The model of Network Coloring Game (NCG) first proposed by Kearns et al. is used to simulate conflict resolving dynamics and consensus reaching procedures in social sciences. In NCG, individual's payoff depends on preference mechanism and is zero when a player shares the same color with someone in the neighborhood. The equilibrium is reached when nobody has incentives to continue choosing a different color. Applications of NCG include resource allocation \cite{Bampas:2013}, timetable scheduling \cite{Seo:2016}, etc., thus numerous literature devoted in estimating the convergence situation and optimizing social payoffs. In this work, we adopted some Markov Chain techniques to further research on NCG. Firstly, with no less than $\Delta + 2$ colors provided, we proposed and proved that the converging time is stochastically bounded by $O_p(\log n)$, through introducing an absorbing Markov Chain to approximate upper bounds for its expectation and variance, which is an improvement on Chaudhuri et al.'s result \cite{Chaudhuri:2008}. Here $n$ is the number of vertices and $\Delta$ is the maximum degree of the network. Secondly, as most literature ignores the dynamics after the conflict is solved, we focused on post-conflict adjustments among the players when a Borda preference mechanism is applied. Markov Chain Monte Carlo (MCMC) methods like Metropolis-Hasting Algorithm and Simulated Annealing Heuristic were employed to simulate payoff-optimizing behaviors and estimate both local and global optimal social welfare. Supporting experimental results were given to illustrate the corresponding procedures.
翻译:Kearns 等人首次提议的网络彩色游戏模式(NGG) 用于模拟冲突解决动态和社会科学中达成共识的程序。 在 NCG 中, 个人的报酬取决于偏好机制, 当玩家与邻居共享相同颜色时, 个人的报酬为零。 当没有人有动力继续选择不同颜色时, 达到平衡。 NCG 的应用包括资源分配 \ cite{Bampas:2013}, 时间表时间安排 \ cite{Seo:2016} 等, 从而在估计趋同状况和优化社会收益方面花费了大量文献。 在这项工作中, 我们采用了某些Markov 链技术来进一步研究 NCG 。 首先, 提供不少于$\ Delta + 2$ 的颜色, 我们提议并证明, 趋同时间的平衡是由$_p(log n) 来约束的, 通过引入吸收 Markov 链以近似于预期和差异的上下限。 Chaudhurial 和 al. 的结果 {Choudhuropie:2008} 以最高级的汇率调整, 当我们使用了货币游戏的货币游戏的汇率的汇率调整过程后, 以最高的汇率调整是