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

今天的社会充斥着数据，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为这些决策提供信息正变得越来越普遍。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，并在两个特定的背景下为NSE研究人员提供清晰和信心。

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.