DOI: 10.5281/zenodo.1217123
title: Complete Numerical Solutions For "Inference Of Ecological And Social Drivers Of Human Brain-Size Evolution" By Mauricio González-Forero And Andy Gardner

description: This zip file contains the complete numerical solutions across the parameter sweep over the P parameters for the six cases considered.

/1RatioForm/ -&gt; solutions for power competence.<br>
/2DiffForm/ -&gt; solutions for exponential competence.

/1RatioForm/1BenchmarkFromSimpleInitialGuess -&gt; solution for the step 1 of initialization (section 5 of the SI).<br>
/1RatioForm/2Benchmark/ -&gt; solution for the step 2 of the initialization (section 5 of the SI).<br>
/1RatioForm/3AdditiveCoop/ -&gt; solutions across the P parameter combinations for additive cooperation.<br>
/1RatioForm/4MultCoop/ -&gt; solutions across the P parameter combinations for multiplicative cooperation.<br>
/1RatioForm/5SubMultCoop/ -&gt; solutions across the P parameter combinations for submultiplicative cooperation.

The contents of /2DiffForm/ are analogous.

The P vector is written in these files in the order (etas,etac,etaC,etag), where<br>
etas -&gt; P1<br>
etac -&gt; P3<br>
etaC -&gt; P2<br>
etag -&gt; P4

The files 1runNotesACMC.pdf and 2runNotesSC.pdf contain the tree structure of the parameter sweep, specifying which parameter combination was used as the resident and which combinations converged to an uninvadable strategy (those with a checkmark).

The file 3runNotesMaternalCareOptimization.pdf contains the 10 parameter combinations that yielded the best adult fit, which then were subject to variation in the parameter phi to find the combination that yielded the best ontogenetic fit.

The file 4runNotesDuplicates.pdf gives the parameter combinations that were not run because they are equivalent to other parameter combinations.

Running [T,N1,run,Tshort,N1short,runShort]=etaCombinations in Matlab and typing run.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i (where i is a number 1,2,...) for PC-AC, EC-MC, PC-SC, and EC-SC. The meaning of "next" is explained in step 4 of the parameter sweep (section 5 of the SI). Typing runShort.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i for PC-MC and EC-AC.

The terminal folders contain the solutions and have the following files:<br>
brainNashDeep.m -&gt; the master file launching the iteration of best responses.<br>
brainMainDeep.m -&gt; the file launching one iteration solving the optimal control problem to find best response.<br>
brainMainTestRunDeep.m -&gt; runs a test to check if there are infeasibility warnings.<br>
brainContinuous.m -&gt; specifies the dynamic constraints.<br>
brainEndpoint.m -&gt; specifies the terminal constraints.<br>
parameters.m -&gt; specifies the parameter values and rescales them to rescale units as specified in section 5 of the SI.<br>
getSolution.m -&gt; extracts solution.<br>
plots.m -&gt; plots solutions over best response iterations.<br>
brainPlot.m -&gt; plots solution of a given best response iteration.<br>
guessDeep.mat -&gt; initial guess and resident used.<br>
solutionNashDeep.mat -&gt; solutions over best response iterations.<br>
solutionDeep.mat -&gt; solution of last best response iteration.