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.     

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.     

Three Controls are Better than One: A Computational Model of Complex Control Systems

This paper investigates theories that integrate and extend currently accepted agency- and transaction-based approaches to organizational control. We use a computational model to build three forms of control systems (market, bureaucratic, clan) and three forms of control targets (input, behavior, output). Using these models, we examine relationships between control systems and both singular and multiple control targets. Results of this study support the emerging broader perspective on organizational control research and suggest that managers can improve organizational performance by focusing attention on multiple control targets. In addition, findings partially support posited relationships between control systems and singular control targets. The authors suggest that results of this study should direct scholars to refocus control research from examinations of singular forms of control to evaluations of more complex control systems.     

The Dynamics of Cultural Influence Networks

This article investigates the behavior of cultural influence networks over time, using a computer simulation based on a formal model of cultural transmission in organizations. In the formal model, every organizational member exerts some cultural influence on, and is influenced by, every other member; these influence paths constitute a dense social network and the weights of paths (ties) vary throughout the network. Over time, each organizational member's enculturation level changes in response to influence from other members, and the influence weight of each path changes in relationship to the cultural similarity of the individuals connected by the path. Virtual experiments explore the configuration and evolution of the cultural influence network under varying demographic conditions and influence principles. Demographic effects are studied by varying organizational size, hiring selectivity and turnover rates. Two principles for determining initial influence path weights are examined, cohort-based influence and random influence. The simulations show that the cultural influence network evolves over time to a robust configuration, fluctuating around a stable dynamic equilibrium as individuals enter and leave the organization. As turnover rates rise, cohort-based influence strengthens the influence network and reduces network inequality. In this model, cohort-based influence processes promote cultural stability in organizations.     

The Effect of Organizational Communication Media on Organizational Culture and Performance: An Agent-Based Simulation Model

This paper examines the mutual relationship between the communication richness of media used for conducting organizational communication and organizational culture. The richness of the media influences how well the organization might maintain its culture. On the other hand, a strong organizational culture allows a more effective use of the media by providing members with some of the necessary common ground to better understand the information exchanged. These relationships are investigated using an agent-based simulation model (ABM). Our ABM incorporates many partial theories into a coherent and fully defined model, which helps formalize and integrate those theories. Our model allows us to analyze non-linearities and interaction effects, which are difficult to investigate using other techniques. Additionally, the ABM allows us to investigate the dynamics of the phenomenon and generate hypotheses that could then be tested using empirical studies. Given the substantial resources necessary to conduct empirical studies, we think that the present ABM is valuable in helping guide data collection efforts. In this paper, we present results that show that organizational culture can influence the effectiveness of the media used for organizational communication and that a high media richness can help maintain and stabilize a culture. The effect of media richness on organizational culture depends on the initial strength of the culture. In general, for a given richness of the media, an initially strong culture stabilizes faster and becomes stronger through time than an initially weak culture. Additionally, the model suggests that a stable network of contacts among agents fosters a high achievement of organizational tasks. Conversely, when agents are forced to establish contacts with agents outside the usual network for doing their work, the accomplishment of tasks decreases.     

高端装备制造企业组织创新演化机制研究——基于混沌理论

以演化视角分析高端装备制造企业组织创新类型和创新演化过程,并基于种群竞争模型,构建企业组织创新演化模型,通过分析模型平衡点稳定性及其演化趋势,发现当创新演化初始点出现在进入鞍点轨线附近时,其微小变化将导致最终演化状态的巨大差别。应用Lyapunov指数法判定高端装备制造企业组织创新演化的混沌性,运用创新演化模型模拟企业组织创新混沌演化过程并分析创新混沌演化规律。研究结果表明：(1)渐进式创新和激进式创新之间较强的竞争效应是企业组织创新演化进入混沌的一般条件;(2)鞍点是企业组织创新混沌演化的转折点,在进入鞍点轨线的影响域中,创新演化轨道的断裂位置和跃迁方向敏感依赖于初始条件;(3)环境变化对激进式创新的压力会增强创新演化的混沌性。     

Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different”

