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Evolving multiplayer networks: Modelling the evolution of cooperation in a mobile population

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  • We consider a finite population of individuals that can move through a structured environment using our previously developed flexible evolutionary framework. In the current paper the behaviour of the individuals follows a Markov movement model where decisions about whether they should stay or leave depends upon the group of individuals they are with at present. The interaction between individuals is modelled using a public goods game. We demonstrate that cooperation can evolve when there is a cost associated with movement. Combining the movement cost with a larger population size has a positive effect on the evolution of cooperation. Moreover, increasing the exploration time, which is the amount of time an individual is allowed to explore its environment, also has a positive effect. Unusually, we find that the evolutionary dynamics used does not have a significant effect on these results.

    Mathematics Subject Classification: 91A22, 60J10, 92D15.


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  • Figure 1.  This plot shows the best response staying propensities for 1 type $C_i$ individual playing against $N-1$ type $C_j$ individuals. Parameter set 1 is used with $\lambda = 0.2$ and $i, j\in \{0.01, 0.02, \ldots, 0.99\}$. The intersection point of the plots gives the unique strategy which is a best response to itself, i.e. the unique cooperator resident Nash equilibrium staying propensity $\gamma_R$, which is somewhere between $0.3$ and $0.4$. This value is similar to the one obtained using the iterative method (see Figure 2). The values from the current figure are approximate only because of the jagged nature of the lines; these occur because of the very large number of simulations that would be necessary to obtain a smooth version (the figure uses 10000 simulations for each combination). The figure is used to illustrate the uniqueness of the solution only.

    Figure 2.  These plots show the effect of movement cost on the evolution of cooperation using parameter set 1. The left (centre) plot shows the staying propensities $\delta_R = 0.99$ ($\gamma_R$) for resident defectors (cooperators) and $\gamma_M$ ($\delta_M$) for a mutant cooperator (defector) used to invade the resident population. In the right plot, we have the fixation probability of a mutant cooperator $C_{\gamma_M}$ (defector $D_{\delta_M}$) against $N-1$ resident defectors $D_{0.99}$ (cooperators $C_{\gamma_R}$).

    Figure 3.  Plots created using parameter set 2. The exploration time $T$ has been decreased from 10 to 5.

    Figure 4.  Plots created using parameter set 3. The exploration time $T$ has been increased from 10 to 25.

    Figure 5.  Plots created using parameter set 4. The population size has been increased from 10 to 20.

    Figure 6.  Plots have been created using parameter set 5. The plots here are against the reward to cost ratio $v/c$ such that $c = 0.04.$

    Figure 7.  Plots have been created using parameter set 6. The plots here are against the reward to cost ratio $v/c$ such that $c = 0.09$.

    Figure 8.  This plot shows the best response cooperator staying propensity (solid line, value shown on the x-axis) versus the range of defector staying propensities on the y-axis, and the best response defector staying propensity (dashed line, value shown on the y-axis) versus the range of cooperator staying propensities (on the x-axis) for $N/2$ cooperators and $N/2$ defectors. Parameter set 1 is used with $\lambda = 0.2$ and the staying propensities are chosen from the set $\{0.01, 0.02, \ldots, 0.99\}$. The best response staying propensities cross at one point only, which is thus the unique Nash equilibrium, where $\gamma\approx 0.7$ and $\delta\approx0.5$. These values are similar to those obtained using the iterative method described earlier (see Figure 9). As before, the values from the current figure are approximate only because of the jagged nature of the lines; the figure is used to illustrate the uniqueness of the solution only.

    Figure 9.  These plots show the effect of movement cost $\lambda$ on the evolution of cooperation and are created using parameter set 1. The plot on the left shows the Nash equilibrium staying propensity $\gamma$ for cooperators and $\delta$ for defectors in a mixed population where there are $N/2$ individuals of each type. The plot in the centre shows the fixation probability of each type from the mixed state with $N/2$ individuals of each type. The plot on the right shows the fixation probability of a mutant cooperator $C_\gamma$ (defector $D_\delta$) in a population of $N-1$ resident defectors $D_\delta$ (cooperators $C_\gamma$).

    Figure 10.  Plots created using parameter set 2. Plots are as in Figure 9 with exploration time $T$ decreased from $10$ to $5$.

    Figure 11.  Plots created using parameter set 3. Plots are as in Figure 9 with exploration time $T$ increased from $10$ to $25$.

    Figure 12.  Plots created using parameter set 4. Plots are as in Figure 9 with population size $N$ increased from $10$ to $20$.

    Figure 13.  Plots created using parameter set 5. Plots are as in Figure 9 but $\lambda$ is fixed and reward to cost ratio $v/c$ varied such that $c = 0.04$.

    Figure 14.  Plots created using parameter set 5. Plots are as in Figure 9 but $\lambda$ is fixed and reward to cost ratio $v/c$ varied such that $c = 0.09$.

    Table 1.  Notation used in the paper.

