论文标题
意见动力学转向使用随机搜索
Opinion Dynamics Steering using Stochastic Search
论文作者
论文摘要
在本文中，我们将随机搜索动态优化框架应用于部分活跃的人群中的转向意见动态。该框架基于随机优化理论，并依赖于指数家族分布的候选解决方案的采样。我们得出开放循环，反馈和新颖的自适应反馈策略的分布参数更新。所有三个策略均在模拟中的两个不同意见动态转向方案上进行了测试，并与手工设计的反馈政策进行了比较。结果展示了意见动力控制的框架和相对于主动代理设置大小的自适应反馈策略的鲁棒性。
In this paper, we apply the stochastic search dynamic optimization framework for steering opinion dynamics in a partially active population. The framework is grounded on stochastic optimization theory and relies on sampling of candidate solutions from distributions of the exponential family. We derive the distribution parameter update for an open loop, a feedback, and a novel adaptive feedback policy. All three policies are tested on two different opinion dynamics steering scenarios in simulation and compared against a hand designed feedback policy. The results showcase the effectiveness the framework for opinion dynamics control and the robustness of the adaptive feedback policy with respect to the active agent set size.
