四值非线性序集逻辑系统中理论的随机发散度

在四值非线性序集逻辑系统L24中,给出了随机相似度和随机逻辑伪距离的基本性质。然后在随机逻辑度量空间中提出了理论的随机发散度,指出全体原子公式之集在随机逻辑度量空间中未必是全发散的,其是否全发散取决于给定的四值概率分布序列。     

信用资产组合优化的"条件在险值-补偿"型随机规划模型

本文对于信用资产组合的优化问题给出了一个稳健的模型，所建模型涉及了条件在险值（CVaR）风险度量以及具有补偿限制的随机线性规划框架，其思想是在CVaR与信用资产组合的重构费用之间进行权衡，并降低解对于随机参数的实现的敏感性．为求解相应的非线性规划，本文将基本模型转化为一系列的线性规划的求解问题．     

三值R_0命题逻辑系统中理论的随机发散度

在三值R_0命题逻辑系统中,给出了随机相似度和随机逻辑伪距离的基本性质.然后在随机逻辑度量空间中提出了理论的随机发散度,指出全体原子公式之集在随机逻辑度量空间中未必是全发散的,其是否全发散取决于给定的随机数序的分布.     

随机度量理论及其应用在我国最近进展的综述

本旨在全面综述随机度量理论及其应用过去十年在我国发展过程中所获得的主要结果与思想方法,本由十节组成,第一节对我们工作的背景-概率度量空间与随机度量空间理论作一简单的介绍;第二节给出某些有关随机泛函分析及取值于抽象空间的可测函数的预备知识,第三节阐明随机泛函分析与原始随机度量理论（本称之为F-随机度量理论）的整体关系,主要结果是在随机元生成空间上给出自然且合理的随机度量与随机范数的构造,从而将随机元与随机算子理论的研究纳入随机度量理论框架,主要思想是将随机泛函分析视为随机度量空间体系上的分析学而统一地发展;从而形成了发展随机泛函分析的一个新的途径-空间随机化途径;除此之外,在本节我们也从随机过程理论的观点出发首次提出对应于随机度量理论原始版本的一种新的随机共轭空间理论（叫作F-随机共轭空间理论）,它的突出优点是能保持象随机过程的样本性质这样更精细的特性（本节由作的工作构成）,在第四节,基于作最近提出的随机度量理论的一个新的版本（本称之为E-随机度量理论）,从传统泛函分析的角度对过去已被发展起来的随机共轭空间理论（本称之为E-随机共轭空间理论）的基本结果进行系统整理并给以全新的处理（本节内容整体上由作最近的一篇论构成,也尤其提到朱林户等人的重要工作）,在本节我们也以相当的篇幅论述F-随机共轭空间理论与E-随机共轭空间理论的内在关系与本质差异,在下面紧跟的两节,致力于E-随机共轭空间理论深层次的结果,尤其突出了E-随机赋范模与传统的赋范空间、E-随机共轭空间与经典共轭空间之间的内在联系;在第五节给出了几类E-随机赋范模的E-随机共轭空间的表示定理（主要由作的工作,作与游兆水及林熙合作的工作,还有巩馥州与刘清荣合作的工作组成）,在六节给出完备E-随机赋范模为随机自反的特征化定理（主要由作及合作的工作组成）,尤其是第五及第六节中,我们给出随机度量理论在随机泛函分析及经典Banach空间中若干实质性的应用;第七节简要给出E-随机赋半范模及E-随机对偶系理论初步;第八节简单阐明随机度量理论与泛函分析的关系;第九节简单阐明了随机度量理论与概率度量空间理论的关系,最后在第十节结合随机度量理论,Banach空间理论及随机泛函分析对发展随机泛函分析的空间随机化途径的合理性与优越性作了进一步的分析。     

