exp-min life-exp-max vision grn-grwn grn-grwth-int %-bst-Lnd

1 TRUE 1 83 1 1 8 10

2 TRUE 1 83 1 1 8 10

3 TRUE 1 83 1 1 8 10

4 TRUE 1 83 1 1 8 10

5 TRUE 1 83 1 5 8 10

6 TRUE 1 83 1 5 8 10

7 TRUE 1 83 1 5 8 10


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8 TRUE 1 83 1 5 8 10

9 TRUE 1 83 1 10 8 10

10 TRUE 1 83 1 10 8 10

11 TRUE 1 83 1 10 8 10

12 TRUE 1 83 1 10 8 10

13 TRUE 1 83 6 1 8 10

14 TRUE 1 83 6 1 8 10

15 TRUE 1 83 6 1 8 10

16 TRUE 1 83 6 1 8 1017 TRUE 1 83 6 5 8 10

18 TRUE 1 83 6 5 8 10

19 TRUE 1 83 6 5 8 10

20 TRUE 1 83 6 5 8 10

21 TRUE 1 83 6 10 8 10

22 TRUE 1 83 6 10 8 10

23 TRUE 1 83 6 10 8 10

24 TRUE 1 83 6 10 8 10

25 TRUE 1 83 11 1 8 10

26 TRUE 1 83 11 1 8 10

27 TRUE 1 83 11 1 8 10

28 TRUE 1 83 11 1 8 10

29 TRUE 1 83 11 5 8 10

30 TRUE 1 83 11 5 8 10

Run # metabolism # ppl [step] gini-coeff the-seed Std-Dev

1 1 250 100 0.622832067 -3.06419E+15 8.90E-162 1 250 100 0.566106107 2.66023E+15 1.00E-15

3 14 250 100 0.440682716 -7.57058E+15 2.78E-15

4 14 250 100 0.420120154 -6.17568E+15 1.39E-15

5 1 250 100 0.478673134 -5.94196E+15 1.78E-15

6 1 250 100 0.554769622 3.18137E+15 2.56E-15

7 14 250 100 0.50497552 4.75641E+14 1.22E-15

8 14 250 100 0.630894189 -6.45686E+15 1.22E-15

9 1 250 100 0.479557715 -5.49949E+14 9.46E-16

10 1 250 100 0.480010085 -1.5088E+15 1.11E-15

11 14 250 100 0.512119288 3.28608E+14 2.00E-15

12 14 250 100 0.612047308 5.81065E+15 2.67E-15

13 1 250 100 0.294942547 8.55834E+15 1.78E-15

14 1 250 100 0.279598848 4.73094E+15 8.34E-16

15 14 250 100 0.399656184 -4.23465E+15 1.33E-15

16 14 250 100 0.392575566 3.23571E+15 1.33E-15

17 1 250 100 0.225383258 4.20535E+15 1.95E-16

18 1 250 100 0.196794208 6.44056E+15 4.17E-16


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19 14 250 100 0.502084109 -6.54923E+14 2.45E-15

20 14 250 100 0.512587848 -1.2435E+15 1.22E-15

21 1 250 100 0.1798979 -1.38871E+15 4.45E-16

22 1 250 100 0.190644613 3.00095E+15 8.34E-16

23 14 250 100 0.524434996 3.91973E+15 2.89E-15

24 14 250 100 0.5197689 6.29865E+15 1.89E-15

25 1 250 100 0.28274441 -8.25909E+15 2.22E-16

26 1 250 100 0.26124385 2.23466E+15 1.06E-1527 14 250 100 0.37082874 5.27403E+15 1.22E-15

28 14 250 100 0.397968425 2.85437E+15 3.34E-16

29 1 250 100 0.154447984 4.47477E+15 1.95E-16

30 1 250 100 0.159194854 2.04986E+15 5.01E-16


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Standard Deviation of the Gini-

Coefficient

0

0.1

0.2

0.3

0.4

0.5

0.6

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Gini-Coefficient

Gini-Coefficient

Computable Economics: Challenge #3

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