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results_cont.m
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results_cont.m
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% Results.m: generates graphs for section 4 in Iskhakov, Rust and Schjerning (2012)
% Fedor Iskhakov, University of New South Wales
% John Rust, University of Maryland
% Bertel Schjerning, University of Copenhagen
%% CLEAR MEMORY AND SWT SWITCHES
clc; clear; close all;
% to be modified by user
paperdir='/Users/bertelschjerning/Dropbox/IRS/leapfrogging/_IOpaper/current/0pdf/';
%%Switches
compile=1;
%% COMPILE
tic
if compile
fprintf('COMPILING... ');
tic
% mex -largeArrayDims$ leapfrog.c -DMAXEQB=3 -DPRINTeqbloop=1 -DPRINTeqbstr=0
mex -largeArrayDims leapfrog.c -DMAXEQB=3 -DPRINTeqbloop=1 -DPRINTeqbstr=0
tc=toc; fprintf('Compiled model in %1.10f (seconds)\n\n',tc);
end
% SET BECNHMARK PARAMETERS
setup;
% Adjustments to sw structure - see setup for description
sw.alternate=true; %alternate move or simultanious move game
sw.esr=5; %equilibrium selection rule to be used: see setup.m or esr.c %ESR:always play first equilibrium
mp.c_tr=-1;
mp.onestep=1;
mp.tpm=[0 1; 1 0];
mp.k2=0;
par.nC=25;
mp.nC=par.nC; % add nC to mp
% %%%%%%%%%%%%%%%%%%%
% %% FIGURE 4
% %%%%%%%%%%%%%%%%%%%
% mp.k1=5;
% for i=1:2;
% mpvec(i)=mp;
% swvec(i)=sw;
% end
% i=1; mpvec(i).c_tr=1;
% i=2; mpvec(i)=mp; swvec(i).esr=7; swvec(i).alternate=false;
% tit1(1)={'Panel (a): Non-monotonic tech. progress'}
% tit1(2)={'Panel (b): Simultaneous move'}
% tit2(1)={'Panel (c): Non-monotonic tech. progress'}
% tit2(2)={'Panel (d): Simultaneous move'}
% for i=1:numel(mpvec);
% par.nC=5;
% if swvec(i).alternate==false
% par.nC=5;
% end
% mpvec(i).nC=par.nC;
% % recalculate depend parameters
% mpvec(i).dt=5/par.nC; % 5 is top cost
% mpvec(i).df=exp(-0.05*mpvec(i).dt);
% par.pti=f_pti(mpvec(i), par);
% swvec(i).esr=99; %equilibrium selection rule to be used: see setup.m or esr.c
% swvec(i).esrmax=10000; %run N feasible eqstrings (set equal to 192736405 for n=5)
% swvec(i).esrstart=0; %
% %% SET MONOPOLY PARAMETERS
% mp_mon=mpvec(i);
% par_mon=par;
% sw_mon=swvec(i);
% mp_mon.tpm=[1 0; 1 0];
% sw_mon.alternate=true; % always solve alternate move to obtain monopoly profits
% sw_mon.esr=1; %equil5ibrium selection rule to be used: see setup.m or esr.c
% sw_mon.esrmax=1; %run N feasible eqstrings (set equal to 192736405 for n=5)
% %% SOLVE MONOPOLY MODEL
% [bne_mon, br_mon, g_mon, eqbstr_mon]=leapfrog(par_mon,mp_mon,sw_mon);
% v10_mon=g_mon(par.nC).solution(1,7);
% v20_mon=g_mon(par.nC).solution(1,9);
% %% SOLVE MODEL AT PARAMETRS mpvec(i), par, swvec(i)
% [bne, br, g, eqbstr, moncmp]=leapfrog(par,mpvec(i),swvec(i),g_mon);
% eqbstr(:,isnan(eqbstr(1,:)))=[];
% eqbstr=eqbstr';
% moncmp=moncmp';
% %Efficiency
% %eqbstr(:,8)=eqbstr(:,8)/(v10_mon+v20_mon);
% eqbstr(:,8)=eqbstr(:,8)/(v10_mon+v20_mon);
% [a, b]=graph.EqbstrPlot(eqbstr,[2 8],(v10_mon+v20_mon),[],sw,tit1{i});
% % title(tit1{i});
% set(gcf, 'PaperPosition', [0 0 6 6]);
% saveas(gcf, sprintf('%sTri_%d',paperdir, i), 'fig')
% saveas(gcf, sprintf('%sTri_%d.eps',paperdir, i), 'psc2')
% % d=analyse(eqbstr);
% dummystr = '[((eqbstr(:,3)~=0).*(eqbstr(:,4)~=0)) ((eqbstr(:,3)==0) | (eqbstr(:,4)==0))]';
% graphstring='graph.EqbstrCDFPlot(eqbstr,[0], dummystr, lbl ,mp,sw,tit2{i},0.5, mfig)';
% % graph.EqbstrCDFPlot(eqbstr,[0 -5 6 7], dummystr, {'vi ne 0','vi=0'} ,[],sw,tit2{i},1);
% graph.EqbstrCDFPlot(eqbstr,[0 7 -5 ], [], [] ,[],sw,tit2{i},1);
% % title(tit2{i});
% set(gcf, 'PaperPosition', [0 0 6 6]);
