Alternative Allocation Strategies Research Articles

Database Decomposition to satisfy the Least Privilege Principle in Healthcare

The Multilevel Database Decomposition Framework is a cybersecurity strategy to enhance system robustness and minimize the impact of data breaches with a focus on healthcare systems. With respect to more conventional normalization methods, the framework prioritizes robustness against cyber threats over mere data redundancy reduction. The key strategy of the framework is the decomposition of a database into smaller databases to restrict user access and mitigate the impact of successful intrusions by satisfying the least privilege principle in a more complete way. For this purpose, each database the decomposition produces is uniquely associated with a set of users and the decomposition ensures that each user can access all and only the data his/her operations need. This limits the potential impact of threat agents impersonating users to the information a compromised user can access.
 To prevent the propagation of an intrusion across the databases it produces, the framework can apply alternative allocation strategies by distributing the databases to distinct virtual or physical entities according to the security requirement of the original application. This flexibility in allocation management ultimately reinforces defenses against evolving cyber threats and it is the main advantage of the deposition.
 As a counterpart of better robustness, some tables will be replicated across the databases the decomposition returns and updates of these tables should be properly replicated to prevent inconsistencies among copies of the same table in distinct databases. The paper includes a performance analysis to evaluate the overheads associated with the alternative allocations. This offers insights into the framework implementation and adaptability to distinct security needs and to evaluate the framework effectiveness for healthcare data systems.

Navigating Uncertainty: Enhancing Markowitz Asset Allocation Strategies through Out-of-Sample Analysis

This research paper explores the complicated connection between uncertainty and the Markowitz asset allocation framework, specifically investigating how mistakes in estimating parameters significantly impact the performance of strategies during out-of-sample evaluations. Drawing on relevant literature, we highlight the importance of our findings. In contrast to common assumptions, our study systematically compares these approaches with alternative allocation strategies, providing insights into their performance in both anticipated and real-world out-of-sample events. The research demonstrates that incorporating methods to address uncertainty enhances the Markowitz framework, challenging the idea that longer sample periods always lead to better outcomes. Notably, imposing a short-sale constraint proves to be a valuable strategy for improving the effectiveness of the initial portfolio. While revealing the complexities of uncertainty, our study also highlights the surprising resilience of basic asset allocation approaches, such as equally weighted allocation, which exhibit commendable performance. Methodologically, we employ a rigorous out-of-sample evaluation, emphasizing the practical implications of parameter uncertainty on asset allocation outcomes. Investors, portfolio managers, and financial practitioners can use these insights to refine their strategies, considering the dynamic nature of markets and the limitations internal to the traditional models. In conclusion, this paper goes beyond the theoretical scope to provide substantial value in enhancing real-world investment decisions.

Communication and Computing Task Allocation for Energy-Efficient Fog Networks.

The well known cloud computing is being extended by the idea of fog with the computing nodes placed closer to end users to allow for task processing with tighter latency requirements. However, offloading of tasks (from end devices to either the cloud or to the fog nodes) should be designed taking energy consumption for both transmission and computation into account. The task allocation procedure can be challenging considering the high number of arriving tasks with various computational, communication and delay requirements, and the high number of computing nodes with various communication and computing capabilities. In this paper, we propose an optimal task allocation procedure, minimizing consumed energy for a set of users connected wirelessly to a network composed of FN located at AP and CN. We optimize the assignment of AP and computing nodes to offloaded tasks as well as the operating frequencies of FN. The considered problem is formulated as a Mixed-Integer Nonlinear Programming problem. The utilized energy consumption and delay models as well as their parameters, related to both the computation and communication costs, reflect the characteristics of real devices. The obtained results show that it is profitable to split the processing of tasks between multiple FNs and the cloud, often choosing different nodes for transmission and computation. The proposed algorithm manages to find the optimal allocations and outperforms all the considered alternative allocation strategies resulting in the lowest energy consumption and task rejection rate. Moreover, a heuristic algorithm that decouples the optimization of wireless transmission from implemented computations and wired transmission is proposed. It finds the optimal or close-to-optimal solutions for all of the studied scenarios.

