EvoLudoLab: Fixation times on the superstar graph: Difference between revisions
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options="-- | options="--module Moran --run --delay 10 --view Statistics_-_Fixation_time --timestep 10 --popupdate B --popsize 96 --geometry S5,5 --init mutant 1,0 --fitness 1,2"| | ||
title=Fixation times on the superstar graph| | title=Fixation times on the superstar graph| | ||
doc=Note that the fixation times are not the same for all vertices. In particular, if the mutant is placed in the hub (vertex \(0\)) or the linear chain (vertices \(1-15\)), its fixation probability is very small. The worst places for the mutant to arise are the 'hot' vertices with many links feeding into them, i.e. the hub as well as the first vertices of the linear chain (vertices \(1-5\)). In the rare case where the mutant does not get wiped out, the fixation time is essentially the same as for any reservoir vertex. For symmetry reasons, all reservoir vertices have the same fixation times. The fixation times for the original Moran process are shown as a dark red line for reference. | doc=Note that the fixation times are not the same for all vertices. In particular, if the mutant is placed in the hub (vertex \(0\)) or the linear chain (vertices \(1-15\)), its fixation probability is very small. The worst places for the mutant to arise are the 'hot' vertices with many links feeding into them, i.e. the hub as well as the first vertices of the linear chain (vertices \(1-5\)). In the rare case where the mutant does not get wiped out, the fixation time is essentially the same as for any reservoir vertex. For symmetry reasons, all reservoir vertices have the same fixation times. The fixation times for the original Moran process are shown as a dark red line for reference. | ||
For the simulations, the population size is \(N=96\) with \(5\) branches and \(k=5\). The fitness of residents is set to \(1\) and that of mutants to \(2\). Interestingly, the fixation times on the superstar graph are slightly less than on the star graph of equal size - even though the superstar acts as a slightly stronger evolutionary amplifier despite the stronger finite size effects.}} | For the simulations, the population size is \(N=96\) with \(5\) branches and \(k=5\). The fitness of residents is set to \(1\) and that of mutants to \(2\). Interestingly, the fixation times on the superstar graph are slightly less than on the star graph of equal size - even though the superstar acts as a slightly stronger evolutionary amplifier despite the stronger finite size effects.}} | ||
Latest revision as of 13:36, 12 August 2024
| Color code: | Residents | Mutants |
|---|---|---|
| New resident | New mutant |
| Payoff code: | Residents | Mutants |
|---|
Fixation times on the superstar graph
Note that the fixation times are not the same for all vertices. In particular, if the mutant is placed in the hub (vertex \(0\)) or the linear chain (vertices \(1-15\)), its fixation probability is very small. The worst places for the mutant to arise are the 'hot' vertices with many links feeding into them, i.e. the hub as well as the first vertices of the linear chain (vertices \(1-5\)). In the rare case where the mutant does not get wiped out, the fixation time is essentially the same as for any reservoir vertex. For symmetry reasons, all reservoir vertices have the same fixation times. The fixation times for the original Moran process are shown as a dark red line for reference.
For the simulations, the population size is \(N=96\) with \(5\) branches and \(k=5\). The fitness of residents is set to \(1\) and that of mutants to \(2\). Interestingly, the fixation times on the superstar graph are slightly less than on the star graph of equal size - even though the superstar acts as a slightly stronger evolutionary amplifier despite the stronger finite size effects.
Data views
| Snapshot of the spatial arrangement of strategies. | |
| 3D view of snapshot of the spatial arrangement of strategies. | |
| Time evolution of the strategy frequencies. | |
| Snapshot of the spatial distribution of payoffs. | |
| 3D view of snapshot of the spatial distribution of payoffs. | |
| Time evolution of average population payoff bounded by the minimum and maximum individual payoff. | |
| Snapshot of payoff distribution in population. | |
| Degree distribution in structured populations. | |
| Statistics of fixation probability for each vertex where the initial mutant arose. | |
| Statistics of conditional fixation times of residents and mutants as well as absorption time for each vertex where the initial mutant arose. | |
| Message log from engine. |
Game parameters
The list below describes only the few parameters related to the evolutionary dynamics of residents and mutants with fixed fitness (constant selection). Numerous other parameters are available to set population structures or update rules on the player as well as population level.
- --fitness <r,m>
- fitness of residents r and of mutants m.