    Table of Notation
    Notation Definition Description
    $N$ $\in \mathbb{Z}^+$Population size.
    $M$ $\in \mathbb{Z}^+$Number of places.
    $I_n$Individual $n$.
    $P_m$Place $m$.
    $m_{n, t}$ $\in\{1, \ldots, M\}$Place where $I_n$ is at time $t$.
    ${\bf{m}}_t$ $ = [m_{n, t}]_{n = 1}^N$Population distribution at time $t$.
    ${\bf{m}}_{<t}$$ = ({\bf{m}}_{t-1}, \ldots, {\bf{m}}_0)$Population distribution history.
    ${p_t}(\bf{m}\left| {{{\bf{m}}_{ < t}}} \right.)$$\in[0, 1]$Probability population has distribution ${\bf{m}}$ at time $t$ given history ${\bf{m}}_{<t}$.
    $\pi_t$ $\in[0, 1]$Population distribution probability function (PDPF).
    $P({\bf{m}}_{<t})$ $\in[0, 1]$Probability that population has history ${\bf{m}}_{<t}$.
    $\pi_{n, t}$ $\in[0, 1]$Individual distribution probability function (IDPF).
    $f_{n, t}$$ \ge 0$Fitness contribution of $I_n$ at time $t$.
    $F_{n, t}$ $>0$Fitness of $I_n$ at time $t$.
    $\mathcal{G}_{n}$ $\subset\{1, 2\ldots, N\}$ Direct group: group that $I_n$ is in.
    $w_{i, j, t}$ $\ge 0$Replacement weight that $I_i$ replaces $I_j$ at time $t$.
    ${\bf{W}}_t$ $ = [w_{i, j, t}]_{i, j = 1, \ldots, N}$Weighted adjacency matrix of evolutionary graph.
    $u_{i, j, t}$$\ge 0$Replacement weight contribution that $I_i$ assigns to $I_j$ at time $t$.
    $A, B$Two types of individuals in the population.
    $\mathcal{S}$ $\subset\{1, 2, \ldots, N\}$Population state, $n\in \mathcal{S}$ if $I_n$ has type $A$.
    $\mathcal{N}$ $ = \{1, 2, \ldots, N\}$State consisting of all type $A$ individuals.
    $P_{\mathcal{S}\mathcal{S}'}$ $\in [0, 1]$Probability of transitioning from $\mathcal{S}$ to $\mathcal{S}'$.
    $\rho^A_\mathcal{S}$ $\in[0, 1]$Fixation probability of type $A$ when the initial state is $\mathcal{S}$.
    $\mathfrak{r}_{ij}$ $ \in [0, 1]$Probability that $I_i$ replaces $I_j$.
    $h_n$ $\in [0, 1]$Probability that $I_n$ stays.
    $\alpha_n$ $\in[0, 1]$ Staying propensity: probability that individual $I_n$ stays when alone.
    $C\ (D)$Cooperator and defector interactive strategy.
    $\beta_C\ (\beta_D)$ $\in \mathbb{R}$Benefit of being with a cooperator (defector).
    $S$ $\in (0, 1)$Sensitivity shown to group members.
    $v$ $>0$Reward as a multiple of background fitness.
    $c$ $\in [0, 1)$Cost as a multiple of background fitness.
    $R_{n}$ $\ge 0$Payoff to $I_n$.
    $\lambda$ $ \in [0, \min(R_n))$Movement cost.
    $T$ $\in \mathbb{Z}^+$Exploration time.
    $C_\alpha\ (D_\alpha)$Cooperator (defector) with staying propensity $\alpha$.
    $\gamma\ (\delta)$ $\in[0, 1]$Nash equilibrium staying propensity of cooperator (defector).
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    Table 2.  Dynamics defined using the replacement weights and fitnesses as in [45]. In each case, B (D) is appended to the name of the dynamics if selection happens in the birth (death) event. For BDB and BDD dynamics $\mathfrak{r}_{ij} = b_{i}d_{ij}$, for DBD and DBB dynamics $\mathfrak{r}_{ij} = d_{j}b_{ij}$.

    BDB $\displaystyle b_i = \frac{ F_i }{\sum_{n} F_n }, \ d_{ij}= \frac{w_{ij} }{\sum_{n} w_{in} }$BDD $\displaystyle b_i= \frac{1}{N}, \ d_{ij}= \frac{ w_{ij}F_j^{-1} }{\sum_{n} w_{in}F_n^{-1} }$
    DBD $\displaystyle d_j= \frac{ F_j^{-1}}{ \sum_{n} F_n^{-1} }, \ b_{ij}= \frac{ w_{ij} }{\sum_{n} w_{nj} }$DBB $\displaystyle d_j= \frac{ 1 }{ N }, \ b_{ij}= \frac{ w_{ij}F_i }{\sum_{n} w_{nj}F_n }$
    LB $\displaystyle\mathfrak{r}_{ij} =\frac{ w_{ij}F_i}{\sum_{n, k} w_{nk} F_n}$LD $\displaystyle \mathfrak{r}_{ij} =\frac{ w_{ij}F_j^{-1} }{\sum_{n, k} w_{nk} F_k^{-1} }$
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    Table 3.  Parameters used for the simulations. The other parameters are fixed such that we have a complete structure with each individual having its own home, $\beta_C = 1$, $\beta_D = -1$, $S = 0.03$ and the dynamics used are BDB.

    Parameter Set123456
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