Optimal Decision Strategies for Large Spatio-Temporal Decision Problems

大型时空决策问题的最优决策策略

• 批准号: 1513579
• 负责人: Eric Laber
• 金额: $ 15万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513579&HistoricalAwards=false
• 关键词: Optimal; Decision; Strategies; Large; Spatio

A number of current public health, ecological, and environmental crises can be conceptualized as spatio-temporal decision problems wherein a harmful replicating agent is spreading across space and, simultaneously, a decision maker must select when, where, and how to allocate limited resources targeted at controlling the spread of the agent. Examples include the spread of an infectious disease across people in a social network, the spread of a computer virus across machines in a network, and the spread of an invasive species across an ecological landscape. The costs of these epidemics are enormous. For example, 32% of global deaths are attributed to infectious diseases, the cost of computer viruses to U.S. businesses is estimated to exceed 60 billion dollars per year, and the cost of invasive species is estimated to exceed 100 billion dollars per year and to affect 100 million acres of land. Thus, improvements to spatio-temporal decision making could have tremendous benefits to all sectors of society. Technological advances have made it increasingly easy to collect, store, and manipulate large amounts of data. This research project takes first steps toward methods that use accumulating data on the spread of a replicating agent to inform resource allocation over time. The methodology adjusts for non-stationary agent dynamics, changing availability of resources, and uncertain or incomplete measurements.A key component of controlling the spread of a replicating agent over space and time is deciding where, when, and how to apply interventions. This control process is formalized as an allocation strategy which comprises a sequence of functions, one per time point, that map up-to-date information on the spread of the agent to a distribution over subsets of locations to receive an intervention. The project formally defines an optimal allocation strategy using potential outcomes, and demonstrates that spatial proximity induces causal interference among locations, thereby preventing direct application of existing methods for sequential treatment assignment. A parametric estimator of the optimal strategy within a pre-specified class of allocation strategies using a systems dynamics model and simulation-optimization is developed. This estimator has low variance and can be applied in a data-impoverished setting, however it may suffer from high bias if the systems dynamics model is misspecified. A semi-parametric estimator of the optimal allocation strategy which does not require correct specification of a systems dynamics model is also developed. Because the semi-parametric estimator relies on fewer assumptions about the underlying systems dynamics, it is potentially robust to model misspecification but may have high variance. To balance bias and variance, and optimize finite sample performance, shrinkage of the semi-parametric estimator toward the parametric estimator will be investigated. The methodologies will be illustrated with an application to the spread of white-nose syndrome in bats.

许多当前的公共卫生，生态和环境危机可以被概念化为时空决策问题，其中有害复制的代理正在跨太空传播，同时，决策者必须选择何时，何时何地，以及如何分配针对代理商传播的有限资源。 例子包括传染病在社交网络中跨人们的传播，计算机病毒在网络中的机器中的传播以及入侵物种在生态景观中的传播。 这些流行病的成本是巨大的。 例如，32％的全球死亡归因于传染病，美国企业的计算机病毒成本估计每年超过600亿美元，据估计，入侵物种的成本每年超过1000亿美元，并影响1亿英亩的土地。 因此，改善时空决策可能会对社会的所有部门带来巨大的好处。技术进步使收集，存储和操纵大量数据变得越来越容易。 该研究项目采取了第一步，迈向了使用累积数据的方法，该数据随着时间的流逝，复制代理的传播信息为资源分配提供了信息。 该方法可以调整非平稳代理动力学，更改资源的可用性以及不确定或不完整的测量。 该控制过程被形式化为一种分配策略，该策略包括一个函数序列，一个时间点，该函数绘制了有关代理商传播到分布到位置子集的分布以接受干预的最新信息。 该项目使用潜在的结果正式定义了最佳分配策略，并证明空间接近性会引起位置之间的因果干扰，从而阻止直接应用现有方法进行顺序治疗分配。 开发了使用系统动力学模型和仿真优化的预先指定的分配策略中最佳策略的参数估计器。 该估计器的方差较低，可以应用于数据破裂的设置，但是如果系统动力学模型被弄清楚，它可能会遭受高偏差。 还开发了不需要正确规范系统动力学模型的最佳分配策略的半参数估计器。 由于半参数估计器依赖于对基础系统动力学的较少假设，因此模拟错误指定的模型可能具有较高的差异，但可能具有很高的差异。 为了平衡偏差和方差并优化有限样本性能，将研究半参数估计器向参数估计器的收缩。 该方法将通过应用白鼻综合征在蝙蝠中的扩散进行说明。