% saveas(gcf, sprintf('%sCDF_%d',paperdir, i), 'fig')
% saveas(gcf, sprintf('%sCDF_%d.eps',paperdir, i), 'psc2')
% end
%%%%%%%%%%%%%%%%%%%
%% FIGURE 6 and 7
%%%%%%%%%%%%%%%%%%%
%clear mpvec, swvec;
mp.k1=2;
for i=1:7;
mpvec(i)=mp;
swvec(i)=sw;
mpvec(i).k1=2;
mpvec(i).nC=250;
end
% Adjust mp structure - see setup for description
% FIGURE 6
i=1; mpvec(i).k1=2; mpvec(i).nC=100; swvec(i).esr=99; tit(i)={'Panel (a): Preemption and rent-dissipation'}
i=2; mpvec(i).k1=2; mpvec(i).nC=25; swvec(i).esr=99; tit(i)={'Panel (b): Underinvestment'}
i=3; mpvec(i).k1=.5; mpvec(i).nC=25; swvec(i).esr=99; tit(i)={'Panel (c): Leap-frogging'}
% FIGURE 7
i=4; mpvec(i).tpm=[0.5 0.5; 0.5 .5]; tit(i)={'Panel (a): Random alternating moves'}
i=5; mpvec(i).c_tr=2; tit(i)={'Panel (b): Non-monotonic tech. progress'}
%i=6; mpvec(i).c_tr=2; mpvec(i).onestep=0; tit(i)={'Panel (c): Non-monotonic multistep tech. progress'}
%i=7; mpvec(i)=mp; swvec(i).esr=7; swvec(i).alternate=false; tit(i)={'Panel (d): Simultaneous move'}
%i=6; swvec(i).esr=7; swvec(i).alternate=false; tit(i)={'Panel (d): Simultaneous move'}
i=6; swvec(i).esr=3; swvec(i).alternate=false; tit(i)={'Panel (d): Simultaneous move'}
%!rm randstream.mat
nMC=1000;
McOut=nan(nMC, 3, numel(mpvec));
%% FIGURE 6 and 7
for i=4:6;
% for i=1:2;
% recalculate depend parameters
par.nC=mpvec(i).nC; % add nC to mp
capT=floor(1.5*par.nC);
mpvec(i).dt=25/par.nC; % 5 is top cost
mpvec(i).df=exp(-0.05*mp.dt);
par.pti=f_pti(mpvec(i), par);
%% SET MONOPOLY PARAMETERS
mp_mon=mpvec(i);
par_mon=par;
sw_mon=swvec(i);
mp_mon.tpm=[1 0; 1 0];
sw_mon.alternate=true; % always solve alternate move to obtain monopoly profits
sw_mon.esr=1; %equil5ibrium selection rule to be used: see setup.m or esr.c
sw_mon.esrmax=1; %run N feasible eqstrings (set equal to 192736405 for n=5)
%% SOLVE MONOPOLY MODEL
[bne_mon, br_mon, g_mon, eqbstr_mon]=leapfrog(par_mon,mp_mon,sw_mon);
v10_mon=g_mon(par.nC).solution(1,7);
v20_mon=g_mon(par.nC).solution(1,9);
%% SOLVE MODEL AT PARAMETRS mpvec(i), par, swvec(i)
[bne, br, g, eqbstr]=leapfrog(par,mpvec(i),swvec(i));
for iMC=1:nMC;
%% Simulare sequences
sp=simul.setup(par); % Run setup for simulation module
sp.T=capT;
s=simul.sequence(g,sp, mpvec(i),par,swvec(i).alternate, 'randseed'); % Sumulate sequences
s_mon=simul.sequence(g_mon,sp, mp_mon,par,sw_mon.alternate, 'randseed'); % Sumulate sequences
simultan=(s.i1==s.i2).*(s.i1>0);
preempt=((s.c1>s.c2).*(s.i2>0) | (s.c2>s.c1).*(s.i1>0)) ;
leap=((s.c1>s.c2).*(s.i1>0) | (s.c2>s.c1).*(s.i2>0));
[s.i1 s.i2 s.c1 s.c2 simultan preempt leap];
% if i<=7;
% s.cmon=s_mon.c1;
% graph.CostSequence(s, mpvec(i), tit{i}, '', s_mon); % plot sequences realized costs
% else
% graph.CostSequence(s, mpvec(i), tit{i}, ''); % plot sequences realized costs
% end
% set(gcf, 'PaperPosition', [0 0 6 5]);
% saveas(gcf, sprintf('%sSeq_%d',paperdir, i), 'fig')
% saveas(gcf, sprintf('%sSeq_nC%d_%d.eps',paperdir, par.nC, i), 'psc2')
preempt_share=sum(preempt)/(sum((s.i1 +s.i2)>0)-1);
leap_share=sum(leap)/(sum((s.i1 +s.i2)>0)-1);
simul_share=sum(simultan)/(sum((s.i1 +s.i2)>0)-1);
McOut(iMC,:, i)=[simul_share preempt_share leap_share];
end
figure(i);
y=McOut(:,:,i)
xi=unique(y);
yi = histc(y,xi);
histc(y,xi)
cdfy=cumsum(yi)./repmat(sum(yi), size(yi,1),1)
stairs(xi, cdfy);
xlim([0 1]);
ylim([0 1]);
legend('Duplicative','Premptive','Leap-frogging', 'location', 'SouthEast');
title(tit{i})
set(gcf, 'PaperPosition', [0 0 6 5]);
saveas(gcf, sprintf('%sCDFSeq_%d',paperdir, i), 'fig')
saveas(gcf, sprintf('%sCDFSeq_nC%d_%d.eps',paperdir, par.nC, i), 'psc2')
end;
return