Computationally modeling organizational learning and adaptability as resource allocation: An artificial adaptive systems approach

A framework for and a computational model of organizational behavior based on an artificial adaptive system (AAS) is presented. An AAS, a modeling approach based on genetic algorithms, enables the modeling of organizational learning and adaptability. This learning can be represented as decisions to allocate resources to the higher performing organizational agents (i.e., individuals, groups, departments, or processes, depending on the level of analysis) critical to the organization's survival in different environments. Adaptability results from the learning function enabling the organizations to change as the environment changes. An AAS models organizational behavior from a micro-unit perspective, where organizational behavior is a function of the aggregate actions and interactions of each of the individual agents of which the organization is composed. An AAS enables organizational decision making in a dynamic environment to be modeled as a satisficing process and not as a maximization process. To demonstrate the feasibility and usefulness of such an approach, a financial trading adaptive system (FTAS) organization is computationally modeled based on the AAS framework. An FTAS is an example of how the learning mechanism in an AAS can be used to allocate resources to critical individuals, processes, functions, or departments within an organization.     

组织学习能力的个性优势识别与评价研究

组织学习能力是个多维概念,定期识别在哪个维度上具备个性优势并在此基础上对组织学习能力状况做出评价将有利于促进组织绩效的提升。本文按照目标规划的思想,依据2-范数距离,提出一种组织学习能力的个性优势识别方法,该方法从尊重每一位被评价者的意见出发识别其个性优势特征,并在此基础上给出综合评价结论。在此基础上以国内某大型软件企业的20个事业部为例进行了应用研究,组织学习能力由发现能力、发明能力、选择能力、执行能力、推广能力、反思能力、获取知识能力、输出知识能力、建立知识库能力九个维度组成。研究结果表明,基于个性优势识别的组织学习能力评价方法符合“人本管理”思想,易于被评价主体接受,且具有客观性的特点,同时可以产生标杆效应。     

地位差异视角下知识分享策略对组织学习的影响研究

组织学习是提高企业竞争能力的重要途径,人际学习是组织学习的重要组成部分。组织成员间普遍存在的地位差异,会直接影响个体的知识分享策略,继而影响组织学习。然而,以往的组织学习模型通常忽略了个体的地位差异,默认假设每个组织成员的地位是相同的。鉴于此,本文在组织学习经典仿真模型的基础上,引入地位这一特征,综合探讨正向差异化(高地位成员更愿意分享知识)、负向差异化(低地位成员更愿意分享知识)、以及同质化(不同地位的成员有相同的知识分享意愿)这三种知识分享策略对组织学习的影响机理。研究结果表明,三种知识分享策略下,知识分享水平和地位认可度对组织学习的作用关系和作用大小存在显著差异。但总体来说,仅当组织成员的地位认可度较高且知识分享水平较低时,同质化策略才最有利于组织学习;其他情境下,负向差异化策略更有利于提高组织学习绩效。     

Structural reconfiguration and informal coordination in administrative organizations

This paper considers the problem of determining the optimal design of public organizations in terms of maximizing their reliability against institutional failures. To capture both the individual and the system-level aspects of organizational decisionmaking, first we present an analytical model that characterizes the optimal decision behavior of a single decision maker (unit, agent, in general: DM) in the context of a binary decision task. In this sense, reliability of a DM against the two possible error types: implementation of the wrong policy (error of comission, Type I error) and failure to act when it is necessary (error of omission, Type II error) are interpreted as the result of a particular decision strategy. Individual expertise is represented in the form of a Relative Operating Characteristic (ROC) curve that, in turn, depicts the necessary trade-off between the two errors when selecting an appropriate decision strategy. Component decisions are then combined along the lines of organizational structure which is described using a graph formalism. We show that the task of finding the best organizational design involves a joint optimization over structure and strategy, and implement the normative model in the context of a detailed example. Our numerical results suggest that when DMs coordinate their decision rules, there is little difference in the performance of various organizational structures.This work was supported by the Office of Naval Research under ONR contract #N0014-93-I-0793.     

Adaptation as a Morphing Process: A Methodology for the Design and Evaluation of Adaptive Organizational Structures

Changes in objectives, in resources, or in the environment may necessitate the adaptation of an organization from one form to another. However, in many cases, the organizations need to continue functioning while adaptation takes place, i.e., it is not possible to stop the organizational activity in order to reorganize, and then start again. In this case, adaptation can be expressed as a morphing process in which the organization transitions from one form with its attendant task allocation to a different one through a series of incremental steps that preserve overall functionality and performance. Coordination between organization members during adaptation is critical. A computational model for this type of organizational adaptation at the operational level is presented. The model is implemented using the Colored Petri Net formulation of discrete event dynamical systems. A design methodology that utilizes this model is outlined and a simple example is used to illustrate the approach.     

