Allocation Rules Research Articles

Infection Causes Trade-Offs between Development and Growth in Larval Amphibians.

AbstractTrade-offs between life history traits are context dependent; they vary depending on environment and life stage. Negative associations between development and growth often characterize larval life stages. Both growth and development consume large parts of the energy budget of young animals. The metabolic rate of animals should reflect differences in growth and developmental rates. Growth and development can also have negative associations with immune function because of their costs. We investigated how intraspecific variation in growth and development affected the metabolism of larval amphibians and whether intraspecific variation in growth, development, and metabolic rate could predict mortality and viral load in larvae infected with ranavirus. We also compared the relationship between growth and development before and after infection with ranavirus. We hypothesized that growth and development would affect metabolism and predicted that each would have a positive correlation with metabolic rate. We further hypothesized that allocation toward growth and development would increase ranavirus susceptibility and therefore predicted that larvae with faster growth, faster development, and higher metabolic rates would be more likely to die from ranavirus and have higher viral loads. Finally, we predicted that growth rate and developmental rate would have a negative association. Intraspecific variation in growth rate and developmental rate did not affect metabolism. Growth rate, developmental rate, and metabolism did not predict mortality from ranavirus or viral load. Larvae infected with ranavirus exhibited a trade-off between developmental rate and growth rate that was absent in uninfected larvae. Our results indicate a cost of ranavirus infection that is potentially due to both the infection-induced anorexia and the cost of infection altering priority rules for resource allocation.

Effectiveness of unified protocol for trans diagnostic treatment in children with anxiety disorders: A randomized control trial

BACKGROUND: The Unified Protocol for Transdiagnostic Treatment of Emotional Disorders in Children (UP-C) is a theory-derived approach that can target the common underlying processes, such as the cognitive, emotional, and behavioral processes in emotional disorders in children aged 8–12 years. This study aimed to investigate UP-C's efficacy in treating children's anxiety disorders compared to cognitive behavior therapy (CBT). MATERIALS AND METHODS: In this randomized control trial (RCT), with pre-test, post-test, and follow-up, 34 participants aged 8–12 with anxiety disorders were selected through the restricted randomization method and allocated to intervention (UP-C) or control (CBT) groups by random allocation rule. UP-C group consisted of 15 weekly individual sessions, and CBT included 16 weekly individual sessions. The Emotion Regulation Questionnaire for Children and Adolescents (ERQ-CA) and The Screen for Child Anxiety Related Disorders (SCARED-71) were completed in the pre-test, post-test, and three-month follow-up stages. The data of participants were analyzed using the repeated measure analysis of variance. A P- value under. 05 was regarded as significant. RESULTS: Based on the repeated measures ANOVA, UP-C, and CBT significantly reduced anxiety symptoms (P = .002) and emotional suppression (P = .032). Moreover, UP-C and CBT significantly increased emotion regulation (P = .000) and cognitive reappraisal (P = .000). CONCLUSION: The individual UP-C can be effective as anxiety-oriented CBT in treating anxiety disorders. Also, in the three months follow-up, the UP-C's effects were more stable and progressive than the CBT.

Allocation rules of indivisible prizes in team contests

Allocation rules of indivisible prizes in team contests

Belatacept with time-limited tacrolimus coimmunosuppression modifies the 3-year risk of eplet mismatch in kidney transplantation

