Fair Resource Allocation Research Articles

“Objectivity” as a bureaucratic virtue

ABSTRACTAcross bureaucratic contexts, “objectivity” is a dominant conception of appropriate conduct. But what does it mean for bureaucrats to work “objectively”? For staffers of the Israeli government's Committee for Health Care Services, objectivity is understood as a key bureaucratic virtue, one that promotes the ethical goal of fair resource allocation. To them, objective decision‐making is based on adopting an “unemotional” attitude. Aware of the life‐and‐death implications of committee decisions, they attempt to work “unemotionally” by engaging what I term a moral sensibility for unemotionality, a tendency to avoid exposure to patients’ subjective experience. Cultivating this sensibility has concrete effects on the committee's decisions and on patients’ place in medical decision‐making. Examining “objectivity” as a morally desired disposition, rather than as a static construct, yields its reconceptualization as an enduring intersubjective achievement. This approach offers another way to examine the workings of power and politics in state bureaucracies. [objectivity, bureaucracy, virtue ethics, morality, emotion, social welfare, health care, Israel]

Lower Complex Resource Allocation Methodology based on Graph Theory and Index Matrix Rearrangement for OFDMA/MIMO-OFDMA Systems

Resource allocation assigns subcarriers and power at the Base Station (BS) to different users, and is an important aspect in multiuser OFDMA downlink (DL) transmission. Various resource allocation approaches differ in the objectives and constraints in defining the problem and in the algorithms developed to solve the respective problems. Complexity of resource allocation mainly depends on the algorithm developed. This paper intends to fill gaps in literature by introducing graph based lower complex methodology to solve a per-symbol rate maximization problem for three different scenarios in which perfect Channel State Information (CSI) is available. Firstly, a Single Input Single Output (SISO) OFDMA system with Sub-band Carrier Assignment Scheme (SCAS) which supports only a single modulation scheme per user is considered. A SISO-OFDMA system with Generalized Carrier Assignment Scheme (GCAS) which supports multiple modulation schemes within a users’ sub-band is considered next, and finally a Multiple Input Multiple Output (MIMO) OFDMA system is considered. A new parameter is introduced to evaluate fairness of resource allocation, and extensive performance evaluation of developed algorithms based on average SNR values and fairness is carried out. MATLAB simulations show that algorithms perform well and allocate resources fairly even when the channel is extremely frequency selective.

Fair Allocation of Potential COVID-19 Vaccines Using an Optimization-Based Strategy

The fair allocation of resources among multiple stakeholders in any area is a complex challenge for decision-making. This paper presents an optimization strategy for the allocation of COVID-19 vaccines, when they are available, through different fairness schemes (social welfare, Nash, Rawlsian justice, and social welfare II scheme). The applicability of the proposed model is illustrated using the case study of Mexico, including the states of the country as stakeholders. We involve several parameters to guide the allocation, such as the size, risk profiles, and fraction of vulnerable groups in the population. Furthermore, different scenarios of the availability of potential COVID-19 vaccines were evaluated. The social welfare approach is the most commonly used scheme for the allocation of resources. However, we demonstrate that this scheme yields non-unique or multiple solutions (through the social welfare II approach). These social welfare approaches provide inequalities in the allocations that become critical when resources are scarce. Specifically, the social welfare scheme favors large stakeholders (greater population) in all scenarios. We also observe how the complexity of the allocation increases with the higher availability of vaccines. Hence, it is relevant to consider allocation schemes to identify fair solutions.

How can we decide a fair allocation of healthcare resources during a pandemic?

Whenever the government makes medical resource allocation choices, there will be opportunity costs associated with those choices: some patients will have treatment and live longer, while a different group of...

QoS-prioritised media delivery with adaptive data throughput in IoT-based home networks

To maintain network efficiency of a home network, it is crucial to moderately distribute limited data throughput and bandwidth resources to various home devices and services that require different network resource provisions. This paper accounts for QoS differentiation and fairness of network resource allocation which are essential to the design of an efficient IoT-based home media delivery mechanism against network traffic dynamics. Accordingly, our study in this paper proposes a QoS-prioritised media delivery mechanism with adaptive data throughput. This mechanism design includes several functions: assigning weights of relative importance to home service types, adjusting media quality of home services, reducing data workload against traffic congestion, and fairly distributing free bandwidth to prioritised home services. Furthermore, our study fulfills a simplified proof-of-concept implementation in a small-scaled IoT-based home network. Practical experiments can generate real measure data for performance examination. Results show that the proposed mechanism is able to sustain not only QoS differentiation like data throughput and delay, but also fair user satisfaction with comfortable media playing quality.

