带风险偏好的区间支付交流结构合作博弈及平均树解北大核心CSCD

针对具有区间支付的限制结盟合作博弈,考虑现实局中人的不同偏好信息,通过引入风险偏好均值,提出了具有风险偏好的区间支付交流结构合作博弈及其平均树解.通过公理化体系对此解的存在性进行了证明,并将此分配方法应用到供应链纵向研发合作企业收益分配的实例中,表明该方法的有效性和可行性.此研究同时考虑了合作结盟的限制约束性和局中人的风险态度差异性,不仅能有效刻画现实结盟情境,且利于分配收益函数的求解.     

带风险偏好的区间支付交流结构合作博弈及平均树解

针对具有区间支付的限制结盟合作博弈,考虑现实局中人的不同偏好信息,通过引入风险偏好均值,提出了具有风险偏好的区间支付交流结构合作博弈及其平均树解.通过公理化体系对此解的存在性进行了证明,并将此分配方法应用到供应链纵向研发合作企业收益分配的实例中,表明该方法的有效性和可行性.此研究同时考虑了合作结盟的限制约束性和局中人的风险态度差异性,不仅能有效刻画现实结盟情境,且利于分配收益函数的求解.     

基于时隙ALOHA协议的数据传输二人随机博弈模型

在一个给定的拓扑网络中研究关于数据传输的二人随机博弈模型.两个局中人（源节点）试图通过一个公共节点向目的节点传输随机数据包,这些数据包被分为重要的数据包和不重要的数据包两类,假设每个局中人都有一个用于存储数据包的有限容量的缓冲器.通过构造数据传输的成本分摊和奖励体系,把这种动态的冲突控制过程建模为具有有限状态集合的随机博弈,研究局中人在这种随机博弈模型下的非合作以及合作行为.在非合作情形下,给出纳什均衡的求解算法;在合作情形下,选择Shapley值作为局中人支付总和的分配方案,并讨论其子博弈一致性,提出使得Shapley值为子博弈一致的分配补偿程序.     

基于时隙ALOHA协议的数据传输二人随机博弈模型

在一个给定的拓扑网络中研究关于数据传输的二人随机博弈模型.两个局中人(源节点)试图通过一个公共节点向目的节点传输随机数据包,这些数据包被分为重要的数据包和不重要的数据包两类,假设每个局中人都有一个用于存储数据包的有限容量的缓冲器.通过构造数据传输的成本分摊和奖励体系,把这种动态的冲突控制过程建模为具有有限状态集合的随机博弈,研究局中人在这种随机博弈模型下的非合作以及合作行为.在非合作情形下,给出纳什均衡的求解算法;在合作情形下,选择Shapley值作为局中人支付总和的分配方案,并讨论其子博弈一致性,提出使得Shapley值为子博弈一致的分配补偿程序.     

基于Stackelberg博弈的时变双层交通分配模型

提出一个时变双层交通分配模型,其中上层网络管理者设立了一个路段的最大排队长度,其目标是使由网络流和排队长度定义的总出行时间最小.目标函数在离散时段内以路段流量和排队长度作为决策变量,同时考虑不同类型的信号交叉口延误的影响.下层网络用户的反应依赖于上层管理者的决策,其选择是使自身感知阻抗最小的路径,服从一个基于成对组合Logit的路径选择模型,构成一个成对组合Logit的均衡分配问题.结合了交通分配和流传播方法,将其表示为一个均衡约束下的双层数学规划问题,形成了一个Stackelberg非合作博弈.使用遗传算法求解该双层规划问题,并采用实证分析来表现模型的特征和算法的计算表现.结果表明路径重叠、路段流量、路段排队长度等因素对网络均衡流分布均有显著影响.     