Solid organ transplant donor-recipient eplet mismatch has been correlated with donor-specific antibody (DSA) formation, antibody-mediated rejection, and overall rejection rates. However, studies have been predominantly in patients on tacrolimus-based immunosuppression regimens and have not fully explored differences in ethnically and racially diverse populations. Evidence indicates that patients on belatacept have lower rates of DSA formation, suggesting mediation of the immunogenicity of mismatched human leukocyte antigen polymorphisms. We performed a retrospective, single-center analysis of class II eplet disparity in a cohort of kidney transplant recipients treated using belatacept with tacrolimus induction (Bela/TacTL) or tacrolimus regimens between 2016 and 2019. Bela/TacTL (n = 294) and tacrolimus (n = 294) cohorts were propensity score-matched with standardized difference <0.15. Single-molecule eplet risk level was associated with immune event rates for both groups. In Cox regression analysis stratified by eplet risk level, Bela/TacTL immunosuppression was associated with a decreased rate of DSA (hazard ratio [HR] = 0.4), antibody-mediated rejection (HR = 0.2), and rejection (HR = 0.45). In the low-risk group, cumulative graft failure was lower for patients on Bela/TacTL (P < .02). Analysis of eplet mismatch burden may be a useful adjunct in identifying high-risk populations with increased immunosuppression requirements and should encourage the design of allocation rules to incentivize lower-risk pairings without negatively impacting equity in access.

Multi-objective location-allocation optimization of temporary delivery points for medical supplies during large-scale epidemic outbreaks

The surge in the demand for medical supplies and the lack of service delivery points in the early stages of an epidemic are likely to exacerbate the spread of the epidemic and create serious inequities in distribution. To more effectively allocate medical supplies during large-scale epidemic outbreaks, we develop a multi-objective mixed integer nonlinear programming model incorporating appointment allocation rules to optimize the locations of delivery points, the flow of medical supplies among different nodes, and the appointment allocation rules. The model aims to minimize the number of infected individuals in the study area, infection risks at delivery points, and total cost and service level deviation at the demand points. To achieve such objectives, we adopt the hierarchy optimization method and ε-constraint method encompassing model complexity and applicability. The practicality of our model is demonstrated through a numerical case for mask allocation in accordance with appointment allocation rules during COVID-19 in 2020 based on real population distribution data of Xipu Subdistrict in Pidu District, Chengdu, China. The results reveal trade-offs between different objectives. In addition, related suggestions are provided for variations in demand satisfaction rate, the number of individuals at each delivery point and appointment allocation rules.

Development of rules for budget allocation and methods for evaluating media plans for the digital space

The article studies methods of advertising campaign budget allocation in digital space using advertising hypotheses. The author proposes a new approach based on the use of advertising hypotheses as an advertising medium. A form of media plan specifically designed for digital space has been presented. Special attention is paid to two types of advertising campaigns: those that pay for clicks or impressions and those that use direct marketing tools (SMS, mobile notifications and email newsletters). A way to evaluate media plan effectiveness has been suggested. The author believes that the main task at the stage of media planning is to determine whether the developed advertising hypothesis is able to achieve the planned indicators. This approach can improve advertising campaigns effectiveness in digital space and ensure more accurate budget allocation. The results of the study can be useful for marketers and advertising agencies, helping them make informed decisions when planning and optimizing advertising campaigns in digital space.

Fair Allocation of Vaccines, Ventilators and Antiviral Treatments: Leaving No Ethical Value Behind in Healthcare Rationing

A priority system has traditionally been the protocol of choice for the allocation of scarce life-saving resources during public health emergencies. COVID-19 revealed the limitations of this allocation rule. Many argue that priority systems abandon ethical values, such as equity, by discriminating against disadvantaged communities. We show that a restrictive feature of the traditional priority system largely drives these limitations. Following minimalist market design, an institution design paradigm that integrates research and policy efforts, we formulate pandemic allocation of scarce life-saving resources as a new application of market design. Interfering only with the restrictive feature of the priority system to address its shortcomings, we formulate a reserve system as an alternative allocation rule. Our theoretical analysis develops a general theory of reserve design. We relate our analysis to debates during COVID-19 and describe the impact of our paper on policy and practice. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis.

