Resource management is an important component in many distributed clusters. A resource manager handles which server a task should run on and which user’s task that should be allocated. If a system has multiple users with similar demands, all users should have an equal share of the cluster, making the system fair. This is typically done today using a centralized server which has full knowledge of all servers in the cluster and the different users. Having a centralized server brings problems such as single point of failure, and vertical scaling on the resource manager. This thesis focuses on fairness for users during task allocation with a decentralized resource manager. A solution called, Parallel Distributed Gradient-based Dominant Resource Fairness, is proposed. It allows servers to handle a subset of users and to allocate tasks in parallel, while maintaining fairness results close to a centralized server. The solution utilizes a gradient network topology overlay to sort the servers based on their users’ current usage and allows a server to know if it has the user with the currently lowest resource usage. The solution is compared to pre-existing solutions, based on fairness and allocation time. The results show that the solution is more fair than the pre-existing solutions based on the gini-coefficient. The results also show that the allocation time scales based on the number of users in the cluster because it allows more parallel allocations by the servers. It does not scale as well though as existing distributed solutions. With 40 users and over 100 servers the solution has an equal time to a centralized solution and outperforms a centralized solution with more users. / Resurshantering är en viktig komponent i många distribuerade kluster. En resurshanterare bestämmer vilken server som skall exekvera en uppgift, och vilken användares uppgift som skall allokeras. Om ett system har flera användare med liknande krav, bör resurserna tilldelas jämnlikt mellan användarna. Idag implementeras resurshanterare oftast som en centraliserad server som har information om alla servrar i klustret och de olika användarna. En centraliserad server skapar dock problem som driftstopp vid avbrott på ett enda ställe, även enbart vertikal skalning för resurshanteraren. Denna uppsats fokuserar på jämnlikhet för användare med en decentraliserad resurshanterare. En lösning föreslås, Parallel Distributed Gradient-based Dominant Resource Fairness, som tillåter servrar att hantera en delmängd av användare i systemet, detta med en liknande jämnlikhet jämförande med en centraliserad server. Lösningen använder en så kallad gradient network topology overlay för att sortera servrarna baserat på deras användares resursanvändning och tillåter en server att veta om den har användaren med lägst resursanvändning i klustret. Lösningen jämförs med existerande lösningar baserat på jämnlikhet och allokeringstid. Resultaten visar att lösningen ger en mer jämnlik allokering än existerande lösningar utifrån gini-koefficienten. Resultaten visar även att systemets skallbarhet angående allokeringstid är beroende på antalet användare i klustret eftersom det tillåter fler parallella allokeringar. Lösningen skalar inte lika bra dock som existerande distribuerade lösningar. Med 40 användare och över 100 servrar har lösningen liknande tid som en centraliserad server, och är snabbare med fler användare.

Although the literature in the field of criminal justice and philosophy is full of ideas of what constitutes "justice," little to nothing has been done to see where the average individual's opinion falls in relation to these ideas. This paper analyzes a cross-sectional convenience sample of students at UCF to determine their preference of six models of justice: utilitarianism, contractarianism, fairness, retributivism, moralism, and libertarianism. Correlating demographic factors are also discussed.

The purpose of this research was to investigate the relation between measurement bias at the item level (differential item functioning, dif) and predictive bias at the test score level. Dif was defined as a difference in the probability of getting a test item correct for examinees with the same ability but from different subgroups. Predictive bias was defined as a difference in subgroup regression intercepts and/or slopes in predicting a criterion. Data were simulated by computer. Two hypothetical subgroups (a reference group and a focal group) were used. The predictor was a composite score on a dimensionally complex test with 60 items. Sample size (35, 70, and 105 per group), validity coefficient (.3 or .5), and the mean difference on the predictor (0, .33, .66, and 1 standard deviation, sd) and the criterion (0 and .35 sd) were manipulated. The percentage of items showing dif (0%, 15%, and 30%) and the effect size of dif (small = .3, medium = .6, and large = .9) were also manipulated. Each of the 432 conditions in the 3 x 2 x 4 x 2 x 3 x 3 design was replicated 500 times. For each replication, a predictive bias analysis was conducted, and the detection of predictive bias against each subgroup was the dependent variable. The percentage of dif and the effect size of dif were hypothesized to influence the detection of predictive bias; hypotheses were also advanced about the influence of sample size and mean subgroup differences on the predictor and criterion. Results indicated that dif was not related to the probability of detecting predictive bias against any subgroup. Results were inconsistent with the notion that measurement bias and predictive bias are mutually supportive, i.e., the presence (or absence) of one type of bias is evidence in support of the presence (or absence) of the other type of bias. Sample size and mean differences on the predictor/criterion had direct and indirect effects on the probability of detecting predictive bias against both reference and focal groups. Implications for future research are discussed.

