Desired Allocation Research Articles

Karma economies for sustainable urban mobility – a fair approach to public good value pricing

City road infrastructure can be considered a public good, and congestion is the intersection of demand for mobility with the limited available transportation supply. Congestion pricing is effective in reducing demand to sustainable levels below network capacity limits, but also controversial, as it introduces equity issues and systematically discriminates against lower-income groups. Karma is a non-monetary, fair, and efficient resource allocation mechanism that employs an artificial currency different from money, incentivises cooperation amongst selfish individuals, and achieves a balance between giving and taking. Where money does not do its job, Karma achieves socially more desirable allocations by being aligned with consumers’ needs rather than their financial power. This work highlights the value proposition of Karma, provides guidance on Karma mechanism design, and equips the reader with a useful software framework to model Karma economies and predict consumers’ behaviour. A case study of road pricing demonstrates Karma’s potential.

Optimization of Balanced Distribution of Online and Offline Biochemistry Teaching Resources Utilizing Machine Learning Algorithms and Big Data

Utilizing machine learning algorithms, the current conventional optimization methods for balanced allocation of teaching resources are enhanced. These methods primarily focus on predicting the current network resource load capacity without calculating the adaptation factor, often leading to suboptimal allocation outcomes. To address this issue, we propose a balanced allocation optimization approach for online and offline biochemistry education resources, taking into account the importance degree and big data. This approach calculates the probabilistic importance of teaching resources and the user delay in distinct modes to identify the optimal balanced allocation of teaching resources. Our experimental results comfirm that this approach achieves a higher resource utilization rate and a more desirable allocation effect compared to previous methods.

Covariate-Adaptive Biased Coin Randomization for Master Protocols with Multiple Interventions and Biomarker-Stratified Allocation

In a multi-arm trial with predefined subgroups for each intervention to target, it is often desirable to enrich assignment to an intervention by enrolling more biomarker-positive participants to the intervention. We describe how to implement a biased coin design to achieve desired allocation ratios among interventions and between the number of biomarker-positive and biomarker-negative participants assigned to each intervention. We illustrate the proposed method with the randomization algorithm implemented in the Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) trial.

Emphasizing egalitarianism in the allocation of China's provincial carbon emission allowances

China plans to allocate the provincial carbon emission allowances (CEA), but the CEA scheme has not been determined. Uncertain decisionmaker preferences lead to the uncertainty of the CEA scheme, hindering provinces from setting climate targets. Thus, we investigate different CEA schemes under various preferences from the fairness—efficiency perspective. We developed a CEA scheme set (consisting of 176851 CEA schemes) based on four criteria: egalitarianism, capacity, responsibility, and efficiency. By constructing a desirability evaluation index, we find the most desirable CEA schemes corresponding to various preferences from the CEA scheme set. The results show that egalitarianism can co-improve fairness and efficiency on top of the existing CEA scheme. Relying mainly on egalitarianism supplemented by efficiency is a desirable allocation mode for any decisionmaker. Regardless of the preferences, these situations generally exist: 1) Guangdong and Shandong will get the highest CEA; 2) Municipalities directly under the central government and less developed provinces will get the lowest CEA; 3) North, Northwest, and Northeast China will bear high emission reduction pressures before 2030. These findings will provide essential guidance for decisionmakers in formulating CEA schemes and provinces in formulating emission reduction policies.

Transit facility allocation: Hybrid quantum-classical optimization.