随机度量理论及其应用在我国最近进展的综述

本旨在全面综述随机度量理论及其应用过去十年在我国发展过程中所获得的主要结果与思想方法。全由十节组成,第一节对我们工作的背景－概率度量空间与随机度量空间理论和一简单的介绍;第二节给出某些有关随机泛函分析及取值于抽象空间的可测函数的预备知识;第三节阐明随机泛函分析与原始随机度量理论（本称之为F－随机度量理论）的整体关系：主要结果是在随机元生成空间给出自然且合理的随机度量与随机范数的构造,从而将随机元与随机算子理论的研究纳入随机度量理论框架;主要思想是将随机泛函分析视为随机度量空间体系上的分析学而统一地发展,从而形成了发展随机泛函分析的一个新的途径－空间随机化途径;除此之外,在本节我们也从随机过程理论观点出发首次提出对应于随机度量理论原始版本的一种新的随机共轭空间理论（叫作F－ 随机共轭空间理论）,它的突出优点是能保持象随机过程的样本性质这样更精细的特性（本节由作的工作构成）;在第四节,基本作最近提出的随机度量理论的一个新的版本（本称之为E－随机度量理论）,从传统泛函分析的角度对过去已被发展起来的随机共轭空间理论（本称之为E－随机共轭空间理论）,从传统泛函分析的角度对过去已被发展起来的随机共轭空间理论（本称之为E－随机共轭空间理论）的基本结果进行系统整理并给以全新的处理（本节内容整体上由作最近后篇论构成,也尤其提到朱林户等人的重要工作）;在本节我们也相当的篇幅论述F－随机共轭空间理论与E－随机共轭空间理论的内存关系与本质差异。在下紧跟的两节,致力于E－随机共轭空间理论深层次的结果,尤其突出了E－随机赋范模与传统的赋范空间、E－随机共轭空间与经典共轭空间之间的内存联系;在第五节给出了几类E－随机赋范模的E－随机共轭空间的表示定理（主要由作的工作,作与游兆永及林熙合作的工作,还有巩馥州与刘清荣合作的工作组成）;在第六节给出完备E－随机赋范模为随机自反的特征化定理（主要由作及合作的工作组成）;在第六节给出完备E－随机赋范模为随机自反的特征化定理（主要由作及合作的工作组成）。尤其在第五及第六节中,我们给出随机度量理论在随机泛函分析及经典Banach空间中若干实质性的应用;第七节简要给出E－随机赋半范模及E－随机对偶系理论初步;第八节简单阐明随机度量理论与泛函分析的关系;第九节阐明了随机度量理论与概率度量空间理论的关系。最后在第十节结合随机度量理论,Banach空间理论及随机泛函分析对发展随机泛函分析的空间随机化途径的合理性与优越性作了进一步的分析。     

带平稳扰动项的连续时间线性系统的随机微分对策

随机微分对策问题在实际中有很多的应用，该问题要求两个控制变量，一个是使性能指标最小，另一个使之最大。在「１」中给出了不带扰动项的连续时间线性系统的随机微分对策问题的解签。在本文中我们研究了带有平稳扰动项的连续时间线性系统的随机微分对策问题，给出了在此种情况下的随机微分对策公式。     

与TSD准则一致的组合证券投资分析

基于预先给定的目标收益率,利用投资者对低于目标收益率的风险损失和高于目标收益率的风险报酬之间的权衡,给出了一些非对称风险度量模型,特别其中一种风险度量是低于参考点的方差和高于参考点的方差的加权和,它利用二阶上偏矩来修正二阶下偏矩,进一步建立了在该非对称风险度量下的组合投资优化模型,并证明了该模型在三阶随机占优的意义下是有效的.此外,还给出了其它3个模型与三阶随机占优准则是否一致的结论,并对所给出的几个组合证券投资模型的求解方法及其应用进行了分析.以上研究和分析为投资者在选择投资模型时避免盲目性、任意性提供了有益的决策参考.     

混合动力公交车能量控制策略的优化模型

针对混合动力公交车在循环工况内功率需求的特点,建立了未来功率需求贝叶斯预测模型;利用2-阶段随机动态规划模型将大规模的随机动态规划问题简化为多个小规模的随机动态规划问题和一个确定型动态规划问题;对于随机动态规划模型的求解,给出了稀疏表示的降维方法,将复杂的泛函极值问题转化为常规的随机动态优化问题,并采用分布估计算法和计算资源最优配置算法的计算机仿真优化算法对随机动态优化问题进行求解;给出了基于查表的在线控制策略,为模型的实际应用进行了有益的探索。     

多目标随机结盟对策的区间模糊ZS-值

研究多重目标随机结盟对策问题,引用区间模糊教有关理论,同时考虑局中人参与程度模糊化和支付函数模糊化的情形以及局中人对不同目标的偏好程度,给出多目标随机结盟对策的区间模糊ZS-值的定义及定理.区间模糊ZS-值能更好解决企业合作中存在的不精确数据时的利益分配问题,最后通过一个实例说明其可行性.     

随机度量空间及其应用

首先证明取值于度量空间(可分或不可分)的随机元可构成随机度量空间;取值于赋范空间的随机元可嵌入到随机赋范空间中.接着给出这些结论对随机算子的应用.最后统一给出赋范空间上几乎处处有界的随机线性泛函的表示.     