模糊环境下合作联盟收益分配模型和算法研究

在合作联盟中,由于不确定性因素的存在,联盟收益不能精确得到分配。文章将合作对策的支付值用三角模糊数来表示,解决模糊收益分配问题,并考虑了联盟的重要性(即权重)及有效性等因素对利益分配的影响。通过构建多权重模糊分配二次规划模型,给出其解析式,据此确定参与合作联盟的局中人的三角形模糊数分配值。最后,通过数值算例验证了该模型的合理性和优越性,从而为合作联盟利益分配提供了一种合理、有效的计算方法。     

合作博弈的粒子群算法求解

随着局中人人数的增加,利用传统的“占优”方法和“估值”方法进行合作博弈求解无论从逻辑上还是计算上都变得非常困难。针对此问题,将合作博弈的求解看作是局中人遵照有效性和个体理性提出分配方案,并按照一定规则不断迭代调整直至所有方案趋向一致的过程。依据该思路,对合作博弈粒子群算法模型进行构建,确定适应度函数,设置速度公式中的参数。通过算例分析,利用粒子群算法收敛快、精度高、容易实现的特点,可以迅速得到合作博弈的唯一分配值,这为求解合作博弈提供了新的方法和工具。     

产业集群背景下模糊联盟结构合作博弈的核心

运用模糊延拓等方法将核心理论扩展到模糊联盟结构合作博弈中,提供一种兼顾局中人的模糊参与度与联盟偏好的稳定分配方法,并且给出模糊联盟结构合作博弈的模糊Owen值稳定的充分条件.基于供应链协同创新中的不确定因素较多,将此跨供应链协作问题抽象为模糊联盟结构合作博弈模型,计算模糊信息下合作利益分配策略,在两个层次上分配额外收益:产业集群,供应链.模糊联盟结构合作博弈理论以及求解方法的研究,理论上拓展了经典合作博弈的应用范围,实证上又为供应链协同创新问题提供了一定分析思路,降低了由于收益分配不均导致的跨区域供应链破裂的概率.     

联盟收益不确定下合作博弈的多目标粒子群扩展算法求解

在传统的合作博弈求解中,通常假设联盟收益确定或者局中人对联盟收益取值意见一致.现实中,联盟收益往往不确定,局中人对联盟收益取值意见不一致,且联盟分配方案的达成通常是局中人基于个体理性与判断进行多轮谈判,互相影响、相互妥协、最终趋同的结果.针对这种情况,本文首先对联盟收益不确定时局中人的收益进行描述,建立合作博弈的扩展模型,再考虑局中人的理性互动与策略博弈,借鉴群智能的建模思想和求解思路,利用多目标粒子群扩展算法对模型进行求解.本文对于联盟收益不确定时合作博弈的求解提供了新的思路与方法.     

产学研合作收益分配的博弈分析

产学研的合作促进了科研成果的产业化进程,同时也加速了企业的产品创新.但由于收益分配的不合理,导致了许多产学研合作的失败.从博弈论的观点来看产学研的组建和运行过程,建立了合作收益最优分配系数模型,分析了考虑学研方技术风险条件下的合作收益分配系数,并对合作各方在协议的收益分配方案下会如何进行自己的最优行动选择进行了非合作博弈分析.     

On a game in manufacturing

We analyse a non-zero sum two-person game introduced by Teraoka and Yamada to model the strategic aspects of production development in manufacturing. In particular we investigate how sensitive their solution concept (Nash equilibrium) is to small variations in their assumptions. It is proved that a Nash equilibrium is unique if it exists and that a Nash equilibrium exists when the capital costs of the players are zero or when the players are equal in every respect. However, when the capital costs differ, in general a Nash equilibrium exists only when the players' capital costs are high compared to their profit rates.     

