Management of a stage-structured insect pest: an application of approximate optimization.
Management of a stage-structured insect pest: an application of approximate optimization.
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阶段结构害虫的管理：近似优化的应用。
DOI：
10.1002/eap.1700
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Hackett SC
中科院分区：
文献类型：
--
作者：
Hackett SC
生态决策问题经常需要随着时间的推移对一系列行动进行优化，而这些行动可能既有直接影响，也有下游影响。只有当维数足够低时，动态规划才能解决这类问题。近似动态规划（ADP）提供了一套以保证最优性为代价，适用于任意复杂性问题的方法。最容易推广的方法是展望策略：这是一种蛮力算法，通过构建和解决一系列在确定的规划范围内完整问题的临时截断近似来确定合理的行动。我们开发和应用这种方法来害虫管理问题的灵感来自地中海果蝇，Ceratitis capitata。该模型旨在最大限度地减少管理行动的累积成本和在单个16周的季节中由苍蝇引起的损失。介蝇种群是阶段结构的，并且持续增长，而管理决策是以离散的、每周的间隔做出的。每周，该模型在不采取行动、使用杀虫剂或从六种不育昆虫释放比例中选择一种。前瞻性政策绩效在一系列规划范围内进行评估，包括两个级别的作物对蝇类的易感性和三个级别的农药持久性。在所有情况下，前瞻性政策所建议的行动与只在当前一周内将成本最小化的短视政策形成对比。我们发现，前瞻性政策总是比短视政策执行得更好，决策质量对成本相对于规划范围的时间分布很敏感：当排除相关成本时，扩展规划范围是有益的。然而，当重大成本即将解决时，较长的规划期限可能会降低决策质量。ADP方法，如本文开发的基于前瞻性策略的方法，使问题难以处理，无法进行动态规划，但应谨慎应用，因为它们的灵活性是以保证最优性为代价的。然而，鉴于许多生态管理问题的复杂性，使用更具代表性的问题提法提出“足够好”的战略的能力可能比从简化模型得出的最佳战略更可取。
Ecological decision problems frequently require the optimization of a sequence of actions over time where actions may have both immediate and downstream effects. Dynamic programming can solve such problems only if the dimensionality is sufficiently low. Approximate dynamic programming (ADP) provides a suite of methods applicable to problems of arbitrary complexity at the expense of guaranteed optimality. The most easily generalized method is the look‐ahead policy: a brute‐force algorithm that identifies reasonable actions by constructing and solving a series of temporally truncated approximations of the full problem over a defined planning horizon. We develop and apply this approach to a pest management problem inspired by the Mediterranean fruit fly,Ceratitis capitata. The model aims to minimize the cumulative costs of management actions and medfly‐induced losses over a single 16‐week season. The medfly population is stage‐structured and grows continuously while management decisions are made at discrete, weekly intervals. For each week, the model chooses between inaction, insecticide application, or one of six sterile insect release ratios. Look‐ahead policy performance is evaluated over a range of planning horizons, two levels of crop susceptibility to medfly and three levels of pesticide persistence. In all cases, the actions proposed by the look‐ahead policy are contrasted to those of a myopic policy that minimizes costs over only the current week. We find that look‐ahead policies always out‐performed a myopic policy and decision quality is sensitive to the temporal distribution of costs relative to the planning horizon: it is beneficial to extend the planning horizon when it excludes pertinent costs. However, longer planning horizons may reduce decision quality when major costs are resolved imminently. ADP methods such as the look‐ahead‐policy‐based approach developed here render questions intractable to dynamic programming amenable to inference but should be applied carefully as their flexibility comes at the expense of guaranteed optimality. However, given the complexity of many ecological management problems, the capacity to propose a strategy that is “good enough” using a more representative problem formulation may be preferable to an optimal strategy derived from a simplified model.
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