On risk minimizing portfolios under a Markovian regime-switching Black-Scholes economy

We consider a risk minimization problem in a continuous-time Markovian regime-switching financial model modulated by a continuous-time, observable and finite-state Markov chain whose states represent different market regimes. We adopt a particular form of convex risk measure, which includes the entropic risk measure as a particular case, as a measure of risk. The risk-minimization problem is formulated as a Markovian regime-switching version of a two-player, zero-sum stochastic differential game. One important feature of our model is to allow the flexibility of controlling both the diffusion process representing the financial risk and the Markov chain representing macro-economic risk. This is novel and interesting from both the perspectives of stochastic differential game and stochastic control. A verification theorem for the Hamilton-Jacobi-Bellman (HJB) solution of the game is provided and some particular cases are discussed.     

Risk minimizing portfolios and HJBI equations for stochastic differential games

In this paper, we consider the problem to find a market portfolio that minimizes the convex risk measure of the terminal wealth in a jump diffusion market. We formulate the problem as a two player (zero-sum) stochastic differential game. To help us find a solution, we prove a theorem giving the Hamilton–Jacobi–Bellman–Isaacs (HJBI) conditions for a general zero-sum stochastic differential game in a jump diffusion setting. We then use the theorem to study particular risk minimization problems. Finally, we extend our approach to cover general stochastic differential games (not necessarily zero-sum), and we obtain similar HJBI equations for the Nash equilibria of such games.     

On Entire Moments of Self-Similar Markov Processes

We consider a risk-based asset allocation problem in a Markov, regime-switching, pure jump model. With a convex risk measure of the terminal wealth of an investor as a proxy for risk, we formulate the risk-based asset allocation problem as a zero-sum, two-person, stochastic differential game between the investor and the market. The HJB dynamic programming approach is used to discuss the game problem. A semi-analytical solution of the game problem is obtained in a particular case.     

A BSDE approach to risk-based asset allocation of pension funds with regime switching

An asset allocation problem of a member of a defined contribution (DC) pension fund is discussed in a hidden, Markov regime-switching, economy using backward stochastic differential equations, (BSDEs). A risk-based approach is considered, where the member selects an optimal asset mix with a view to minimizing the risk described by a convex risk measure of his/her terminal wealth. Firstly, filtering theory is adopted to transform the hidden, Markov regime-switching, economy into one with complete observations and to develop, (robust), filters for the hidden Markov chain. Then the optimal asset allocation problem of the member is formulated as a two-person, zero-sum stochastic differential game between the member and the market in the economy with complete observations. The BSDE approach is then used to solve the game problem and to characterize the saddle point of the game problem. An explicit expression for the optimal asset mix is obtained in the case of a convex risk measure with quadratic penalty and it can be considered a generalized version of the Merton ratio. An explicit expression for the optimal strategy of the market is also obtained, which leads to a risk-neutral wealth dynamic and may provide some insights into asset pricing in the economy with inflation risk and regime-switching risk. Numerical examples are provided to illustrate financial implications of the BSDE solution.     

Stochastic differential portfolio games for an insurer in a jump-diffusion risk process

We discuss an optimal portfolio selection problem of an insurer who faces model uncertainty in a jump-diffusion risk model using a game theoretic approach. In particular, the optimal portfolio selection problem is formulated as a two-person, zero-sum, stochastic differential game between the insurer and the market. There are two leader-follower games embedded in the game problem: (i) The insurer is the leader of the game and aims to select an optimal portfolio strategy by maximizing the expected utility of the terminal surplus in the “worst-case” scenario; (ii) The market acts as the leader of the game and aims to choose an optimal probability scenario to minimize the maximal expected utility of the terminal surplus. Using techniques of stochastic linear-quadratic control, we obtain closed-form solutions to the game problems in both the jump-diffusion risk process and its diffusion approximation for the case of an exponential utility.     

Total risk aversion,stochastic optimal control,and differential games

We present a connection between the theory of risk in the context of a stochastic optimal control problem and its relation to the theory of differential games. In particular, we define the notion of total risk aversion from the viewpoint of the upper value of a differential game. We prove that as the index of absolute risk aversion of a utility function in a stochastic control problem converges to infinity the (certainty equivalent) optimal payoff converges to the upper value of an associated deterministic differential game. The two main points of this paper are (1) a precise characterization oftotal risk aversion and (2) the construction of a stochastic optimal control problem intimately connected to a deterministic differential game.Partially supported by the Air Force Office of Scientific Research Grant No. AFOSR-86-0202.Partially supported by a grant from the National Science Foundation.     

