Allocation Rules Research Articles

Grandfathering or benchmarking? The impact of carbon quota allocation rule on the joint emission reduction supply chain

The choice of carbon quota allocation rule (CQA rule) is crucial for the efficient operation of cap-and-trade policy. This paper analyses the impact of grandfathering (GF) rule and benchmarking (BM) rule on the joint emission reduction (JER) supply chain by using a differential game approach. In the proposed model, the upstream manufacturer achieves its emission reduction target through green technology and recycling, and the downstream retailer promotes these products. Our results show that compared with the benchmark case, both CQA rules do not always encourage the manufacturer to increase emission reduction investments. Moreover, the environmental and economic performance of different CQA rules is time-varying. For low-emission products, both CQA rules may yield higher total carbon emissions. For medium and high-emission products, the BM rule has a better short-term environmental performance, while the GF rule always has the best long-term environmental performance. As such, the government should prioritise the inclusion of medium and high-emission manufacturers in the carbon trading market and adopt the BM rule at the initial stage, and then transition to the GF rule. We also provide practical insights on the timing of transition, setting free carbon quotas, balancing economic and environmental performance, and dealing with uncertain market changes.

Qini Curves for Multi-Armed Treatment Rules

Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of the Qini curve to multiple costly treatment arms that quantifies the value of optimally selecting among both units and treatment arms at different budget levels. We develop an efficient algorithm for computing these curves and propose bootstrap-based confidence intervals that are exact in large samples for any point on the curve. These confidence intervals can be used to conduct hypothesis tests comparing the value of treatment targeting using an optimal combination of arms with using just a subset of arms, or with a non-targeting assignment rule ignoring covariates, at different budget levels. We demonstrate the statistical performance in a simulation experiment and an application to treatment targeting for election turnout.

Efficient and Fair Healthcare Rationing

The rationing of healthcare resources has emerged as an important issue, which has been discussed by medical experts, policy-makers, and the general public. We consider a rationing problem where medical units are to be allocated to patients. Each unit is reserved for one of several categories, and each category has a priority ranking over the patients. We present a class of allocation rules that respect the priorities, comply with the eligibility requirements, allocate the largest feasible number of units, and do not penalize agents for rising in the priority ranking of a category. The rules characterize all possible allocations that satisfy the first three properties and are polynomial-time computable.

Towards data auctions with externalities

The design of data markets has gained importance as firms increasingly use machine learning models fueled by externally acquired training data. A key consideration is the externalities firms face when data, though inherently freely replicable, is allocated to competing firms. In this setting, we demonstrate that a data seller's optimal revenue increases as firms can pay to prevent allocations to others. To do so, we first reduce the combinatorial problem of allocating and pricing multiple datasets to the auction of a single digital good by modeling utility for data through the increase in prediction accuracy it provides. We then derive welfare and revenue maximizing mechanisms, highlighting how the form of firms' private information – whether the externalities one exerts on others is known, or vice-versa – affects the resulting structures. In all cases, under appropriate assumptions, the optimal allocation rule is a single threshold per firm, where either all data is allocated or none is.

Stochastic Resource Allocation Problems: Minmax and Maxmin Solutions

Problem definition: Min-max or max-min objective criteria naturally arise in many resource allocation applications, especially when the attendant goal involves distribution of a limited resource across multiple subsystems, locations, or activities. Each has a different consumption rate of the resource. In this setting, the decision maker is interested in optimizing the system performance, which can be measured in terms of the smallest or largest order statistic across subsystems. Examples include the allocation of health resources to maximize the first runout time or to minimize the maximum delay. Methodology/results: In the literature, there are no unified theoretical and computational results on the optimal allocations for these problems. Thus, in this paper, we develop key theoretical properties of the attendant stochastic resource allocation problems, providing a technical condition for the objective functions to have favorable properties, upon which we derive novel optimal allocation rules with clear interpretation. Managerial implications: The proportional allocation rule can be interpreted as a robust solution when the full distribution of the interarrival time is unknown. It generally has poor performance, especially when the resource consumption rates vary across locations, which happens regularly in practice. Our allocation rule is exact and should be used for the allocation of scarce resources—that is, when the number of resources per location is limited, which is often encountered in healthcare settings.

