sodium_nitride

That's true, but I don't think settlers are considered civilians under international law either way

Tariffs and tax cuts are basically the only 2 things in which trump actually believes.

Funniest possible outcome. The Germans found out it was stolen, try to get it back, fail, then agree agree for the money to be spent on Ukraine as a compensation. The money is never spent on Ukraine.

Maybe it could be possible. I'll have to look into it.

No, I don't know what I'd do with them.

Actually maybe I could use them to compute some things but that'd be a different investigation entirely.

Thanks!

It's the matlab operator for transposing a matrix or vector

She capitulated to the right and the dem base didn’t hold the line, which is up for debate who is at fault I guess. Ultimately she and the DNC didn’t offer a likable and enticing enough alternative to get people to vote. And they’ll never learn the lesson they need to, they’ll just keep pushing right.

Second to the top comment.

Even Liberals are beginning to recognize patterns and connect dots.

We are truly living in the end times.

It's not even "no consequences to anything", but "the people dying and suffering are doing so outside my bubble"

Interpretation of the graphs.

Graph 1: We still see the same result. When the prices of an economy are at those predicted by the LTV, the income of every sector shrinks to 0, leading to perfect economic reproduction. However, we see that many economies have economic reproduction even without LTV prices. I have a hypothesis for this. Some of the randomly generated economies in the simulation are "disconnected", meaning that the different industries don't buy and sell to each other. In this case, the effect of prices of one industry on another are minimum, so the prices stop mattering much.

Graph 2: Same as graph 1, but the shape of the curve is different. Not really sure what to say about this

Graph 3: I found it very interesting that no matter how much I tried to increase the wages (at one point, I had a wage basket 2 times bigger than what the economy could actually produce on its own), the trade balance remained stubbornly positive for the overwhelming majority of the data points.

This could happen because the sectors were reorganizing themselves to exploit comparative advantage, even though I never coded them to do this!

Say the people of the country were consuming 1 million tons of grain, and 100,000 cars every time step. Producing a car takes 1 person-year, and producing a ton of grain takes 0.1 person years. This level of consumption would then require 2 million person-years of labor (1 million for the cars, 1 million for the grains).

Even if there were only 1.5 million people in the economy, they could, for example, spend all their labor producing cars. So they would make 150,000 cars and export 50,000 cars. If the price of the cars is much higher than the price of grains, they could just exchange the cars for enough grains while still maintaining a trade surplus.

This was one of the most surprising results I saw from this model.

Graph 4: This here was to test an assumption that many economists make about the economy. They assume that the profit rates of industries equalise over time. However, in my simulation at least, this never happens. There is like an invisible floor to how low the differences in profit rates can get.

I will be taking requests if someone wants me to generate data. I can change the number of sectors, the amount of wages. I can try different price seeking strategies, etc.

Also, I never thought I'd reach the "post your research annonymously on Hexbear" stage of my academic career.

' %%%%%%%%%%

time = 100;
n = 10;
N = 100000;
connectivity = (2*n)^0.5; %The average number of intermediate commodities that go into making a commodity
threshold = connectivity/n;
e_l = 0.025;                 %proportionality rate at which hirings change per timestep
e_p = 0.025;                 %proportionality rate at which prices can change per timestep

Data = zeros([5 N*time]); %Pre allocating data matrix. Necessary to speed up simulation
Data_final = zeros([5 N]); %Pre allocating data matrix. Holds data on final time steps of each economy
w = 0.5; %Percentage of national production that the economy aims to give to labor

%%%%%%%%%%LOOP

for i = 1:N

    %Generate random workforce distribution between sectors
    L = rand([n time])*0.998 + 0.001;
    L(:,1) = L(:,1)./sum(L(:,1));    %Normalise the population to 1

    %Randomly generate direct labor use
    l = rand([n 1])*0.998 + 0.001;
    
