EvoLudoLab: Spatial 2x2 Game - Dominance A: Difference between revisions

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options="--run --delay 200 --view 0 --reportfreq 1.0 --popsize 100x --popupdate r --playerupdate i --updateprob 1.0 --switchpref 0.0 --geometry m --intertype a1 --numinter 1 --reprotype a8 --initfreqs 995:5 --mutation 0.0 --basefit 1.0 --selection 1.0 --reward 1.0 --punishment 0.0 --temptation 0.9 --sucker 0.1"|
options="--run --delay 200 --view 0 --reportfreq 1.0 --popsize 100x --popupdate r --playerupdate i --updateprob 1.0 --switchpref 0.0 --geometry m --intertype a1 --numinter 1 --reprotype a8 --initfreqs 995:5 --mutation 0.0 --basefit 1.0 --selection 1.0 --reward 1.0 --punishment 0.0 --temptation 0.9 --sucker 0.1"|
title=Type A dominates|
title=Type A dominates|
doc=In well-mixed populations type <math>A</math> players dominate and take over the population irrespective of the initial configuration. Essentially the same happens in spatially structured populations.
doc=In well-mixed populations type \(A\) players dominate and take over the population irrespective of the initial configuration. Essentially the same happens in spatially structured populations.


In the case of by-product mutualism, the success of cooperation hinges on the presence of a sufficiently big cluster of cooperators. The required cluster size is small (often two adjacent cooperators are enough but depends on the payoffs and stochasticity of the update rules). It is also interesting to observe the difficulties in eliminating all defectors - increasing the stochasticity such that players occasionally switch to worse performing strategies would speed up that process.
In the case of by-product mutualism, the success of cooperation hinges on the presence of a sufficiently big cluster of cooperators. The required cluster size is small (often two adjacent cooperators are enough but depends on the payoffs and stochasticity of the update rules). It is also interesting to observe the difficulties in eliminating all defectors - increasing the stochasticity such that players occasionally switch to worse performing strategies would speed up that process.


The parameters above are set to <math>R = 1, P = 0, T = 0.9</math> and <math>S = 0.1</math> with players imitating better strategies proportional to the payoff difference on a 100×100 lattice with only 0.5% cooperators initially. With some small probability cooperators will go extinct. To reduce this risk, you can either increase the system size or the initial fraction of cooperators.}}
The parameters above are set to \(R = 1, P = 0, T = 0.9\) and \(S = 0.1\) with players imitating better strategies proportional to the payoff difference on a 100×100 lattice with only 0.5% cooperators initially. With some small probability cooperators will go extinct. To reduce this risk, you can either increase the system size or the initial fraction of cooperators.}}


[[Category: Christoph Hauert]]
[[Category: Christoph Hauert]]

Revision as of 08:25, 12 June 2012

Color code: Cooperators Defectors
New cooperator New defector
Payoffs:
Low High

Note: The gradient of the payoff scale is augmented by pale shades of the strategy colours to mark payoffs that are achieved in homogeneous populations of the corresponding type.

Type A dominates

In well-mixed populations type \(A\) players dominate and take over the population irrespective of the initial configuration. Essentially the same happens in spatially structured populations.

In the case of by-product mutualism, the success of cooperation hinges on the presence of a sufficiently big cluster of cooperators. The required cluster size is small (often two adjacent cooperators are enough but depends on the payoffs and stochasticity of the update rules). It is also interesting to observe the difficulties in eliminating all defectors - increasing the stochasticity such that players occasionally switch to worse performing strategies would speed up that process.

The parameters above are set to \(R = 1, P = 0, T = 0.9\) and \(S = 0.1\) with players imitating better strategies proportional to the payoff difference on a 100×100 lattice with only 0.5% cooperators initially. With some small probability cooperators will go extinct. To reduce this risk, you can either increase the system size or the initial fraction of cooperators.

Data views

Strategies - Structure

Snapshot of the spatial arrangement of strategies.

Strategies - Mean

Time evolution of the strategy frequencies.

Fitness - Structure

Snapshot of the spatial distribution of payoffs.

Fitness - Mean

Time evolution of average population payoff bounded by the minimum and maximum individual payoff.

Fitness - Histogram

Snapshot of payoff distribution in population.

Structure - Degree

Degree distribution in structured populations.

Statistics - Fixation probability

Statistics of fixation probabilities.

Statistics - Fixation time

Statistics of fixation and absorption times.

Stationary distribution

Statistics of the stationary distribution of the numbers of each strategic type. Note, only available for non-zero mutation rates.

Console log

Message log from engine.

Module parameters

The list below describes only the few parameters related to the Prisoner's Dilemma, Snowdrift and Hawk-Dove games. Follow the link for a complete list and detailed descriptions of the user interface and further parameters such as spatial arrangements or update rules on the player and population level.

--paymatrix <a00,a01;a10,a11>
2x2 payoff matrix. Type \(A\) has index 0 and type \(B\) index 1.
--inittype <type>
type of initial configuration:
frequency <f0>,<f1>...
random distribution with given trait frequencies, f0, f1,.... Note, only available for frequency based modules and models.
density <d0>,<d1>...
initial trait densities <d1,...,dn>. Note, only available for density based modules and models.
uniform
uniform random distribution, equal frequencies of all traits.
monomorphic <t>[,<v>]
monomorphic initialization with trait t. Note, for modules with variable population densities, the optional parameter v indicates the initial frequency of vacant sites. If omitted the monomorphic trait is initialized at its (estimated) carrying capacity.
mutant <m>,<r>[,<v>]
single mutant with trait m in homogeneous resident population of type r. The mutant is placed in a location selected uniformly at random (mutants arising through cosmic rays). Note, for modules with variable population densities, the optional parameter v indicates the initial frequency of vacant sites. If omitted the resident trait is initialized at its (estimated) carrying capacity.
temperature <m>,<r>[,<v>]
single mutant with trait m in homogeneous resident population of type r. The mutant is placed in a location selected proportional to the in-degree of nodes (temperature initialization, mutants arising through errors in reproduction). Note, for modules with variable population densities, the optional parameter v indicates the initial frequency of vacant sites. If omitted the resident trait is initialized at its (estimated) carrying capacity.
stripes
stripes of traits. Note, only available for 2D lattices.
kaleidoscopes
configurations that produce evolutionary kaleidoscopes for deterministic updates (players and population). Note, only available for some modules.

Note, for modules that admit multiple species, the initialization types for each species can be specified as an array separated by ;. With more species than initialization types, they are assigned in a cyclical manner.