An essential consideration in urban transit facility planning is service efficiency and accessibility. Previous research has shown that reducing the number of facilities along a route may increase efficiency but decrease accessibility. Striking a balance between these two is a critical consideration in transit planning. Transit facility consolidation is a cost-effective way to improve the quality of service by strategically determining the desirable allocation of a limited number of facilities. This paper develops an optimization framework that integrates Geographical Information systems (GIS), decision-making analysis, and quantum technologies for addressing the problem of facility consolidation. Our proposed framework includes a novel mathematical model that captures non-linear interactions between facilities and surrounding demand nodes, inter-facility competition, ridership demand and spatial coverage. The developed model can harness the power of quantum effects such as superposition and quantum tunnelling and enables transportation planners to utilize the most recent hardware solutions such as quantum and digital annealers, coherent Ising Machines and gate-based universal quantum computers. This study presents a real-world application of the framework to the public transit facility redundancy problem in the British Columbia Vancouver metropolitan area. We demonstrate the effectiveness of our framework by reducing the number of facilities by 40% while maintaining the same service accessibility. Additionally, we showcase the ability of the proposed mathematical model to take advantage of quantum annealing and classical optimization techniques.

Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures.

Reallocations of time between daily activities such as sleep, sedentary behavior and physical activity are differentially associated with markers of physical, mental and social health. An individual's most desirable allocation of time may differ depending on which outcomes they value most, with these outcomes potentially competing with each other for reallocations. We aimed to develop an interactive app that translates how self-selected time reallocations are associated with multiple health measures. We used data from the Australian Child Health CheckPoint study (n = 1685, 48% female, 11-12 y), with time spent in daily activities derived from a validated 24-h recall instrument, %body fat from bioelectric impedance, psychosocial health from the Pediatric Quality of Life Inventory and academic performance (writing) from national standardized tests. We created a user-interface to the compositional isotemporal substitution model with interactive sliders that can be manipulated to self-select time reallocations between activities. The time-use composition was significantly associated with body fat percentage (F = 2.66, P < .001), psychosocial health (F = 4.02, P < .001), and academic performance (F = 2.76, P < .001). Dragging the sliders on the app shows how self-selected time reallocations are associated with the health measures. For example, reallocating 60 minutes from screen time to physical activity was associated with -0.8 [95% CI -1.0 to -0.5] %body fat, +1.9 [1.4 to 2.5] psychosocial score and +4.5 [1.8 to 7.2] academic performance. Our app allows the health associations of time reallocations to be compared against each other. Interactive interfaces provide flexibility in selecting which time reallocations to investigate, and may transform how research findings are disseminated.

Three-player game-theoretic allocation of indivisible resources during natural disasters

Resource allocation is an integral part of disaster management. After a natural disaster, multiple concurrent emergencies in distinct locations often make resource allocation challenging for the disaster management authority. This article focuses on disaster resource allocation based on a novel three-player, non-cooperative, single-stage, strategic game where the emergency locations are considered as the players. The decision-making authority solves a game-theoretic algorithm to determine a suitable strategy for allocating indivisible resources among three disaster locations where the available resources are insufficient to satisfy all the players simultaneously. Based on a unique non-monetary cost function, each player incurs a penalty for any possible allocation strategy. Mathematical analysis shows that, for the proposed game, at least one pure strategy Nash equilibrium (PSNE) always exists, which can be a desirable allocation to the players. Payoff dominance and utopia-point-based solutions are used to select a single PSNE from a set of multiple PSNEs.

Building and Maintaining a Desired Exposure to Private Markets: Commitment Pacing, Cash Flow Modeling, and Beyond

Unlike public markets, private market holdings are a class of self-liquidating assets. Consequently, there is no passive strategy, e.g., buy-and-hold, to invest in private markets. All investments require an active management. In order to build and maintain a desired allocation to private markets one needs a commitment pacing plan that balances several objectives including stable exposure, performance, cash flow management, diversification over funds and time, and maintaining relationships with GPs. A good commitment pacing plan is often seen as the lynchpin of a private capital program and can account for much of the dispersion in performance across LPs. We present a simulation-based framework to show the tradeoff between the steady-state NAV and the speed with which it can be built using a simple, yet powerful, commitment strategy and a popular cash flow model. Our framework also reveals the tradeoff between liquidity and risk-adjusted performance assuming the existence of private market premium in US buyout investments.

