风险厌恶因子不确定时的二级供应链定价与订货策略

研究了风险中性的供应商与风险厌恶的零售商所构成的二阶供应链的定价与订货策略,零售商的风险厌恶由条件价值风险来度量.研究结果表明,当供应商不确定零售商的风险厌恶因子时,一定会造成其期望利润的下降,从而也体现了信息的价值.特别地,当风险厌恶因子服从均匀分布时,其期望订货量正是需求的截断随机变量的期望.     

最佳动力换档规律自学习算法的收敛性分析

针对汽车电控机械自动变速器 (AMT)最佳动力性换档规律的获取方法工作量大且耗资多,所得换档规律对其它车辆适应性差等问题,根据迭代自学习控制理论,提出了一种在线、实时寻求最佳动力性换档规律的自学习算法,并从理论上证明了该算法的收敛性,给出了收敛条件,讨论了自学习算法的快速收敛问题.分析结果表明,此法可以应用于实际AMT系统.     

骄阳热点 期期评

热点1:谈谈网络游戏中玩家心态的种类发起人:骄阳@亦岷玩家回复:1.为了虚荣心而游戏的玩家:这类玩家大多是网络游戏中的练级狂,疯狂的练级打装备以满足自己的虚荣心。2.为了金钱利益的玩家:为了金钱利益的玩家大多是外挂以及木马的散布者,盗取别人的ID和装备以寻求金钱的利益。3.为了游戏而游戏的玩家:为了游戏而游戏的玩家,大多是因为此游戏的特色而游戏的,并不是常时间的在线游戏,而只是体验此游戏的乐趣。4.为了交友而游戏的玩家:为了交友而游戏的玩家,这类玩家就是创造真挚友情和爱情的玩家,他们比较注重交友,注重友情和爱情,胜过那些…     

航海世纪：任务攻略

在《航海世纪》中，任务分为三种，最简单的工作任务；当玩家达到一定要求时才会出现的剧情任务；和只占盟会的任务，如何从各种任务中获取最大利益，是很多玩家关心的话题，下面就让我们来看一看。     

基于风险的供需网契约协调仿真研究

针对供需网的一个典型节点, 研究随机需求情形下零售商主导的供需网契约协调问题。考虑由一个风险厌恶的零售商和一个风险厌恶的制造商构成的两层供需系统, 零售商制定最优批发价, 制造商决定最优供货量, 构建了基于收益共享费用共担的供需网协调模型。仿真分析结果表明, 当整个供需系统的风险厌恶系数高于制造商和零售商的风险厌恶系数时, 收益共享费用共担契约可以实现系统的显著改善甚至完美协调。     

基于节点博弈漏洞攻击图的网络风险分析方法

鉴于当前的漏洞风险分析方法未考虑攻防双方的相互制约关系,尝试将博弈论引入漏洞攻击图的节点分析过程,提出了基于节点博弈漏洞攻击图的风险分析模型RAMVAG。在此基础上,提出一种基于连通矩阵的漏洞风险分析算法VRAA。算法建立了攻击图的连通矩阵,在分析信息系统漏洞的自身风险和传播风险的基础上,对漏洞全局风险进行综合评价,评价结果能够帮助管理者确定网络系统的关键漏洞。实例分析证明了模型和算法的有效性。     

基于最优风险与预防模型的医疗数据挖掘算法

为有效地实现疾病的早期诊断和预防,提出一种带权重的、基于最优风险与预防模型的医疗数据挖掘算法。利用最优风险与预防模型产生和疾病相关的特征属性值项,通过带权重的风险和预防集算法确定每个特征属性值项的权重。在2个标准医疗数据集中的测试结果表明,该算法能获取医疗数据中具有代表性的特征属性值项,并且每个特征属性值项都被赋予一个权重,使其获得较好的挖掘效果。     

一种在线汉字签名鉴别方法

本文针对在线汉字签名鉴别系统中存在的误拒绝真实签名问题和误接收伪造签名问题,提出了相应的解决方法.首先引入伪匹配起点的概念,使得用动态规划算法求出的最优匹配路径更具合理性;采用了多模板的方法,认为最终的鉴别距离是被测签名与多个参考签名距离的平均值;根据最小风险贝叶斯决策规则,求出距离阈值h使系统的平均风险达到最小.实验结果证明了本文提出方法的有效性.     

