基于期权博弈的跨国并购实证分析——以联想并购IBMPC为例

将实物期权定价方法引入到讨价还价谈判博弈中,分别建立了完全信息和不完全信息跨国并购期权博弈模型,并应用纳什讨价还价定理求出了其中不完全信息博弈的唯一解,以及完全信息博弈的解区间.该模型证明期权估值法具有普遍意义, DCF估值法是跨国并购中的一种特例,最后,通过联想并购IBM PC案例检验了模型的基本结论.     

运输问题的合作对策求解方法

产地间或销地间往往存在竞争,在这种情况下,使用运输问题最优化方法是不合理的。因此,从个体理性的视角提出运输问题的合作对策求解方法,方法将运输问题看作是一个博弈问题,各个产地或销地是博弈的局中人,求解其纳什均衡与纳什讨价还价解。在此基础上,说明了运输问题的非合作形式是一个指派问题,并证明指派问题的最优解是一个纳什均衡点。接着,通过实验验证运输问题的最优解是一个纳什讨价还价解,满足产地或销地的自身利益。在此基础上,针对纳什讨价还价解不唯一的问题,从决策者的视角给出最大可能激励成本的计算方法。最后,为弥补纳什讨价还价解不唯一及纳什讨价还价解不允许出现子联盟的缺陷,给出运输收益分配或成本分摊的Shapely值计算方法。     

数据包络分析SBM超效率模型无可行解问题的两阶段求解法

数据包络分析是一种得到广泛应用的基于线性规划的非参数技术效率分析方法,其超效率模型是将被评价DMU从参照集中排除从而使求解得出的效率值可能大于1.超效率模型在文献中用于对有效的DMU进行排序、探测异常值、敏感性和稳定性分析、分析生产率变化的Malmquist模型、二人博弈模型等.超效率模型存在的一个缺陷是在规模收益可变的假设下会出现无可行解的问题.提出了一种基于两阶段求解的SBM超效率模型,并保持了与传统SBM超效率模型的兼容性:在传统的投入(产出)导向SBM超效率模型有可行解时,两阶段法获得的结果与之相同;在传统的投入(产出)导向SBM超效率模型无可行解时,两阶段超效率模型可以得出最接近投入(产出)导向定义的可行解.算例采用实际数据对方法进行了验证.     

三方相互威慑讨价还价模型

研究了三方相互威慑讨价还价问题,从合作博弈的角度建立了三方相互威慑讨价还价模型.并且通过对均衡存在性和冲突可能性的分析,给出了三方相互威慑问题冲突发生的条件以及存在均衡状态时的均衡解.最后通过实验验证了本文分析的正确性.     

考虑消费者低碳偏好的供应链企业减排成本分摊比较研究

在低碳环境下,研究了单一制造商和单一零售商组成的两级低碳供应链成本分摊决策问题。考虑消费者具有环保意识,因此在购买产品时会考虑产品的碳排放。分别讨论了零售商参与减排成本分摊契约和双方Nash讨价还价成本分摊契约两种形式对碳减排、产品定价和整个供应链及其成员收益的影响。研究发现,在两种契约形式中,零售商都可以实现与制造商共同降低碳排放的目标,从而促进制造商提高碳减排率,提高供应链利润。但是基于Nash讨价还价方式的减排成本分摊契约的供应链绩效高于零售商成本分摊契约下的供应链绩效。同时消费者低碳偏好增加可以激励零售商更多地分摊减排成本,在此基础上,利用Nash讨价还价模型确定了零售商为制造商提供减排成本分摊的比例范围及其最优解。最后,通过数值分析验证了减排因子对不同决策结构的定价、零售商减排分摊比例、供应链利润等决策的影响。     

三方相互体谅讨价还价模型

对于较重要的三方合作利益分配博弈问题,将相互体谅作为基本遵循并基于适应性预期模型,建立三方相互体谅讨价还价模型.运用这个模型,可以把三方相互体谅讨价还价达成均衡,归结为单调有界数列收敛与常系数非齐次线性差分方程组求解的结合运用.三方相互体谅讨价还价属于不完全信息动态合作博弈.对模型求解,一般地揭示三方相互体谅讨价还价达成均衡的过程与结果,阐明讨价还价有满足初始条件的唯一解和充要条件,以及充要条件的经济含义和数学意义.三方相互体谅讨价还价模型是对于两方相互体谅讨价还价模型的拓展,可以为建立更多方相互体谅讨价还价模型提供借鉴.     

