On Approximating a Scheduling Problem

Given a set of communication tasks (best described in terms of a weighted bipartite graph where one set of nodes denotes the senders, the other set the receivers, edges are communication tasks, and the weight of an edge is the time required for transmission), we wish to minimize the total time required for the completion of all communication tasks assuming that tasks can be preempted (that is, each edge can be subdivided into many edges with weights adding up to the edge's original weight) and that preemption comes with a cost. In this paper, we first prove that one cannot approximate this problem within a factor smaller than unless P=NP. It is known that a simple approximation algorithm achieves within a ratio of two (H. Choi and S.L. Hakimi, Algorithmica, vol. 3, pp. 223–245, 1988). However, our experimental results show that its performance is worse than the originally proposed heuristic algorithm (I.S. Gopal and C.K. Wong, IEEE Transactions on Communications, vol. 33, pp. 497–501, 1985). We devise a more sophisticated algorithm, called the potential function algorithm which, on the one hand, achieves a provable approximation ratio of two, and on the other hand, shows very good experimental performance. Moreover, the way in which our more sophisticated algorithm derives from the simple one, suggests a hierarchy of algorithms, all of which have a worst-case performance at most two, but which we suspect to have increasingly better performance, both in worst case and with actual instances.     

Modelling Labour Demand Dynamics beyond the Frictionless Environment

Abstract. In this study we develop a simple econometric model for labour demand dynamics by explicitly considering deviations from the standard frictionless economic environment. In particular, we explore the potential effect on labour demand of capital market imperfections, labour market institutional rigidities in the form of union power, and the impact of uncertainty. In addition, in the presence of uncertainty, adjustment costs and irreversibility emerge as an important factor that introduces an option value of waiting. According to our results, leverage exerts a direct negative impact on employment dynamics indicating that capital market imperfections are indeed present. Moreover, leverage is found to also affect the adjustment process of employment. Finally, we document asymmetric effects of frictions on labour demand across different size groups that imply a higher vulnerability of small and medium sectors with respect to debt levels. Uncertainty exerts a significantly negative impact on labour demand, which is compatible with presence of partial irreversibility of employment decisions. Finally, unions over and above their effect via higher wages are positively related to employment, a finding that is indicative of their pursue for job security.     

Analyzing propensity matched zero-inflated count outcomes in observational studies

Determining the effectiveness of different treatments from observational data, which are characterized by imbalance between groups due to lack of randomization, is challenging. Propensity matching is often used to rectify imbalances among prognostic variables. However, there are no guidelines on how appropriately to analyze group matched data when the outcome is a zero-inflated count. In addition, there is debate over whether to account for correlation of responses induced by matching and/or whether to adjust for variables used in generating the propensity score in the final analysis. The aim of this research is to compare covariate unadjusted and adjusted zero-inflated Poisson models that do and do not account for the correlation. A simulation study is conducted, demonstrating that it is necessary to adjust for potential residual confounding, but that accounting for correlation is less important. The methods are applied to a biomedical research data set.     

Modeling patient arrivals in community clinics

We develop improved methods for modeling and simulating the streams of patients arriving at a community clinic. In previous practice, random (unscheduled) patient arrivals were often assumed to follow an ordinary Poisson process (so the corresponding patient interarrival times were randomly sampled from an exponential distribution); and for scheduled arrivals, each patient's tardiness (i.e., the deviation from the scheduled appointment time) was often assumed to be randomly sampled from a normal distribution. A thorough analysis of patient arrival times, obtained from detailed workflow observations in nine community clinics, indicates these assumptions are not generally valid, and the tardiness data sets for this study are best modeled by unbounded Johnson distributions. We also propose a nonhomogeneous Poisson process to model the random patient arrivals; we review a nonparametric approach to estimating the associated mean-value function; and we describe an algorithm for generating random patient arrivals from the estimated model. The adequacy of this model of random patient arrivals can be assessed by standard goodness-of-fit tests. These findings are important since testable scheduling optimization strategies must be based upon accurate models for both random and scheduled patient arrivals. The impacts on modeling, as well as implications for practice management, are discussed.     

Evidence of ARCH(1) Errors in the Context of Spurious Regressions

The spurious regression phenomenon is related to first-order serially correlated errors. This study, using a Monte Carlo analysis, finds that this phenomenon is also related to ARCH(1) type errors.     

Robust Linear Calibration

We regard the simple linear calibration problem where only the response y of the regression line y = β₀ + β₁ t is observed with errors. The experimental conditions t are observed without error. For the errors of the observations y we assume that there may be some gross errors providing outlying observations. This situation can be modeled by a conditionally contaminated regression model. In this model the classical calibration estimator based on the least squares estimator has an unbounded asymptotic bias. Therefore we introduce calibration estimators based on robust one-step-M-estimators which have a bounded asymptotic bias. For this class of estimators we discuss two problems: The optimal estimators and their corresponding optimal designs. We derive the locally optimal solutions and show that the maximin efficient designs for non-robust estimation and robust estimation coincide.     

Educating tomorrow’s reformists: factors affecting the development of undergraduates’ social progressivism

Researchers have unpacked the ways in which students participate in democracy through voting and other forms of civic engagement. However, very little empirical work has delved into how students develop socially progressive values, despite their unprecedented importance to young people during their years in higher education. Rooted in a rich historical context of campus demonstration spanning the past 75 years and current events in the United States, this inspection of college students’ social progressivism was grounded by Pascarella’s model of students’ learning and cognitive development, and uses OLS (ordinary least squares) regression to investigate the phenomenon. Analyzing data collected by the Cooperative and Institutional Research Program from 159 institutions across the United States, this study explored the predictive capacity of students’ interaction with influential agents of socialization and other variables central to undergraduates’ college experiences. Results indicate that socially progressive students tended to interact more with faculty outside of office hours and had a higher cumulative GPA. Additional findings and implications are discussed.     

A Newsvendor Problem with Revenue Sharing and Channel Coordination*

Increasingly manufacturers and retailers are implementing revenue‐sharing policies to coordinate distribution channels more effectively. This article considers a standard newsvendor problem in a single manufacturer–retailer channel and compares the expected profits that each party receives in a traditional ordering environment with those that can be achieved under a revenue‐sharing policy designed to completely eliminate double marginalization. It is shown that the retailer always benefits from the transition to revenue sharing by capturing a portion of the incremental channel profit generated by the complete elimination of double marginalization. A necessary demand‐distribution‐dependent condition is derived under which the transition to revenue sharing benefits the manufacturer as well. The findings of this research are illustrated in a numerical example for the uniform demand distribution.     

Veblen and the entrepreneur

Ideas originating from the pioneering work of Thorstein Veblen provide the theoretical backbone of this paper. His work emerged and acquired a position of prominence more or less at the same time with the publication of seminal contributions by Schumpeter, Knight and Hayek in the field of entrepreneurial studies. However, Veblen failed to influence the evolution of ideas in the emerging field of study. In fact, this is the first systematic attempt in deploying the conceptual ‘tools’ developed by Veblen in entrepreneurship research. More specifically, this paper sets out to provide an alternative conceptualisation of the entrepreneur that does not simply relax but breaks away from the restrictive assumptions that constrained the development of the field in economics. In doing so, particular emphasis is placed upon the interface between agency and context.