Decision Making in the Face of Uncertainty: Comparative Probability Metrics and Network Surveillance

面对不确定性的决策：比较概率度量和网络监控

• 批准号: RGPIN-2019-04212
• 负责人: Stevens, Nathaniel
• 金额: $ 1.68万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714701
• 关键词: Decision; Making; Face; Uncertainty; Comparative

Society today is inundated with data and this data revolution is changing the way decisions are being made; in all natural science and engineering (NSE) fields making decisions in the face of uncertainty and using data to inform these decisions is becoming commonplace. Here I propose a research program whose main objective is to develop tools and methodologies that facilitate such decision-making processes and that provide NSE researchers with clarity and confidence in two specific contexts. First, we consider comparisons in which the similarity or superiority of two or more characteristics is to be evaluated. Traditionally, such comparisons are made via hypothesis tests for which the null hypotheses assume equality. However, in practice it is often more natural to assume the characteristics are different until there is sufficient evidence to justify considering them equal. Importantly, in such contexts exact equivalence is not always necessary and instead practical equivalence is sufficient. Both of these considerations are gracefully handled by what I call the probability of agreement and other tailored comparative probability metrics (CPMs). These metrics and this way of thinking constitute an innovative and elegant means of formally evaluating and quantifying similarity and superiority while accounting for practical importance. Here I propose several novel CPM-based strategies for use in a variety of application areas including the comparison measurement devices, lifetime distributions, and outcomes in different experimental conditions. I expect that this research program will lead to a paradigm shift in decision-making strategies and to the replacement of hypothesis testing as the standard approach for making such comparisons. Second, we consider the field of network surveillance which is concerned with identifying anomalous activity in social networks. As the prevalence of social networks has increased in recent years, so also has the importance of network surveillance. My work has been devoted to the development of real-time monitoring methodologies that seek to identify when and how interactions within a network significantly change. Such techniques have been successful in identifying spammers, terrorists and fraudulent activity in social networks. This research area is burgeoning with many open research problems and monitoring strategies to explore. In this proposal I highlight my plans to develop a unified methodology that quickly and accurately detects anomalies while accounting for community structure, individual propensities for interaction, the direction of interactions as well as contextual information that might be useful. Such a methodology will advance the current state-of-the-art and greatly improve our ability to identify nefarious activity in social networks. This research program will involve both graduate and undergraduate students who will all conduct timely and impactful research and acquire highly sought-after skills.

当今社会充斥着数据，这场数据革命正在改变决策的方式;在所有自然科学和工程（NSE）领域，面对不确定性做出决策并使用数据来告知这些决策变得越来越普遍。在这里，我提出了一个研究计划，其主要目标是开发工具和方法，以促进这种决策过程，并提供清晰度和信心，在两个特定的环境中的NSE研究人员。 首先，我们考虑比较，其中两个或多个特征的相似性或优越性是要评估的。传统上，这种比较是通过假设检验进行的，其中零假设假定相等。然而，在实践中，在有足够的证据证明它们是平等的之前，通常更自然地假设这些特征是不同的。重要的是，在这种情况下，精确的对等并不总是必要的，实际的对等就足够了。这两个考虑因素都被我称为协议概率和其他定制的比较概率度量（CPM）优雅地处理了。这些指标和这种思维方式构成了一种创新和优雅的方式，在考虑实际重要性的同时，正式评估和量化相似性和优越性。在这里，我提出了几种新的CPM为基础的策略，用于在各种应用领域，包括比较测量设备，寿命分布，并在不同的实验条件下的结果。我希望这项研究计划将导致决策策略的范式转变，并取代假设检验作为进行此类比较的标准方法。 其次，我们考虑网络监控领域，它涉及到识别社交网络中的异常活动。随着近年来社交网络的普及，网络监控的重要性也在增加。我的工作一直致力于开发实时监控方法，以确定网络内的交互何时以及如何发生重大变化。这些技术在识别垃圾邮件发送者、恐怖分子和社交网络中的欺诈活动方面取得了成功。这一研究领域正在蓬勃发展，有许多开放的研究问题和监测策略有待探索。在这份提案中，我强调了我的计划，即开发一种统一的方法，快速准确地检测异常，同时考虑社区结构、个人互动倾向、互动方向以及可能有用的上下文信息。这种方法将推进当前的最新技术，并大大提高我们识别社交网络中恶意活动的能力。 这项研究计划将涉及研究生和本科生谁都将进行及时和有影响力的研究，并获得备受追捧的技能。