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.

A computational laboratory is a “place ” where we can: ask a question about an organization and its processes, build a computational experiment, design and conduct an experiment, and answer or comment on the question. The questions can be: what is, what might be, and what should be. Validation is a fundamental concern in science; the validity of a laboratory and model depends upon the question being addressed. A laboratory for a descriptive what is question may not be valid for a what should be design question. Docking—the alignment of two models—goes beyond validity. Docking juxtaposes two models to investigate whether they proceed in like manner or yield similar results. I argue that docking provides a guide in the use of different laboratories to address organization questions; and, further computational and non computational models can be docked to deepen and broaden our understanding of organization science.

Organizations sometimes face critical situations or crises that can induce severe consequences or even disasters if wrong decisions are made. The bulk of crisis management research has relied heavily on case study methods yet often with rhetorical or even inconsistent suggestions. With an exclusive focus on crisis prevention, the issue of how organizations can maintain good performance when faced with critical situations has largely remained unexplored. There is also an apparent lack of consideration regarding how aspects of organizational design and task environment interact and affect organizational performance under critical situations. In this paper, we attempt to address this issue from an open system’s perspective and integrate techniques of computational modeling with the analyses of two crisis cases, the Vincennes incident and the Hinsdale incident. The use of a computational model with strong organization theory foundation has provided a systematic mechanism for abstracting empirical information and generating theoretical results, thus complementing conventional case analyses, which thrive on in-depth information but are often limited by the lack of analytical ability to provide theoretical insight that goes beyond empirical descriptions. For the two crisis cases, the study shows, through detailed quantitative illustrations, that the computer model can be very effective in predicting organizational performance and suggesting designs that organizations can employ to mitigate the impact of crises. This study has demonstrated that our approach of computational case analysis can be very successful in providing systematic and explicit guidance for effective crisis mitigation both theoretically and empirically.

NSF GRT9354995 and by the ONR N00014-97-1-0037. Managers are continually designing and redesigning their teams and organizations. Design decisions tend to be based on trial and error, with little attention to long term experience and little or no attempt at verification. Organizational researchers interested in design have generated a vast compendium of design knowledge, much of which goes under the heading – Contingency Theory. As the name implies, the right design for an organization is seen to be contingent on a large number of complex and interacting factors. The complexity of the findings is such that, on the practical side, little guidance can be given to the manager and, on the theoretical side, advances in understanding are hampered by the overwhelming complexity. Computational models are ideally suited for reasoning about large complex systems composed of multiple interacting parts. This thesis addresses these pragmatic and theoretical problems by developing a computational toolkit for reasoning about organizational design that can be used to design teams or organizations, examine the impact of design changes, and reason theoretically about

This study explores the organizational impact of a variety of important promotion systems commonly practiced in organizations including up-or-out systems, absolute merit-based systems, relative merit-based systems, and seniority-based systems. Through the computer simulation of organizations in a distributed decision making setting, the results indicate that the effectiveness of any promotion system is dependent on a range of factors including the nature of the task environment, the design of the organizational structure, the frequency of monitoring, the criteria of performance, and the transferability of task knowledge. This study has implications not only for understanding organizational promotion systems from the contingency perspective, but also for bridging the fields of strategic human resource management and computational organization theory.