2. Go

Now we turn to action! In this section, we will implement the go procedure that will handle the two main stages of our model: the Game Play Stage and the Evolutionary Stage.

to go
    if (all? players [strategy = "ALLC"] or all? players [strategy = "ALLD"]) [stop]
    game-play-stage
    evolutionary-stage
    recolor-players
    tick
end

Game Play Stage¶

In the Game Play Stage, each agent will interact with its 8 neighbors and accumulate payoffs based on their strategies. We will define a procedure called game-play-stage to handle this.

to game-play-stage
    ask players [
        set payoff 0  ; reset payoff before interactions
        let n_C count neighbors with [strategy = "ALLC"]
        let n_D 8 - n_C
        ifelse (strategy = "ALLC") [
            set payoff (n_C * (benefit - cost)) + (n_D * (- cost))
        ] [
            set payoff (n_C * benefit) + (n_D * 0)
        ]
    ]
end

Evolutionary Stage¶

In the Evolutionary Stage, agents will update their strategies based on the payoffs received during the Game Play Stage. An agent may adopt the strategy of a neighbor with a higher payoff. We will define a procedure called evolutionary-stage to handle this.

to evolutionary-stage
    ask players [
        let best-neighbor max-one-of neighbors [payoff]
        if (best-neighbor != nobody and [payoff] of best-neighbor > payoff) [
            set strategy [strategy] of best-neighbor
        ]
    ]
end

With these procedures in place, our model is now capable of simulating the dynamics of cooperation and defection among agents in a lattice structure. Each tick of the simulation will represent one round of interactions and strategy updates.