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Patrol Strategies to Maximize Pristine Forest Area

机译:最大化原始森林面积的巡逻策略

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摘要

Illegal extraction of forest resources is fought, in many developing countries, by patrols that try to make this activity less profitable, using the threat of confiscation. With a limited budget, officials will try to distribute the patrols throughout the forest intelligently, in order to most effectively limit extraction. Prior work in forest economics has formalized this as a Stackelberg game, one very different in character from the discrete Stackelberg problem settings previously studied in the multiagent literature. Specifically, the leader wishes to minimize the distance by which a profit-maximizing extractor will trespass into the forest-or to maximize the radius of the remaining "pristine" forest area. The follower's cost-benefit analysis of potential trespass distances is affected by the likelihood of being caught and suffering confiscation. In this paper, we give a near-optimal patrol allocation algorithm and a 1/2-approximation algorithm, the latter of which is more efficient and yields simpler, more practical patrol allocations. Our simulations indicate that these algorithms substantially outperform existing heuristic allocations.
机译:在许多发展中国家,巡逻与非法没收森林作斗争,巡逻试图利用没收的威胁使这项活动的利润减少。在预算有限的情况下,官员们将尝试在整个森林中智能地巡逻,以最有效地限制采伐。先前在森林经济学中的工作将其形式化为Stackelberg游戏,其特征与先前在多主体文献中研究的离散Stackelberg问题设置大不相同。具体地说,领导者希望使利润最大化的采掘者侵入森林的距离最小化,或使剩余的“原始”森林区域的半径最大化。追随者对潜在侵入距离的成本效益分析受到被捕和被没收的可能性的影响。在本文中,我们给出了一种接近最优的巡逻分配算法和一个1/2逼近算法,后者效率更高,并且产生的巡逻分配更简单,更实用。我们的仿真表明,这些算法大大优于现有的启发式分配。

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