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{{EvoLudoLab:CSD| | {{EvoLudoLab:CSD| | ||
options="--game cSD --run --delay 100 --view Strategies_-_Distribution --reportfreq 50 --popsize 5000 --popupdate | options="--game cSD --run --delay 100 --view Strategies_-_Distribution --reportfreq 50 --popsize 5000 --popupdate async --playerupdate imitate --geometry M --intertype r1 --numinter 1 --references r1 --benefitfcn 12 --benefitparams 1 --costfcn 3 --costparams 1,0.6 --traitmax 5 --init 2.8,0.02 --inittype gaussian --mutation 0.02 --mutationtype g --mutationsdev 0.02"| | ||
title=Continuous Snowdrift game: Attractor & Repellor| | title=Continuous Snowdrift game: Attractor & Repellor| | ||
doc=For more complicated payoff functions several singular strategies \(x^*\) may be found. In this example we use \(B(x) = b_1 \sqrt{x+y}\) and \(C(x) = c_1 \ln(c_2 x+1)\). For the parameters indicated below this results in a repellor near \(x_1^*\approx 3.9\) together with a branching point near \(x^*_2 \approx 0.7\). Starting with \(x_0 < 3.9\) drives the population towards lower investments until the branching point is reached. At \(x^*_2\) two branches emerge and diverge until the upper branch reaches the boundary of the trait range. Note that for the dimorphic population the repellor near \(x_1^*\approx 3.9\) no longer exists. The trait range in the above simulation is \([0,5]\). | doc=For more complicated payoff functions several singular strategies \(x^*\) may be found. In this example we use \(B(x) = b_1 \sqrt{x+y}\) and \(C(x) = c_1 \ln(c_2 x+1)\). For the parameters indicated below this results in a repellor near \(x_1^*\approx 3.9\) together with a branching point near \(x^*_2 \approx 0.7\). Starting with \(x_0 < 3.9\) drives the population towards lower investments until the branching point is reached. At \(x^*_2\) two branches emerge and diverge until the upper branch reaches the boundary of the trait range. Note that for the dimorphic population the repellor near \(x_1^*\approx 3.9\) no longer exists. The trait range in the above simulation is \([0,5]\). |
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