Optimizing global COVID-19 vaccine allocation: An agent-based computational model of 148 countries.

BackgroundBased on the principles of equity and effectiveness, the World Health Organization and COVAX formulate vaccine allocation as a mathematical optimization problem. This study aims to solve the optimization problem using agent-based simulations.MethodsWe built open-sourced agent-based models to simulate virus transition among a demographically representative sample of 198 million people in 148 countries using advanced computational services. All countries continuing their current vaccine progress is defined as the baseline scenario. Comparison scenarios include achieving minimum vaccination rates and allocating vaccines based on pandemic levels.FindingsThe simulations are fitted using the pandemic data from 148 countries from January 2020 to June 2021. Under the baseline scenario, the world will add 24.36 million cases and 468,945 deaths during the projection period of three months. Inoculating at least 10%, 20%, and 26% of populations in all countries requires 1.12, 3.31, and 5.00 million additional vaccine doses every day, respectively. Achieving these benchmarks reduces new cases by 0.56, 2.74, and 3.32 million, respectively. If allocated by the current global distribution, 5.00 million additional vaccine doses will only avert 1.45 million new cases. If those 5.00 million vaccines are allocated based on projected cases in each country, the averted cases will increase more than six-fold to 9.20 million. Similar differences between allocation methods are observed in averted deaths.ConclusionThe global distribution of COVID-19 vaccines can be optimized to achieve better outcomes in terms of both equity and effectiveness. Alternative vaccine allocation methods may avert several times more cases and deaths than the current global distribution. With reasonable requirements on additional vaccines, COVAX could adopt alternative allocation strategies that reduce cross-country inequity and save more lives.

Mitigating Inequities and Saving Lives with ICU Triage during the COVID-19 Pandemic.

The burdens of the coronavirus disease (COVID-19) pandemic have fallen disproportionately on disadvantaged groups, including the poor and Black, Latinx, and Indigenous communities. There is substantial concern that the use of existing ICU triage protocols to allocate scarce ventilators and critical care resources—most of which are designed to save as many lives as possible—may compound these inequities. As governments and health systems revisit their triage guidelines in the context of impending resource shortages, scholars have advocated a range of alternative allocation strategies, including the use of a random lottery to give all patients in need an equal chance of ICU treatment. However, both the save-the-most-lives approach and random allocation are seriously flawed. In this Perspective, we argue that ICU triage policies should simultaneously promote population health outcomes and mitigate health inequities. These ethical goals are sometimes in conflict, which will require balancing the goals of maximizing the number of lives saved and distributing health benefits equitably across society. We recommend three strategies to mitigate health inequities during ICU triage: introducing a correction factor into patients’ triage scores to reduce the impact of baseline structural inequities; giving heightened priority to individuals in essential, high-risk occupations; and rejecting use of longer-term life expectancy and categorical exclusions as allocation criteria. We present a practical triage framework that incorporates these strategies and attends to the twin public health goals of promoting population health and social justice.

Dynamic Resource Allocation on Multi-Category Two-Sided Platforms

Platform businesses are typically resource-intensive and must scale up their business quickly in the early stage to compete successfully against fast-emerging rivals. We study a critical question faced by such firms in the novel context of multicategory two-sided platforms: how to optimally make investment decisions across two sides, multiple categories, and different time periods to achieve fast and sustainable growth. We first develop a two-category two-period theoretical model and propose optimal resource allocation strategies that account for heterogeneous within-category direct and indirect network effects and cross-category interdependence. We find that the proposed strategy shares the spirit of the allocation rules for multiproduct nonplatform firms and single-product platform firms, yet it does not amount to a simple combination of the existing rules. Interestingly, the business model that platforms adopt crucially determines the optimal strategy. Platforms that charge by user should adopt a “reinforcing” rule for both within- and cross-category allocations by allocating more resources toward the stronger growth driver. Platforms that charge by transaction should also adopt the reinforcing rule for within-category allocation, but follow a “compensatory” rule for cross-category and intertemporal allocations by allocating more resources toward the weaker growth driver. We use data from the daily deals industry to empirically identify the network effects, propose alternative allocation strategies stemming from our theoretical findings, and use simulations to show the benefits of these strategies. For instance, we show that reallocating 10% of the average observed investment from Fitness to Beauty can increase profits by up to 15.5% for some cities. This paper was accepted by Matthew Shum, marketing.

Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

Fluctuations in electricity tariffs induced by the sporadic nature of demand loads on power grids has initiated immense efforts to find optimal scheduling solutions for charging and discharging plug-in electric vehicles (PEVs) subject to different objective sets. In this paper, we consider vehicle-to-grid (V2G) scheduling at a geographically large scale in which PEVs have the flexibility of charging/discharging at multiple smart stations coordinated by individual aggregators. We first formulate the objective of maximizing the overall profit of both, demand and supply entities, by defining a weighting parameter. We then propose an online decentralized greedy algorithm for the formulated mixed integer non-linear programming (MINLP) problem, which incorporates efficient heuristics to practically guide each incoming vehicle to the most appropriate charging station (CS). The better performance of the presented algorithm compared to an alternative allocation strategy is demonstrated through simulations in terms of the overall achievable profit and flatness of the final electricity load. Moreover, the results of simulations reveal the existence of optimal number of deployed stations at which the overall profit can be maximized.

Rectifying social inequalities in a resource allocation task

To investigate whether children rectify social inequalities in a resource allocation task, participants (N=185 African-American and European-American 5–6year-olds and 10–11year-olds) witnessed an inequality of school supplies between peers of different racial backgrounds. Assessments were conducted on how children judged the wrongfulness of the inequality, allocated new resources to racial ingroup and outgroup recipients, evaluated alternative allocation strategies, and reasoned about their decisions. Younger children showed ingroup favorability; their responses differed depending on whether they had witnessed their ingroup or an outgroup at a disadvantage. With age, children increasingly reasoned about the importance of equal access to school supplies and correcting past disparities. Older children judged the resource inequality negatively, allocated more resources to the disadvantaged group, and positively evaluated the actions of others who did the same, regardless of whether they had seen their racial ingroup or an outgroup at a disadvantage. Thus, balancing moral and social group concerns enabled individuals to rectify inequalities and ensure fair access to important resources regardless of racial group membership.

An assessment of the current status of the chokka squid fishery in South Africa and an evaluation of alternative allocation strategies

The South African squid fishery is based on a single species, Loligo reynaudi, locally referred to as chokka and commonly found around the Agulhas Bank and West Coast shelf of South Africa. The existing long term rights for participation in the fishery expire at the end of 2013 and a new set of rights will have to be issued. Progress made to date in achieving the objectives of the South African Marine Living Resources Act of 1998 (MLRA), including progress towards greater equity, will be an important consideration in allocation decisions. The draft Small-Scale Fisheries Policy, scheduled to be adopted in 2013, could also influence the criteria to be used. This study updates the information on the current structure and functioning of the squid jig sector and considers the likely performance of two alternative access allocation scenarios against the current and evolving policy goals. The study reaffirms that the squid fishery is an important contributor to human well-being in South Africa and particularly in the Eastern Cape Province but there is scope for improvement in the extent of transformation and equity. The study concludes that, as a result of a common lack of experience and capital amongst potential new entrants, the risks associated with transformation in this fishery are likely to be high, especially if the option of allocating rights to community-based legal entities was to be used. The risks could be reduced by ensuring that a number of pre-conditions were met. These include, amongst others, not allowing total effort in the fishery to increase; ensuring effective management by the responsible government authorities; adequate capacity building of new entrants, whether individuals or cooperatives; and ensuring new entrants have viable business plans.