Pick-an-Object Mechanisms

We introduce a new family of mechanisms for one-sided matching markets, denoted pick-an-object (PAO) mechanisms. When implementing an allocation rule via PAO, agents are asked to pick an object from individualized menus. These choices may be rejected later on, and these agents are presented with new menus. When the procedure ends, agents are assigned the last object they picked. We characterize the allocation rules that can be sequentialized by PAO mechanisms and the ones that can be implemented in a robust truthful equilibrium. We justify the use of PAO as opposed to direct mechanisms by showing that its equilibrium behavior is closely related to the one in obviously strategy-proof (OSP) mechanisms, but implements commonly used rules, such as Gale-Shapley DA and top trading cycles, which are not OSP implementable. We run laboratory experiments comparing truthful behavior when using PAO, OSP, and direct mechanisms to implement different rules. These indicate that agents are more likely to behave in line with the theoretical prediction under PAO and OSP implementations than their direct counterparts. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This research is funded by the National Natural Science Foundation of China [Grant 72033006]. R. Hakimov was supported by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Project 100018_189152]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4908 .

Merit first, need and equality second: hierarchies of justice

AbstractThe question of “Justice” still divides social research, moral philosophy, and public discourse. Three principles of distributive justice (allocation rules) occupy center stage in the debate: merit (equity, proportionality), need, and equality. Yet their relation remains diffuse, and current theory does not inform political practice. Here, we aim to develop a coherent picture with an interdisciplinary analysis. From an evolutionary point of view, the foundational principle of justice is reciprocity in social exchange (what corresponds to merit). But besides being just, justice must be effective, efficient, and communicable, thereby making justice rather a social bargain and an optimization problem. Social-psychological insights (intuitions, rules of thumb, self-bindings) can inform us when and why the two allocation principles need and equality are more likely to succeed than merit would. But both are governed by reciprocal considerations, and self-bindings help to interpret altruism as “very generalized reciprocity.” Regarding politics, the reciprocal social norm Meritocratic Principle can be implemented, and its controversy avoided, by concentrating on “non-merit,” i.e., institutionally draining the wellsprings of undeserved incomes (economic rents). Avoiding or taxing away economic rents is an effective implementation of justice in market economies.

Grantmaking, Grading on a Curve, and the Paradox of Relative Evaluation in Nonmarkets

Abstract The article develops a model of nonmarket allocation of resources such as the awarding of grants to meritorious projects, honors to outstanding students, or journal slots to quality publications. On the supply side, the available budget of grants is awarded to applicants who are evaluated most favorably according to the noisy information available to reviewers. On the demand side, stronger candidates are more likely to obtain grants and thus self-select into applying, given that applications are costly. We establish that if evaluation is perfect, grading on a curve inefficiently discourages even the very best candidates from applying. More generally, when the budget is insufficient to award grants to all applicants, the equilibrium unravels if information is symmetric enough—the paradox of relative evaluation. Leveraging a technique based on the quantile function pioneered by Lehmann, we characterize a broad set of nonmarket allocation rules under which an increase in evaluation noise in a field (or course) raises equilibrium applications in that field, and reduces applications in all other fields. We empirically confirm these comparative statics by exploiting a change in the rule for apportioning the total budget to applications in different fields at the European Research Council, showing that a 1 standard deviation increase in own evaluation noise leads to a 0.4 standard deviation increase in the number of applications and budget share. Moreover, we derive insights for the design of evaluation institutions, particularly regarding the endogenous choice of noise by fields or courses and the optimal aggregation of fields into panels.

Influential process nodes identification strategy for aircraft assembly system based on complex network and improved PageRank

In the manufacturing network, the fluctuation of process operation time can result in the delay of the total completion time, which brings a huge challenge to controlling entire production schedule. For aircraft manufacturing industries, the processes that have a significant impact on the completion time of the assembly line need to be identified and monitored in order to ensure the final assembly efficiency and reliability. However, due to the large number of process nodes and the complexity of resource constraints and relationship constraints, traditional node centrality algorithms cannot identify influential nodes accurately. Therefore, based on complex network theory, this paper studies the influential nodes identification problem in the context of assembly lines. Firstly, The Resource-Process Coupling Network for the assembly line is constructed based on the production background and network resource characteristics. Then, based on PageRank algorithm and the idea of resource iteration, Improved PageRank Algorithm (IPRA) is proposed to identify influential process nodes, which brings the resource and time parameters into the allocation rules. Finally, according to simulation results, a comparative analysis of IPRA and existing algorithms is conducted and concludes that our method can better identify influential nodes in actual complex production networks. Furthermore, this paper identifies influential process nodes of aircraft assembly line based on the case of the commercial aircraft manufactory.

