On the population monotonicity of independent set games

An independent set game is a cooperative game dealing with profit sharing in the maximum independent set problem. A population monotonic allocation scheme is a rule specifying how to share the profit of each coalition among its participants such that every participant is better off when the coalition expands. In this paper, we provide a necessary and sufficient characterization for independent set games admitting population monotonic allocation schemes. Moreover, our characterization can be verified efficiently.     

Optimized Selection of Water Resource Allocation Schemes Based on Improved Connection Entropy in Beijing’s Southern Plain

Increased urbanization has caused problems such as increasing water consumption and the continuous deterioration of the groundwater environment. It is necessary to consider the groundwater quality in the water resource optimization system and increase the rate of reclaimed water development to reduce the amount of groundwater exploitation and achieve sustainable development of water resources. This study used the Daxing District, a region of Beijing’s southern plain, as an example to evaluate water quality by analyzing water quality data of surface and groundwater from 2012 to 2016 and actual water-use schemes from 2006 to 2016. Three groundwater extraction modes were set up based on NO₃–N concentrations, water resources were optimized under three extraction modes, and water resource optimization schemes were determined based on the improved connection entropy. The results show that (1) the surface water quality was poor, and the proportion of V₄ type water in the indexes of NH₃–N and chemical oxygen demand (COD) was the largest. The surface water can only be used for agricultural irrigation. The pollution sources contributing most to NH₃–N and COD were domestic and agricultural pollution sources. (2) The groundwater quality was good. The NO₃–N index was primarily type I–III water, accounting for 95.20% of the total samples. Severe NH₃–N pollution areas were mainly in the northern region, and most regional groundwater can be used for various purposes. (3) Taking 2016 as an example, three groundwater exploitation modes were set to optimize water resource allocation, and the results showed that the rate of groundwater development and NO₃–N pollution decreased significantly after optimization. (4) Connection entropy is an evaluation method that combines connection numbers and entropy, including identify, difference, and opposition entropy. As connection entropy being a kind of complete entropy, which can reflect the difference of the system in different states, based on the improved connection entropy, the connection entropies of optimal water resource allocation and actual water-use schemes were calculated. The connection entropy of groundwater exploitation mode 3 was less than that of groundwater exploitation modes 1 and 2 and actual water-use schemes from 2006 to 2016. Therefore, exploitation mode 3’s water resource optimization scheme was recommended. In the paper, satisfactory results have been obtained. As a kind of complete entropy, connection entropy has great research value in dealing with complex hydrological problems. This study’s research methods and outcomes can provide methodological and theoretical lessons for water management in freshwater-deficient areas.     

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.     

基于MetropolisQ学习的蜂窝网络在线信道分配

针对现有的蜂窝网络的在线动态分配模型具有的信道需求量大、呼叫动态变化时阻塞率高和收敛速度慢的缺点,设计了一种基于MetropoisQ学习的蜂窝网络的在线信道分配方法。首先,在考虑同信道限制、邻居信道限制和同小区限制的基础上,设计了在线信道分配的数学模型,然后在Q-Learning算法基础上的设计了一种基于资格迹的Q(λ)算法实现信道的在线分配,为了进一步提高收敛速度,采用Metropois规则对算法中动作的选择方式进行改进,实现探索和利用的平衡。为了验证文中方法,采用Matlab工具上进行实验,仿真实验结果表明文中方法能实现蜂窝通信网络的在线信道分配,且与其它方法比较,具有较少的信道需求量、较低的阻塞率和收敛速度,较其它方法具有较大优越性。     

考虑边流量有限的网络路径博弈问题研究

考虑每条边有流量约束的网络路径博弈问题, 根据收益函数单调递增的特点分析其内在零和性质, 并建模为存在公共边的路径博弈模型。在寻找均衡解的过程中, 首先考虑非合作的情形, 在局中人风险中性的假设下, 给出了求Nash均衡流量分配的标号法并证明该均衡分配的唯一性。接着进一步考虑局中人合作的可能性, 给出模型求得所有局中人的整体最大收益, 并基于纳什谈判模型给出目标函数为凸函数的数学模型确定唯一收益分配方案。事实上, 该方案是对剩余价值的平均分配。最后给出一个算例, 验证本文理论和方法的可行性。关键词:流量约束; 均衡流量; 网络路径博弈; 收益分配     

考虑联盟结构的联合采购合作剩余分配博弈分析

联合采购合作剩余分配本质是在最大联盟剩余值与最小期望分配值之间的谈判协商过程。当前联合采购合作剩余分配研究主要基于参与企业地位无差别假设,忽略了联盟结构对剩余分配具有明显影响。基于联盟形成的层次结构,研究论证了采购企业通过组建小联盟形式参与更大联盟的联合采购,可提升其在分配谈判中的话语权。通过算例分析表明,考虑联盟结构的联合采购合作剩余分配相对于不考虑联盟结构得到的分配方案更具公平性和合理性。     

A linear program for the two-hub location problem

This paper considers the discrete two-hub location problem. We need to choose two hubs from a set of nodes. The remaining nodes are to be connected to one of the two hubs which act as switching points for internodal flows. A configuration which minimizes the total flow cost needs to be found. We show that the problem can be solved in polynomial time when the hub locations are fixed. Since there are at most ways to choose the hub locations, the two-hub location problem can be solved in polynomial time. We transform the quadratic 0–1 integer program of the single allocation problem in the fixed two-hub system into a linear program and show that all extreme points of the polytope defined by the LP are integral. Also, the problem can be transformed into a minimum cut problem which can be solved efficiently by any polynomial time algorithm.     

A machine learning efficient frontier

We propose a simple approach to bridge between portfolio theory and machine learning. The outcome is an out-of-sample machine learning efficient frontier based on two assets, high risk and low risk. By rotating between the two assets, we show that the proposed frontier dominates the mean–variance efficient frontier out-of-sample. Our results, therefore, shed important light on the appeal of machine learning into portfolio selection under estimation risk.     

Optimal asset allocation for pension funds under mortality risk during the accumulation and decumulation phases

In a financial market with one riskless asset and n risky assets whose prices are lognormal, we solve in a closed form the problem of a pension fund maximizing the expected CRRA utility of its surplus till the (stochastic) death time of a representative agent. We consider a unique asset allocation problem for both accumulation and decumulation phases. The optimal investment in the risky assets must decrease during the first phase and increase during the second one. We accordingly suggest it is not optimal to manage the two phases separately, and outsourcing of allocation decisions should be avoided in both phases. JEL: G23, G11 MSC 2000: 62P05, 91B28, 91B30, 91B70, 93E20