First consensus document of waiting list prioritization for liver transplantation by the Spanish Society of Liver Transplantation (SETH).

Spain is worldwide leader in deceased donation rates per million habitants and count on a strong network of twenty-five liver transplant institutions. Although the access to liver transplantation is higher than in other countries, approximately 10% of patients qualifying for liver transplantation in Spain will die in the waiting list or would be excluded due to clinical deterioration. A robust waiting list prioritization system is paramount to grant the sickest patients with the first positions in the waiting list for an earlier access to transplant. In addition, the allocation policy may not create or perpetuate inequities, particularly in a public and universal healthcare system. Hitherto, Spain lacks a unique national allocation system for elective liver transplantation. Most institutions establish their own rules for liver allocation and only two autonomous regions, namely Andalucía and Cataluña, share part of their waiting list within their territory to provide regional priority to patients requiring more urgent transplantation. This heterogeneity is further aggravated by the recently described sex-based disparities for accessing liver transplantation in Spain, and by the expansion of liver transplant indications, mainly for oncological indications, in absence of clear guidance on the optimal prioritization policy. The present document contains the recommendations from the first consensus of waiting list prioritization for liver transplantation issued by the Spanish Society of Liver Transplantation (SETH). The document was supported by all liver transplant institutions in Spain and by the Organización Nacional de Trasplantes (ONT). Its implementation will allow to homogenize practices and to improve equity and outcomes among patients with end-stage liver disease.

Aid allocation with optimal monitoring: Theory and policy

We explore the implications of allowing a poverty-averse donor to monitor aid use within the familiar context of the needs vs. aid effectiveness tradeoff. The paper focuses on the optimal aid allocation between two countries when the donor simultaneously decides about aid shares and country-specific monitoring effort aimed at increasing the amount reaching the poor. Endogenizing aid effectiveness is shown to raise the poor’s income in the worse-governed country, yet not necessarily in the better-governed one, whereas the effect on country aid shares is essentially ambiguous. Those results still hold when the basic model is extended in various directions. Conventional aid allocation rules should be re-examined in their light.

Interpretation of Fairness Concept and Economic Impact Analysis of Carbon Quota Allocation between Industries in Indonesia

Abstract Quota allocation is critical in implementing supporting policies for carbon reduction efforts, such as the carbon cap and trade mechanism. Depending on the allocation rules, certain company types may benefit more economically. Inequality conditions can happen and reduce the willingness to participate in sustainability efforts. This study aims to propose fairness interpretations in the context of company-level carbon quota allocation. Four industries in Indonesia with inherently different emission and financial profiles were selected as case studies: energy, oil palm, basic materials, and finance. Fairness principles were analyzed to represent the interests of companies based on their financial and environmental performance. Indicators were selected to quantify the equality principles and aggregated into a Comprehensive Index (CI) for quota allocation, where the companies’ quota surplus/deficit is quantified into monetary value to estimate the economic impact with varying reduction targets. According to our analysis, the sectoral advantages/disadvantages would depend on the viewpoint of fairness principles, and the oil palm and financial sectors be less impacted than the basic materials and energy sectors with increased emission reduction targets. The sensitivity analysis suggests that the vertical, horizontal, polluter pays, and historical responsibility principles are more sensitive to weighting than the merit and basic needs principles.

Allocation Rules and Age-Dependent Waiting Times for Kidney Transplantation.

Rigid age limits in the current allocation system for post-mortem donor kidneys in Germany may have problematic effects. The new German national transplantion registry enables data analysis with respect to this question. Using anonymized data from the German national transplantion registry, we extracted and evaluated information on the recipients and postmortem donors of kidneys that were allocated in Germany through Eurotransplant over the period 2006-2020. Data on 19 664 kidney transplantations in Germany from 2006 to 2020 were analyzed. The median waiting time for kidney transplantation was 5.8 years. Persons under age 18 waited a median of 1.7 years; persons aged 18 to 64, 7.0 years; and persons aged 65 and older, 3.8 years. Over the period of observation, postmortem kidneys were transplanted into 401 people of age 64 (2.0% of all organ recipients) and 1,393 people of age 65 (7.1% of all organ recipients). The difference in waiting times between allocation programs for persons under age 65 (ETKAS, "Eurotransplant Kidney Allocation System") and those aged 65 and older (ESP, "Eurotransplant Senior Program") increased over the period of observation, from 2.6 years in 2006-2010 to 4.1 years in 2017-2020. The rigid age limits in the current allocation rules for post-mortem kidney donations in Germany are prolonging the waiting times for transplants among patients aged 18 to 64. We think these rules need to be fundamentally reassessed.