Stochastic Portfolio Theory Optimization and the Origin of Alternative Asset Allocation Strategies

What is the theoretical reason why a particular alternative allocation strategy, or a combination thereof, should offer a superior return vs. risk tradeoff? Can we derive an optimal alternative allocation strategy from first principles, both from an absolute return perspective, to identify the most appropriate long-term strategic benchmark, and from a relative return perspective, to identify the alternative allocation strategy with the highest expected information ratio relative to a market-cap weighted index? We attempt to answer these questions, by building on the stochastic portfolio theory framework of Fernholz, to study the evolution of portfolio wealth, both in absolute terms and relative to a market index. We prove that the portfolio maximizing the expected value of logarithmic portfolio wealth at a fixed level of volatility differs from the traditional mean-variance portfolio solution by the linear combination of three further terms: an equally weighted portfolio, a risk parity portfolio, and a high cash flow rate of return portfolio. Most importantly, we prove that, given any market capitalization weighted index, the portfolio maximizing relative logarithmic growth with respect to this index deviates from the market benchmark by the linear combination of four subportfolios: an equally weighted portfolio, a risk parity portfolio, a high cash flow rate of return portfolio, and a global minimum variance portfolio. Consistent with previous empirical research, we prove that an investor can profit from diversification effects when spreading his investment across different alternative asset allocation methods, and we show the four alternative asset allocation building blocks which constitute the optimal portfolio.

Private Crop Insurers and the Reinsurance Fund Allocation Decision

This research investigates the strategic behavior of private crop insurance firms reinsured by the USDA through the Standard Reinsurance Agreement. This arrangement allows the private firm to strategically allocate individual policies into different risk‐sharing arrangements. Thus, firm earnings are conditioned upon accurately forecasting policy loss experience. Our analysis begins with models investigating the characteristics explaining the placement of policies into the assigned risk fund. Then a simulation model of the SRA is used to compare the post‐SRA returns of actual firm allocations to two alternative allocation strategies based on a aggregate models and a policy‐level econometric forecasting model.

A Look at the Long-run Effectiveness of Multimedia Advertising and Its Implications for Budget Allocation Decisions

<h3>ABSTRACT</h3> This study examines the long-term effectiveness of multimedia advertising in a competitive setting and its implications for budget allocation decisions, using multivariate persistence methodology. Analysis of network TV, spot TV, and magazine advertising for the two major competitors in the U.S. SUV industry suggested that long-run advertising effectiveness differed considerably among media. These differences were attributed to the media lifespan, retrieval, and content of the message they convey. The authors propose that budget allocation decisions should consider the long-run effectiveness of the different media employed to increase the productivity of advertising campaigns. They also conduct a simulation experiment to further investigate long-run sales effects of alternative allocation strategy scenarios.

Sequential balancing: A simple method for treatment allocation in clinical trials

Although minimisation methods have frequently been advocated for treatment allocation in clinical trials, they are not widely used. As this may partly be due to the complexity of the methods, we devised a new and simple minimisation method to balance for prognostic factors, called sequential balancing. Each factor is dealt with sequentially and when a new subject enters the trial, he or she is allocated the treatment that leads to improved balance of the first factor over the treatments. If the balance of the first factor was already satisfactory, then the treatment is allocated that leads to improved balance of the second factor and so on. The algorithm requires no calculations. We simulated a realistic trial and compared the performance of this method to the performance of alternative allocation strategies: the variance minimisation method, simple randomisation and stratification. The sequential balancing method led to better balance than randomisation and stratification. In the case of four factors or less, the performance of the sequential balancing method and the variance minimisation method were comparable and the sequence of the factors was not very relevant. When more factors were introduced, the balance of the sequential method remained comparable with the balance achieved with the variance minimisation method for the first four factors, but it started to decrease from the fifth factor onwards. We conclude that the ease and simplicity of the new method make it an attractive option when balance is required for four factors or less. If there are more than four factors, the sequential balancing method may still be an acceptable option, but the advantage of simplicity has to be weighed against the loss of performance compared to other minimisation methods.

