Computational and mathematical organization theory is an inter-disciplinary 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.

This article develops a model of organizational knowledge acquisition in terms of modern psychological, sociological, economic and management theories by deconstructing the terms involved: an organization as a collective agent having goals and capabilities to achieve them; knowledge as the hidden state variables imputed to an agent as the basis of its capabilities; and acquisition as the reproduction of dispositions. This form of model enables one to relate the knowledge processes involved to existing models of organizational processes, and to understand such phenomena as knowledge economics and knowledge management. The breadth of the notion of organization encompasses markets, firms and societies; the operational definition of knowledge clarifies its role and the utility of the notion; and the focus on reproduction of dispositions in knowledge acquisition enables the management of knowledge acquisition to be analyzed. ______________________________________________________________________________________ 1.

In this paper we examine the adaptability of the Japanese style lean organization system and the traditional American style mass organization system under changing environments. From an organizational design perspective, key structural aspects of the two organizations are modeled in a problem solving context using computational methods. Organizational-level performance in terms of decision making accuracy and severity of errors is measured as an indicator of organizational adaptability under conditions where the task environment shifts between predictable to unpredictable or vise versa. Our study shows that both organizations have their respective advantages under different task environments and that they adapt to environmental shifts in different forms. Specifically, when the time pressure is high the lean organization system’s performance is virtually identical to the mass organization system, even though the lean organization system’s members are more proactive. When the time pressure is low, the mass organization system shows a much faster adaptability when the environment shifts to a predictable one but it is also more vulnerable when the environment shifts to an unpredictable one. In contrast, the lean organization system’s response to the changing environment is characterized by its slower adaptability. When the environment shifts to an unpredictable one, the lean organization system shows a gradual improvement till reaching a high level. When the environment shifts to a predictable one, however, the lean organization system shows a gradual

Simulation modeling provides a powerful methodology for advancing theory and research on complex behaviors and systems, yet it has been embraced more slowly in management than in some associated social science disciplines. We suspect that part of the reason is that simulation methods are not well understood. We therefore aim to promote understanding of simulation methodology and to develop an appreciation of its potential contributions to management theory by describing the nature of simulations, its attractions, and its special problems, as well as some uses of computational modeling in management research.

Organizations are occasionally faced with technology-based and accident-triggered crises that may cause costly disasters if not handled properly. Questions arise: How should organizations, with their complex processes and human involvement, be designed if they are to perform well in such crises? Would organizations benefit from structural changes during crises? From a neo-information processing perspective that views organizations as composed of cognitively restricted, socially situated, and task-oriented actors, we argue that the causes and consequences of crises may be better understood through the systematic examination of both environmental and organizational factors. We address our research questions using a rather unique approach: a matched analysis of 80 real organizational cases and 80 computer-simulated organizations. The findings show that a crisis can present critical challenges to organizational performance both externally and internally, and that there is no design guarantee that a high-performing organization will continue to perform well during a crisis situation. In addition, when organizations restructure to adapt to crisis situations, they often face the serious challenges of having to understand not only the external environment, but also organizational design traps. Key words: organizational performance; organizational design; computational modeling; real-crisis cases Whether theories of organization can be applied to nonconventional events or crisis situations has largely been assumed but certainly not fully explored (Carley

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Building on the literature of dynamic capability and organizational learning, we examine strategy execution in hyper-competition as a problem of how organizations can re-configure their learning capability to match with their radically different learning demands. Organizations in hyper-competitive environments face an increasing gap between their learning opportunities and needs, and actual learning performance. In order to survive they must improve their absorptive capacity so that they can learn simultaneously broad, deep and fast. We define such a learning contingency as hyper-learning. To do so, the organization must systematically interlace exploration—that seeks to maximize learning breadth— and exploitation—that seeks to maximize learning depth. Unlike in traditional learning cycles, exploration and exploitation during periods of hyper-learning are not insulated from each other through time or structure. We explore seven software firms engaged in Web system development during the hey-day on dot.com frenzy and investigate how these companies were able to hyper-learn. We distinguish two mechanisms to speed up exploration: distributed gate-keeping and extended grafting of external knowledge; and two mechanisms to speed up exploitation: simple design patterns and peer networks.

Many industries today behave like a massively interconnected network of organizations, technologies, consumers and products. Perhaps the most dramatic and widely known example is the computing industry. In contrast with the vertically integrated environment of the 1960s and 1970s, today’s industry is divided into a large

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As an exploratory step toward integrating differing views in the field of top management succession, this study takes an open system’s perspective and examines the effect of top management succession on organizational performance, in particular decision-making accuracy, under conditions where various organizational and environmental factors can interact. Through a formal computer simulation model that captures the basic behaviors of human decision making and the fundamental characteristics of organizational and environmental settings, the results show that whether top management successions may affect organizational performance, and if so, how organizational performance may be affected, depends on such contextual variables as industry environment, organizational structure, succession type, and organizational age. This study has also demonstrated the importance of moving toward a more systematic and precise contingency approach and the power of computer modeling in understanding the multilevel process of top management succession in complex and dynamic organizations.