Who Is Leading the Dance? or Should the Main Principles of Taxation Be Adjusted in Accordance with Coordination Principles of Social Security? – Teleworking and Beyond

Enabling telework for a few days each week is increasingly becoming the new normal for millions of individuals. Since 1 July 2023, there has been a Framework Agreement in place within the EU, allowing employees to remain covered by the social security system of their state of employment when teleworking from home in a cross-border context. This article discusses whether it is possible to change the allocation rules for taxation of employment income in these situations, to be levied in the same state as the social security contributions (a hypothesis). Arguments pro and contra are highlighted, and additional changes are considered. By using two scenarios (Sweden-Germany and Sweden-Denmark) the outcome of the hypothesis as well as the implications for the new Framework Agreement and similar agreements are highlighted.

Funding lotteries for research grant allocation: An extended taxonomy and evaluation of their fairness

Abstract Some research funding organizations (funders) are experimenting with random allocation of funding (funding lotteries), whereby funding is awarded to a random subset of eligible applicants evaluated positively by review panels. There is no consensus on which allocation rule is fairer—traditional peer review or funding lotteries—partly because there exist different ways of implementing funding lotteries, and partly because different selection procedures satisfy different ideas of fairness (desiderata). Here we focus on two desiderata: that funding be allocated by ‘merit’ (epistemic correctness) versus following ethical considerations, for example without perpetuating biases (unbiased fairness) and without concentrating resources in the hands of a few (distributive fairness). We contribute to the debate first by differentiating among different existing lottery types in an extended taxonomy of selection procedures; and second, by evaluating (via Monte Carlo simulations) how these different selection procedures meet the different desiderata under different conditions. The extended taxonomy distinguishes “Types” of selection procedures by the role of randomness in guiding funding decisions, from null (traditional peer review), to minimal and extensive (various types of funding lotteries). Simulations show that low-randomness Types (e.g. ‘tie-breaking’ lotteries) do not differ meaningfully from traditional peer review in the way they prioritize epistemic correctness at the cost of lower unbiased and distributive fairness. Probably unbeknownst to funders, another common lottery Type (lotteries where some favorably-evaluated proposals bypass the lottery) displays marked variation in epistemic correctness and fairness depending on the specific bypass implementation. We discuss implications for funders who run funding lotteries or are considering doing so.

Allocation rules for communication situations with incompatibilities

In this article we analyze certain situations with restricted cooperation. To do this we introduce a model that combines two types of games well studied in the literature: graph-restricted games and games with incompatible players. In particular, our model extends Myerson’s model for communication situations and Bergantiños’ model for incompatible relationships. Our approach is based on the concept of profit measure, which allows us to deal simultaneously with both types of bilateral relationships. We show that in the situations considered there are multiple possible definitions of the profit achievable for each coalition. This leads us to introduce different allocation rules for these cooperative situations.

Cooperative supply game and its revenue allocation method considering location of transportation hub

PurposeThis paper aims to study the establishment of cooperative supply game model considering transportation hub location, and design the profit allocation rule of the cooperative supply coalition.Design/methodology/approachBased on the economic lost-sizing (ELS) game model and considering the location of transportation hub and the topology design of basic traffic network, we build a supply game model to maximize the profit of cooperative supply coalition. Based on the principle of proportion and the method of process allocation, we suppose the procedural proportional solution of the supplier cooperative supply game.FindingsThrough numerical examples, the validity and applicability of the proposed model and the procedural proportional solution were verified by comparing the procedural proportional solution with the weighted Shapley value, the equal division solution and the proportional rule.Originality/valueThis paper constructs a feasible mixed integer programming model for cooperative supply game. We also provide the algorithm of the allocation rule of cooperative supply game and the property analysis of the allocation rule.