Distributional Effects of Carbon Allowance Trading: How Government Decisions Determine Winners and Losers

If the U.S. should limit carbon dioxide emissions, an allowance trading policy may offer one method of achieving that goal in a cost-effective manner. The distributional effects of such a program could be large, far in excess of the actual cost to the economy. This paper examines how two key decisions that the government would need to make in designing a carbon trading program would determine those distributional effects. Those decisions are how to allocate the allowances and how to use the revenue that the government would receive under alternative allocation strategies. Distributional effects are estimated for both domestic and international trading programs. We distribute deadweight losses due to policy-induced declines in both carbon consumption and factor supplies. Further, we separate the distributional effects associated with government carbon consumption from private sector carbon consumption. Finally, we highlight the problems in measuring distributional effects that are caused by poor data on households' consumption and income.

Regional economic growth by policy-induced capital flows: II. Policy simulation results

This second part of the paper refers to a model, described in Part I, that has been designed to analyze alternative allocation strategies of regional economic growth policy: “movement of the workers to the jobs” (passive factor stocks adjustment) or “jobs to the workers” (active factor stocks adjustment). After concentrating on the main properties of the model structure, the references of the parameters and initial values to empirical data are discussed. Model results derived on the basis of parameters representative for the case of passive adjustment policy are studied, also extensively taking account of the impacts of parameter variations. Then follows the analysis of simulation results of active adjustment. The main purpose of all calculations is to demonstrate that the model is able to generate reasonable and consistent policy effects. Moreover, with respect to the model and its parameters this paper attempts to contribute to finding a solid standpoint as to the preconditions of a successful active regional economic policy.

Regional economic growth by policy-induced capital flows: I. Theoretical approach

In view of regional income disparities this paper discusses alternative allocation strategies of regional economic growth policy: “movement of the workers to the jobs” (passive factor stocks adjustment) versus “jobs to the workers” (active factor stocks adjustment). For policy evaluation of this classical question a dynamic two-regions model is presented (Part I). Its implications will be analyzed on the basis of selected parameters to give an example of possible model applications (Part II). In Part I the analysis will start with general remarks on model construction (stressing the relevance of supply-side growth barriers), then describe the relationships of the model and finally refer to some selected aspects of possible extensions (introduction of a third region as the outside world of the two regions, modification of the model to deal with the no-growth case, consideration of the size of regions, and incorporation of independent regional investment functions). The concluding remarks will focus on the essential policy parameters of the model.

Acquisition and Allocation of Resources: Genetic (CO) Variances, Selection, and Life Histories

We investigate a genetic model in which two traits result from the acquisition and allocation of a single resource. Phenotypic values for the two traits are written as a product of the total amount of the resource acquired and the proportion allotted to each of them. Although multiplicative gene action determines the traits, the epistasis at the gene level is mainly expressed in the additive genetic variance and covariance at the level of the measured traits. Phenotypic and additive genetic covariances between the two traits can be positive or negative; a negative additive genetic covariance can be accompanied by a positive phenotypic covariance. An acquisition-allocation model is the only model of multiplicative gene action that allows simultaneous selection on two traits to be written in matrix form. We use the model of resource acquisition and allocation to find the life-history consequences of acquisition of a resource and allocation to two traits. Two alternative allocation strategies--priority allocation to viability or to fecundity--lead to different evolutionarily stable strategies (ESSs) in life-history components. Primary allocation to fecundity has allocation fractions of zero or one as its stable state. Primary allocation to viability leads to an ESS allocation fraction that depends on resource availability, population growth rate, and the age structure of the population. In a poor environment and for inherently long-lived animals, the ESS allocation fraction tends in the direction of higher viability.