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The topic is over. Carry on with your daily RPing and please remember you are EXPECTED to be respectful of ANY and ALL regions in the game. This is our RMB, but any other is THEIR RMB and we respect their homes. Period.
*Elegarth presses a the nuke launch button*
Anyone wants to guess who is this aimed at?
Rockemsockem and Wubdich
You legends having a nice day today? Or nice evening?
Right to change the subject. We need to put The Seeker of Power on trial for his misuse of dad jokes. He has grown too powerful and the dad jokes are getting cornier and thus he must be sent to a hugger camp
I'll accept the nuke for a hamburger
I could provide a list for that.
But I want this to be over myself. So, I'll head back to the game I'm playing.
Does it look like it?
Yeah, that was a little insensitive of me. Hope things get better for you bro.
[To change the subject, how many of you have read my article on a more realistic way to calculate rich and poor incomes from income inequality and average income?]
The Model
Here, we will be using an income inequality model described by two parameters, χ and α. The model is I = χ/(1-P)^α, where I is income, P is proportion of the population are below you in income level, χ is the minimum income for a full-time worker, and α is the inequality parameter. It is closer to 1 with higher inequality, and closer to zero with lower inequality. Using this equation, we can calculate many of the parameters shown in NationStates, and some not shown, but that are important, using χ and α, by applying or integrating the formula shown above. It should be noted that this formula assumes a minimum income level, which is simply the constant χ. These derived formulas are shown in appendix A.
This model comes from a Stanford paper on modelling inequality (see references), the description of which is as follows. "It is well known that the upper tail of the income distribution follows a power law. One way of thinking about this is to note that income inequality is fractal in nature, as we document more carefully below. In particular, the following questions all have essentially the same answer: What fraction of the income going to the top 10 percent of earners accrues to the top 1 percent? What fraction of the income going to the top 1 percent of earners accrues to the top 0.1 percent? What fraction of the income going to the top 0.1 percent of earners accrues to the top 0.01 percent? The answer to each of these questions - which turns out to be around 40 percent in the United States today - is a simple function of the parameter that characterizes the power law." (Jones and Kim, 1786)
Applications
As formulas for the average income (which we will call μ) and the average income of the rich (top 10%) divided by the average income of the poor (bottom 10%) (we will call this massive number ι) can be derived, and these formulas are the ones provided by NationStates, we can use them to derive the values of χ and α, and thus the rest of the formulas in appendix A can be expressed in terms of ι and μ. Unfortunately, the only way to compute these numbers is using numerical methods (such as newton's method), as the system of equations generated cannot be inverted using ordinary mathematical functions. The code required to do this is in appendix C.
Results
As an example, Rho Ophiuchi has an average income of 88,084, and has ι, the "inequality score" equal to 14.2647. Using newton's method, we can calculate that χ = 28,383.9 and α = 0.677764. In contrast, the Municipalities of Antarctica has an average income of 199,607, and an "inequality score" equal to 1.20169. Using newton's method, we can calculate that χ = 177,408 and α = 0.111211. Found in appendix B are the other parameters, calculated form the derived χ and α values.
Notice how both the average income of the poor and the average income of the rich calculated using this model are higher than the displayed value in NationStates, when calculated from the income inequality and mean income. This is due to much less complex modelling being used in the internals of NationStates itself, and the values displayed here are likely more realistic.
References
Jones, Charles, and Jihee Kim. "A Schumpeterian Model of TopIncome Inequality." Journal of Political Economy, 2018, vol. 126, no. 5. https://web.stanford.edu/~chadj/inequality.pdf.