QoS-prioritised media delivery with adaptive data throughput in IoT-based home networks

To maintain network efficiency of a home network, it is crucial to moderately distribute limited data throughput and bandwidth resources to various home devices and services that require different network resource provisions. This paper accounts for QoS differentiation and fairness of network resource allocation which are essential to the design of an efficient IoT-based home media delivery mechanism against network traffic dynamics. Accordingly, our study in this paper proposes a QoS-prioritised media delivery mechanism with adaptive data throughput. This mechanism design includes several functions: assigning weights of relative importance to home service types, adjusting media quality of home services, reducing data workload against traffic congestion, and fairly distributing free bandwidth to prioritised home services. Furthermore, our study fulfills a simplified proof-of-concept implementation in a small-scaled IoT-based home network. Practical experiments can generate real measure data for performance examination. Results show that the proposed mechanism is able to sustain not only QoS differentiation like data throughput and delay, but also fair user satisfaction with comfortable media playing quality.

Resource Allocation Based on Hierarchical Game for D2D Underlaying Communication Cellular Networks

The paper studies the device-to-device (D2D) underlaying communication in the case of sufficient spectrum resources available in cellular networks. A hierarchical game is used to solve the problem of mode selection, spectrum allocation and power control for D2D underlaying communication. The mode selection and spectrum allocation are solved by a hedonic coalition game. A utility function based on the number of participants in the coalition is proposed to maximize the system throughput while ensuring that users have fair access to resources. After the initial formation of the coalition, a power control algorithm based on the non-cooperative game is presented to further improve the performance. The simulation results show that the algorithm can not only solve the fairness of user resource allocation, but also improve the system throughput.

BEHAVE: Behavior-Aware, Intelligent, and Fair Resource Management for Heterogeneous Edge-IoT Systems

Data-driven approaches are envisioned to build future Edge-IoT systems that satisfy IoT devices demands for edge resources. However, significant challenges and technical barriers exist which complicate resource management of such systems. IoT devices can demonstrate a wide range of behaviors in the devices resource demand that are extremely difficult to manage. In addition, the management of resources fairly and efficiently by the edge in such a setting is a challenging task. In this paper, we develop a novel data-driven resource management framework named BEHAVE that intelligently and fairly allocates edge resources to IoT devices with consideration of their behavior of resource demand (BRD). BEHAVE aims to holistically address the management technical barriers by 1) building an efficient scheme for modeling and assessment of the BRD of IoT devices based on their resource requests and resource usage; 2) expanding a new Rational, Fair, and Truthful Resource Allocation (RFTA) model that binds the devices BRD and resource allocation to achieve fair allocation and encourage truthfulness in resource demand; and 3) developing an enhanced deep reinforcement learning (EDRL) scheme to achieve the RFTA goals. The evaluation results demonstrate BEHAVE's capability to analyze the IoT devices BRD and adjust its resource management policy accordingly.

Cluster Design and Optimization of SWIPT‐Based MEC Networks with UAV Assistance

In recent years, service isolation and service miniaturization have become very popular. The large services are dismantled into multiple low‐cost and simple small services to improve the scalability and disaster tolerance of the entire services. A service network composed of unmanned aerial vehicles (UAVs) and MEC servers is proposed in this paper, which aims at decoupling multiple services of the SWIPT‐MEC network. In this network, UAVs take charge of energy transmission and calculation task scheduling and MEC servers are focused on task calculation. To meet the resource requirements of the ground nodes (GNs) in the network, we designed a distributed iterative algorithm to solve the resource allocation decision problem of GNs and used the modified expert bat algorithm to complete the UAV’s trajectory planning in a two‐dimensional space. The results show that the algorithm can formulate a more fair resource allocation strategy, and its performance is improved by 7% compared with the traditional bat algorithm. In addition, the algorithm in this paper can also adapt to changes in task length and has a certain degree of stability.

