Articulating the Role of Artificial Intelligence in Collective Intelligence: A Transactive Systems Framework

Abstract

Human society faces increasingly complex problems that require coordinated collective action. Artificial intelligence (AI) holds the potential to bring together the knowledge and associated action needed to find solutions at scale. In order to unleash the potential of human and AI systems, we need to understand the core functions of collective intelligence. To this end, we describe a socio-cognitive architecture that conceptualizes how boundedly rational individuals coordinate their cognitive resources and diverse goals to accomplish joint action. Our transactive systems framework articulates the inter-member processes underlying the emergence of collective memory, attention, and reasoning, which are fundamental to intelligence in any system. Much like the cognitive architectures that have guided the development of artificial intelligence, our transactive systems framework holds the potential to be formalized in computational terms to deepen our understanding of collective intelligence and pinpoint roles that AI can play in enhancing it.

Beyond collective intelligence: Collective adaptation

“…Adaptive landscapes and the resulting attractors change over time as new problems emerge endogenously and exogenously. As pointed out by Gupta & Woolley [ 15 ], more than optimizing performance on any particular task, it is important to understand the processes through which collectives adapt to different tasks and contexts.…”

“…Prominent definitions include ‘groups of individuals acting collectively in ways that seem intelligent’ [ 20 ] and ‘the general ability of a group to perform a wide variety of tasks' [ 12 ] . More recently, researchers have identified different collective processes underlying collective intelligence, such as transactive memory, attention, and reasoning systems [ 15 ] and skill congruence [ 21 ] which provide groups the capability to better translate individual ability into collective performance [ 22 ]. Collective intelligence has been studied extensively in organizational science [ 23 – 25 ], cognitive science [ 11 , 26 – 28 ], business [ 29 , 30 ], managing intellectual capital [ 31 ] and crisis management [ 32 , 33 ], among other fields.…”

Human Performance in Competitive and Collaborative Human-Machine Teams

“…The importance of collaboration between the pilot and scout is paramount to minimising loss of life in this safety-critical setting. In this scenario, effective performance occurs when the pilot and scout form a team where members share a common goal and understand both the skills of the other and which tasks are being completed (Gupta & Woolley, 2021). That is, the team operates with a collective intelligence that encompasses the team and the task, and the ability to integrate new information to update this shared understanding.Machine agents that support human operators have become increasingly prevalent, particularly in teams used within safety-critical settings.…”

“…abilities of each team-member, (ii) the tasks and roles each team-member is completing, and (iii) other teammembers' goals and motivations (Gupta & Woolley, 2021). Whilst intelligent machines are rapidly developing they still lack the ability to share, produce, and efficiently integrate group-level knowledge.…”

“…However, competition also leads to erratic behaviours (Niehorster et al, 2019) and sub-optimal decision-making (Bennett et al, 2020). Critically, HH teams demonstrate coordination and anticipation of teammate behaviour (Demir, McNeese, & Cooke, 2017;Grimm, Demir, Gorman, & Cooke, 2018;McNeese, Demir, Chiou, & Cooke, 2021), two key behaviours observed in teams with collective intelligence (Gupta & Woolley, 2021). Social factors are also relevant to HM research.…”