In this dissertation, the central objective is to achieve fairness in bandwidth sharing amongst selfish users in a distributed system. Because of the inherent contention-based nature of the distributed medium access and the selfishness of the users, the distributed medium access is modeled as a non-cooperative game; designated as the Access Game. A p-CSMA type medium access scenario is proposed for all the users. Therefore, in the Access Game, each user has two actions to choose from: "transmit" and "wait". The outcome of the Access Game and payoffs to each user depends on the actions taken by all the users. Further, the utility function of each user is constructed as a function of both Quality of Service (QoS) and Battery Power (BP). Various scenarios involving the relative importance of QoS and BP are considered. It is observed that, in general the Nash Equilibrium of the Access Game does not result into fairness. Therefore, Constrained Nash Equilibrium is proposed as a solution. The advantage of Constrained Nash Equilibrium is that it can be predicated on the fairness conditions and the solution will be guaranteed to result in fair sharing of bandwidth. However, Constrained Nash Equilibrium is that it is not self-enforcing. Therefore, two mechanisms are proposed to design the Access Game in such a way that in each case the Nash Equilibrium of the Access Game satisfies fairness and maximizes throughput. Hence, with any of these mechanisms the solution of the Access Game becomes self-enforcing.

The third general standard of fieldwork requires auditors to maintain a skeptical mindset with regards to the collection and critical assessment of audit evidence. While professional skepticism is frequently referenced by professional standards, a lack of precision in defining the concept presumably leads to variation in how skepticism is exercised in practice. Drawing on theories from the fields of psychology, economics and organizational justice, this dissertation considers different perspectives of what constitutes sufficient professional skepticism and examines how those perspectives differ between audit practitioners and regulators. First, I consider competing perspectives of professional skepticism – neutral versus presumptive doubt – and whether asking auditors to adopt alternative perspectives of skepticism may have implications for audit efficiency and effectiveness. While, too little skepticism may endanger audit effectiveness and lead to audit failure or enforcement action, too much skepticism may arguably lead to unnecessary costs and inefficiency. Second, I consider whether the nature of the auditor-client relationship threatens an auditor’s ability to maintain an attitude of professional skepticism. For example, theoretical perspectives from the fields of psychology and economics suggest that auditors may, consciously or unconsciously, be less skeptical of clients with whom they have developed close, positive working relationships or financial dependencies. More specifically, I consider whether skeptical behavior is impeded by management who display low-risk attitudes towards fraud or by client’s who are considered to be highly important to the profitability of the local office. Finally, I examine how professional skepticism is defined from a regulator’s perspective. When a public company is accused of fraudulent financial reporting, regulators may determine iii that the audit performed on the fraudulent financial statements was deficient. Prior research has suggested that in such cases, insufficient skepticism is often a leading cause of alleged audit failure. Within a fairness theory framework, this study examines enforcement actions against auditors between 1999 and 2009, and identifies certain factors that are associated with a citation for a lack of professional skepticism. Overall, results suggest that regulators approach the issue by determining whether auditors should have been more skeptical. Factors found to affect this determination include whether the auditor was perceived as having been aware of an elevated risk of fraud or whether the client was accused of having provided the auditor with false or misleading information during the course of their investigation.

Sensor network congestion avoidance and control primarily aims to reduce packet drops while maintaining fair bandwidth allocation to existing network flows. The design of a congestion control algorithm suited for all types of applications in sensor networks is a challenging task due to the application-specific nature of these networks. With numerous sensors transmitting data simultaneously to one or more base stations (also called sinks), sensor nodes located near the base station will most likely experience congestion and packet loss. In this thesis, we propose a novel distributed congestion avoidance algorithm which calculates the ratio of the number of downstream and upstream nodes. This ratio value (named Characteristic ratio) is used to take a routing decision and incorporate load balancing while also serving as a pointer to the congestion state of the network. Available queue sizes of the downstream nodes are used to detect incipient congestion. Queue characteristics of candidate downstream nodes are used collectively to implement both congestion avoidance and fairness by adjusting the node's forwarding rate and next hop destination. Such an approach helps to minimize packet drops, improve energy efficiency and load balancing. In cases of severe congestion, the source is signaled to reduce its sending rate and enable the network recovery process. This is essentially a transport layer algorithm and would work best with a multi-path routing protocol and almost any MAC layer standard. We present the design and implementation of the proposed protocol and compare it with the existing avoidance protocols like Global rate control and Lightweight buffering. Our simulation results show a higher packet delivery ratio with greater node buffer utilization for our protocol in comparison with the conventional mechanisms.