The Enron email corpus is appealing to researchers because it represents a rich temporal record of internal communication within a large, real-world organization facing a severe and survival-threatening crisis. We describe how we enhanced the original corpus database and present findings from our investigation undertaken with a social network analytic perspective. We explore the dynamics of the structure and properties of the organizational communication network, as well as the characteristics and patterns of communicative behavior of the employees from different organizational levels. We found that during the crisis period, communication among employees became more diverse with respect to established contacts and formal roles. Also during the crisis period, previously disconnected employees began to engage in mutual communication, so that interpersonal communication was intensified and spread through the network, bypassing formal chains of communication. The findings of this study provide valuable insight into a real-world organizational crisis, which may be further used for validating or developing theories and dynamic models of organizational crises; thereby leading to a better understanding of the underlying causes of, and response to, organization failure. Jana Diesner is a Research Associate and Linguistic Programmer at the Center for Computational Analysis of Social and Organizational Systems at the School of Computer Science (CASOS), Carnegie Mellon University (CMU). She received her Masters in Communications from Dresden University of Technology in 2003. She had been a research scholar at the Institute for Complex Engineered System at CMU in 2001 and 2002. Her research combines computational linguistics, social network analysis and computational organization theory. Terrill L. Frantz is a post-doc researcher at the Center for Computational Analysis of Social and Organizational Systems (CASOS) in the School of Computer Science at Carnegie Mellon University. His research involves studying the dynamics of organization social-networks and behavior via computer modeling and simulation. He is developing an expertise in workforce integration strategy and policy evaluation during organization mergers. He earned his doctorate (Ed.D. in Organization Change) from Pepperdine University, a MBA from New York University and a BS in Business Administration (Computer Systems Management) from Drexel University. Prior to entering academic research, for nearly 20 years he was a software applications development manager in the global financial services and industrial chemicals industries; most recently as a Vice President in Information Technology at Morgan Stanley in Hong Kong, New York and London. Kathleen M. Carley is a professor at the Institute for Software Research International in the School of Computer Science at Carnegie Mellon University. She is the director of the center for Computational Analysis of Social and Organizational Systems (CASOS) <http://www.casos.cs.cmu.edu/>, a university wide interdisciplinary center that brings together network analysis, computer science and organization science (www.casos.ece.cmu.edu) and has an associated NSF funded training program for Ph.D. students. She carries out research that combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are computational social and organization theory, group, organizational and social adaptation and evolution, social and dynamic network analysis, computational text analysis, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations.     

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.     

Modeling centralized organization of organizational change

Organizations change with the dynamics of the world. To enable organizations to change, certain structures and capabilities are needed. As all processes, a change process has an organization of its own. In this paper it is shown how within a formal organization modeling approach also organizational change processes can be modeled. A generic organization model (covering both organization structure and behavior) for organizational change is presented and formally evaluated for a case study. This model takes into account different phases in a change process considered in Organization Theory literature, such as unfreezing, movement and refreezing. Moreover, at the level of individuals, the internal beliefs and their changes are incorporated in the model. In addition, an internal mental model for (reflective) reasoning about expected role behavior is included in the organization model.     

A Model to Evaluate the Effect of Organizational Adaptation

When an organization's output declines due to either internal changes or changes in its external environment, it needs to adapt. In order to evaluate the effectiveness of different adaptation strategies on organizational performance, an organizational model composed of individual models of a five stage interacting decision maker was designed using an object oriented design approach and implemented as a Colored Petri net. The concept of entropy is used to calculate the total activity value, a surrogate for decision maker workload, based on the functional partition and the adaptation strategy being implemented. The individual decision maker's total activity is monitored, as overloaded decision makers constrain organizational performance. A virtual experiment was conducted; organizations implementing local and global adaptation strategies were compared to a control organization with no adaptation. The level of tolerance of the organization, the workload limit based on the concept of the bounded rationality constraint, was used to determined when a decision maker was overloaded: the limiting effect of the workload on performance. The timeliness of the organization's response was used in order to evaluate organizational output as a function of adaptation strategy.     

Organizational Structure and Firm Innovation in a Retail Chain

Two organizational structures for a retail chain are examined fortheir effect on the rate of firm innovation. A centralized organization isdefined as one in which store practices are mandated from corporateheadquarters (HQ) and this results in HQ being the sole source of new ideas.A decentralized organization gives freedom to store managers to adopt theirown ideas and disseminates innovations made by store managers. Thedifference in average profit between the centralized and decentralizedorganizations is found to be a non-monotonic function of innovativeopportunities. The centralized organization is preferred when innovativeopportunities are moderate while the decentralized organization is preferredwhen such opportunities are rich. Centralization also tends to fare betterin environments in which ideas are relatively complex.     