Is Family-Priority Rule the Right Path? An Experimental Study of the Chinese Organ Allocation System

Abstract China is experiencing an organ shortage crisis. We experimentally test the effectiveness of the family-priority allocation rule on organ donation and argue that such incentive would be highly motivating in Chinese family-oriented culture. Results of our experiment show that introducing the family-priority rule can not only increase donor registration but also promote family consent. Such priority rule would be particularly effective to increase deceased organ donation in China, as it will significantly promote donor registration and meanwhile generate a consistent higher family consent rate in a more family-orientated culture.

Matching inventory and demand in a Fast Moving Consumer Goods company: A Decision Support System

Demand fulfilment and its core, order promising, is a key business function that directly impacts the company’s ability to generate revenue through sales. For companies operating under a Make-To-Stock (MTS) production strategy, it is tantamount to matching both on-hand and projected stock to customer demand. The prevalent manner to carry out this task is to use either First-Come-First-Served policies, or simple allocation rules embedded in commercially available software. However, order promising is, in general, quite complex due to uncertainties both in demand and supply, and it can become extremely challenging when demand exceeds the stock. Therefore, several contributions have established the advantages of order promising using optimisation models and/or sophisticated algorithms. However, these works assume that customers can be clustered according to their profitability/importance and it remains open to test whether this also holds for homogeneous customers. Furthermore, due to the exploratory nature of most studies, they assume rather stylised scenarios (such as e.g. single product-orders, partial shipments) that do not clarify how model-based order promising would perform in a realistic setting.In this paper we present a decision framework for order promising in a Fast Moving Consumer Goods (FMCG) company in the brewery sector where customers cannot be clustered and that incorporates many realistic features (such as e.g. multi-product orders, on-full delivery, or perishability constraints). The decision framework relies on several iterations of a MILP model that allocates the stock to customer and reserves some portion of the stock for orders arriving later on. These functionalities are embedded in a Decision Support System (DSS) that also helps with the renegotiation of the orders that cannot be served. Results reported from the usage of the DSS show its ability to obtain high service levels and to favourably compare to other order promising policies. Furthermore, in order to analyse the instance sizes that can be solved in reasonable time, and the parameters that influence the performance and results of the MILP model, an extensive computational experience has been carried out using a testbed that covers a wide range of business scenarios.

The Effect of Adding Daily 50 mg Intramuscular Progesterone to 800 mg Progesterone Suppository on The In Vitro Fertilization Success Rate in Women with Low Progesterone Levels: A Clinical Trial Study.

A low progesterone level on the embryo transfer (ET) day significantly reduces the pregnancy rate. Therefore, the present study aims to investigate the effect of adding daily 50 mg intramuscular progesterone to a total of 800 mg progesterone suppository on the in vitro fertilization (IVF) success rate in women with low progesterone levels. This parallel open-label clinical trial was performed on 218 IVF candidate infertile women who had <9.2 ng/ml progesterone levels on the ET day. These women were randomised to the intervention or control group using the randomisation allocation rule. In the intervention group, 50 mg progesterone was prescribed intramuscularly once daily in addition to 400 mg of progesterone suppository every 12 hours from the day of ET. The control group received only 400 mg of progesterone suppositories every 12 hours. In the case of pregnancy, the drugs above were continued until 12 weeks after the ET. Clinical pregnancy occurred in 54 (50.0%) women in the intervention group and in 39 (36.8%) women in the control group, which was significantly different (P=0.035). Ongoing pregnancy occurred in 47 (43.5%) women in the intervention group, and 33 (31.1%) women in the control group, which was significantly different (P=0.042). There were no significant differences in terms of abortion and multiple pregnancy rates between the two groups. Intramuscular injection of 50 mg progesterone significantly increases the clinical and ongoing pregnancy rates (registration number: IRCT20150105020558N6).

