The effects of agent activeness and cooperativeness on team decision efficiency: A computational simulation study using Team-Soar

Within an organizational context, an agent type reflects the behavioral tendency that a member might employ across tasks. Until now, however, the impacts of agent types on team performance are not well understood. To address this issue, this study examines the relationships of agent activeness and cooperativeness with team decision efficiency at different degrees of information redundancy by using a team model consisting of four AI agents. This study presents the team model called “Team-Soar” and describes how the model implements agent activeness at two levels (active and passive) and agent cooperativeness at three levels (cooperative, neutral, and selfish). Then a computational simulation experiment is described. Results of the simulation indicate that the impacts of the agent type depend on the amount of information to be processed and active style boosts the effects of agent cooperativeness on team efficiency. The results also indicate that active agents do not always contribute team efficiency more than passive agents.