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Internet technologies have created unprecedented opportunities for people to come together and through their collective effort generate large amounts of data about human behavior. With the increased popularity of grounded theory, many researchers have sought to use ever-increasingly large datasets to analyze and draw patterns about social dynamics. However, the data is simply too big to enable a single human to derive effective models for many complex social phenomena. Computational methods offer a unique opportunity to analyze a wide spectrum of sociological events by leveraging the power of artificial intelligence. Within the human factors community, machine learning has emerged as the dominant AI-approach to deal with big data. However, along with its many benefits, machine learning has introduced a unique challenge: interpretability. The models of macro-social behavior generated by AI are so complex that rarely can they translated into human understanding. We propose a new method to conduct grounded theory research by leveraging the power of machine learning to analyze complex social phenomena through social network analysis while retaining interpretability as a core feature.

With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams.

In this paper we propose a new model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. We do this by first characterizing what sets team cognition and collectively intelligence apart, and then reviewing the literature on “superforecasting” and the ability for effectively coordinated teams to outperform predictions by large groups. Lastly, we delve into the ways in which teamwork can be enhanced by artificial intelligence through our model, finally highlighting the many areas of research worth exploring through interdisciplinary efforts.

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