Adapting to the Changing Environment: A Theoretical Comparison of Decision Making Proficiency of Lean and Mass Organization Systems

In this paper weexamine the adaptability of the Japanese style leanorganization system and the traditional American style mass organizationsystem under changing environments. From an organizational designperspective, key structural aspects of the two organizations are modeled ina problem solving context using computational methods. Organizational-levelperformance in terms of decision making accuracy and severity of errors ismeasured as an indicator of organizational adaptability under conditionswhere the task environment shifts between predictable to unpredictable orvise versa. Our study shows that both organizations have their respectiveadvantages under different task environments and that they adapt toenvironmental shifts in different forms. Specifically, when the timepressure is high the lean organization system's performance isvirtually identical to the mass organization system, even though the leanorganization systemÆs members are more proactive. When the timepressure is low, the mass organization system shows a much fasteradaptability when the environment shifts to a predictable one but it is alsomore vulnerable when the environment shifts to an unpredictable one. Incontrast, the lean organization systemÆs response to the changingenvironment is characterized by its slower adaptability. When theenvironment shifts to an unpredictable one, the lean organization systemshows a gradual improvement till reaching a high level. When the environmentshifts to a predictable one, however, the lean organization system shows agradual decrease of performance. Our study further shows that the leanorganization system, with its strong team decision making emphasis, can bemore successful in avoiding severe errors when compared with the massorganization system, even under a predictable task environment.     

企业内部的随机分权

在一个信息传递模型框架中考察多部门组织内部权力的随机配置. 研究结论表明,在特定参数范围内,随机分权能提升组织治理局部适应和部门协调的能力;而且,数值分析表明最优组织结构下的集权概率随着部门协调需求和部门经理层激励冲突的上升而增加. 因此,组织主动引入权力配置不确定性本身可能就是最优组织结构的组成部分,从而为随机分权提供了一种理论解读.     

Intra-organizational Networks and Performance: A Review

Intra-organizational network research had its first heyday during the empirical revolution in social sciences before World War II when it discovered the informal group within the formal organization. These studies comment on the classic sociological idea of bureaucracy being the optimal organization. Later relational interest within organizational studies gave way to comparative studies on the quantifiable formal features of organizations. There has been a resurgence in intra-organizational networks studies recently as the conviction grows that they are critical to organizational and individual performance. Along with methodological improvements, the theoretical emphasis has shifted from networks as a constraining force to a conceptualization that sees them as providing opportunities and finally, as social capital. Because of this shift it has become necessary not only to explain the differences between networks but also their outcomes, that is, their performance. It also implies that internal and external networks should no longer be treated separately.Research on differences between intra-organizational networks centers on the influence of the formal organization, organizational demography, technology and environment. Studies on outcomes deal with diffusion and adaptation of innovation; the utilization of human capital; recruitment, absenteeism and turnover; work stress and job satisfaction; equity; power; information efficiency; collective decision making; mobilization for and outcomes of conflicts; social control; profit and survival of firms and individual performance.Of all the difficulties that are associated with intra-organizational network research, problems of access to organizations and incomparability of research findings seem to be the most serious. Nevertheless, future research should concentrate on mechanisms that make networks productive, while taking into account the difficulties of measuring performance within organizations, such as the performance paradox and the halo-effect.     

Finding common weights based on the DM's preference information

Data Envelopment Analysis (DEA) is basically a linear programming-based technique used for measuring the relative performance of organizational units, referred to as Decision Making Units (DMUs). The flexibility in selecting the weights in standard DEA models deters the comparison among DMUs on a common base. Moreover, these weights are not suitable to measure the preferences of a decision maker (DM). For dealing with the first difficulty, the concept of common weights was proposed in the DEA literature. But, none of the common weights approaches address the second difficulty. This paper proposes an alternative approach that we term as ‘preference common weights’, which is both practical and intellectually consistent with the DEA philosophy. To do this, we introduce a multiple objective linear programming model in which objective functions are input/output variables subject to the constraints similar to the equations that define production possibility set of standard DEA models. Then by using the Zionts–Wallenius method, we can generate common weights as the DM's underlying value structure about objective functions.     

