网络异步模仿动力学的控制
Control of Asynchronous Imitation Dynamics on Networks
摘要：
模仿在决策主体群体中被广泛观察到。使用我们最近的收敛结果的异步模仿动态网络，我们考虑如何这样的网络可以有效地驱动到一个理想的平衡状态，通过提供回报激励使用一定的策略，无论是统一的或针对个人。特别是，如果对于每个可用的策略，代理人玩该策略时，他们的邻居玩相同的策略获得最大的回报，我们表明，提供激励，代理人在网络中，是在平衡将导致收敛到一个独特的新的平衡。对于一个统一的激励可以提供给所有代理的情况下，这个结果允许使用二进制搜索算法的最佳激励的计算。当激励措施可以针对个人代理，我们提出了一种算法来选择代理人应该选择的基础上迭代最大化的代理人谁采取所需的策略，以获得这些代理人这样做所需的回报激励的数量的比率。仿真结果表明，所提出的算法计算出接近最优的有针对性的支付激励的范围内的网络和协调博弈中的支付分布。
英文摘要：
Imitation is widely observed in populations of decision-making agents. Using our recent convergence results for asynchronous imitation dynamics on networks, we consider how such networks can be efficiently driven to a desired equilibrium state by offering payoff incentives for using a certain strategy, either uniformly or targeted to individuals. In particular, if for each available strategy, agents playing that strategy receive maximum payoff when their neighbors play that same strategy, we show that providing incentives to agents in a network that is at equilibrium will result in convergence to a unique new equilibrium. For the case when a uniform incentive can be offered to all agents, this result allows the computation of the optimal incentive using a binary search algorithm. When incentives can be targeted to individual agents, we propose an algorithm to select which agents should be chosen based on iteratively maximizing a ratio of the number of agents who adopt the desired strategy to the payoff incentive required to get those agents to do so. Simulations demonstrate that the proposed algorithm computes near-optimal targeted payoff incentives for a range of networks and payoff distributions in coordination games.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
