viewpoints. It is a tool for building and testing computational theories of coordination. TÆMS is compatible with both formal computational agent-centered approaches and experimental approaches. The framework allows us to both mathematically analyze (when possible) and quantitatively simulate the behavior of multi-agent systems with respect to interesting characteristics of the computational task environments of which they are part. We believe that it provides the correct level of abstraction for meaningfully evaluating centralized, parallel, and distributed control algorithms, negotiation strategies, and organizational designs. This chapter will briefly describe the TÆMS modeling framework for representing abstract task environments, concentrating particularly on its support for simulation. I will describe how to model each of several different multi-agent problem-solving environments, such as This work was supported by DARPA contract N0

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.

Abstract not found

We discuss the application of Social Network Analysis concepts to military C4ISR

... level that individual work practice—collaboration, communication, ‘off-task ’ behaviors, multi-tasking, interrupted and resumed activities, informal interactions, use of tools and movements—is left out, making the description of how the work in an organization actually gets done impossible. This paper describes the Brahms modeling and simulation environment, developed at NASA Ames Research Center. The Brahms modeling language is geared towards modeling people’s activity behavior, making it an ideal environment for simulating organizational processes at a level that allows the analysis of the work practice and designing new work processes at the implementation level.

doi 10.1287/mnsc.1070.0712

Discrete event simulation is generally recognized as a valuable aid to the strategic and tactical decision making that is required in the evaluation stage of the manufacturing systems design and redesign processes. It is common practice to represent workers within these simulation models as simple resources, often using deterministic performance values derived from time studies. This form of representing the factory worker ignores the potentially large effect that human performance variation can have on system performance and it particularly affects the predictive capability of simulation models with a high proportion of manual tasks. The intentions of the chapter are twofold: firstly, to raise awareness of the importance of considering human performance variation in such simulation models and secondly, to present some conceptual ideas for developing a worker agent for representing worker performance in manufacturing systems simulation models. 1

Over time organizations change and coordinate personnel in new ways. Such changes may be precipitated by actual or anticipated changes in personnel, the environment, technologies, legislation, or the top management team. This adaptation is constrained and not all forms of coordination are feasible. Since organizations are inherently computational entities insight is gained by examining the adaptation of organizations using intelligent artificial agents. Using ORGAHEAD, a multi-agent model of organizational behavior, a series of virtual experiments were run to examine issues of organizational adaptation. Results suggest the concurrent occurrence of experiential learning and structural learning generates within the organization the ability to learn meta-change strategies which can be either adaptive or maladaptive. Such meta-change strategies effectively lock organizations into divergent paths of behavior which produce heterogeneity of form across the population of organizations. Organizational performance and form depend on a complex of array of factors including environmental change, experiential and structural learning, and the emergence of institutionalized strategies. 1

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

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