Making Organizational Learning Operational: Implications from Learning Classifier Systems

The concepts of organizational learning in organization and management science cover a very wide range of organization-related activities in organization. Since socially situated intelligence is one of such activities, this paper makes the concept of organizational learning operational from the computational viewpoint for investigating socially situated intelligence. In particular, this paper focuses on the characteristics of multiagent learning as one kind of socially situated intelligence, and analyzes them using four operationalized learning mechanisms in organizational learning. A careful investigation on the characteristics of multiagent learning has revealed the following implications: (1) there are two levels in the learning mechanisms for multiagent learning (the individual level and organizational level) and each mechanism is divided into two types (single- and double-loop learning). The integration of these four learning mechanisms improves socially situated intelligence; and (2) the following properties support socially situated intelligence: (a) different dimensions in learning mechanisms, (b) interaction among various levels and types of learning mechanisms in addition to interaction among agents, and (c) combination of exploration at an individual level and exploitation at an organizational level.     

A concept of a robust solution of a multicriterial linear programming problem

A new concept of a robust solution of a multicriterial linear programming problem is proposed. The robust solution is understood here as the best starting point, prepared while the preferences of the decision maker with respect to the criteria are still unknown, for the adaptation of the solution to the preferences of the decision maker, once they are finally known. The objective is the total cost of the initial preparation and of the later potential adaptation of the solution. In the starting robust solution the decision variables may have interval values. The problem can be solved by means of the simplex algorithm. A numerical example illustrates the approach.     

A Trajectory for Validating Computational Emulation Models of Organizations

Validation of complex simulation models is a challenging problem in computational organization theory research. In this paper, we describe a validation strategy suitable for emulation simulation systems, and show how a comprehensive validation consists of a sequence of steps that evaluate different aspects of a computational organizational simulation model. We demonstrate how this strategy can be applied to the evaluation of the Virtual Team Alliance (VTA), an emulation simulation system designed to guide managers charged with organizational change. VTA required a &201C;trajectory&201D; of successive validation experiments, before managers where willing to follow the recommendations of VTA. Ultimately, we believe this validation approach can be applied to a wide range of different simulation systems, and will make identification of the strengths and weaknesses of organizational simulations easier.     

Exploring Depth Versus Breadth in Knowledge Management Strategies

This paper provides an early attempt at operationalizing and testing the concept of knowledge strategy. Using a computer-simulated product development process, we compare the performance of generalist and specialist knowledge management strategies under conditions of market turbulence. The generalist knowledge strategy emphasizes breadth of knowledge in product development teams, while the specialist strategy focuses on depth of knowledge. Our generalist and specialist strategies are grounded in Eastern and Western perspectives of knowledge management, respectively. A primary difference between these two approaches is the strong emphasis on cross-learning, or learning across team members, in the Eastern perspective relative to the Western perspective. As such, we examine the performance implications of different modes of cross-learning for teams utilizing the generalist knowledge strategy. Specifically, we examine three modes of cross-learning, which are reflected in the use of three learning decision rules: (1) averaging, (2) majority, and (3) hot hand. A learning decision rule indicates how decision-makers learn from their fellow team members. Under the first rule, the decision-maker adopts an average of the beliefs held by fellow team members. Under the second rule, if a majority of fellow team members agree on a particular solution, then the decision-maker adopts the beliefs held by the majority. Under the third rule, the decision-maker learns from the team member whose beliefs have been consistent with market desires most recently. Surprisingly, we find that specialist strategies outperform generalist strategies under conditions of low and high market turbulence. We also find that cross-learning can be beneficial or detrimental, contingent upon the mode of learning. Generalist teams utilizing the averaging decision rule perform significantly worse, while generalist teams utilizing the hot hand decision rule perform significantly better.     

企业跨国冲突策略三维模型研究

企业跨国经营过程中,冲突策略的选择至关重要.本文针对传统二维冲突模型在指导组织间冲突时存在的不足,通过引入环境变量,构建了一个跨国冲突策略三维模型,并以实例应用对模型的使用方法给出了说明.该模型很好地弥补了二维模式存在的缺陷,为企业决策者提供了一个科学而合理的决策方法.     

