Model of Collective Judgment Formation for Sequential Teams in Information-Dense Scenarios

In complex domains such as intelligence analysis and healthcare, teams often need to continuously work toward long-term goals. These scenarios involve sequential work, with teammates performing their roles in shifts. Existing teamwork research acknowledges the myriad factors that influence how information is cognitively processed and collaboratively deliberated to arrive at a shared decision. Yet, much of this research describes synchronous and/or reciprocal interdependence rather than sequential interdependence. This paper presents a descriptive model of collective judgment formation (CJF) in sequential teams. CJF involves the development of judgments by drawing on information from diverse sources, including both humans and AI, such that a mosaic comes into form even as pieces appear and others change shape. CJF also often entails situations with an overwhelming variety, volume, and velocity of information. Our model can be applied to numerous sequential scenarios that incorporate shift handovers and processing data into intelligence.