层次系统DEA模型的研究

经典的DEA模型视决策单元为黑匣子,不考虑内部结构.实际上,决策单元DMU可能具有各种各样的结构.对DMU进行效率评价时,尽管最初的输入和最终的输出相同,但考虑DMU结构与忽视DMU结构得到的效率不同.基于这样一种思想,提出了一种基于层次系统的DEA模型.     

考虑公平性的两阶段系统固定成本分摊DEA模型

针对两阶段系统固定成本分摊问题,已有研究大多是基于效率不变性或构造满意度指标等提出不同方法,少有考虑固定成本分摊方案的公平性。本文首先通过非自利原则得到系统整体与各阶段的成本分摊上限,针对具有群组式组织结构的两阶段系统,提出新的固定成本分摊模型,所得分摊结果更为公平,最后通过实际算例验证了本文模型的可行性和有效性。     

带有乘积型多目标权函数的调度谈判问题

在供应链环境下的生产活动中,各成员对所辖资源具有独立的支配权,因此需要合理的机制使得协同调度方案得以实施,以提高供应链整体的效率.研究由具备不同讨价还价能力的成员所组成的供应链,建立了以纳什讨价还价公理体系为基础的调度谈判模型.在装配系统中,讨论两供应商关于交付顺序的协商.为求取纳什谈判解,提出了一类新的以多目标乘积项作为目标函数的调度问题.对于单机型供应商,新问题的计算复杂性尚未确定,设计了一种多项式时间的启发式算法以求得近优解,并通过数值算例进行验证.该谈判模型为供应链中各成员提供了一种合理的调度协调机制.     

基于线性规划博弈的供应链配送问题

着重建立了供应链配送问题的线性规划模型,分析说明了配送合作联盟对各成员是有利的.在配送博弈中,根据配送问题的对偶最优解和配送博弈核心之间的关系,构造了收益分配函数并证明了配送博弈的核心非空、所对应的分配方案稳定等性质.最后通过算例应用验证了模型和分配函数的有效性.     

考虑决策单元竞争合作动态变化的DEA博弈交叉效率方法

利用DEA方法进行相对效率评估时,决策单元通常需要考虑多重目标,且随着目标的变化,决策单元间竞争合作状态也会发生动态变化。传统竞合模型虽然考虑了决策单元间竞争与合作同时存在的现象,但忽视了竞争合作关系动态变化的过程。本文以竞争合作对策为切入点,将多目标规划中的优先因子引入传统DEA博弈交叉效率模型中,提出了带有优先等级的多目标DEA博弈交叉效率模型,即动态竞合博弈交叉效率模型。该模型充分体现了不同目标下决策单元间竞争合作关系的动态变化,其焦点由传统竞合模型对多重最优权重现象的改善,转向对最优效率得分的直接寻找。利用DEA动态竞合博弈交叉效率模型,本文对环境污染约束下2014年长三角地区制造业投入产出绩效进行了客观的评估。分析结果表明:DEA动态竞合博弈交叉效率模型收敛速度优于传统DEA博弈交叉效率模型,其交叉效率得分收敛于唯一的纳什均衡点;不同目标重要性的差异程度,对最终排名结果不产生明显影响,不需要确切指出。     

A bargaining game model for measuring performance of two-stage network structures

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs), where the internal structures of DMUs are treated as a black-box. Recently DEA has been extended to examine the efficiency of DMUs that have two-stage network structures or processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model not only provides an overall efficiency score for the entire process, but also yields an efficiency score for each of the individual stages. The current paper develops a Nash bargaining game model to measure the performance of DMUs that have a two-stage structure. Under Nash bargaining theory, the two stages are viewed as players and the DEA efficiency model is a cooperative game model. It is shown that when only one intermediate measure exists between the two stages, our newly developed Nash bargaining game approach yields the same results as applying the standard DEA approach to each stage separately. Two real world data sets are used to demonstrate our bargaining game model.     

An equitable method for allocating fixed costs by using data envelopment analysis

This paper concerns the shared cost allocation problem by using Data Envelopment Analysis (DEA), which is observed in practical applications such as public services and production processes. In the management context, the cost allocation problem tries to balance the different desires of two management layers: central manager and each sector manager. The cost can be assigned in an equitable way to the various Decision Making Units (DMUs). To achieve this goal, we present a new DEA-based method for dividing a fixed cost among DMUs. In the proposed method, the fixed cost is assigned to DMUs such that the efficiency measures and the Returns to Scale classifications of all DMUs before and after assigning the fixed cost remain unchanged. Also, the gaps among the costs allocated to DMUs will be minimized. The proposed method has the flexibility to consider the management standpoints. Finally, numerical results of an elucidatory example are furnished to demonstrate the applicability and reliability of our scheme.     

