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. Author manuscript; available in PMC: 2012 Feb 29.
Published in final edited form as: J Theor Biol. 2010 Jun 9;265(4):624–632. doi: 10.1016/j.jtbi.2010.06.010

Figure 2.

Figure 2

Evolutionary outcomes of the restricted strategy set without anti-social punishment on a regular square lattice. When β is small, DN wins regardless of initial frequencies of strategies; when β is large, CP wins regardless of initial frequencies of strategies. Starting from the specified initial frequency, 50 agent based simulations are run and the winning strategy is recorded. The blue portion of each circle indicates the fraction of runs where CP wins or CP and CN coexist; the red portion, the fraction of runs where DN wins; the white portion, the fraction of runs in which there was no convergence after 125,000,000 generations. We explore the (b, β) parameter space, setting c = 1 and α = 1. We consider small errors, ε = 0.01, and a 50 × 50 lattice for a total population size N = 2500. We use viability updating with parameter values γ = 0.1 and θ = 0.1. (A) Initial frequency of strategies: DN=0.79, DP=0.07, CN=0.07, CP=0.07. (B) Initial frequency of strategies: DN=0.07, DP=0.07, CN=0.07, CP=0.79.