Performance-Fairness Trade-off for Wi-Fi and LTE-LAA Coexistence

Long Term Evolution (LTE)-License Assisted Access (LAA), which leverages unlicensed resource sharing with the Wi-Fi network, is a promising technique to address the spectrum scarcity issue in present and future wireless networks. However, unlicensed spectrum sharing between Wi-Fi and LAA requires fair resource allocation with specific performance guarantees for both sets of Wi-Fi and LAA stations. In this paper, an optimal communication policy is devised for LAA stations coexisting on a single unlicensed channel with Wi-Fi stations. The inter-network collisions are avoided through non-overlapping transmission phases for Wi-Fi and LAA networks. The throughput performance of LAA network is maximized while guaranteeing a proportionally fair performance among LAA stations and a fair share for Wi-Fi stations. The proposed scheme, unlike the state-of-the-art coexisting mechanism, jointly optimizes the transmission probability and the transmission rate for each LAA station. The formulated optimization problem to maximize network throughput is solved analytically. The numerical results demonstrate a significant improvement in the LAA throughput, more than 75%, as compared to the case when transmission probabilities are not optimized. Moreover, a notable gain of 8-9% in the fairness index reflects the intra-network fairness of the proposed LAA network over the conventional LAA network.

Global Commons as an Emerging Arena of Contestation of Global Governance Structures and Norms

Abstract The legal framework governing global commons still remains largely unexplored and consequently unregulated, opening the possibility of innovations to resolve transnational global problems. This results in building a dialogue between multiple stakeholders including international organisations, civil society, and emerging powers, resulting in shaping of the modern international law. With China’s growth in global commons domain, on account of reorientation in its foreign policy and increased engagement with international law, it is essential to understand in totality its contribution towards formulation and implementation of public policies at the national, international, and transnational levels. In this backdrop, the article attempts to understand how China influences development of regulatory frameworks governing the internet, space activities, and climate change. Focusing on China’s role as a ‘norm-taker’, ‘norm-maker’ or ‘norm-shaker’ of global governance, the article seeks to analyse its engagement in enhancing or impeding the preservation and fair allocation of resources.

Optimal Configuration of LoRa Networks in Smart Cities

Long range (LoRa) is a wireless communication standard specifically targeted for resource-constrained Internet of Things (IoT) devices. LoRa is a promising solution for smart city applications as it can provide long-range connectivity with a low energy consumption. The number of LoRa-based networks is growing due to its operation in the unlicensed radio bands and the ease of network deployments. However, the scalability of such networks suffers as the number of deployed devices increases. In particular, the network performance drops due to increased contention and interference in the unlicensed LoRa radio bands. This results in an increased number of dropped messages and, therefore, unreliable network communications. Nevertheless, network performance can be improved by appropriately configuring the radio parameters of each node. To this end, in this article we formulate integer linear programming models to configure LoRa nodes with the optimal parameters that allow all devices to reliably send data with a low energy consumption. We evaluate the performance of our solutions through extensive network simulations considering different types of realistic deployments. We find that our solution consistently achieves a higher delivery ratio (up to 8% higher) than the state of the art with minimal energy consumption. Moreover, the higher delivery ratio is achieved by a large percentage of nodes in each network, thereby resulting in a fair allocation of radio resources. Finally, the optimal network configurations are obtained within a short time, usually much faster than the state of the art. Thus, our solution can be readily used by network operators to determine optimal configurations for their IoT deployments, resulting in improved network reliability.

Equity and efficiency of health care resource allocation in Jiangsu Province, China

BackgroundJiangsu was one of the first four pilot provinces to engage in comprehensive health care reform in China, which has been on-going for the past 5 years. This study aims to evaluate the equity, efficiency and productivity of health care resource allocation in Jiangsu Province using the most recent data, analyse the causes of deficiencies, and discuss measures to solve these problems.MethodsData were extracted from the Jiangsu Health/Family Planning Statistical Yearbook (2015–2019) and Jiangsu Statistical Yearbook (2015–2019). The Gini coefficient (G), Theil index (T) and health resource density index (HRDI) were chosen to study the fairness of health resource allocation in Jiangsu Province. Data envelopment analysis (DEA) and the Malmquist productivity index (MPI) were used to analyse the efficiency and productivity of this allocation.ResultsFrom 2014 to 2018, the total amount of health resources in Jiangsu Province increased. The G of primary resource allocation by population remained below 0.15, and that by geographical area was between 0.14 and 0.28; additionally, the G of health financial resources was below 0.26, and that by geographical area was above 0.39. T was consistent with the results for G and Lorenz curves. The HRDI shows that the allocated amounts of health care resources were the highest in southern Jiangsu, except for the number of health institutions. The average value of TE was above 0.93, and the DEA results were invalid for only two cities. From 2014 to 2018, the mean TFPC in Jiangsu was less than 1, and the values exceeded 1 for only five cities.ConclusionThe equity of basic medical resources was better than that of financial resources, and the equity of geographical allocation was better than that of population allocation. The overall efficiency of health care resource allocation was high; however, the total factor productivity of the whole province has declined due to technological regression. Jiangsu Province needs to further optimize the allocation and increase the utilization efficiency of health care resources.