Parametric Synthesis of a Robust Controller Based on the Method of Dominant Poles

In the paper a linear control system described by its characteristic polynomial with interval coefficients including parameters of controller linearly is considered. Problem of the research is finding parameters of a controller guaranteeing dynamic characteristics of a system despite interval parametric uncertainty of its object. It is proposed to base a controller synthesis on root quality indices: minimal stability degree and maximal oscillability degree. Desired values of these indices will be provided with the help of dominant poles method. Applying this method consists in placing a pair of complex-conjugate dominant poles; all other poles — unrestricted poles — will be placed by defining a right border of their allocation area on a complex plane. To apply dominant poles method, a feature of stability degree and oscillability degree to be determined by images of certain vertices of a parametric polytope was used. To synthesize a controller, it is proposed to divide its parameters in two groups: dependent ones and unrestricted ones. The first group of controller parameters is to provide desired allocation of dominant poles in one of vertices of parametric polytope (a dominant vertex). Unrestricted parameters of a controller are to provide desired distance between dominant poles and allocation area of unrestricted poles. To find coordinates of a dominant vertex and verifying vertices providing unrestricted poles allocation, an interval extension of basic phase equation of a root locus theory was developed. This resulted in interval phase inequalities, whose solution allows finding coordinates of desired vertices of characteristic polynomials coeffi cients polytope. Knowing a dominant vertex polynomial and dominant poles allows expressing dependent parameters of a controller from unrestricted ones. Obtained expressions allow placing unrestricted poles in a desired area of a complex plane. To do this, a D-partition by unrestricted parameters of a controller is performed in all verifying vertices of parametric polytope of a system. After choosing values of unrestricted parameters from intersection of all stability domains obtain during D-partition, dependent parameters of a controller can be calculated. An example of synthesizing a PID-controller guaranteeing desired values of dynamics characteristics for an interval control system of the fourth order is provided.

Parametric synthesis of a robust PID-controller for interval control system with aperiodic transient process

The paper is dedicated to a problem of synthesizing a PID-controller, which provides and aperiodical transient process of desired duration in a control system of the third order with interval parametric uncertainty. To solve the problem, a new method of synthesis was developed. It is based on a decomposing a characteristic polynomial of a considered system in two multipliers: dominant polynomial, which determines allocation of a dominant pole and, consequently, quality of a transient process in a considered system, and non-dominant polynomial, which determines allocation of all other system poles. In order to provide a desired allocation of system poles, parameters of a PID-controller are divided to unrestricted ones and dependent ones. The research resulted in algebraic expressions for calculating dependent parameters of a PID-controller and a system of inequalities for finding its unrestricted parameter. Developed method of a PID-controller parametric synthesis allows placing an interval real dominant pole according to desired control quality and placing all other poles of a system in a desired part of a complex plane to provide necessary degree of a robust pole domination.

A Social Exchange Analysis of Adaptive Governance in Water Allocation Processes, the Kafue Flats, Zambia

Adaptive governance is an emergent approach to water allocation that alludes to the social conditions that enable the range of interactions among actors as they agree and pursue mutually desired water allocation outcomes. Limited successful efforts aimed at implementing adaptive governance suggest a dearth in understanding and implementing adaptive governance. Using the analytic lens of social exchange theory, we draw attention to relations of dependence by examining efforts aimed at establishing an equitable water allocation regime on the Kafue Flats of Zambia. Relations of dependence are as a result of differential access and control of hydromentality. In the context of this research, hydromentality includes discourse on water allocation, institutional arrangements and techniques for control of hydraulic infrastructure. The research used in-depth interviews, stakeholder workshops and document analysis. The research illustrates the case in which hydropower and commercial irrigation farmers, access and control hydromentality. In this way, these few key actors are able to either facilitate or constrain adaptive governance processes thereby influecing water allocation outcomes on the Kafue Flats. We conclude by asserting that adaptive governance does not occur in a vacuum and as such require greater attention to relations of dependence as social actors pursue desired water allocation outcomes.