双边信息不对称参与者风险厌恶的应急数量折扣契约

探寻双边信息不对称、一个参与者风险厌恶时,应急数量折扣契约协调供应链应对突发事件的内在规律.借助"利他委托人"理论,科学设置参与者的激励和参与约束条件,在市场价格随机的条件下构建双边信息不对称、一个参与者风险厌恶的应急数量折扣契约.分析信息不对称和风险厌恶对供应链上各决策变量、供应链及链上成员绩效的影响,并通过具体的算...     

支持向量机中核函数的性能评价策略

继神经网络方法之后,支持向量机成为机器学习领域中的有效方法,但是核函数的评价和选取问题一直存在。该文从结构风险出发,通过经验风险和置信区间2个方面对核函数的性能进行量化,给出评价核函数性能的公式,指出传统经验风险定义的缺陷,并提出了一个新的定义。实验证明了该算法的可行性和有效性。     

Risk consideration and cooperation in the iterated prisoner’s dilemma

This paper investigates the cooperative behavior in the two-player iterated prisoner’s dilemma (IPD) game with the consideration of income stream risk. The standard deviation of one-move payoffs for players is defined for measuring the income stream risk, and thus the risk effect on the cooperation in the two-player IPD game is examined. A two-population coevolutionary learning model, embedded with a niching technique, is developed to search optimal strategies for two players to play the IPD game. As experimental results illustrate, risk-averse players perform better than risk-seeking players in cooperating with opponents. In particular, in the case with short game encounters, in which cooperation has been demonstrated to be difficult to achieve in previous work, a high level of cooperation can be obtained in the IPD if both players are risk-averse. The reason is that risk consideration induces players to negotiate for stable gains, which lead to steady mutual cooperation in the IPD. This cooperative pattern is found to be quite robust against low levels of noise. However, with increasingly higher levels of noise, only intermediate levels of cooperation can be achieved in games between two risk-averse players. Games with risk-seeking players get to even lower cooperation levels. By comparing the players’ strategies coevolved with and without a high level of noise, the main reason for the reduction in the extent of cooperation can be explained as the lack of contrition and forgiveness of players in the high-noise interactions. Moreover, although increasing encounter length is helpful in improving cooperation in the noiseless and low-noise IPD, we find that it may enforce the absence of contrition and forgiveness, and thus make cooperation even more difficult in the high-noise games.     

Risk-sensitive filtering, prediction and smoothing for discrete-time singular systems

This paper is concerned with steady-state risk-sensitive filtering, prediction and smoothing problems for discrete-time singular systems. It is shown that a risk-sensitive estimator can be obtained by ensuring the minimum of an indefinite quadratic form to be maximum (minimum) when the risk-sensitivity parameter θ is negative (positive). An auxiliary state-space signal model and an innovation sequence in Krein space are introduced to simplify the derivation of the estimator. The estimator is calculated based on one J-spectral factorization for risk-seeking (θ<0) or one H₂ spectral factorization for risk-averse (θ>0). A numerical example is given to demonstrate the applicability of the result.     

基于改进前景理论的直觉模糊随机多准则决策方法

针对方案准则值为直觉模糊数、准则权重信息部分已知的随机多准则决策问题,提出一种基于改进前景理论的决策分析方法.首先,定义一个新的记分函数,据此可将直觉模糊数转化为实数.其次,考虑到决策者并非完全理性及决策者风险态度的差异性,将决策者分为保守型、 中间型及冒险型,引入改进前景理论,根据不同决策者类型调整参数,构建改进前景决策矩阵.再次,建立以准则值总差异最大化且准则权重差异最小化为目标的非线性二次偏差优化定权模型,计算准则权重.进而,结合改进前景决策矩阵及准则权重计算各方案的综合效用值,并以此确定方案的顺序排列.最后,通过算例验证所提出直觉模糊随机多准则决策方法的有效性和合理性.     

A note on envy-free cake cutting with polynomial valuations

The cake cutting problem models the fair allocation of a heterogeneous divisible resource among multiple players. The central fairness criterion is envy-freeness and a major open question in this domain is the design of a bounded protocol that can compute an envy-free allocation of the cake for any number of players. The only existing finite envy-free cake cutting protocol for any number of players, designed by Brams and Taylor [4], has the property that its runtime can be made arbitrarily large by setting up the valuation functions of the players appropriately. Moreover, there is no closed formula that relates the valuation functions to the number of queries required by the protocol.     