    %Technical matrix:
    A = rand([n n]);
    A = A.*(A<=threshold);
    a = (eye(n)-A)\eye(n); %Storing the productivity matrix so it doesn't have to be recalculated over and over

    while sum(sum(a<0))>0     %If a has negative components, regenerate the economy and try again
        A = rand([n n]);
        A = A.*(A<=threshold);
        a = (eye(n)-A)\eye(n);
    end

    %LTV prices calculation
    LTV = sum(a.*l)';

    %Consumption
    basket = rand([n 1]);
    basket = w*basket./(sum(basket.*LTV)); %Consumption is scaled so that it can be in theory satisfied by the work of half the workforce

    %net production
    %o = zeros([n time]);

    %net income of sectors + agregate measures (pre-allocation)
    M = zeros([n time]);
    trade_balance = zeros([1 time]);
    profit_var = zeros([1 time]);
    
    %Randomised prices are generated for starting timestep (pre-allocation)
    P = zeros([n time]);

    %P(:,1) = rand([n 1]);             %randomly generates a set of prices

    
    %P(:,1) = (eye(n) - A - Cw)\rand([n 1]);
    P(:,1) = rand([n 1]);

    for k = 1:time
        
        if k>1
            hirings = e_l*(M(:,k-1))/sum(basket.*P(:,k-1));  %New Hirings are in proportion to the income available divided by wages
            L(:,k) = L(:,k-1) + hirings;   
            L(:,k) = L(:,k).*(L(:,k)>=(0.001/n)) + (L(:,k)<(0.001/n))*(0.01/n);   %This puts a floor on the size of sectors. Helps prevent the code from exploding.
            P(:,k) = P(:,k-1).*(1 - e_p*(hirings./L(:,k-1)));  %If the size of a sector doubles, the price decreases by e_p percent (from competititon)
            
            L(:,k) = L(:,k)./sum(L(:,k));    
        end

        P(:,k) = P(:,k).*((P(:,k)>=(0.001))) + (P(:,k)<(0.001))*(0.01);   %This puts a floor on the price. Helps prevent the code from exploding.

        %Calculate gross output of industries
        O = L(:,k)./l;

        P(:,k) = P(:,k)./sum(O.*P(:,k));%Normalises these prices so that total economy wide revenue is always 1

        Cw = basket * l';
        profit_var(:,k) = var(((eye(n) - A - Cw)*P(:,k))./P(:,k));

        %Calculate net production
        o = O - A*O;       %Net production can be negative. We will assume the existence of imports
                                %negative net production will show up as
                                %negative sales (the external market is
                                %selling to the economy)

        %Inter-industry sales
        R = O.*P(:,k);  %Market value of gross production by sector
        C = A' .*O*P(:,k); %Costs of inputs to production by sector
        
        %Industry to market sales
        S = o.*P(:,k); %Sales to consumers by sector
        Y = sum(S); %Total industry income from market sales
                    %Under balanced conditions, this income would be
                    %exactly matched by industry outflows to consumers
                    %(wages + dividends)

                    %Here it is assumed that the industry pays enough in
                    %(wages + dividends) to afford a fixed basket of 
                    % consumption.

                    %Any leftover income is the trade balance

        trade_balance(k) = sum((o - basket).*P(:,k));

        W = L(:,k).*(sum(basket.*P(:,k))); %Wages paid out vector by industry
        
        M(:,k) = R - C - W; %Net Income by industry
        
        M_per_worker = (1/n)*M./L(:,k); %I want to see if this givees any interesting results

        %Accounting identities
        % Y = sum(W) + trade_balance
        %Y = sum(R - C)

    end 
    %%%%%%%%%%%%%%Computing more time steps%%%%%%%%%%%%%%%%%%%%%%%%
    
    
    %%%%%%%%%%%%%Processing data%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    %poopoo = LTV./LTV(1,:); 
    %Peepee = P./P(1,:); 

    ratios = P./LTV;
    ratios = log(ratios);
    ratios = ratios - mean(ratios);
    specific_price = sum(abs(ratios))/n;

