Cohumain Building The Socio Cognitive Architecture Of Collective Human

In recent years, we have experienced rapid development of advanced technology, machine learning, and artificial intelligence (AI), intended to interact with and augment the abilities of humans in practically every area of life. With the rapid growth of new capabilities, such as those enabled by generative AI (e.g., ChatGPT), AI is increasingly at the center of human communication and collaboration, resulting in a growing recognition of the... However, there are many unanswered questions regarding how human-AI collective intelligence will emerge and what the barriers might be. Truly integrated collaboration between humans and intelligent agents may result in a different way of working that looks nothing like what we know now, and it is important to keep the essential goal of... In this special issue, we begin to scope out the underpinnings of a socio-cognitive architecture for Collective HUman-MAchine INtelligence (COHUMAIN), which is the study of the capability of an integrated human and machine (i.e.,... This topic consists of nine papers including a description of the conceptual foundation for a socio-cognitive architecture for COHUMAIN, empirical tests of some aspects of this architecture, research on proposed representations of intelligent agents...

Keywords: Artificial intelligence; Collaboration; Collective intelligence; Human–AI; Human–machine teaming. This paper introduces COHUMAIN (Collective Human-Machine Intelligence), a research agenda and framework for studying the complex dynamics of human-AI collaboration. The authors argue that existing research on human-machine interaction is often fragmented across different disciplines, leading to social science models that don't fully account for technology and technical systems that don't consider foreseeable "unexpected"... COHUMAIN proposes a holistic and interdisciplinary approach to designing and developing sociotechnical systems where humans and AI agents can effectively work together. A central goal of COHUMAIN is to foster collective intelligence (CI), which is a group's ability to solve a wide range of problems. The framework is built upon sociocognitive architectures, which provide the underlying infrastructure for how a complex system, like a human-AI team, perceives, understands, and acts productively.

The paper identifies four core problems that any sociocognitive architecture must address to enable CI: 1. Mental States: How individuals perceive their own and others' mental states (e.g., goals, beliefs). 2. Cognitive Resources: How individuals perceive and represent their own and others' specialized knowledge and skills. Correspondence should be sent to Pranav Gupta, Gies College of Business, University of Illinois, Urbana‐Champaign, 6 Wohlers Hall, 1206 S.

Sixth St., Champaign, IL 61820, USA. Email: pranavgu@illinois.edu Revised 2023 Jun 12; Received 2022 Jun 30; Accepted 2023 Jun 12; Issue date 2025 Apr. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence?

Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain—Collective Human‐Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence...

We connect this with synergistic work on a compatible cognitive architecture, instance‐based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human–machine...

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