Ex-post implementation with interdependent values

We characterize ex-post implementable allocation rules for single object auctions under quasi-linear preferences with interdependent value functions. We show that requiring ex-post implementability is equivalent to requiring a weakening of monotonicity, which is a familiar condition used to characterize dominant strategy implementation. We illustrate that non-monotone rules may be necessary to achieve objectives such as efficiency and revenue maximizations, even in standard models such as the maximum signal model studied in Bulow and Klemperer (2002), and Bergemann et al. (2020).

Water Supply Reliability Analysis of Multi-Purpose Dams in Preparation for Water Disasters

Purpose: In this study, in order to find a way to minimize water supply shortage, reliability analysis of water supply was conducted by operating the Andong and the Imha Dam. The purpose is to find a way to minimize water shortage by the allocation distribution model from the reliability analysis results.&#x0D; Method: In this study, in order to analyze the water supply reliability of Andong and Imha Dam, using the Allocation rule presented by Park et al(2007), based on the planned water supply of Andong and Imha Dam for a total of 360 months from 1993 to 2022, analyzed. From the analysis results, the reliability of stable water supply was evaluated.&#x0D; Results: In the case of supplying the planned water supply of Andong and Imha Dam, the analysis result of Rule(B), which considers the ratio of the storage and inflow of the dam in the reservoir operation analysis result, showed that the shortage of Andong Dam occurred the most at 23 months, Regarding the number of shortages of control points, Rule(A), which considers only the storage capacity of the dam, was found to be short at 39 months, which is the largest number of shortages. As for the frequency standard reliability, Rule(B) showed the highest reliability of 90%, but in the case of quantitative reliability, the reliability was similar in all cases.&#x0D; Conclusion: In the water supply reliability, the reliability of stable water supply by supplying only the planned water supply amount is 94%, 93%, and 90% in Rule(B) of the allocation distribution model. 5% at the Andong dam, 7% at the Imha dam, and 10% at the control point. When water supply is evaluated with frequency reliability, it is judged that countermeasures for the shortfall are necessary. Next, in the case of quantitative reliability, when only the planned water supply was supplied, it was analyzed to be 95%, 94%, and 95% in all methods, so that the quantitative reliability was higher than the frequency reliability.

An efficient simulation procedure for the expected opportunity cost using metamodels

Many decision problems in modern industries involve the performance optimization of discrete-event dynamic systems. Because of the stochasticity of these systems, performance usually is evaluated through simulation. Finding the optimal designs of these systems can be modeled as a ranking and selection problem. This research considers the problem of selecting the best design from among numerous designs. We minimized the expected opportunity cost (EOC) of incorrect selection with a limited computing budget. The entire domain of designs could be divided into several adjacent partitions, and the performances of designs in each partition were estimated using a regression metamodel. We assumed the underlying function in each partition to be quadratic. Using large deviations theory, we then derived an asymptotically optimal budget allocation rule and developed a sequential allocation algorithm to implement the allocation rule. The EOC measure was compared with the probability of correct selection measure in a set of large-scale simulation optimization problems. Numerical experiments verified the effectiveness of our proposed simulation procedure.

Fuzzy cooperative game with intersecting priori coalition: A generalized configuration value

A fuzzy game with a configuration structure (i.e., a fuzzy configuration structure game) is introduced, which not only allows the fuzzy participation of players but also admits the intersection of fuzzy priori coalitions. A kind of fuzzy configuration structure games is proposed by the linear expression of cooperative game. In order to seek appropriate solutions for the proposed fuzzy configuration structure game, we prove that the fuzzy value for a fuzzy configuration structure game (i.e., FC value) can be denoted by value of cooperative game by different ways (or formulas). This kind of FC values contains several kinds of values, which is a generalized value of fuzzy cooperative games. Moreover, a further generalized extension of the proposed FC value is also given, which is also seen as an extension of ESL value (satisfying the efficiency, linearity and symmetry). Thus, by fuzzy configuration structure game and its generalized value, the application scope of the cooperative game with a configuration structure is extended to the fuzzy field. A general profit allocation rule is gotten for most kinds of cooperative games, including cooperative game, coalition structure game, configuration structure game and their fuzzy forms.

