Consistency of Higher Order Risk Preferences

Risk aversion (a second‐order risk preference) is a time‐proven concept in economic models of choice under risk. More recently, the higher order risk preferences of prudence (third‐order) and temperance (fourth‐order) also have been shown to be quite important. While a majority of the population seems to exhibit both risk aversion and these higher order risk preferences, a significant minority does not. We show how both risk‐averse and risk‐loving behaviors might be generated by a simple type of basic lottery preference for either (1) combining “good” outcomes with “bad” ones, or (2) combining “good with good” and “bad with bad,” respectively. We further show that this dichotomy is fairly robust at explaining higher order risk attitudes in the laboratory. In addition to our own experimental evidence, we take a second look at the extant laboratory experiments that measure higher order risk preferences and we find a fair amount of support for this dichotomy. Our own experiment also is the first to look beyond fourth‐order risk preferences, and we examine risk attitudes at even higher orders.     

Capacity Planning and Allocation for Web‐Based Applications

Motivated by the technology division of a financial services firm, we study the problem of capacity planning and allocation for Web‐based applications. The steady growth in Web traffic has affected the quality of service (QoS) as measured by response time (RT), for numerous e‐businesses. In addition, the lack of understanding of system interactions and availability of proper planning tools has impeded effective capacity management. Managers typically make decisions to add server capacity on an ad hoc basis when systems reach critical response levels. Very often this turns out to be too late and results in extremely long response times and the system crashes. We present an analytical model to understand system interactions with the goal of making better server capacity decisions based on the results. The model studies the relationships and important interactions between the various components of a Web‐based application using a continuous time Markov chain embedded in a queuing network as the basic framework. We use several structured aggregation schemes to appropriately represent a complex system, and demonstrate how the model can be used to quickly predict system performance, which facilitates effective capacity allocation decision making. Using simulation as a benchmark, we show that our model produces results within 5% accuracy at a fraction of the time of simulation, even at high traffic intensities. This knowledge helps managers quickly analyze the performance of the system and better plan server capacity to maintain desirable levels of QoS. We also demonstrate how to utilize a combination of dedicated and shared resources to achieve QoS using fewer servers.     

Comparison of intracalss correlation coefficients with the ratio of two independent f-statistics

The ratio of analysis of variance F-statistics is proposed as a test criterion for comparing the intraclass correlation coefficients of two independent groups of classes. Selected percentage points of this ratio’s distribution are tabulated for the special case when there are two observations in each class. Such tables should be useful when investigating heritability from a study of identical and nonidentical twin pairs.     

MODEL FORMULATION FOR FLEET SIZE ANALYSIS OF A UNIVERSITY MOTOR POOL

A GPSS/360 model was developed to investigate the behavior of a university's motor pool dispatch fleet. Time-series data were collected and frequency distributions were constructed for vehicle request patterns and trip-duration times. Regression analysis was performed to formulate trip mileage generators as functions of service time. Appropriate statistical goodness-of-fit tests were conducted to ascertain the extent of congruence between actual system behavior and expectations based on a number of theoretical distributions. Analysis of simulated response variables indicates that the model's internal structure reproduces reality to a high degree. Fifteen years of simulated experience were generated for six alternative fleet size configurations; results suggest that reductions in existing fleet capacity could be realized without effecting undue impairment in service levels.     

Class and class conflict: An objective-subjective interactive approach

Class conflict is one of the biggest sources of social tension. But why do some individuals perceive more class conflict than others? In this article, we consider how subjective self-placement interacts with objective social class to shape people's perception of class conflict. We argue that the common finding that individuals often misidentify with the incorrect class can have implications on how they perceive class conflict in their society. Analyzing data from the International Social Survey Programme (ISSP 1999 and 2009), we find that while, overall, perception of class conflict is higher among the working class and lower among salariats, self-placing lower in the social structure can bring perceptions closer together. Specifically, we show that salariats who deflate their class identities to levels expected of the working class perceive levels of class conflict similar to the working class. This study is among the first to document the disparate effects of the interplay between objective and subjective class on perception of class conflict across countries and over time.     

The gender citation gap: Why and how it matters

The weight of evidence suggests that articles written by men and women receive citations at comparable rates. This suggests that research quality or gender-based bias in research evaluation and citing behaviors may not be the reason why academic women accumulate fewer citations than men at the career level. In this article, I outline a career perspective that highlights women's disadvantages in career progression as the root causes for the gender citation gap. I also consider how the gender citation gap may perpetuate the unequal pay between genders in science. My analysis of two different datasets, one including paper and citation information for over 130,000 highly cited scholars during the 1996–2020 period and another including citation and salary information for nearly 2,000 Canadian scholars over the 2014–2019 period, shows several important findings. First, papers written by women on average receive more citations than those written by men. Second, the gender citation gap grows larger with time as men and women progress in their careers, but the opposite pattern holds when research productivity and collaborative networks are considered. Third, higher citations lead to higher pay, and gender differences in citations explain a significant share of the gender wage gap. Findings demonstrate the critical need for more attention toward gender differences in career progression when investigating the causes and solutions for gender disparities in science.