知识创造行为与组织惯例的演化博弈及其仿真研究

以演化博弈模型为主要理论工具,在对知识创造行为与组织惯例关系予以描述的基础上,构建知识创造行为与组织惯例的演化博弈模型。通过求解复制动态方程,分析不同条件下知识创造行为与组织惯例分别达到演化稳定均衡的策略。研究结果表明:知识创造行为与组织惯例的匹配属于动态、重复博弈过程,参与博弈的预期收益、激励成本、转换成本直接决定演化稳定策略且影响个体对知识创造行为与组织惯例的选择,知识创造行为则倾向以承袭为主的保守策略。演化博弈方法的引入为知识创造行为和组织惯例的研究开辟了全新视角,也为相关领域的进一步探索提供有利的理论支持。     

Learning to align organizational design and data

The relationship between organizational learning and organizational design is explored. In particular, we examine the information processing aspects of organizational learning as they apply to a two-valued decision making task and the relation of such aspects to organizational structure. Our primary contribution is to extend Carley's (1992) model of this process. The original model assumes that all data input into the decision making processes are of equal importance or weight in determining the correct overall organizational decision. The extension described here allows for the more natural situation of non-uniform weights of evidence. Further extensions to the model are also discussed. Such organizational learning performance measures provide an interesting framework for analyzing the recent trend towards flatter organizational structures. This research suggests that flatter structures are not always better, but rather that data environment, ultimate performance goals, and relative need for speed in learning can be used to form a contingency model for choosing organizational structure.     

The utility efficient set and its interactive reduction

We assume a decision situation under risk with incomplete information on preferences modelled as a vector utility function. We consider an additive aggregation of its components and partial information on the scaling constants. We develop the concept of utility efficiency to identify efficient strategies in discrete problems when the information about the scaling constants of the decision maker is in the form of a polyhedral cone. A characterization of the utility efficient set provides a practical way to compute such efficient strategies. We then discuss an interactive method based on the assessment of the scaling constants via an interactive paired comparison with its convergence. The method is complemented by a procedure to reduce the utility efficient set to aid in the process of reaching a final strategy.     

Performance of Organizational Design Models and Their Impact on Organization Learning

The performance of various organizational structures is an essential parameter in the reengineering of organizations, particularly in the current rapidly changing, competitive and information technology-dependent environment. This situation has generally resulted in a smaller workforce confronted with voluminous information-processing requirements. Naturally, the focus has been on the design of organzations that will support effective and efficient learning processes. This specific study is on the contingency modely by Ouksel et al. (1997) on the impact of organizational structure on organizational learning and performance. It expands the empirical evaluation of the model to test its robustness. This is done by conducting extensive simulations with (a) a larger number of variables in the decision tasks, and (b) a larger set of numbers for each of the numeric variables, than has been used in the past. Overall, the validity of previous results achieved has been confirmed, but the selection of the most appropriate design, that which will indeed improve learning, is even more nuanced than past studies have concluded.     

基于多Agent仿真的双元创新平衡与组织绩效关系研究

运用多主体仿真方法,探讨了双元创新平衡对组织绩效的作用机理,并且检验了个体间模仿学习速度、任务复杂度、人员流动、环境扰动等情境因素与双元创新平衡对组织绩效的交互影响。仿真实验的结果表明,当组织处于稳定环境下,任务复杂程度适中,并且个体间模仿学习速度中等时,双元创新平衡参数与组织绩效间呈现倒U型的影响关系;当任务过于复杂或者过于简单时,任务本身的特性削弱了双元创新平衡参数对组织绩效的影响,不同平衡模式对应的组织绩效没有明显的差距;当个体间模仿学习速度比较慢时,增强了组织的探索性,此时具有利用式创新倾向的组织可以更好地提升组织绩效水平。但当个体间模仿学习速度过快时,组织更多的倾向探索式创新对组织绩效的提升更有利;当组织存在人员流动和外部环境扰动时,更多的利用式创新可以提升组织对现有知识进行重组和整合的能力,从而更好地适应环境的动态变化。     

A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior

Recently, nature-inspired algorithms have increasingly attracted the attention of researchers. Due to the fact that in BPSO the position vectors consisting of ‘0’ and ‘1’ can be seen as a decision behavior (support or oppose), in this paper, we propose a BPSO with hierarchical structure (BPSO_HS for short), on the basis of multi-level organizational learning behavior. At each iteration of BPSO_HS, particles are divided into two classes, named ‘leaders’ and ‘followers’, and different evolutionary strategies are used in each class. In addition, the mutation strategy is adopted to overcome the premature convergence and slow convergent speed during the later stages of optimization. The algorithm was tested on two discrete optimization problems (Traveling Salesman and Bin Packing) as well as seven real-parameter functions. The experimental results showed that the performance of BPSO_HS was significantly better than several existing algorithms.