Risk sensitivity and assortment in social dilemmas

Uncertainty and risk are key features of many social dilemmas, where individual decisions are often made with imperfect knowledge and variance in outcomes. In this paper, we investigate the evolutionary dynamics of the well-known public goods game using a recently introduced modelling framework encapsulating risk-sensitive assortment. Here, the population of mobile agents playing the game is divided into fixed-sized interaction groups. Individuals are defined by a single genetic trait—a risk sensitivity trait—that guides their decision-making. This trait is mapped to a continuous range of investment levels and also provides a mechanism to guide mobility (migration) decisions. Detailed computational simulation experiments confirm the relationship between risk orientation, decision-making and mobility in the game. As the size of each group increases, assortment levels tend to decrease and risk-averse individuals tend to dominate the population. However, in many scenarios, there was high variance in the proportion of ‘cooperators’ both in groups and between different groups, suggesting that risk-seeking behaviour is an emergent property of mobility induced positive assortment.     

The framing of drivers' route choices when travel time information is provided under varying degrees of cognitive load

In two experiments, participants chose between staying on a main route with a certain travel time and diverting to an alternative route that could take a range of travel times. In the first experiment, travel time information was displayed on a sheet of paper to participants seated at a desk. In the second experiment, the same information was displayed in a virtual environment through which participants drove. Overall, participants were risk-averse when the average travel time along the alternative route was shorter than the certain travel time of the main route but risk-seeking when the average travel time of the alternative route was longer than the certain travel time along the main route. In the second experiment, in which cognitive load was higher, participants simplified their decision-making strategies. A simple probabilistic model describes the risk-taking behavior and the load effects. Actual or potential applications of this research include the development of efficient travel time information systems for drivers.     

A risk-averse approach to simulation optimization with multiple responses

This article considers risk-averse simulation optimization problems, where the risk measure is the well-known Average Value-at-Risk (also known as Conditional Value-at-Risk). Furthermore, this article combines Taguchi’s robustness with Response Surface Methodology (RSM), which results in a novel, robust RSM to solve such risk-averse problems. In case the risk-averse problem is convex, the conic quadratic reformulation of the problem is provided, which can be solved very efficiently. The proposed robust RSM is applied to both an inventory optimization problem with a service-level constraint and a call-center problem; the results obtained for the risk-averse problem and its benchmark problem, where the risk measure is the variance, are intuitively sound and interesting.     

Mechanism design with partial verification and revelation principle

In the case of mechanism design with partial verification, where agents have restrictions on misreporting, the Revelation Principle does not always hold. Auletta et al. (J Auton Agent Multi-Agent Syst, to appear) proposed a characterization of correspondences for which the Revelation Principle holds, i.e., they described restrictions on misreporting under which a social choice function is implementable if and only if it is truthfully implementable. In this paper, we demonstrate that the characterization proposed in [1] is incorrect, and, building on their work, give a correct characterization. We also provide an example that demonstrates that our characterization is different from that of Auletta et al.     

Maximal Introspection of Agents

This paper concerns the representation of introspective belief and knowledge in multi-agent systems. An introspective agent is an agent that has the ability to refer to itself and reason about its own beliefs. It is well-known that representing introspective beliefs is theoretically very problematic. An agent which is given strong introspective abilities is most likely to have inconsistent beliefs, since it can use introspection to express self-referential beliefs that are paradoxical in the same way as the classical paradoxes of self-reference. In multi-agent systems these paradoxical beliefs can even be expressed as beliefs about the correctness and completeness of other agents' beliefs, i.e., even without the presence of explicit introspection. In this paper we explore the maximal sets of introspective beliefs that an agent can consistently obtain and retain when situated in a dynamic environment, and when treating beliefs “syntactically” (that is, formalizing beliefs as axioms of first-order predicate logic rather than using modal formalisms). We generalize some previous results by Perlis [1985] and des Rivières & Levesque [1988].     

Risk management policies for dynamic capacity control

Consider a dynamic decision making model under risk with a fixed planning horizon, namely the dynamic capacity control model. The model describes a firm, operating in a monopolistic setting and selling a range of products consuming a single resource. Demand for each product is time-dependent and modeled by a random variable. The firm controls the revenue stream by allowing or denying customer requests for product classes. We investigate risk-sensitive policies in this setting, for which risk concerns are important for many non-repetitive events and short-time considerations.Numerically analysing several risk-averse capacity control policies in terms of standard deviation and conditional-value-at-risk, our results show that only a slight modification of the risk-neutral solution is needed to apply a risk-averse policy. In particular, risk-averse policies which decision rules are functions depending only on the marginal values of the risk-neutral policy perform well. From a practical perspective, the advantage is that a decision maker does not need to compute any risk-averse dynamic program. Risk sensitivity can be easily achieved by implementing risk-averse functional decision rules based on a risk-neutral solution.