Abstract

Here we will use results of Cox, Durrett, and Perkins for voter model perturbations to study spatial evolutionary games on $\mathbb{Z}^d$, $d\ge 3$ when the interaction kernel is finite range, symmetric, and has covariance matrix $\sigma^2 I$. The games we consider have payoff matrices of the form $1+ wG$ where $1$ is matrix of all 1's and $w$ is small and positive. Since our population size $N=\infty$, we call our selection small rather than weak which usually means $w =O(1/N)$. The key to studying these games is the fact that when the dynamics are suitably rescaled in space and time they convergence to solutions of a reaction diffusion equation (RDE). Inspired by work of Ohtsuki and Nowak and Tarnita et al we show that the reaction term is the replicator equation for a modified game matrix and the modifications of the game matrix depend on the interaction kernel only through the values of two or three simple probabilities for an associated coalescing random walk. Two strategy games lead to an RDE with a cubic nonlinearity, so we can describe the phase diagram completely. Three strategy games lead to a pair of coupled RDE, but using an idea from our earlier work, we are able to show that if there is a repelling function for the replicator equation for the modified game, then there is coexistence in the spatial game when selection is small. This enables us to prove coexistence in the spatial model in a wide variety of examples where the replicator equation of the odified game has an attracting equilibrium with all components positive. Using this result we are able to analyze the behavior of four evolutionary games that have recently been used in cancer modeling.

Highlights

  • Game theory was invented by John von Neumann and Oscar Morgenstern [1] to study strategic and economic decisions of humans

  • To investigate this using evolutionary game theory, Basanta et al [45] considered a three strategy game in which cells are initially characterized as having autonomous growth (AG), but could switch to glycolysis for energy production (GLY ), or become increasing mobile and invasive (IN V )

  • V (x, y) = A(x − x∗ log x) + B(y − y∗ log y) is a Lyapunov function for the Lotka-Volterra equation, i.e., it is decreasing along solutions of (7.4), and x∗, y∗ is an attracting fixed point

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Summary

Introduction

Game theory was invented by John von Neumann and Oscar Morgenstern [1] to study strategic and economic decisions of humans. Evolutionary game theory has been applied to study cancer This provides an important motivation for our work, so we will consider four examples beginning with one first studied by Tomlinson [48]. The prevalence of glycolytic cells in invasive tumor suggests that their presence could benefit the emergence of invasive phenotypes To investigate this using evolutionary game theory, Basanta et al [45] considered a three strategy game in which cells are initially characterized as having autonomous growth (AG), but could switch to glycolysis for energy production (GLY ), or become increasing mobile and invasive (IN V ). OC cells produce osteoclast activating factors that stimulate the growth of M M cells where as M M cells are not effected by the presence of OB cells These considerations lead to the following game matrix. Swierniak and Krzeslak’s survey [47] contains the four examples we have covered here, as well as a number of others

Overview
Voter model perturbations
Voter model duality
PDE limit
Phase diagram
Concrete examples
ODEs for the three strategy games
Projective transformation
Reduction to Lotka-Volterra systems
Classifying three strategy games
Three edge fixed points
Two edge fixed points
One edge fixed point
Spatial three strategy games
Repelling functions
Boundary lemmas
Results for three classes of examples
Almost constant sum games
Tarnita’s formula
Multiple myeloma
Glycolytic phenotype
Tumor-stroma interactions
10 Voter model duality: details
11 Proofs of the coalescence identities
12 Derivation of the limiting PDE
13 Two strategy games with Death-Birth updating
13.2 Proof of Tarnita’s formula
14 Equilibria for three strategy games
Full Text
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