Visualizing the Beer Game: The Value of Interactions During Dynamic Decision Making

Humans have several exceptional abilities, one of which is the perceptual tasks of the visual sense. Humans have unique abilities to perceive data and identify patterns, trends, and outliers. This research investigates the design of interactive visualizations to identify the value and benefits of dynamic decision-making situations. Results from the Beer Distribution Game are analyzed to determine the value of visualizations (V = T + I + E + C). More precisely, how users obtain insight by using a visualization tool and how those insights inform decisions. The results provide evidence of the value and benefits of interactive visualization for dynamic decision-making situations

Theoretical properties of Bayesian Student-tt linear regression

Student-$t$ linear regression is a commonly used alternative to the normal model in Bayesian analysis when one wants to gain robustness against outliers. The assumption of heavy-tailed error distribution makes the model more adapted to a potential presence of outliers by assigning higher probabilities to extreme values. Even though the Student-$t$ model is often used in practice, not a lot is known about its theoretical properties. In this paper, we aim to fill some gaps by providing analyses in two different asymptotic scenarios. In the first one, outliers are considered to be further and further away from the bulk of the data. The analysis allows to characterize the limiting posterior distribution, a distribution in which a trace of the outliers is present, making the approach partially robust. The impact of the trace is seen to increase with the degrees of freedom of the Student-$t$ distribution assumed. The second asymptotic scenario is one where the sample size increases and the normal model is the true generating process to be able to compare the efficiency of the robust estimator to the ordinary-least-squares one when the latter is the benchmark. The asymptotic efficiency is comparable, in the sense that the variance of the robust estimator is inflated but only by a factor, and this factor converges to 1 as the degrees of freedom increase. The trade-off between robustness and efficiency controlled through the degrees of freedom is thus precisely characterized (at least asymptotically)

Adaptive Thresholding Heuristic for KPI Anomaly Detection

A plethora of outlier detectors have been explored in the time series domain, however, in a business sense, not all outliers are anomalies of interest. Existing anomaly detection solutions are confined to certain outlier detectors limiting their applicability to broader anomaly detection use cases. Network KPIs (Key Performance Indicators) tend to exhibit stochastic behaviour producing statistical outliers, most of which do not adversely affect business operations. Thus, a heuristic is required to capture the business definition of an anomaly for time series KPI. This article proposes an Adaptive Thresholding Heuristic (ATH) to dynamically adjust the detection threshold based on the local properties of the data distribution and adapt to changes in time series patterns. The heuristic derives the threshold based on the expected periodicity and the observed proportion of anomalies minimizing false positives and addressing concept drift. ATH can be used in conjunction with any underlying seasonality decomposition method and an outlier detector that yields an outlier score. This method has been tested on EON1-Cell-U, a labeled KPI anomaly dataset produced by Ericsson, to validate our hypothesis. Experimental results show that ATH is computationally efficient making it scalable for near real time anomaly detection and flexible with multiple forecasters and outlier detectors

More Than Forty Amish Affiliations? Charting the Fault Lines

The Amish are notoriously difficult to chart in terms of affiliations. However, defining affiliations is important to researchers: as a suitable measurement of conservatism, as a useful context for making sense of a particular district or settlement, for tracing socio-religious change over time, and for depicting both the unity and diversity that characterize contemporary Amish socio-ecclesiastical life. Until recently, scholars followed John Hostetler's definition of an affiliation as a group of church districts that fellowship together and share a common Ordnung. But in The Amish, Donald Kraybill, Karen Johnson-Weiner, and Steven Nolt offer an entirely new definition of an affiliation as a cluster of two or more districts with at least twenty years of shared history. They conclude that there are at least 40 Amish affiliations. I argue against this haphazard fragmentation, identifying six major affiliations and a handful of outliers. I then apply my traditional-modified model to several scenarios to demonstrate the model's utility

Optimistic Robust Optimization With Applications To Machine Learning

Robust Optimization has traditionally taken a pessimistic, or worst-case viewpoint of uncertainty which is motivated by a desire to find sets of optimal policies that maintain feasibility under a variety of operating conditions. In this paper, we explore an optimistic, or best-case view of uncertainty and show that it can be a fruitful approach. We show that these techniques can be used to address a wide variety of problems. First, we apply our methods in the context of robust linear programming, providing a method for reducing conservatism in intuitive ways that encode economically realistic modeling assumptions. Second, we look at problems in machine learning and find that this approach is strongly connected to the existing literature. Specifically, we provide a new interpretation for popular sparsity inducing non-convex regularization schemes. Additionally, we show that successful approaches for dealing with outliers and noise can be interpreted as optimistic robust optimization problems. Although many of the problems resulting from our approach are non-convex, we find that DCA or DCA-like optimization approaches can be intuitive and efficient

Returns to Schooling: A Quantile Regression

This paper contributes to the large body of economic literature that attempts to estimate the returns to schooling. It uses quantile regression to estimate the effect of an additional year of education on monthly wage for earners in different quantiles. Using data from the young men’s cohort of the National Longitudinal Survey, the paper attempts to control for ability, family background, geography, and race, and finds that the returns to schooling is approximately 3.49% for men. Furthermore, the paper finds that while the effect of education on earnings is not significantly different from quantile to quantile, the significance of education increases with earnings

Enhancing Employee Communication Behaviors for Sensemaking and Sensegiving in Crisis Situations: Strategic Management Approach for Effective Internal Crisis Communication

Purpose The purpose of this paper is to explore the organizational effectiveness of internal crisis communication within the strategic management approach, whether it enhanced voluntary and positive employee communication behaviors (ECBs) for sensemaking and sensegiving. By doing so, this study provides meaningful insight into: new crisis communication theory development that takes a strategic management approach, emphasizing employees’ valuable assets from an organization, and effective crisis communication practice that reduces misalignment with employees and that enhances voluntary and positive ECBs for the organization during a crisis. Design/methodology/approach This study conducted a nationwide survey in the USA among full-time employees (n=544). After dimensionality check through confirmatory factor analysis, this study tested hypothesis and research question by conducting ordinary least squares multiple regression analyses using STATA 13. Findings This study found that strategic internal communication factors, including two-way symmetrical communication and transparent communication, were positive and strong antecedents of ECBs for sensemaking and sensegiving in crisis situations, when controlling for other effects. The post hoc analysis confirmed theses positive and strong associations across different industry areas. Originality/value This study suggests that voluntary and valuable ECBs can be enhanced by listening and responding to employee concerns and interests; encouraging employee participation in crisis communication; and organizational accountability through words, actions and decisions during the crisis. As a theoretical implication, the results of this study indicate the need for crisis communication theories that emphasize employees as valuable assets to an organization