Kerr, MacCoun, & Kramer, 1996). Teamwork is not guaranteed to provide either of these advantages. With respect to (1) combining complementary inputs, increasing the size of an organization tends to reduce its overall efficiency unless there is also an increase in departmentalization and standardization of tasks (Blau, 1970). The latter features, however, reduce flexibility of response in a changing or novel environment (Donaldson, 1995). A related problem is goal displacement, in which specialized units lose sight of the larger organizational purpose, and pursue their own goals as if they were fixed ends rather than means, which should be reevaluated when conditions change (Scott, 1998). With respect to (2) better decisions, groups may be affected by socialization biases, such as groupthink, which induce conformity rather than diversity of thought (Janus, 1972; March, 1996.). For this reason, group decisions tend to be better when individuals think about the problem independently befo

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Good team players are often defined in trait terms; that is, they are described as dependable, flexible, or cooperative. Our goal is to examine the relationship between team member personality traits and team effectiveness. However, to understand the effects of personality on team performance requires greater specificity in how personality is described and in how team effectiveness is described. A hierarchical model of team member personality is presented that defines higherlevel personality traits and specific facets relevant to team performance. Next, a classification of the core teamwork dimensions underlying effective team performance is presented. Finally, predictions are derived linking team member personality facets to specific teamwork requirements.

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a series of technology research roadmaps, or plans, developed over a three year period by the Federation of American Scientists and the Learning Federation, a partnership among industry, academia, and private foundations to stimulate research and development in learning science and technology. The full series of research roadmaps is available at www.FAS.org/learningfederation. We thank Dr. Jan Cannon-Bowers for her major contribution in writing this roadmap. And, we thank Dr. Marianne Bakia, who left FAS just prior to completion of the roadmap, for her contributions to the Learning Federation Project and development of this roadmap. We gratefully acknowledge the funding support of the 2003 Congressional appropriation to the Federation of American Scientists for the Digital Opportunity Investment Trust (DO IT). A major part of that funding supported the Learning Federation's Learning Sciences and Technology Research and Development Roadmap, which appears in the DO IT Report to Congress. We also gratefully acknowledge the additional funding support of the organizations that sponsored this work and helped make possible the Roadmap:

This report documents the design, development, and implementation of refinements to a prototype staff training package for future forces. These training refinements were made to a prototype staff training package described in the report Prototype Staff Training and Evaluation Methods for Future Forces, Throne et al., 1999. The training refinements were implemented in a simulation-based experiment examining the impact of digital systems on Future Battle Command at the battalion and brigade level. This report focuses on the training support package designed to improve performance of staffs using advanced command, control, communications, computer, and intelligence (C 4 I) systems. Documentation is provided on the analysis, design and development of four staff training products developed under this effort: a Surrogate Command, Control, Communications, and Computers (SC 4) System Demonstration, Digital Staff Drills, Team Training Sessions (TTSs) and TTSs Trainer Guide, and refinements to Tactical Decision-Making Exercises (TDXs). The formative evaluation is described for those products with survey results and project team observations reported by product type. Lessons learned on future staff training are documented and may help direct the Army’s effort to develop command and staff training support packages (TSPs) for future forces.

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Task analysis is a critical first step in understanding a new complex domain. Currently, task analysis is a mostly manual process with weak automation support. This paper introduces the first phase of the SAVVII prototype as a proof-of-concept for early validation of task analysis activities. Early validation is supported by the transference of semantics from data values to data structures. Rough estimations of discrepancies between tasks are used to focus the knowledge elicitor's attention on questionable areas, thereby reducing much of the tediousness and time-intensive nature of validation. SAVVII was shown to work on the developmental domain of parables. It is currently undergoing experimentation in two real-world knowledge acquisition activities. Introduction Many systems engineering methodologies call for extensive efforts in knowledge acquisition along with design, implementation, and test. Task analysis is a critical phase in many knowledge acquisition processes. ...

Intelligent agents can be quite useful as entities in combat simulations. Recently, there has been a great deal of research on developing enhanced methods for implementing intelligent agents in combat simulations, such as by introducing models of teamwork and collaborative behavior. However, modeling of command-and-control has lagged behind. Much is known about command-and-control in human tactical decision-making (TDM) teams from studies in cognitive science and organizational psychology. These studies suggest that human decision-makers tend to follow a Naturalistic Decision-Making process, in which situation awareness plays a key role. Hence command-and-control is heavily focused on information-gathering and information-fusion activities, oriented toward reducing uncertainty and identifying the situation, based on which an appropriate response can be applied (or adapted) from experience or training. In this paper, we provide a brief survey of multi-agent systems architectures, with a focus on combat simulations, and a survey of the cognitive literature on human situation awareness and tactical decision-making. Then we describe a new computational model for command-and-control in multi-agent systems. Primarily, the model focuses on a procedural representation of situation assessment and attempts to capture the decisions regarding information-gathering and informationmanagement activities, though we also discuss how to integrate these activities with other on-going aspects of C2 (mission, threat-handling, etc.) using prioritization. We then discuss an approach to extending this procedure to a team task, which should automatically generate the interactions and information flow necessary to simulate distributed situation awareness.

While most collaboration technologies are concerned with supporting task accomplishment, members of work teams do not always have the skills necessary for effective teamwork. In this research I propose that providing dynamic feedback generated by automated analysis of language behavior can help team members reflect on and subsequently improve their teamwork behaviors. This prospect is developed based on research in multiple disciplines, including teamwork effectiveness and social behaviors, feedback for training and regulating behaviors, and use of language in group conversations. To support this research, I directed the design and development of GroupMeter, a web-based chat system that analyzes conversations using a dictionarybased word count technique and visualizes indicators of language. I present a set of requirements for the GroupMeter system and the iterative process in which its design evolved. Findings from experiment 1 included a set of linguistic indicators that may serve as a useful source of automated feedback, such as agreement words and selfreferences, and that were embedded into the GroupMeter system. Experiments