In this podcast episode, we explore the fascinating concept of collective intelligence, drawn from recent research on how groups can outperform individual members in solving complex problems. The study presents a mathematical model that explains how collective intelligence arises from the integration of individual members' knowledge, conceptualized as 'mental maps.' It reveals a non-linear relationship between group intelligence and the average intelligence of its members, depending on task difficulty. Additionally, we delve into how past studies may have yielded conflicting results due to variations in task complexity. The episode sheds light on the intersection between theoretical models and real-world applications, offering insights into the evolving field of collective intelligence research.

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Original paper: https://jasss.soc.surrey.ac.uk/23/3/4.html

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