EvoLudoLab: Moran process on the complete graph
| Color code: | Residents | Mutants |
|---|---|---|
| New resident | New mutant |
| Payoff code: | Residents | Mutants |
|---|
Evolutionary dynamics on the complete graph
NOTE: due to the many links of a complete graph, the dynamical layout of the graph is likely taking some time. Depending on the speed of your computer some patience may be required.
Because mutants can spread to any part of the graph in a single step, the invasion process progresses quickly and does not exhibit any characteristic patterns.
For the simulations, the population size is \(N=100\) and hence \(4'950\) 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. Typically it takes only between \(10-15\) generations for the mutant to reach fixation.
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.