Strategic Manipulation Approach for Solving Negotiated Transfer Pricing Problem

This paper suggests an approach for solving the transfer pricing problem, where negotiation between divisions is carried out considering the manipulation game theory model for a multidivisional firm. The manipulation equilibrium point is conceptualized under the Machiavellian social theory, represented by three concepts: views, tactics and immorality. In this approach, we are considering a non-cooperative model for the transfer pricing problem: a game model involving manipulating and manipulated players engaged cooperatively in a Nash game, restricted by a Stackelberg game. The cooperation is represented by the Nash bargaining solution. The transfer pricing problem is conceptualized as a strong Stackelberg game involving manipulating and manipulated divisions. This structure established conditions of unequal relative power among divisions, where high-power divisions tend to be abusive and less powerful divisions have a tendency to behave compliantly. For computation purposes, we transform the Stackelberg game model into a Nash game, where every division is able of manipulative behavior to some degree: the Nash game relaxes the interpretation of the manipulation game and the equilibrium selection for the transfer pricing problem. The manipulation dynamics and rationality proposed for the transfer pricing problem correspond to many real-world negotiation situations. We present an example, that illustrates how manipulation can be employed to solve the transfer pricing problem in a multidivisional firm.     

A Bidding Game with Heterogeneous Players

A one-sided limit order book is modeled as a noncooperative game for several players. Agents offer various quantities of an asset at different prices, competing to fulfill an incoming order, whose size is not known a priori. Players can have different payoff functions, reflecting different beliefs about the fundamental value of the asset and probability distribution of the random incoming order. In a previous paper, the existence of a Nash equilibrium was established by means of a fixed point argument. The main issue discussed in the present paper is whether this equilibrium can be obtained from the unique solution to a two-point boundary value problem, for a suitable system of discontinuous ordinary differential equations. Some additional assumptions are introduced, which yield a positive answer. In particular, this is true when there are exactly two players, or when all players assign the same exponential probability distribution to the incoming order. In both of these cases, we also prove that the Nash equilibrium is unique. A counterexample shows that these assumptions cannot be removed, in general.     

Collusive game solutions via optimization

A Nash-based collusive game among a finite set of players is one in which the players coordinate in order for each to gain higher payoffs than those prescribed by the Nash equilibrium solution. In this paper, we study the optimization problem of such a collusive game in which the players collectively maximize the Nash bargaining objective subject to a set of incentive compatibility constraints. We present a smooth reformulation of this optimization problem in terms of a nonlinear complementarity problem. We establish the convexity of the optimization problem in the case where each player's strategy set is unidimensional. In the multivariate case, we propose upper and lower bounding procedures for the collusive optimization problem and establish convergence properties of these procedures. Computational results with these procedures for solving some test problems are reported. It is with great honor that we dedicate this paper to Professor Terry Rockafellar on the occasion of his 70th birthday. Our work provides another example showing how Terry's fundamental contributions to convex and variational analysis have impacted the computational solution of applied game problems. This author's research was partially supported by the National Science Foundation under grant ECS-0080577. This author's research was partially supported by the National Science Foundation under grant CCR-0098013.     

Game theoretic approach for fertilizer application: looking for the propensity to cooperate

This paper continues the research implemented in previous work of (Schreider et al. in Environ. Model. Assess. 15(4):223–238, 2010) where a game theoretic model for optimal fertilizer application in the Hopkins River catchment was formulated, implemented and solved for its optimal strategies. In that work, the authors considered farmers from this catchment as individual players whose objective is to maximize their objective functions which are constituted from two components: economic gain associated with the application of fertilizers which contain phosphorus to the soil and environmental harms associated with this application. The environmental losses are associated with the blue-green algae blooming of the coastal waterways due to phosphorus exported from upstream areas of the catchment. In the previous paper, all agents are considered as rational players and two types of equilibria were considered: fully non-cooperative Nash equilibrium and cooperative Pareto optimum solutions. Among the plethora of Pareto optima, the solution corresponding to the equally weighted individual objective functions were selected. In this paper, the cooperative game approach involving the formation of coalitions and modeling of characteristic value function will be applied and Shapley values for the players obtained. A significant contribution of this approach is the construction of a characteristic function which incorporates both the Nash and Pareto equilibria, showing that it is superadditive. It will be shown that this approach will allow each players to obtain payoffs which strictly dominate their payoffs obtained from their Nash equilibria.     

