• Asia pacific journal of information systems
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• v.10 no.3
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• pp.105-120
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• 2000

The objective of this paper is to show how team study can be advanced with the aid of a current computer technology, that is distributed Artificial Intelligence(DAI). Studying distributed problem solving by using groups of artificial agents, DAI can provide important ideas and techniques for the study of team behaviors like team decision making. To demonstrate the usefulness of DAI models as team research tools, a DAI model called 'Team-Soar' was built and a simulation experiment done with the model was introduced, Here, Team-Soar models a naval command and control team consisting of four members whose mission was to identify the threat level of aircraft. The simulation experiment was performed to examine the relationships of team decision scheme and member incompetence with team performance. Generally, the results of the Team-Soar simulation met expectations and confirmed previous findings in the literature. For example, the results support the existence of main and interaction effects of team decision scheme and member competence on team performance. Certain results of the Team-Soar simulation provide new insights about team decision making, which can be tested against human subjects or empirical data.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.1
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• pp.93-104
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• 2000

Passive agents participate in team activities passively, that is, only upon requst, whereas active agents involve themselves voluntarily. Teams composed of active agents are generally believed to perform better than those with passive agents. In this paper, by using a computational simulation model we examine the effect of agent activeness on the efficiency of decision-making teams that access different amout of information. "Team-Soar" is a computational fraemwork that consists of a group of interconnected individual Al agents (i.e., Soar). A simulation experiment using Tearm-Soar was performed. Results of the simulation provide valuable insights on the roles of agent activeness. For example, the impact of having more active agents becomes more sigfniciant as the amout of information to process increases and when the team decision efficiency is important. Some of the results are counter-intultive and therefore provides an opportunity to understand the roles of the agnet activeness more deeply. For instance, the simulation results reveal that having more active agents did not always enhance team efficiency. Conclusively, the simulation experiment demonstrates how computational models contribute to the research of agents social characteristics.teristics.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.37-59
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• 2004

Team decision making is a collective behavior that needs to be understood by considering properties belonging to team and individual member domains together. This paper introduces a conceptual model called "Dual-Level(DL)" model that describes a team decision-making process in terms of team level, member level, and the relationship between them. The team-level view explains the decision-making process by considering the team as a wholeand divides the process into three stages: Problem Conceptualization, Alternative Generation, and Selection. The member-level view describes what happens to individual members when they go through the group process and splits it into the five phases: Individual Cognitive Mapping, Problem Decomposition, Subproblem Session, Subproblem Integration, and Team Decision. The DL model works as a theoretical framework to explore team decision making by using a set of computational models of team design and team members. In practice, the conceptual framework is used to build a computational model of decision making team, called "Team-Soar."

• Asia pacific journal of information systems
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• v.12 no.2
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• pp.197-216
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• 2002

Cooperativeness within an organization can be conceptualized as the degree of members' willingness to work with others. The simulation study investigates the relationships of cooperativeness with team performance at different levels of information redundancy by using a multi-agents model called Team-Soar. The model consists of a group of four individual Al agents situated in a network, which models a naval command and control team consisting of four members. The study used a $9{\times}3$ design in which agent cooperativeness was manipulated at nine levels by gradually replacing selfish team members with increasing numbers of neutral and cooperative members, while information redundancy was controlled at three different levels(i.e., low, medium, and high). Results of the Team-Soar simulation show that cooperation has positive impacts on team performance. Further, the results reveal that the impact of agent cooperativeness on team performance depends on the amount of information needed to be processed during the decision making process.