Expected values for variable network games

A network game assigns a level of collectively generated wealth to every network that can form on a given set of players. A variable network game combines a network game with a network formation probability distribution, describing certain restrictions on network formation. Expected levels of collectively generated wealth and expected individual payoffs can be formulated in this setting. We investigate properties of the resulting expected wealth levels as well as the expected variants of well-established network game values as allocation rules that assign to every variable network game a payoff to the players in a variable network game. We establish two axiomatizations of the Expected Myerson Value, originally formulated and proven on the class of communication situations, based on the well-established component balance, equal bargaining power and balanced contributions properties. Furthermore, we extend an established axiomatization of the Position Value based on the balanced link contribution property to the Expected Position Value.

The in-group egalitarian Owen values

This paper proposes a new class of allocation rules, which is referred to as the in-group egalitarian Owen values, that integrates two seemingly conflicting principles—marginalism and egalitarianism—in the framework of cooperative games with coalition structures. This class of allocation rules facilitates the use of different principles of allocation in different layers of a social structure such as allocation across coalitions and within a coalition. Therefore, each coalition can employ its specific distributive principle for intra-coalition distribution. Our main results provide axiomatic foundations for the class of allocation rules, in which monotonicity properties highlight the importance of the proposed solutions. We also show that various monotonicity properties generate various allocation rules, where the Owen value and the in-group egalitarian division value are special cases. These results extend the egalitarian Shapley values to the games with coalition structures and offer a rationale for monotonic allocation rules in the presence of social structures.

Private disclosure with multiple agents

This study examines a mechanism design problem where the principal can affect the agents' knowledge of a payoff-relevant state, namely, the principal designs and commits to an information disclosure policy that generates agent-specific private signals, while the principal directly observes neither the state nor the signal profile. We solve this problem by constructing a novel class of disclosure policies that exhibit individually uninformative, aggregately revealing, and immune to unilateral misreporting properties and show that the principal achieves the same payoff as if she could directly observe the state and implement state-contingent allocation rules. Moreover, we prove that our disclosure policy is robust to information-sharing among a certain number of agents and remains optimal in various settings.

Prioritized organ allocation rules under compatibility constraints

We study the efficacy of the donor-priority rule in promoting deceased organ donation under blood-type compatibility constraints in both theory and the laboratory. Compared with an allocation policy in which transplantations are performed only within the same blood-type group, we find that transferring organs across blood-type compatible groups discourages the donation incentives of hard-to-match agents and reduces the supply of the more widely acceptable type of organ. Meanwhile, the easy-to-match agents have higher incentives to donate. At the aggregate level, both our theoretical and experimental results suggest that the aggregate donation rate is lower under allocation policies that allow blood-type compatible transplants among different blood-type groups.

Graded Subsidy Policy-Based Equilibrium Strategy Applied to Investment in Electric Vehicle Chargers: A Case Study in Chengdu

The Chinese government has released plenty of incentive policies to stimulate the diffusion rate of electric vehicles (EV) and the number of public charging stations, such as charging parking lots (PLs). This paper explores cooperative relationships between regional governments and PL owners in the process of investment in EV charging equipment, and a bi-level optimization model is built to examine their hierarchical relationships. Karush-Kuhn-Tucker conditions are utilized to solve the NP-hard problem. Five PLs in Chengdu are selected as examples to examine the validity of this model. The optimal allocation decision of the government and the optimal investment decisions of PL owners are concluded through the calculation results. Compared with non-graded subsidies, the priority of the graded subsidy rules is confirmed. In line with the calculation results, subsidies can promote investors to provide more charging services, and a graded allocation rule can maximize the incentive performance of subsidies.