论文标题
在图形约束下动态的社会学习
Dynamic social learning under graph constraints
论文作者
论文摘要
我们引入了一个图形受限的动态选择模型，并通过积极的$α$均匀奖励建模的强化。我们表明，它的经验过程可以将其写入马尔可夫噪声的随机近似递归，具有与某些顶点加强随机行走相同的概率定律。我们使用这种等价性表明，对于$α> 0 $，当“退火”时，渐近结果将在特定限制意义上围绕最佳，而慢慢地让$α\ uparrow \ infty $缓慢。
We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively $α$-homogeneous rewards. We show that its empirical process, which can be written as a stochastic approximation recursion with Markov noise, has the same probability law as a certain vertex reinforced random walk. We use this equivalence to show that for $α> 0$, the asymptotic outcome concentrates around the optimum in a certain limiting sense when `annealed' by letting $α\uparrow\infty$ slowly.
