Community engagement plays a critical role in anti-poaching efforts, yet existing mathematical models aimed at enhancing this engagement often overlook direct participation by community members as alternative patrollers. Unlike professional rangers, community members typically lack flexibility and experience, resulting in new challenges in optimizing patrol resource allocation. To address this gap, we propose a novel game-theoretic model for community-participated patrol, where a conservation agency strategically deploys both professional rangers and community members to safeguard wildlife against a best-responding poacher. In addition to a mixed-integer linear program formulation, we introduce a Two-Dimensional Binary Search algorithm and a novel Hybrid Waterfilling algorithm to efficiently solve the game in polynomial time. Through extensive experiments and a detailed case study focused on a protected tiger habitat in Northeast China, we demonstrate the effectiveness of our algorithms and the practical applicability of our model.

翻译：社区参与在反盗猎工作中发挥着关键作用，然而，现有旨在提升此类参与度的数学模型往往忽视了社区成员作为替代巡护员的直接参与。与专业护林员不同，社区成员通常缺乏灵活性和经验，这为优化巡护资源配置带来了新的挑战。为弥补这一不足，我们提出了一种新颖的社区参与式巡护博弈论模型，其中保护机构策略性地部署专业护林员与社区成员，以应对最佳响应的盗猎者，从而保护野生动物。除了给出混合整数线性规划模型，我们还引入了二维二分搜索算法和一种新颖的混合注水算法，以在多项式时间内高效求解该博弈。通过大量实验以及针对中国东北一处老虎栖息保护地的详细案例研究，我们验证了所提算法的有效性以及模型的实际适用性。