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Patrol Strategies to Maximize Pristine Forest Area

机译:最大化原始林区的巡逻策略

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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:在许多发展中国家的巡逻中,在许多发展中国家巡逻,巡逻违反了非法提取森林资源,这些活动利用没收威胁将这项活动较少。预算有限,官员将在智能地分配整个森林的巡逻,以便最有效地限制提取。在森林经济学中的事先工作已经将其正式化为Stackelberg游戏,其中一个在多层文学中先前研究过的离散Stackelberg问题设置中的字符非常不同。具体地,领导者希望最小化利润最大化提取器将侵入森林的距离 - 或者最大化剩余的“原始”林区域的半径。追随潜在侵入距离的跟随者的成本效益分析受到被捕获和遭受没收的可能性的影响。在本文中,我们提供了近乎最佳的巡更分配算法和1/2近似算法,后者更有效,更高,更实际的巡逻拨款。我们的模拟表明这些算法大大倾向于现有的启发式分配。

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