This can be split into different aspects such as observation (integrating citizen inputs and other data sources); models (for example, a shared digital model of how a city or organisation works); memory (records of past systems and their performance); creativity (innovation, for example) and empathy (sensing and understanding feelings). This provides a framework for seeing how they can be advanced, with technology advancing some rapidly (eg observation) and others much less so. This approach makes it possible to use CI for diagnosis and problem-solving by bringing together new methods for understanding problems, generating solutions, implementing them and learning from them.
Filling the gaps in our understanding will be a major challenge. Research has shown that collective cognition is not uniform and is shaped by the social structures in which the groups operate,20 a significant observation since current approaches to studying human interaction struggle to account for social dynamics. While there has been progress on defining and even measuring CI itself,21 deciding what characterises "intelligent" group behaviour remains subjective.
Increasing use of technology to enhance CI also necessitates the development of new metrics of collective cognition that can be collected unobtrusively and then used by digital platforms or AI to improve group collaboration. Taking a multidisciplinary approach that pulls insights from biology, computer science, and the social sciences will be crucial for developing a holistic view of CI.22