EvoLudoLab: Continuous Snowdrift Game  Branching (exp)
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 popup 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.
Continuous Snowdrift game: Saturating investments
In all examples so far, if higher investments were advantageous (at least in one branch) then the investments would continue to increase until the upper boundary of the trait range is reached. This must not be the case. In this last example we choose \(B(x+y) = b_0[1\exp(b_1 x)]\) and \(C(x) =c_0 \ln(c_1 x+1)\). For the parameters below, we again observe a branching point near \(x^*_1\approx 0.7\) accompanied by a repellor near \(x^*_2\approx 0.2\). Starting with a population \(x_0 > 0.2\), selection and mutations drive the population towards the branching point but now the emerging upper branch grows only to trait values of around \(2.2\). Obviously, when starting with \(x_0 < 0.2\) branching can not occur and investment levels stay close to zero. Again note that the dimorphic population no longer has a repellor near \(x^*_2\approx 0.2\) and therefore the lower branch evolves straight to minimal investments.
The parameters are set to \(b_0 = 5, b_1 = 1, c_0 = 1, c_1 = 10\) with players imitating better strategies proportional to the payoff difference and an initial traits/investment of \(0.2 \pm 0.05\) in a population of \(5'000\) individuals. Mutations occur with a probability of 1% and the standard deviation of the Gaussian distributed mutations is \(0.01\).
Data views 

Snapshot of the spatial arrangement of strategies.  
Time evolution of the strategy frequencies.  
Snapshot of strategy distribution in population  
Time evolution of the strategy distribution  
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.  
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.