Trustful double auction design for Peer-to-Peer energy trading between interconnected micro-grids with supply–demand imbalance

Peer-to-Peer (P2P) energy trading between Interconnected Micro-Grids (IMGs) presents a promising approach for enhancing the economic advantages for prosumers while mitigating supply–demand imbalances within individual Micro-Grids (MGs). This paper proposes a trustful double auction mechanism for P2P energy trading among prosumers in IMGs, structured into two distinct stages. Initially, concurrent auctions are conducted within each MG to facilitate intra-grid trading, followed by subsequent auctions facilitating inter-grid trading across IMGs. We figure out the allocation and pricing rules that satisfy the required properties in mechanism design even when the auction consists of two stages. Given that energy transactions across IMGs entail non-negligible power losses, equitable allocation of these losses among prosumers is paramount. To address this, we integrate a fair cost distribution methodology into our auction mechanism, implemented by an iterative algorithm. Rigorous analysis substantiates our proposed auction mechanism’s incentive compatibility, individual rationality, and budget balance, thereby fostering truthful and voluntary prosumer participation while averting potential market deficits. Numerical analysis underscores the efficacy of our approach, showcasing a significant improvement in P2P transactions and supply–demand balance enhancement compared to trading solely within individual MGs.

Implementing randomized allocation rules with outcome-contingent transfers

We study a mechanism design problem where the allocation rule is randomized and transfers are contingent on outcomes. In this problem, an agent reports his private information, and an exogenous randomized allocation rule assigns an outcome based on the report. A planner designs an outcome-contingent transfer to incentivize the agent to report truthfully. We say that the allocation rule is implementable if such transfers exist. For this implementation problem, we derive two sufficient and necessary conditions. Each has a geometric interpretation. Moreover, when the allocation rule is implementable, we construct transfers that implement the allocation rule.

Effects of climate variability and change on groundwater impacts of forestry plantations

AbstractQuantifying water use of various water consumers is an essential part of sustainable water management. Annual evapotranspiration (ET) of plantation forests often exceeds that of dryland agriculture, which in South Africa and South Australia has resulted in restrictions on plantation development. In the latter case, water licences are issued to commercial forestry plantations to account for higher ET compared to dryland pasture. Unlike irrigated crops, it is not practicable to measure water use of plantations directly and so in South Australia a set of ‘deemed’ average water use rates has been applied since 2013, based on species and depth to groundwater. Since South Australia's ‘deemed’ rates were calculated, additional plot‐scale measurements of annual ET from plantations &lt;2 years old and post‐canopy closure have been used to quantify various components of ET. This has enabled development of two empirical ET models for plantations in South Australia's Lower Limestone Coast, and facilitated an advanced understanding of the effect of plantations on hydrological processes, particularly in relation to groundwater use. In this study, we applied these models to estimate rotation‐averaged annual ET and net groundwater impacts (net groundwater extraction plus recharge reduction compared to pasture) of plantations, driven by climate and groundwater depth, for comparison with the deemed rates. The modelling suggests that the groundwater impacts of plantations vary in space and time and that the deemed rates over‐estimate these impacts, on average. Accounting for variation in the effects of climate on the various components of ET, both spatially and temporally, may allow for more flexible rules for water resource allocation than using any simple, rule‐of‐thumb approach.

Vaccination for communicable endemic diseases: optimal allocation of initial and booster vaccine doses.

For some communicable endemic diseases (e.g., influenza, COVID-19), vaccination is an effective means of preventing the spread of infection and reducing mortality, but must be augmented over time with vaccine booster doses. We consider the problem of optimally allocating a limited supply of vaccines over time between different subgroups of a population and between initial versus booster vaccine doses, allowing for multiple booster doses. We first consider an SIS model with interacting population groups and four different objectives: those of minimizing cumulative infections, deaths, life years lost, or quality-adjusted life years lost due to death. We solve the problem sequentially: for each time period, we approximate the system dynamics using Taylor series expansions, and reduce the problem to a piecewise linear convex optimization problem for which we derive intuitive closed-form solutions. We then extend the analysis to the case of an SEIS model. In both cases vaccines are allocated to groups based on their priority order until the vaccine supply is exhausted. Numerical simulations show that our analytical solutions achieve results that are close to optimal with objective function values significantly better than would be obtained using simple allocation rules such as allocation proportional to population group size. In addition to being accurate and interpretable, the solutions are easy to implement in practice. Interpretable models are particularly important in public health decision making.