Through the development and usage of an agent‐based model, this article investigates firms' adaptive strategies against disruptions in a supply chain network. Viewing supply chain networks as complex adaptive systems, we first construct and analyze a real‐world supply chain network among 2,971 firms spanning 90 industry sectors. We then develop an agent‐based simulation to show how the model of firms' adaptive behaviors can leverage competition relationships within a supply chain network. The simulation also models how disruptions propagate in the supply chain network through cascading failures. With the simulation, we seek to understand if a firm's adaptive behaviors can reduce the impact of disruptions in supply chain networks. Therefore, we propose, evaluate, and analyze two types of adaptive strategies a firm can leverage to reduce the negative effects of supply chain network disruptions. First, we deploy in our model a reactive strategy, which restructures the network in response to a disruption event among first‐tier suppliers. Next, we develop and propose proactive strategies, which are used when a distant disruption is observed but has not yet hit the focal firm. We discuss the implications related to how and when firms can improve their resilience against supply disruptions by leveraging adaptive strategies.

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The goal of this article is to understand the multidisciplinary field of public affairs. Based on data and text mining on the profiles and publications of all faculty members from a list of research‐oriented U.S. public affairs programs, we describe the landscape of public affairs schools and scholars, identify 15 topics in public affairs research and discuss their trends of change between 1986 and 2015, and show the clustering and hiring networks of public affairs schools. Our results suggest a broader approach to understanding the field of public affairs than the public administration focus in the literature. Although public administration is highly visible in the field, which is evidenced by the journals most favored by public affair scholars, various specific policy areas (such as health, social, urban, environmental, global, and education policies) show strong representations based on our topical analysis of public affairs research.

The past 2 decades have witnessed the emergence of information as a scientific discipline and the growth of information schools around the world. We analyzed the current state of the iSchool community in the U.S. with a special focus on the evolution of the community. We conducted our study from the perspectives of acquiring talents and producing research, including the analysis on iSchool faculty members' educational backgrounds, research topics, and the hiring network among iSchools. Applying text mining techniques and social network analysis to data from various sources, our research revealed how the iSchool community gradually built its own identity over time, including the growing number of faculty members who received their doctorates from the field that studies information, the deviation from computer science and library science, the rising emphasis on the intersection of information, technology, and people, and the increasing educational and research homogeneity as a community. These findings suggest that iSchools in the U.S. are evolving into a mature and independent discipline with a more established identity.

Modeling human behavioral data is challenging due to its scale, sparseness (few observations per individual), heterogeneity (differently behaving individuals), and class imbalance (few observations of the outcome of interest). An additional challenge is learning an interpretable model that not only accurately predicts outcomes, but also identifies important factors associated with a given behavior. To address these challenges, we describe a statistical approach to modeling behavioral data called the structured sum-of-squares decomposition (S3D). The algorithm, which is inspired by decision trees, selects important features that collectively explain the variation of the outcome, quantifies correlations between the features, and partitions the subspace of important features into smaller, more homogeneous blocks that correspond to similarly-behaving subgroups within the population. This partitioned subspace allows us to predict and analyze the behavior of the outcome variable both statistically and visually, giving a medium to examine the effect of various features and to create explainable predictions. We apply S3D to learn models of online activity from large-scale data collected from diverse sites, such as Stack Exchange, Khan Academy, Twitter, Duolingo, and Digg. We show that S3D creates parsimonious models that can predict outcomes in the held-out data at levels comparable to state-of-the-art approaches, but in addition, produces interpretable models that provide insights into behaviors. This is important for informing strategies aimed at changing behavior, designing social systems, but also for explaining predictions, a critical step towards minimizing algorithmic bias.