Closed-loop task allocation in robot swarms using inter-robot encounters

In swarm robotics systems, coordinated behaviors emerge via local interactions among the robots as well as between robots and the environment. For a swarm of robots performing a set of pre-defined tasks in an enclosed region, this paper develops a decentralized mechanism to allocate tasks to each robot by leveraging the spatial interactions occurring among the robots as they move around the domain. With the aim of achieving a desired percentage of the swarm performing each task, the developed strategy allows individual robots to switch between different tasks with a certain probability when they encounter other robots in the region. We develop an analytical model to describe the inter-robot encounters occurring in a densely packed swarm of robots using ideas from the Enskog theory of dense gases and illustrate how the swarm can leverage this model to achieve the desired allocation levels. Furthermore, the inter-robot encounters enable the robots to measure the current allocation of the swarm, which is then used to regulate the rate at which they switch between tasks. This allows the swarm to speed up or slow down the rate of transitions between tasks depending on how far the current allocation is from the desired values, ultimately facilitating uninterrupted task execution by the robots. The methods introduced in this paper illustrate how naturally occurring encounters among robots in a swarm can be used to allocate tasks to the robots in a closed-loop manner. The developed algorithm is completely decentralized and can be deployed on minimalistic robots without the need for communication or a central coordinator. The performance of the algorithm is demonstrated on a swarm of real robots.

Connecting Strategies to Resources: Scale Development of a Resource Bundle for Internet Retailing

Service strategy literature emphasizes the alignment of target market, service concept, and service delivery system. This article reports scale development of a resource bundle that is useful to implement service strategies in Internet retailing. Three dimensions of the resource bundle are proposed in this study. They cover three alignment parts between target market and service concept, service concept and service delivery system, and target market and service delivery system. To rigorously develop the measurement scale, this study uses a two-stage procedure which comprises an item-level study that uses judges and a scale-level study that employs a survey of Internet retailers. Finally, a second-order measurement model of the resource bundle with a total of 14 measurement items was established. Results of the confirmatory factor analysis reveal empirical evidence of resource complementarities, thereby providing critical information about desirable resource allocation for Internet commerce and, in turn, carrying some managerial implications.

On the benefits of currency reform

Money allows agents to achieve allocations that are not possible without it. However, currency in most economies is a uniform object, and there may be incentive compatible allocations that cannot be implemented with a uniform currency. We show that currency reform, i.e., changing the monetary base by replacing one currency with another, is a powerful tool that can enable a monetary authority to achieve a desired allocation. Our monetary mechanism with currency reform is anonymous and features a nonlinear exchange rate between currencies and a monotone value of money. These results help interpret the characteristics of currency reforms observed in practice.

Remittances, poverty and human capital: evidence from developing countries

PurposeThe purpose of this paper is to analyze the relationship between remittances and poverty through the human capital channel in developing countries, which has received less attention in the literature.Design/methodology/approachThe paper applied the system GMM developed by Arellano and Bond (1991) and Arellano and Bover (1995) containing 54 developing countries. This estimator is appropriate compared to a cross-section technique because it controls for the endogeneity of all explanatory variables, includes unobserved country-specific effects and allows for the inclusion of lagged dependent variables.FindingsThe results suggest that, while remittances reduced poverty, the effect is moderated via education. A 1 percent increase in remittances reduces the poverty headcount by 0.47 percent, while the reduction is 0.33 percent via education. The marginal effect of remittances is negatively related to the level of education, indicating that education mitigates the effect of remittances on poverty.Practical implicationsThis paper includes the implications for the policymakers to justify the need for more effective approaches. It is useful to identify whether and how remittances and human capital interact in their effect on poverty when deciding the most desirable allocation of available resources between these two priorities.Originality/valueThis paper takes a step forward filling the limited evidence on the role of human capital in remittances–poverty relationship in developing countries. Different from the existing studies which have used the traditional panel estimators, this study utilizes the dynamic panel estimators such as system GMM to tackle the specification issues of endogeneity, measurement errors and heterogeneity.