Computational and mathematical organization theory: Perspective and directions

Computational and mathematical organization theory is an interdisciplinary scientific area whose research members focus on developing and testing organizational theory using formal models. The community shares a theoretical view of organizations as collections of processes and intelligent adaptive agents that are task oriented, socially situated, technologically bound, and continuously changing. Behavior within the organization is seen to affect and be affected by the organization's, position in the external environment. The community also shares a methodological orientation toward the use of formal models for developing and testing theory. These models are both computational (e.g., simulation, emulation, expert systems, computer-assisted numerical analysis) and mathematical (e.g., formal logic, matrix algebra, network analysis, discrete and continuous equations). Much of the research in this area falls into four areas: organizational design, organizational learning, organizations and information technology, and organizational evolution and change. Historically, much of the work in this area has been focused on the issue how should organizations be designed. The work in this subarea is cumulative and tied to other subfields within organization theory more generally. The second most developed area is organizational learning. This research, however, is more tied to the work in psychology, cognitive science, and artificial intelligence than to general organization theory. Currently there is increased activity in the subareas of organizations and information technology and organizational evolution and change. Advances in these areas may be made possible by combining network analysis techniques with an information processing approach to organizations. Formal approaches are particularly valuable to all of these areas given the complex adaptive nature of the organizational agents and the complex dynamic nature of the environment faced by these agents and the organizations.This paper was previously presented at the 1995 Informs meeting in Los Angeles, CA.     

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.     

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。     

Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings

Social action is situated in fields that are simultaneously composed of interpersonal ties and relations among organizations, which are both usefully characterized as social networks. We introduce a novel approach to distinguishing different network macro-structures in terms of cohesive subsets and their overlaps. We develop a vocabulary that relates different forms of network cohesion to field properties as opposed to organizational constraints on ties and structures. We illustrate differences in probabilistic attachment processes in network evolution that link on the one hand to organizational constraints versus field properties and to cohesive network topologies on the other. This allows us to identify a set of important new micro-macro linkages between local behavior in networks and global network properties. The analytic strategy thus puts in place a methodology for Predictive Social Cohesion theory to be developed and tested in the context of informal and formal organizations and organizational fields. We also show how organizations and fields combine at different scales of cohesive depth and cohesive breadth. Operational measures and results are illustrated for three organizational examples, and analysis of these cases suggests that different structures of cohesive subsets and overlaps may be predictive in organizational contexts and similarly for the larger fields in which they are embedded. Useful predictions may also be based on feedback from level of cohesion in the larger field back to organizations, conditioned on the level of multiconnectivity to the field.     

Can tools help unify organization theory? Perspectives on the state of computational modeling

Scholars engaged in the study of work group and organizational behavior are increasingly calling for the use of integrated methods in conducting research, including the wider adoption of computational models for generating and testing new theory. Our review of the state of modern computational modeling incorporating social structures reveals steady increases in the incorporation of dynamic, adaptive, and realistic behaviors of agents in network settings, yet exposes gaps that must be addressed in the next generation of organizational simulation systems. We compare 28 models according to more than two hundred evaluation criteria, ranging from simple representations of agent demographic and performance characteristics, to more richly defined instantiations of behavioral attributes, interaction with non-agent entities, model flexibility, communication channels, simulation types, knowledge, transactive memory, task complexity, and resource networks. Our survey assesses trends across the wide set of criteria, discusses practical applications, and proposes an agenda for future research and development. Michael J. Ashworth is a doctoral candidate in computational organization science at Carnegie Mellon University, where he conducts research on social, knowledge, and transactive memory networks along with their effects on group and organizational learning and performance. Practical outcomes of his work include improved understanding of the impact of technology, offshoring, and turnover on organizational performance. Mr. Ashworth has won several prestigious grants from the Sloan Foundation and the National Science Foundation to pursue his research on transactive memory networks. Journals in which his research has appeared include Journal of Mathematical Sociology, International Journal of Human Resource Management, and Proceedings of the International Conference on Information Systems. His recent work on managing human resource challenges in the electric power industry has been featured in the Wall Street Journal and on National Public Radio's ``Morning Edition.' Mr. Ashworth received his undergraduate degree in systems engineering from the Georgia Institute of Technology. Kathleen M. Carley is a professor at the Institute for Software Research International in the School of Computer Science at Carnegie Mellon University. She is the director of the center for Computational Analysis of Social and Organizational Systems (CASOS), a university-wide interdisciplinary center that brings together network analysis, computer science and organization science (www.casos.ece.cmu.edu). Prof. Carley carries out research that combines cognitive science, dynamic social networks, text processing, organizations, social and computer science in a variety of theoretical and applied venues. Her specific research areas are computational social and organization theory; dynamic social networks; multi-agent network models; group, organizational, and social adaptation, and evolution; statistical models for dynamic network analysis and evolution, computational text analysis, and the impact of telecommunication technologies on communication and information diffusion within and among groups. Prof. Carley has undergraduate degrees in economics and political science from MIT and a doctorate in sociology from Harvard University.     

