Privacy-Preserving Public Information for Sequential Games

In settings with incomplete information, players can find it difficult to coordinate to find states with good social welfare. For example, in financial settings, if a collection of financial firms have limited information about each other's strategies, some large number of them may choose the same high-risk investment in hopes of high returns. While this might be acceptable in some cases, the economy can be hurt badly if many firms make investments in the same risky market segment and it fails. One reason why many firms might end up choosing the same segment is that they do not have information about other firms' investments (imperfect information may lead to `bad' game states). Directly reporting all players' investments, however, raises confidentiality concerns for both individuals and institutions. In this paper, we explore whether information about the game-state can be publicly announced in a manner that maintains the privacy of the actions of the players, and still suffices to deter players from reaching bad game-states. We show that in many games of interest, it is possible for players to avoid these bad states with the help of privacy-preserving, publicly-announced information. We model behavior of players in this imperfect information setting in two ways -- greedy and undominated strategic behaviours, and we prove guarantees on social welfare that certain kinds of privacy-preserving information can help attain. Furthermore, we design a counter with improved privacy guarantees under continual observation

TRADE-OFF BALANCING FOR STABLE AND SUSTAINABLE OPERATING ROOM SCHEDULING

The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR planning and scheduling with disturbances in demand and case times, and inconsistencies among the performance measures. We also present an extension of these algorithms that addresses production scheduling for sustainability. We demonstrate the effectiveness and efficiency of these algorithms via simulation and statistical analyses

Autonomous grid scheduling using probabilistic job runtime scheduling

Computational Grids are evolving into a global, service-oriented architecture – a universal platform for delivering future computational services to a range of applications of varying complexity and resource requirements. The thesis focuses on developing a new scheduling model for general-purpose, utility clusters based on the concept of user requested job completion deadlines. In such a system, a user would be able to request each job to finish by a certain deadline, and possibly to a certain monetary cost. Implementing deadline scheduling is dependent on the ability to predict the execution time of each queued job, and on an adaptive scheduling algorithm able to use those predictions to maximise deadline adherence. The thesis proposes novel solutions to these two problems and documents their implementation in a largely autonomous and self-managing way. The starting point of the work is an extensive analysis of a representative Grid workload revealing consistent workflow patterns, usage cycles and correlations between the execution times of jobs and its properties commonly collected by the Grid middleware for accounting purposes. An automated approach is proposed to identify these dependencies and use them to partition the highly variable workload into subsets of more consistent and predictable behaviour. A range of time-series forecasting models, applied in this context for the first time, were used to model the job execution times as a function of their historical behaviour and associated properties. Based on the resulting predictions of job runtimes a novel scheduling algorithm is able to estimate the latest job start time necessary to meet the requested deadline and sort the queue accordingly to minimise the amount of deadline overrun. The testing of the proposed approach was done using the actual job trace collected from a production Grid facility. The best performing execution time predictor (the auto-regressive moving average method) coupled to workload partitioning based on three simultaneous job properties returned the median absolute percentage error centroid of only 4.75%. This level of prediction accuracy enabled the proposed deadline scheduling method to reduce the average deadline overrun time ten-fold compared to the benchmark batch scheduler. Overall, the thesis demonstrates that deadline scheduling of computational jobs on the Grid is achievable using statistical forecasting of job execution times based on historical information. The proposed approach is easily implementable, substantially self-managing and better matched to the human workflow making it well suited for implementation in the utility Grids of the future

Diminishing Inequalities? A Study on Reconstituted Gender Relations in Bangladeshi Households During the COVID-19 Crisis

This article explores the gendered impact of the COVID-19 crisis in Bangladesh by analysing everyday practices within the household. Conceptually, we have followed R.W. Connell’s model of the structures of gender and Naila Kabeer’s perspective on women’s power to examine how a normative gender order involving heterosexual marital partners tends to be sustained during ‘normal’ times but can often be destabilised in the context of an unprecedented crisis. Based on an analysis of data collected through an online survey and in-depth interviews, our findings show that the COVID-19 crisis has generated an opportunity for challenging gender inequalities by diminishing the public-private divide and expanding the horizon of responsibility sharing between women and men. Facing this ‘new normal’ reality, some women have been able to consider life choices and revise unequal relationships with spouses. In contrast, others have reproduced pre-existing inequalities and continued life ‘as usual’ under the regime of men

Development of a resource model for greening environmental resilience: socio-eco efficiency framework analysis at Kombolcha Industrial Zone, Ethiopia

