Improving Community-Participated Patrol for Anti-Poaching

TL;DR

This paper introduces a game-theoretic model and algorithms for optimizing community-participated patrols in anti-poaching efforts, addressing challenges of resource allocation and community involvement.

Contribution

It presents a novel game-theoretic framework and algorithms for deploying both professional rangers and community members in anti-poaching patrols, considering community participation.

Findings

Algorithms efficiently solve the patrol game in polynomial time.

Model effectively improves anti-poaching resource allocation.

Case study demonstrates practical applicability in tiger habitats.

Abstract

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.