Internal correlation in repeated games

This paper characterizes the set of all the Nash equilibrium payoffs in two player repeated games where the signal that the players get after each stage is either trivial (does not reveal any information) or standard (the signal is the pair of actions played). It turns out that if the information is not always trivial then the set of all the Nash equilibrium payoffs coincides with the set of the correlated equilibrium payoffs. In particular, any correlated equilibrium payoff of the one shot game is also a Nash equilibrium payoff of the repeated game.For the proof we develop a scheme by which two players can generate any correlation device, using the signaling structure of the game. We present strategies with which the players internally correlate their actions without the need of an exogenous mediator.     

Nonzero-sum stochastic games with unbounded costs: Discounted and average cost cases

We treat non-cooperative stochastic games with countable state space and with finitely many players each having finitely many moves available in a given state. As a function of the current state and move vector, each player incurs a nonnegative cost. Assumptions are given for the expected discounted cost game to have a Nash equilibrium randomized stationary strategy. These conditions hold for bounded costs, thereby generalizing Parthasarathy (1973) and Federgruen (1978). Assumptions are given for the long-run average expected cost game to have a Nash equilibrium randomized stationary strategy, under which each player has constant average cost. A flow control example illustrates the results. This paper complements the treatment of the zero-sum case in Sennott (1993a).     

An experimental study of constant-sum centipede games

In this paper, we report the results of a series of experiments on a version of the centipede game in which the total payoff to the two players is constant. Standard backward induction arguments lead to a unique Nash equilibrium outcome prediction, which is the same as the prediction made by theories of “fair” or “focal” outcomes. We find that subjects frequently fail to select the unique Nash outcome prediction. While this behavior was also observed in McKelvey and Palfrey (1992) in the “growing pie” version of the game they studied, the Nash outcome was not “fair”, and there was the possibility of Pareto improvement by deviating from Nash play. Their findings could therefore be explained by small amounts of altruistic behavior. There are no Pareto improvements available in the constant-sum games we examine. Hence, explanations based on altruism cannot account for these new data. We examine and compare two classes of models to explain these data. The first class consists of non-equilibrium modifications of the standard “Always Take” model. The other class we investigate, the Quantal Response Equilibrium model, describes an equilibrium in which subjects make mistakes in implementing their best replies and assume other players do so as well. One specification of this model fits the experimental data best, among the models we test, and is able to account for all the main features we observe in the data.     

Forward-backward stochastic differential equation games with delay and noisy memory

Abstract

The goal of this paper is to study a stochastic game connected to a system of forward-backward stochastic differential equations (FBSDEs) involving delay and noisy memory. We derive sufficient and necessary maximum principles for a set of controls for the players to be a Nash equilibrium in the game. Furthermore, we study a corresponding FBSDE involving Malliavin derivatives. This kind of equation has not been studied before. The maximum principles give conditions for determining the Nash equilibrium of the game. We use this to derive a closed form Nash equilibrium for an economic model where the players maximize their consumption with respect to recursive utility.     

How to Select a Solution in Generalized Nash Equilibrium Problems

We propose a new solution concept for generalized Nash equilibrium problems. This concept leads, under suitable assumptions, to unique solutions, which are generalized Nash equilibria and the result of a mathematical procedure modeling the process of finding a compromise. We first compute the favorite strategy for each player, if he could dictate the game, and use the best response on the others’ favorite strategies as starting point. Then, we perform a tracing procedure, where we solve parametrized generalized Nash equilibrium problems, in which the players reduce the weight on the best possible and increase the weight on the current strategies of the others. Finally, we define the limiting points of this tracing procedure as solutions. Under our assumptions, the new concept selects one reasonable out of typically infinitely many generalized Nash equilibria.