Spectrum Allocation and Power Control for Non-Orthogonal Multiple Access in HetNets

In this paper, a novel resource allocation design is investigated for non-orthogonal multiple access (NOMA) enhanced heterogeneous networks (HetNets), where small cell base stations (SBSs) are capable of communicating with multiple small cell users (SCUs) via the NOMA protocol. With the aim of maximizing the sum rate of SCUs while taking the fairness issue into consideration, a joint problem of spectrum allocation and power control is formulated. In particular, the spectrum allocation problem is modeled as a many-to-one matching game with peer effects . We propose a novel algorithm where the SBSs and resource blocks interact to decide their desired allocation. The proposed algorithm is proved to converge to a two-sided exchange-stable matching. Furthermore, we introduce the concept of ‘exploration’ into the matching game for further improving the SCUs’ sum rate. The power control of each SBS is formulated as a non-convex problem, where the sequential convex programming is adopted to iteratively update the power allocation result by solving the approximate convex problem. The obtained solution is proved to satisfy the Karush-Kuhn–Tucker conditions. We unveil that: 1) the proposed algorithm closely approaches the optimal solution within a limited number of iterations; 2) the ‘exploration’ action is capable of further enhancing the performance of the matching algorithm; and 3) the developed NOMA-enhanced HetNets achieve a higher SCUs’ sum rate compared with the conventional OMA-based HetNets.

General covariate-adaptive randomization targeting unequal allocation ratio

In clinical trials, a well-designed randomization procedure should have the potential to achieve compound objectives. Unequal treatment allocation is frequently designed, in that it has ethical and economic advantages. In this paper, we develop a general covariate-adaptive randomization procedure that can target any desired allocation ratio. To ensure the consistency of ratio at different levels, covariates are adjusted according to overall ratio, and a weighted imbalance measure is constructed with three types of imbalance under desired allocation ratio. Moreover, theoretical results are obtained under easily satisfied conditions. Finally, we investigate the performance of the proposed design and validate theoretical results through simulations.

Bullwhip and reverse bullwhip effects under the rationing game

When an unreliable supplier serves multiple retailers, the retailers may compete with each other by inflating their order quantities in order to obtain their desired allocation from the supplier, a behavior known as the rationing game. We introduce capacity information sharing and a capacity reservation mechanism in the rationing game and show that a Nash equilibrium always exists. Moreover, we provide conditions guaranteeing the existence of the reverse bullwhip effect upstream, a consequence of the disruption caused by the supplier. In contrast, we also provide conditions under which the bullwhip effect does not exist. In addition, we show that a smaller unit reservation payment leads to more bullwhip and reverse bullwhip effects, while a large unit underage cost results in a more severe bullwhip effect. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 203–216, 2017

Auctions for Essential Inputs

We study the design of auctions for the allocation of essential inputs, such as spectrum rights, transmission capacity or airport landing slots, to firms using these inputs to compete in a downstream market. When welfare matters in addition to auction revenues, there is a tradeoff: provisions aimed at fostering post-auction competition in the downstream market typically results in lower prices for consumers, but also in lower auction proceeds. We first characterize the optimal auction design from the standpoints of consumer and total welfare. We then examine how various regulatory instruments can be used to implement the desired allocation.

Manipulation via endowments in auctions with multiple goods

We study manipulation via endowments in a market in an auction setting with multiple goods. In the market, there are buyers whose valuations are their private information, and a seller whose set of endowments is her private information. A social planner, who wants to implement a socially desirable allocation, faces the seller’s manipulation via endowments, in addition to buyers’ manipulation of misreporting their valuations. We call a mechanism immune to the seller’s manipulation via endowments destruction-proof. In general, there exists no mechanism which is destruction-proof, together with strategy-proofness of the buyers, efficiency, and participation. Nevertheless, we find a restricted domain of the buyers’ valuation profiles satisfying a new condition called per-capita goods–buyer submodularity. We show that, in this domain, there exists a mechanism which is destruction-proof, together with the above properties. The restriction is likely to be met when each winner’s valuation is close to the next-highest valuation. We also provide a relation to monopoly theory, and argue that per-capita goods–buyer submodularity is independent of the standard elasticity argument.