Reliable packet transmission in WBAN with dynamic and optimized QoS using multi-objective lion cooperative hunt optimizer

Recent studies demonstrate that handling a patient’s health-related data needs dynamic Quality of Service (QoS) to cope up with significant changes in the functioning of a Patient Monitoring Wireless Body Area Network (PA-WBAN). This kind of system demands a remarkable improvement in the QoS as it is dealing with the reliable transmission of a large volume of assorted data in a frequently changing environment. The QoS in a PA-WBAN is not only about how it senses or reads data from various body parts but is how it deciphers the sensed data. In the patient monitoring system, if the required data is not available in a readable format or not in a specific time, then this data becomes useless and is of no use. However, to harmonize these requirements, two approaches are proposed in this paper. The first protocol is developed for obtaining dynamic QoS for reliable packet transmission. It offers both node and packet-level dynamic-priority assignment policy, which further helps in fair and dynamic resource allocation, queuing, scheduling, retransmission, drop, and delay. It provides fair queuing and percentile scheduling policies, which estimates service rate for each priority queue and serves only highly significant packets with high waiting time during scheduling. Additionally, it offers application-specific reliability through a predictive retransmission and loss recovery policy. During retransmission and loss recovery, it calculates a retransmission rate for each sensor node and retransmits only that amount of packets from each sensor node. It also controls congestion with its dynamic priority-based rate adjustment and packet drop policies. It further provides a concept of time-bound based packet transmission policy that minimizes delay and jitter more appropriately. The second protocol is designed for attaining optimized QoS in the dynamic and assorted PA-WBAN. It applies a Lion Cooperative Hunt Optimization (LCHO) technique for the optimization of multi-objective QoS. Both theoretical and simulation results examine the usefulness of the proposed protocols and illustrate its advantage over the existing protocols.

Development of a structured process for fair allocation of critical care resources in the setting of insufficient capacity: a discussion paper

Early in the COVID-19 pandemic there was widespread concern that healthcare systems would be overwhelmed, and specifically, that there would be insufficient critical care capacity in terms of beds, ventilators...

Benefit-of-the-doubt approach to workload indicators: Simplifying the use of case weights in court evaluations

In practice, the calculation of workloads in different entities and the allocation of resources between such entities is often done using simple task weighting models. Different tasks are assigned weights and the total workload is the weighted sum of the tasks performed. Traditional approaches for establishing the weights, including the using of Delphi methods or detailed time-studies, are however expensive, and the resulting weights are often challenged by the evaluated entities. In this paper, we discuss how to mitigate these problems.We study the problem within the context of the judiciary system. To ensure an efficient judiciary, it is usually considered necessary to have a reliable case weighting system (CWS). Different types of court cases, e.g. criminal versus civil cases, have different resource needs, and to get a relevant aggregate measure of the tasks at hand, it is therefore necessary to weight the different court cases, i.e. to construct a case mix corrected workload measure.We suggest a “benefit-of-the-doubt” (BoD) approach inspired by recent developments in Data Envelopment Analysis (DEA). We allow for uncertain weights based on only partial information about the “true” weights, and we evaluate individual courts with the weights that put them in their most favorable light. In addition to making the weight setting easier and less disputable in applications, our approach dispenses with several limitations of a traditional weighted caseload approach, including the implicit assumptions of constant returns to scale and a constant rate of substitution between caseloads. Moreover, most of the applications of detailed case weights are still available using our BoD approach. We can continue to evaluate the efficiency of individual courts and device sound and fair resource allocation procedures that are robust to the remaining uncertainty about weights.We illustrate our approach by several real-world applications and discuss relevant extensions and applications in other areas, including regulation.