When does management matter in a dog-eat-dog world: An “Interaction Value Analysis” model of organizational climate

Interaction Value Interaction Value Analysis (I.V.A.) models a network of rational actors who generate value by interacting with each other. This model can be used to understand human organizations. Since people form organizations to facilitate interactions between productive individuals, the value added by interaction is the contribution of the organization. This paper examines the result of varying the queuing discipline used in selecting among back-logged interaction requests. Previously developed I.V.A. models assumed a First-in-first-out (FIFO) discipline, but using other disciplines can better represent the “Climate” of an organization. I.V.A. identifies circumstances under which organizations that control members’ interaction choices outperform organizations where individuals choose their own interaction partners. Management can be said to “matter” when individual choices converge to a point where interactions generate a lower than optimal value. In previous I.V.A. models, relinquishing central control of interaction choices reduced the aggregate value by anything from 0% to 12%, depending on circumstances. This paper finds the difference between the two modes of organization to go as high as 47% if actors display preferences between interaction partners instead of treating all equally. A politically divided, dog-eat-dog, “Capitalist” climate follows one queuing discipline, which is found to generally increase the value that a strong control structure can add. A chummy, in-bred “Fraternal” climate gains from control in some circumstances (low interdependence or low differentiation), but not in others (high or medium interdependence and differentiation under low diversity, for example). These are compared to the previous version of I.V.A., in which the queuing discipline was FIFO and the climate deemed “Disciplined”. Previously published findings on Organizational Climate are duplicated and extended with a higher level of detail. Priority queuing in an I.V.A. model is thus a useful proxy for Organizational Climate, open to future validation because its detailed predictions can be confirmed or falsified by observation. Walid Nasrallah is currently Assistant Professor in the Engineering Management program at the American University of Beirut (AUB). He received his Ph.D. from the Construction Engineering and Management program at Stanford University in 2000 and his Master’s degree at MIT in 1989. Between the two, he occupied several positions in the construction and software engineering fields. His research interests today include simulation, decision theory, and the evolution of organizations in response to new technologies.     

Organizational Design of Post Corporation Structure Using Fuzzy Multicriteria Decision Making

A model for organizational design of post corporation structure is developed in this paper. Alternative organization solutions have been designed taking into account the post environment characterized by private operators' competition and development of new message transmission technologies. Criteria for organizational design have been considered as numerical and uncertain linguistic variables describes by fuzzy sets. The model has been tested on a numerical example.     

Change in the Form of Evolution: Transition from Primate to Hominid Forms of Social Organization

In this paper I sketch a model for the transition from biologically to culturally based forms of social organization. The impetus for the transition arises from increased individualization among the non-human primates that can be observed as one moves phylogenetically from the Cercopithecoids and Ceboids (Old and New World monkeys) to the hominoids, especially the African apes. Increased individualization introduced a conflict with coherent and stable social integration that was only resolved among the hominid ancestors to modern Homo sapiens by shifting to a cultural/conceptual, rather than a behavioral/biological, basis for social organization. The shift entailed a change from evolution driven by individual fitness to evolution driven by the structural coherency of a conceptual system for social organization; that is, to selection based on group, rather than individual, level traits. Conceptually the transition depended upon the evolution of mental capacities such as a theory of mind and recursion, both of which are absent or occur only in minimal form among the non-human primates.