    M = sum(abs(M));
    M_per_worker = sum(abs(M_per_worker));
    
    %specific_price = sum(abs(log(Peepee./poopoo)))/(n-1);

    % trade_balance; trade_intensity
    Data(:,(1+ (i-1)*time ):(i*time)) = [specific_price; M; M_per_worker; trade_balance; profit_var];
    Data_final(:,(1+ (i-1) ):(i)) = [specific_price(time); M(time); M_per_worker(time); trade_balance(time); (profit_var(time)).^0.5];

end
%%%%%%%%%%LOOP end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

resolution = 1001;
scale = 1;

ptsy = linspace(0, 1, resolution);
ptsx = linspace(0, scale, resolution);
%ptsx = linspace(-0.1, 5, 1001);
%H = log(histcounts2(Data_final(2,:), Data_final(1,:), pts, pts));
H = log(histcounts2(Data(2,:), Data(1,:), ptsy, ptsx));
imagesc(ptsx, ptsy, H);
axis xy;
set(gca, 'XLim', ptsx([1 end]), 'YLim', ptsy([1 end]), 'YDir', 'normal');
colormap copper
a=colorbar;
a.Label.String = "Density of simulation outcomes [natural log scale]";
xlabel {Deviation from LTV prices [natural log scale]}
ylabel {Deviation from reproduction [linear scale]}
title {Absolute sector income vs LTV pricing}
exportgraphics(gcf,"repro_inv_M10.png","Resolution",600);

figure 
ptsy = linspace(0, 1, resolution);
ptsx = linspace(0, scale, resolution);
%H = log(histcounts2(Data_final(3,:), Data_final(1,:), pts, pts));
H = log(histcounts2(Data(3,:), Data(1,:), ptsy, ptsx));
imagesc(ptsx, ptsy, H);
axis xy;
set(gca, 'XLim', ptsx([1 end]), 'YLim', ptsy([1 end]), 'YDir', 'normal');
colormap copper
a=colorbar;
a.Label.String = "Density of simulation outcomes [natural log scale]";
xlabel {Deviation from LTV prices [natural log scale]}
ylabel {Deviation from reproduction (scaled by employment) [linear scale]}
title {Per worker sector income vs LTV pricing}
exportgraphics(gcf,"repro_inv_Mw10.png","Resolution",600);

figure 

ptsy = linspace(-1, 1, resolution);
ptsx = linspace(0, scale, resolution);
%H = log(histcounts2(Data_final(4,:), Data_final(1,:), pts, pts));
H = log(histcounts2(Data(4,:), Data(1,:), ptsy, ptsx));
imagesc(ptsx, ptsy, H);
axis xy;
set(gca, 'XLim', ptsx([1 end]), 'YLim', ptsy([1 end]), 'YDir', 'normal');
colormap copper
a=colorbar;
a.Label.String = "Density of simulation outcomes [natural log scale]";
xlabel {Deviation from LTV prices [natural log scale]}
ylabel {Trade balance [linear scale]}
title {Trade balance vs LTV pricing}
exportgraphics(gcf,"repro_inv_T10.png","Resolution",600);

figure 

ptsy = linspace(0, 2.5, resolution);
ptsx = linspace(0, scale, resolution);
%H = log(histcounts2(Data_final(5,:), Data_final(1,:), pts, pts));
H = log(histcounts2(Data(5,:), Data(1,:), ptsy, ptsx));
imagesc(ptsx, ptsy, H);
axis xy;
set(gca, 'XLim', ptsx([1 end]), 'YLim', ptsy([1 end]), 'YDir', 'normal');
colormap copper
a=colorbar;
a.Label.String = "Density of simulation outcomes [natural log scale]";
xlabel {Deviation from LTV prices [natural log scale]}
ylabel {STD of profitability rates of sectors}
title {Profit STD vs LTV pricing}
exportgraphics(gcf,"repro_inv_p10.png","Resolution",600);

'