Sentence examples for population games from inspiring English sources
A population game F belongs to the class of stable population games when F is monotone, then, from Theorem 2.3, we can assert that the space of stable population games is homeomorphic to the graph space of their solution mappings.
Population games have become a powerful tool for solving resource-allocation problems in a distributed manner, and for the design of non-centralized optimization-based controllers.
The algorithms, inspired by a class of evolutionary dynamics that emerge in the context of population games, generate on-line Shahshahani gradient-like systems able to achieve extremum seeking under simplex-like constraints on the positive orthant.
In this work, we propose the usage of population games, evolutionary and learning dynamics in the study of node coevolution for selfish protocol selection, and, most importantly, its impact on system performance.
Game theory requires solving for the Nash equilibrium, which in population games is a strategy such that no sufficiently small group of individuals can achieve a higher payoff by adopting a different strategy [19].
For any population game (F Xrightarrowmathbb{R}^{n}), each Nash equilibrium point (x^) of the game F is equivalent to (x^) being a solution of variational inequality ((y-x^)^{T} F(x^ leq0); see [22].
This relevant case can only occur, when taking into account self-interactions in multi-population games.
Our treatment of heterogeneous preferences is based on multi-population games [33] [37].
In the multi-population games with interactions across populations, the risk-dominance concept is not sufficient to understand the dynamics and outcome of the game.
As we will show in the following, the dynamics and outcome of the multi- population stag hunt and snowdrift games with incompatible preferences are more complicated and in marked contrast to the corresponding one-population games.
The aim of this paper is to illustrate the advantages of two recently introduced population-game approaches in comparison to other classical optimization methods.
