具有领导者的交互步行者群体的最优招募策略
Optimal recruitment strategies for groups of interacting walkers with leaders
摘要：
我们引入了一个模型的相互作用的随机游动的有限一维链上的吸收边界或目标的末端。步行者有两种类型：向给定目标弹道式移动的信息粒子，以及偏向于接近信息粒子的扩散不信息粒子。这个模型模拟了动物等级群体的动态，其中一个知情的个体试图说服和领导其同种动物的运动。我们的特点是成功的说服的第一次通过概率的不知情的粒子的目标，我们解释的速度知情的粒子作为一个战略参数，粒子的调谐，以最大限度地提高其成功。我们发现，成功概率是非单调的，达到最大值在一个中间的速度，其值增加的扩散率的未知粒子。当两个不同的团队在相反的方向竞争时，通常最大的团队是最成功的。然而，少数人可以通过两种不同的策略来扭转这种局面，成为最有可能的赢家：增加其吸引力强度或调整其速度到相对于多数人速度的最佳值。
英文摘要：
We introduce a model of interacting random walkers on a finite one dimensional chain with absorbing boundaries or targets at the ends. Walkers are of two types: informed particles that move ballistically towards a given target, and diffusing uninformed particles that are biased towards close informed particles. This model mimics the dynamics of hierarchical groups of animals, where an informed individual tries to persuade and lead the movement of its conspecifics. We characterize the success of the persuasion by the first-passage probability of the uninformed particle to the target, and we interpret the speed of the informed particle as a strategic parameter that the particle tunes to maximize its success. We find that the success probability is non-monotonic, reaching its maximum at an intermediate speed whose value increases with the diffusing rate of the uninformed particle. When two different groups of informed leaders traveling in opposite directions compete, usually the largest group is the most successful. However, the minority can reverse this situation and become the most probable winner by following two different strategies: increasing its attraction strength or adjusting its speed to an optimal value relative to the majority's speed.
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。