This study used the socio-eco efficiency framework as an application tool to resilience the green environment at Kombolecha industrial zone by balancing the water consumption growth and green environmental tradeoffs. In addition, it aimed to determine the significant indicators, which associated with the water consumption and recycling efficiency. The consumers (factories and households) socio-eco efficiency practices were limited and then caused groundwater degradation and green environmental depletion. Previous studies, for instance, BASF (2009), ESCAP (2011) eco-efficiency, and Sailing et al., (2013) SEE balance (socio-eco efficiency) analysis targeted the company’s product portfolio and quality improvement. This study, however, considered both factories and household’s consumption activities that were proven to manifest in a complex water consumption compared to the production process. The study integrated social, economic and environmental indicators and determined the socio-eco efficiency effects on theresource consumption growth and green environment tradeoffs; water consumption and recycling efficiency. Subsequently, the study then developed a socio-eco efficiency model that used to balance the gaps between water consumption and recycling intensity inefficiency. The socio- eco efficiency indicators could, thus, be an applied tool that could be measured by employing the binary logistic regression, instrumental variable model, simultaneous equation model and the propensity score matching estimation. Based on this, this study results indicated that the household’s awareness, perception and consumption behaviours concerning the green mind adoption, product, market, technology and jobs use were strongly associated and influenced by the water resource consumption growth and green environment tradeoffs at the 5 percent significance level. Particularly, the household’s social aspects, consumer’s culture, behaviour and poverty; economic (monthly income) and environmental aspects (waterquantity limit and waste recycle) were found to bestatistically significant and strongly altered the water resource consumption and recycling efficiency by 0.000 values at the 95 percent confidence level. This study implication was thesocio-eco efficiency framework, which was key the finding of the study that holds the three key indicators, did directly associate and significant determine the factories and household’s groundwater consumption and recycling intensity differently by 0.000 values at the 95 percent confidence level. The socio- eco efficiency model could thus be an analytical tool that could be applied into groundwater consumption and recycling process. The socio-eco efficiency resource model, which is a key tool to resilient the green environment, optimized the water consumption and recycling efficiency and could be incorporated into the groundwater and green environment protection policy of Ethiopia. This study, in a circular fashion, proved socio-eco efficiency application and resolved some of the consumption paradox in the factories and household’s groundwater consumption and recycling processes. Thenon-integrated indicators and inapplicability of the socio-eco efficiency framework, nonetheless, made the green environment cautiously. So that a tactical integrative socio-eco efficiency resource model, particularly, green finances, such as green water tax, lease, paymenhave to be incorporated during the groundwater consumption that recovers the green environment attainments in Kombolecha and at large in Ethiopia.Environmental SciencesPh. D. (Environment Management

Evolutionary Solutions and Internet Applications for Algorithmic Game Theory

The growing pervasiveness of the internet has created a new class of algorithmic problems: those in which the strategic interaction of autonomous, self-interested entities must be accounted for. So motivated, we seek to (1) use game theoretic models and techniques to study practical problems in load balancing, data streams and internet traffic congestion, and (2) demonstrate the usefulness of evolutionary game theory's adaptive learning model as an analytical and evaluative tool.First we consider the evolutionary game theory concept of stochastic stability, and propose the price of stochastic anarchy as an alternative to the price of anarchy for quantifying the cost of having no central authority. Unlike Nash equilibria, stochastically stable states are the result of natural dynamics of large populations of computationally bounded agents, and are resilient to small perturbations from ideal play. To illustrate the utility of stochastic stability, we study the load balancing game on related machines, which has an unbounded price of anarchy, even in the case of two jobs and two machines. We show that in contrast, even in the general case, the price of stochastic anarchy is bounded.Next, we propose auction-based mechanisms for admission control of continuous queries to a Data Stream Management System. When submitting a query, each user also submits a bid: how much she is willing to pay for her query to run. Our mechanisms must admit queries and set payments in a way that maximizes system revenue while incentivizing customers to use the system honestly. We propose several manipulation-resistant payment mechanisms and prove that one guarantees a profit close to a standard profit benchmark, and the others perform well experimentally.Finally, we study the long standing problem of congestion control at bottleneck routers on the internet. We examine the effectiveness of commonly-used queuing policies when each network endpoint is self-interested and has no information about the other endpoints' actions or preferences. By employing evolutionary game theory, we find that while bottleneck routers face heavy congestion at stochastically stable states under policies being currently deployed, a practical policy that was recently proposed yields fair and efficient conditions with no congestion