Template:EvoLudoLab:CSD

Along the bottom of the applet there are several buttons to control the execution and the speed of the simulations - for details see the EvoLudo GUI documentation. Of particular importance are the parameters button and the data data views pop-up list along the top. The former opens a panel that allows to set and change various parameters concerning the game as well as the population structure, while the latter displays the simulation data in different ways.

Color code:	Maximum	Minimum	Mean

Strategy code:	Defect Cooperate

Payoff code:	Low High

Note: The shades of grey of the payoff scale are augmented by blueish and reddish shades indicating payoffs for mutual cooperation and defection, respectively.

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	Data views
Strategies - Structure	Snapshot of the spatial arrangement of strategies.
Strategies - Mean	Time evolution of the strategy frequencies.
Strategies - Histogram	Snapshot of strategy distribution in population
Strategies - Distribution	Time evolution of the strategy distribution
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.
Console log	Message log from engine.

Game parameters

The list below describes only the few parameters related to the continuous snowdrift game. Follow the link for a complete list and descriptions of all other parameters e.g. referring to update mechanisms of players and the population.

Benefit/Cost Functions

A variety of different combinations of cost and benefit functions can be selected.

Benefit \(b_0,\ b_1\)

Two parameters for the benefit function. Note that not all functions require both.

Cost \(c_0,\ c_1\)

Two parameters for the cost function. Note that not all functions require both.

Mean invest

Mean trait value of initial population.

Sdev invest

Standard deviation of initial population. If set to negative values, the population will be initialized with uniform distributed traits.