Managing workload allocation to ensure fairness and equity amongst staff can be a challenge in any organisation and managing the workload allocation of autonomy seeking academic staff in a business school can be even more so. In this study, the researcher aimed to review a recently designed and implemented academic workload allocation model in a South African business school in order to establish whether the model and implementation system has been successful in contributing to actual and perceived fairness and equity in workload distribution amongst their academic staff. The researcher did this by using a sequential exploratory mixed methods approach, first reviewing documentary evidence, which informed the design of an online survey with the academic staff, followed by semi-structured interviews with a sample group. The study reveals that the model, and the way it was implemented and managed, failed to achieve its intended aims of increased equitable and fair workloads amongst academic staff. These implementation failures have resulted in negative consequences for the organisational culture. Staff satisfaction and engagement with the model, its implementation and management does not present positively in the findings of this study. In the South African context where there are very few studies related to academic workload allocation models, the results of this study may be valuable for higher education institutions considering the introduction or review of workload models amongst their academic staff. The study highlights the importance of an inclusive and careful design approach, change management considerations during the implementation phase, and the transparent management of the workload allocation process and results.

Expectations of children to perform parenting tasks above their development level, a phenomenon known as parentification, is said to be destructive in western literature. However, in other cultures, children taking on some parenting duties may not be considered a detriment but rather an attribute of filial responsibility. The current study examined the relationships between identity distress and parentification as it differs by culture. A comparison of parentification by ethnicity revealed that individuals who did not classify as an ethnic minority experienced significant levels of identity distress compared to ethnic minority individuals regarding parentification. This variation may be attributed to elements of parentification seen as an expectation by some ethnic groups. Further analyses of the data and implications for understanding cultural bias in our assumptions of the adverse effects of parentification is discussed.

Addressing fairness among users and risk mitigation in the context of resource allocation in transportation systems under uncertainties poses a crucial challenge yet to be satisfactorily resolved. This dissertation attempts to address this challenge, focusing on achieving a balance between system-wide efficiency and individual fairness in stochastic transportation resource allocation problems. To study complicated fair and risk-averse resource allocation problems - from public transit to urban air mobility and multi-stage infrastructure maintenance - we develop three models: DrFRAM, FairUAM, and FCMDP. Each of these models, despite being proven NP-hard even in a simplistic case, inspires us to develop efficient solution algorithms. We derive mixed-integer linear programming (MILP) formulations for these models, leveraging the unique properties of each model and linearizing non-linear terms. Additionally, we strengthen these models with valid inequalities. To efficiently solve these models, we design exact algorithms and approximation algorithms capable of obtaining near-optimal solutions. We numerically validate the effectiveness of our proposed models and demonstrate their capability to be applied to real-world case studies to adeptly address the uncertainties and risks arising from transportation systems. This dissertation provides a foundational platform for future inquiries of risk-averse resource allocation strategies under uncertainties for more efficient, equitable, and resilient decision-making. Our adaptable framework can address a variety of transportation-related challenges and can be extended beyond the transportation domain to tackle resource allocation problems in a broader setting. / Doctor of Philosophy / In transportation systems, decision-makers constantly strive to devise the optimal plan for the most beneficial outcomes when facing future uncertainties. When optimizing overall efficiency, individual fairness has often been overlooked. Besides, the uncertainties in the transportation systems raise serious questions about the adaptability of the allocation plan. In response to these issues, we introduce the concept of fair and risk-averse resource allocation under uncertainties in this dissertation. Our goal is to formulate the optimal allocation plan that is both fair and risk-averse amid uncertainties. To tackle the complexities of fair and risk-averse resource allocation problems, we propose innovative methods and practical algorithms, including creating novel formulations as well as deriving super-fast algorithms. These solution approaches are designed to accommodate the fairness, uncertainties, and risks typically in transportation systems. Beyond theoretical results, we apply our frameworks and algorithms to real-world case studies, thus demonstrating our approaches' adaptability to various transportation systems and ability to achieve various optimization goals. Ultimately, this dissertation aims to contribute to fairer, more efficient, and more robust transportation systems. We believe our research findings can help decision-makers with well-informed choices about resource allocation in transportation systems, which, in turn, lead to the development of more equitable and reliable systems, benefiting all the stakeholders.

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