Two-stage non-cooperative games with risk-averse players

This paper formally introduces and studies a non-cooperative multi-agent game under uncertainty. The well-known Nash equilibrium is employed as the solution concept of the game. While there are several formulations of a stochastic Nash equilibrium problem, we focus mainly on a two-stage setting of the game wherein each agent is risk-averse and solves a rival-parameterized stochastic program with quadratic recourse. In such a game, each agent takes deterministic actions in the first stage and recourse decisions in the second stage after the uncertainty is realized. Each agent’s overall objective consists of a deterministic first-stage component plus a second-stage mean-risk component defined by a coherent risk measure describing the agent’s risk aversion. We direct our analysis towards a broad class of quantile-based risk measures and linear-quadratic recourse functions. For this class of non-cooperative games under uncertainty, the agents’ objective functions can be shown to be convex in their own decision variables, provided that the deterministic component of these functions have the same convexity property. Nevertheless, due to the non-differentiability of the recourse functions, the agents’ objective functions are at best directionally differentiable. Such non-differentiability creates multiple challenges for the analysis and solution of the game, two principal ones being: (1) a stochastic multi-valued variational inequality is needed to characterize a Nash equilibrium, provided that the players’ optimization problems are convex; (2) one needs to be careful in the design of algorithms that require differentiability of the objectives. Moreover, the resulting (multi-valued) variational formulation cannot be expected to be of the monotone type in general. The main contributions of this paper are as follows: (a) Prior to addressing the main problem of the paper, we summarize several approaches that have existed in the literature to deal with uncertainty in a non-cooperative game. (b) We introduce a unified formulation of the two-stage SNEP with risk-averse players and convex quadratic recourse functions and highlight the technical challenges in dealing with this game. (c) To handle the lack of smoothness, we propose smoothing schemes and regularization that lead to differentiable approximations. (d) To deal with non-monotonicity, we impose a generalized diagonal dominance condition on the players’ smoothed objective functions that facilitates the application and ensures the convergence of an iterative best-response scheme. (e) To handle the expectation operator, we rely on known methods in stochastic programming that include sampling and approximation. (f) We provide convergence results for various versions of the best-response scheme, particularly for the case of private recourse functions. Overall, this paper lays the foundation for future research into the class of SNEPs that provides a constructive paradigm for modeling and solving competitive decision making problems with risk-averse players facing uncertainty; this paradigm is very much at an infancy stage of research and requires extensive treatment in order to meet its broad applications in many engineering and economics domains.     

Kalman-Bucy filtering equations of forward and backward stochastic systems and applications to recursive optimal control problems

This paper is concerned with Kalman-Bucy filtering problems of a forward and backward stochastic system which is a Hamiltonian system arising from a stochastic optimal control problem. There are two main contributions worthy pointing out. One is that we obtain the Kalman-Bucy filtering equation of a forward and backward stochastic system and study a kind of stability of the aforementioned filtering equation. The other is that we develop a backward separation technique, which is different to Wonham's separation theorem, to study a partially observed recursive optimal control problem. This new technique can also cover some more general situation such as a partially observed linear quadratic non-zero sum differential game problem is solved by it. We also give a simple formula to estimate the information value which is the difference of the optimal cost functionals between the partial and the full observable information cases.     

Mean-field type forward-backward doubly stochastic differential equations and related stochastic differential games

We study a kind of partial information non-zero sum differential games of mean-field backward doubly stochastic differential equations, in which the coefficient contains not only the state process but also its marginal distribution, and the cost functional is also of mean-field type. It is required that the control is adapted to a sub-filtration of the filtration generated by the underlying Brownian motions. We establish a necessary condition in the form of maximum principle and a verification theorem, which is a sufficient condition for Nash equilibrium point. We use the theoretical results to deal with a partial information linear-quadratic (LQ) game, and obtain the unique Nash equilibrium point for our LQ game problem by virtue of the unique solvability of mean-field forward-backward doubly stochastic differential equation.     

Stochastic bankruptcy games

We study bankruptcy problems where the estate and the claims have stochastic values and we allow these values to be correlated. We associate a transferable utility game with uncertainty to a stochastic bankruptcy problem and use the Weak Sequential Core as a solution concept for such games. We test the stability of a number of well known division rules in this stochastic setting and find that all of them are unstable, with the exception of the stochastic extension of the Constrained Equal Awards rule which leads to a Weak Sequential Core element.