US Clinicians’ Experiences and Perspectives on Resource Limitation and Patient Care During the COVID-19 Pandemic

Little is known about how US clinicians have responded to resource limitation during the coronavirus disease 2019 (COVID-19) pandemic. To describe the perspectives and experiences of clinicians involved in institutional planning for resource limitation and/or patient care during the pandemic. This qualitative study used inductive thematic analysis of semistructured interviews conducted in April and May 2020 with a national group of clinicians (eg, intensivists, nephrologists, nurses) involved in institutional planning and/or clinical care during the COVID-19 pandemic across the United States. Emergent themes describing clinicians' experience providing care in settings of resource limitation. The 61 participants (mean [SD] age, 46 [11] years; 38 [63%] women) included in this study were practicing in 15 US states and were more heavily sampled from areas with the highest rates of COVID-19 infection at the time of interviews (ie, Seattle, New York City, New Orleans). Most participants were White individuals (39 [65%]), were attending physicians (45 [75%]), and were practicing in large academic centers (≥300 beds, 51 [85%]; academic centers, 46 [77%]). Three overlapping and interrelated themes emerged from qualitative analysis, as follows: (1) planning for crisis capacity, (2) adapting to resource limitation, and (3) multiple unprecedented barriers to care delivery. Clinician leaders worked within their institutions to plan a systematic approach for fair allocation of limited resources in crisis settings so that frontline clinicians would not have to make rationing decisions at the bedside. However, even before a declaration of crisis capacity, clinicians encountered varied and sometimes unanticipated forms of resource limitation that could compromise care, require that they make difficult allocation decisions, and contribute to moral distress. Furthermore, unprecedented challenges to caring for patients during the pandemic, including the need to limit in-person interactions, the rapid pace of change, and the dearth of scientific evidence, added to the challenges of caring for patients and communicating with families. The findings of this qualitative study highlighted the complexity of providing high-quality care for patients during the COVID-19 pandemic. Expanding the scope of institutional planning to address resource limitation challenges that can arise long before declarations of crisis capacity may help to support frontline clinicians, promote equity, and optimize care as the pandemic evolves.

Ethics of triage for intensive-care interventions during the COVID-19 pandemic: Age or disability related cut-off policies are not justifiable

Public health emergencies such as pandemics can put health systems in a position where they need to ration medical equipment and interventions because the resources available are not sufficient to meet demand. In public health management, the fair allocation of resources is a permanent and cross-sector issue since resources, and especially economic resources, are not infinite. During the COVID-19 pandemic resources need to be allocated under conditions of extreme urgency and uncertainty. One very problematic aspect has concerned intensive care medicine and age discrimination has been among the most hotly discussed issues, as age has been touted as a probable criterion for selection. In this paper we analyse some documents originating from scientific societies and medical associations, mainly related to EU sphere and available in English, French, Spanish and Italian (Switzerland, Spain, Belgium, France, England and Italy), concerning the criteria for admission to the intensive care units. We highlights how, in most of these documents, it is explicitly stated that “age itself” is not a criterion for patient selection. Our conclusion is that these criteria should be defined in advance of a crisis situation and be grounded in clinical indicators. Establishing “cut-off” policies with regard to criteria such as age or chronic disability is definitely an unjustifiable form of discrimination even in the context of a public health emergency.

Costing of Cesarean Sections in a Government and a Non-Governmental Hospital in Cambodia-A Prerequisite for Efficient and Fair Comprehensive Obstetric Care.

Knowing the cost of health care services is a prerequisite for evidence-based management and decision making. However, only limited costing data is available in many low- and middle-income countries. With a substantially increasing number of facility-based births in Cambodia, costing data for efficient and fair resource allocation is required. This paper evaluates the costs for cesarean section (CS) at a public and a Non-Governmental (NGO) hospital in Cambodia in the year 2018. We performed a full and a marginal cost analysis, i.e., we developed a cost function and calculated the respective unit costs from the provider’s perspective. We distinguished fixed, step-fixed, and variable costs and followed an activity-based costing approach. The processes were determined by personal observation of CS-patients and all procedures; the resource consumption was calculated based on the existing accounting documentation, observations, and time-studies. Afterwards, we did a comparative analysis between the two hospitals and performed a sensitivity analysis, i.e., parameters were changed to cater for uncertainty. The public hospital performed 54 monthly CS with an average length of stay (ALOS) of 7.4 days, compared to 18 monthly CS with an ALOS of 3.4 days at the NGO hospital. Staff members at the NGO hospital invest more time per patient. The cost per CS at the current patient numbers is US$470.03 at the public and US$683.23 at the NGO hospital. However, the unit cost at the NGO hospital would be less than at the public hospital if the patient numbers were the same. The study provides detailed costing data to inform decisionmakers and can be seen as a steppingstone for further costing exercises.

Fair allocation of scarce medical resources in the time of COVID-19: what do people think?

The COVID-19 pandemic has placed an enormous burden on health systems, and guidelines have been developed to help healthcare practitioners when resource shortage imposes the choice on who to treat....