Redistribution of tax resources: a cooperative game theory approach

We consider the problem of how to distribute public expenditure among the different regions of an economic entity after all taxes have been collected. Typical examples are: the regions that make up a country, the states of a federal country, or the countries of a confederation of countries. We model the problem as a cooperative game in coalitional form, called the tax game. This game estimates the fiscal resources collected in each region, or coalition of regions, by differentiating between what comes from economic activity within each region and what comes from trade with the other regions. This methodology provides a measure of the disagreement within a region, or coalitions of regions, with respect to the budget received. Similarly, the stability of a budget allocation can be inferred by its situation within the core of the corresponding tax game. We consider the Spanish case as an example and show that the current regional financial system has a moderate degree of instability. We introduce two budget allocation rules, both borrowed from the cooperative games literature: the balanced allocation, which coincides with the nucleolus and with the Shapley value of the tax game, and the weighted balanced allocation, which coincides with the weighted Shapley value. We compare both budget allocation rules with the current Spanish financial system.

Research on Vehicle Scheduling of Cross-Regional Collection Using Hierarchical Agglomerative Clustering and Algorithm Optimization

At present, municipal solid waste (MSW) collection is based on the divide-regional operation mode, which has many deficiencies. This paper proposes a cross-regional operation scheme. Through the initial assignment, type labeling, and reassignment, and use the improved hierarchical agglomerative clustering (IHAC) algorithm and garbage collecting route optimization (GCRO) algorithm to realize intelligent allocation of garbage and scheduling route planning of collection vehicles. The experimental results demonstrate the proposed scheme improves the utilization of vehicle resources, reduces the operating cost, realizes the balanced allocation of garbage, and solves the problems caused by limitations of the original operation scheme, which demonstrates the feasibility and effectiveness of the cross-regional operation.

Human Resources Balanced Allocation Method Based on Deep Learning Algorithm

At present, the economics and social developments show the characteristics of diversification, and the focus of social enterprise management is driven by the allocation of human resources. Human resource allocation is a way of appropriate allocation and reasonable placement of human resources. It means that, under the guidance of science, human resources can maintain the best combination with other resources at any time. Nevertheless, the irregularities in management teams and the balanced differences of talent quality have a great effect on the balanced development of an enterprise. Based on this, this paper studies the establishment of a recurrent neural network (RNN) model to realize the allocation of human resources and the balanced development of enterprise management. Firstly, a deep learning model, based on the recurrent neural network, is established. Then, the human resources data is analyzed to calculate the matching degree between the human resources and posts. Finally, personnel scheduling is carried out according to the matching degree score between the human resources and posts, to obtain the optimal balanced allocation result of the human resources. Experimental results show that our method can bring significant improvements to personnel position matching and effectively enhance the efficiency of human resource allocation based on the cloud environment.

Efficacy of BioFire FilmArray Gastrointestinal Panel (FGP) to Reduce Hospital Costs Associated With Contact Isolation: A Pragmatic Randomized Controlled Trial

Background: Molecular syndromic panels can rapidly detect common pathogens responsible for acute gastroenteritis in hospitalized patients. Their impact on both patient and healthcare system outcomes is uncertain compared to conventional stool testing. This randomized trial evaluates the impact of molecular testing on in-hospital resource utilization compared to conventional stool testing.Methods: Hospitalized patients with acute diarrheal illness were randomized 1:1 to either conventional or molecular stool testing with the BioFire FilmArray gastrointestinal panel (FGP). The primary outcome was duration of contact isolation, and secondary outcomes included other in-hospital resource utilization such as diagnostic imaging and antimicrobial use. Results: A total of 156 patients were randomized. Randomization resulted in a balanced allocation of patients across all 3 age strata (<18, 18-69, ≥70 years old). The proportion of positive stools was 20.5% vs 29.5% in the control and FGP groups, respectively (p=0.196). The median duration of contact isolation was 51 hours (interquartile range (iqr) 66) and 69 hours (iqr 81) in the conventional and FGP groups, respectively (p=0.0513). There were no significant differences in other in-hospital resource utilization between groups. Conclusions: There were no differences in in-hospital resource utilization observed between FGP and conventional stool testing groups. Trials Registration: ClinicalTrials.gov NCT04189874

Entry-Proofness and Discriminatory Pricing under Adverse Selection

This paper studies competitive allocations under adverse selection. We first provide a general necessary and sufficient condition for entry on an inactive market to be unprofitable. We then use this result to characterize, for an active market, a unique budget-balanced allocation implemented by a market tariff making additional trades with an entrant unprofitable. Motivated by the recursive structure of this allocation, we finally show that it emerges as the essentially unique equilibrium outcome of a discriminatory ascending auction. These results yield sharp predictions for competitive nonexclusive markets. (JEL D11, D43, D82, D86)

Enhancement of cloud performance metrics using dynamic degree memory balanced allocation algorithm

In cloud computing, load balancing among the resources is required to schedule a task, which is a key challenge. This paper proposes a dynamic degree memory balanced allocation (D2MBA) algorithm which allocate virtual machine (VM) to a best suitable host, based on availability of random-access memory (RAM) and microprocessor without interlocked pipelined stages (MIPS) of host and allocate task to a best suitable VM by considering balanced condition of VM. The proposed D2MBA algorithm has been simulated using a simulation tool CloudSim by varying number of tasks and keeping number of VMs constant and vice versa. The D2MBA algorithm is compared with the other load balancing algorithms viz. Round Robin (RR) and dynamic degree balance with central processing unit (CPU) based (D2B_CPU based) with respect to performance parameters such as execution cost, degree of imbalance and makespan time. It is found that the D2MBA algorithm has a large reduction in the performance parameters such as execution cost, degree of imbalance and makespan time as compared with RR and D2B CPU based algorithms

Fuzzy Balanced Allocation Problem with Efficiency on Servers

This paper deals with the problem of allocation customers to servers with regards to some fuzzy parameters. In this problem each customer is allocated to the nearest server, and assignment of a customer to a server involves the cost to the customer, which is due to the customer's fuzzy distance to the server. Each server has a fuzzy efficiency which is calculated by the data envelopment analysis method with fuzzy parameters. The higher efficiency of the server to which a customer is assigned, cause more profit for the customer. The goal is allocation of all customers to the servers such that the profitability of the least profits for the customers is maximized. In addition, to prevent queuing in some servers, we consider the balancing on allocation customers to the servers. Therefore, the second goal is minimizing the difference between the maximum and minimum number of customers that are assigned to different servers. A fuzzy bi-objective programming model is presented for the problem, then two fuzzy approaches are proposed for solving this model.