Improving Community-Participated Patrol for Anti-Poaching

作     者：Wu, Yufei Xu, Yixuan Even Zhang, Xuming Liu, Duo Zhu, Shibing Fang, Fei 

作者机构：Shanghai Jiao Tong University China Carnegie Mellon University United States World Wide Fund For Nature China Heilongjiang Academy of Sciences China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Mixed integer linear programming 

摘      要：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. Copyright © 2024, The Authors. All rights reserved.