Appendix A
Value Type | Formula using χ and α | Formula using ι and μ |
Average income | χ/(1-α) | μ |
Average income of poor (bottom 10%) | (1-0.9^(1-α))∙10χ/(1-α) | N/A |
Average income of rich (top 10%) | 10χ∙0.1^(1-α)/(1-α) | N/A |
Lowest income for a full-time job | χ | N/A |
10th percentile income | χ/0.9^α | N/A |
Median income | χ/0.5^α | N/A |
90th percentile income | χ/0.1^α | N/A |
99th percentile income | χ/0.01^α | N/A |
Income of rich divided by income of poor | (0.1^(1-α))/(1-0.9^(1-α)) | ι |
Appendix B
Value Type | Rho Ophiuchi: (ι, μ) = (14.2647, 88,084) | The Municipalities of Antarctica: (ι, μ) = (1.20169, 199,607) |
α | 0.677764 | 0.111211 |
χ | 28,383.9 | 177,408 |
Average income | 84,084.2 | 199,606 |
Average income of poor (bottom 10%) | 29,403.5 | 178,433 |
Average income of rich (top 10%) | 419,432 | 257,861 |
Lowest income for a full-time job | 28,383.9 | 177,408 |
10th percentile income | 30,484.9 | 179,499 |
Median income | 45,404.5 | 191,624 |
90th percentile income | 135,156 | 229,184 |
99th percentile income | 643,576 | 296,090 |
Income of rich divided by income of poor | 14.2647 | 1.20169 |
Appendix C
i = 14.2647#this is inequality
m = 88084.0#this is average income
import numpy as np
def f1x(x,y):
f1x = 0
return f1x
def f1y(x,y):
f1y = (((0.1**(1-y)) + (0.1**(1-y) * y * np.log(10))) / (1-(9/10)**(1-y))) + (((0.09**(1-y) * y * np.log(10 / 9))) / (1-(9/10)**(1-y)) ** 2)
return f1y
def f1(x,y):
f1 = (0.1**(1-y))/(1-0.9**(1-y)) - i
return f1
def f2(x,y):
f2 = x/(1-y) - m
return f2
def f2x(x,y):
f2x = 1/(1-y)
return f2x
def f2y(x,y):
f2y = x/(1-y)**2
return f2y
def j(x,y):#jacobian
j = np.array([[f1x(x,y),f1y(x,y)],[f2x(x,y),f2y(x,y)]])
return j
def fv(x,y):#vector function
fv2 = np.array([f1(x,y),f2(x,y)])
return fv2
def newton_i(xn,j,fv):# iteration of multivariable newton method
x = xn[0]
y = xn[1]
x1 = x - np.matmul(np.linalg.inv(j(x,y)),fv(x,y))[0]
y1 = y - np.matmul(np.linalg.inv(j(x,y)),fv(x,y))[1]
xn1 = np.array([x1,y1])
return xn1
xn = np.array([0.5,0.5])#starting point
x = [xn]# all x
xnm1 = [0,2]#previous estimate of x
iteration = 0
while iteration < 500:
xnm1 = xn
xn = newton_i(xn,j,fv)
iteration += 1
x.append(xn)
error = np.linalg.norm(xn-xnm1)
print('The solution is',xn[0],xn[1],', which took',iteration,'iterations to produce. The 2-norm of the residual is',np.linalg.norm(fv(xn[0],xn[1])),'.')
Dad jokes are IN this year
Post by Mestovakia suppressed by The Seeker of Power.
Arrivederci.
Ele you put 13 people in laughing fits. Your dad jokes have gotten too powerful
well i’m numb, mentally and now physically. And the only thing keeping me going is this Kill La Kill OST I found in my recommended. Thank god and algorithm for small favors..
Anyways:
https://i.pinimg.com/originals/32/8f/14/328f146a07b4422e0937c8b00d34037e.png
I think i’m getting better at the bridges of battleships.. Or whatever those are called, the general area where all the doo-hickeys are at..
So what's going on now?
That's cuz I'm Dad Vader... :p
In my CK3 Haesteinn run I'm duke of Kent and plan on founding a new kingdom. Should I keep the name kent or rename it to Haesteinn?
Name it Caius.
Does Caius mean something significant?
The exiled Earl of Kent renamed himself as Caius in King Lear.
I’m tempted to make the easy Mr Streamer reference.
You are not Dad Vader. You are The Seeker of Power.
Wait. Dangit this was supposed to be a joke, wasn't it? sigh, alright, I'll do self serve and post this on r/Wooosh.
wooooooosh
Seconds ago: Following new legislation in The Greater Portuguese Confederation, the government extracts trade concessions from poor nations in exchange for humanitarian aid.
LMAO!!!
Indeed it was a joke :p
After watching a lot of SCP videos, I think I can summarize it pretty well.
Summarized 1: Some dudes saying 'Hey maybe we should stop paranormal activities', with the paranormal activities usually maintaining some form of phyisicality.
Summarized 2: *announcer voice* In the left corner, we have the world infamous emo giant lizard that can't die™, and on the right corner, we have an orange blob of goo, fight!
(10 seconds later)
*Still in the announcer voice* Somehow, the orange goo thing has started to make the emo giant lizard laugh, which is nearly impossible to do- wait, is it tickling him??? What the hell?
«12. . .30,10030,10130,10230,10330,10430,10530,106. . .44,48644,487»
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