EvoLudoLab: Fixation probabilities on random regular graphs
| Color code: | Residents | Mutants |
|---|---|---|
| New resident | New mutant |
| Payoff code: | Residents | Mutants |
|---|
Fixation probabilities on random regular graphs
Since random regular graphs are circulations, the fixation probability of a mutant has to be the same irrespective of its initial location and must be identical to the one of the original Moran process in unstructured populations.
For the simulations, the population size is \(N=81\) with \(k=4\) neighbours, which results in a total of \(162\) links. The fitness of residents is set to \(1\) and that of mutants to \(2\). Thus, a single mutant has approximately a \(50\%\) chance to take over the population. For reference, the analytical fixation probabilities of the original Moran process are indicated by a dark red line.
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.