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EvoLudoLab: Moran process on the linear chain graph: Difference between revisions
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EvoLudoLab: Moran process on the linear chain graph (view source)
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{{EvoLudoLab:Moran| | {{EvoLudoLab:Moran| | ||
options="--game Moran --run --delay 50 --view Strategies_-_Structure --reportfreq 0.5 --popupdate B --popsize 150 --geometry af -- | options="--game Moran --run --delay 50 --view Strategies_-_Structure --reportfreq 0.5 --popupdate B --popsize 150 --geometry af --init 0,1 --inittype mutant --mutation 0 --basefit 1 --selection 1 --fitness 1,2"| | ||
title=Evolutionary dynamics on the linear chain graph| | title=Evolutionary dynamics on the linear chain graph| | ||
doc=The invasion dynamics of a beneficial mutation typically unfolds by mutants taking over all downstream vertices. Nevertheless, this is a fleeting success because subsequently, the offspring of the slower reproducing resident vertices upstream of the initial mutant gradually, and inevitably, recapture the mutants' territory. With slightly smaller chances, the initial mutant gets wiped out before managing to establish a lineage of its own. | doc=The invasion dynamics of a beneficial mutation typically unfolds by mutants taking over all downstream vertices. Nevertheless, this is a fleeting success because subsequently, the offspring of the slower reproducing resident vertices upstream of the initial mutant gradually, and inevitably, recapture the mutants' territory. With slightly smaller chances, the initial mutant gets wiped out before managing to establish a lineage of its own. | ||
For the simulations, the population size is \(N=150\), the fitness of residents is set to \(1\) and that of mutants to \(2\). Initially, a single mutant is placed in a random position along the linear chain. The state of the population is shown as a function of time such that each row displays the state of the population at a particular time such that the present is at the top and the past at the bottom.}} | For the simulations, the population size is \(N=150\), the fitness of residents is set to \(1\) and that of mutants to \(2\). Initially, a single mutant is placed in a random position along the linear chain. The state of the population is shown as a function of time such that each row displays the state of the population at a particular time such that the present is at the top and the past at the bottom.}} | ||