Allocating the fixed cost as a complement of other cost inputs: A DEA approach

In cost allocation problem, traditional DEA approaches allocate the fixed cost among a group of decision making units (DMUs), and treat the allocated cost as an extra input of each DMU. If costs except for the fixed cost are regarded as inputs in the cost allocation problem, then it is obvious that the fixed cost is a complement of other inputs rather than an extra independent input. Therefore it is necessary to combine the allocated cost with other cost measures in cost allocation problem. Based on this observation, this paper investigates the relationship between the allocated cost and the DEA efficiency score and develops a DEA-based approach to allocate the fixed cost among various DMUs. An example of allocating advertising expenditure between a car manufacturer and its dealers is presented to illustrate the method proposed in this paper.     

Fixed cost and resource allocation based on DEA cross-efficiency

In many managerial applications, situations frequently occur when a fixed cost is used in constructing the common platform of an organization, and needs to be shared by all related entities, or decision making units (DMUs). It is of vital importance to allocate such a cost across DMUs where there is competition for resources. Data envelopment analysis (DEA) has been successfully used in cost and resource allocation problems. Whether it is a cost or resource allocation issue, one needs to consider both the competitive and cooperative situation existing among DMUs in addition to maintaining or improving efficiency. The current paper uses the cross-efficiency concept in DEA to approach cost and resource allocation problems. Because DEA cross-efficiency uses the concept of peer appraisal, it is a very reasonable and appropriate mechanism for allocating a shared resource/cost. It is shown that our proposed iterative approach is always feasible, and ensures that all DMUs become efficient after the fixed cost is allocated as an additional input measure. The cross-efficiency DEA-based iterative method is further extended into a resource-allocation setting to achieve maximization in the aggregated output change by distributing available resources. Such allocations for fixed costs and resources are more acceptable to the players involved, because the allocation results are jointly determined by all DMUs rather than a specific one. The proposed approaches are demonstrated using an existing data set that has been applied in similar studies.     

Characterizing an equitable allocation of shared costs: A DEA approach

In many applications to which DEA could be applied, there is often a fixed or common cost which is imposed on all decision making units. This would be the case, for example, for branches of a bank which can be accessed via the numerous automatic teller machines scattered throughout the country. A problem arises as to how this cost can be assigned in an equitable way to the various DMUs. In this paper we propose a DEA approach to obtain this cost allocation which is based on two principles: invariance and pareto-minimality. It is shown that the proposed method is a natural extension of the simple one-dimensional problem to the general multiple-input multiple-output case.     

A DEA game model approach to supply chain efficiency

Data envelopment analysis (DEA) is a useful method to evaluate the relative efficiency of peer decision making units (DMUs). Based upon the definitions of supply chain efficiency, we investigate the efficiency game between two supply chain members. It is shown that there exist numerous Nash equilibriums efficiency plans for the supplier and the manufacturer with respect to their efficiency functions. A bargaining model is then proposed to analyze the supplier and manufacturer's decision process and to determine the best efficiency plan strategy. DEA efficiency for supply chain operations is studied for the central control and the decentralized control cases. The current study is illustrated with a numerical example.     

Output–input ratio analysis and DEA frontier

Data envelopment analysis (DEA) is a mathematical programming technique for identifying efficient frontiers for peer decision making units (DMUs). The ability of identifying frontier DMUs prior to the DEA calculation is of extreme importance to an effective and efficient DEA computation. In this paper, we present mathematical properties which characterize the inherent relationships between DEA frontier DMUs and output–input ratios. It is shown that top-ranked performance by ratio analysis is a DEA frontier point. This in turn allows identification of membership of frontier DMUs without solving a DEA program. Such finding is useful in streamlining the solution of DEA.     

基于DEA的资源配置状况分析

DEA在经济系统中的应用实质是对资源配置状况的分析，但由于分析结果过于简单，构成了该方法推广应用的障碍，章通过深入分析，发现导致决策单元无效的三大成因，把DEA对决策单元的分析结果由两种增加到四种，并相应的提出价格无效、绝对冗余、相对冗余等新概念，从而为管理改善决策提供明确的指导方向。     

DTZ model with independent subsystems

Data envelopment analysis (DEA) is a mathematical programming approach to assess relative efficiency of a group of decision-making units. In view of the defects of existing models in evaluating efficiency of the system with P independent subsystems, Yang et al. [10] introduced YMK model with the assumption that decision-making unit (DMU) is independent of each other. But in some production systems, decision-making units usually have some relationships in this way or that. In this paper, DEA model is given by assuming that DMUs can cooperate with others in its subgroups. Some property and the efficiency relationship of the whole system and its subsystems are given.