CI is an emerging field, drawing from a broad range of disciplines including biology, psychology, economics and computer science.1 That said, CI methods are already widely used in various fields, such as citizen science, experiments in democracy, mobilising consumers for product design and predictions in finance.
The methods of CI make it possible to look more systematically at how teams, organisations and even entire communities think, observe, analyse, plan and create. Better understanding of these processes can then be used to help groups think more successfully.
Principles from CI are being applied in areas as varied as organisational design, citizen science and open democracy, and can help to improve everything from social-media moderation to predicting and responding to natural disasters. Harnessing CI could provide a new, more inclusive and more effective model for global governance,2 and play a vital role in tackling climate change,3 the UN's Sustainable Development Goals4 and a host of other thorny societal problems.5
In the past decade, there has been significant growth in the use of technology to enhance the CI of groups both large and small.6 This ranges from web platforms designed to coordinate large-scale collaboration to the use of AI to facilitate group discussions. Crowdsourcing information or small chunks of work from large numbers of people can tackle challenges as varied as training AI and predicting floods.7
Citizen-science projects engage the general public to help scientists collect and analyse data. Open innovation platforms like Kaggle and InnoCentive let companies outsource engineering challenges to independent experts. Deliberative democracy is involving everyday citizens in political decision-making in countries.8 The advent of AI chatbots and agents has created a powerful new way for humans to interface with machines, making it increasingly important to consider how AI-human teams will operate in the future.
However, efforts to apply ideas from CI in the real world are piecemeal. The vast majority of organisations are failing to employ simple principles that could significantly enhance their effectiveness, suggesting a need for more translational research. And where there are attempts to harness CI, the practice is often well ahead of the theory. Significant research is required to improve our understanding of the fundamentals underpinning CI and how to design and apply new tools to enhance it. Current models of collective action have delivered innovation, but they themselves have remained remarkably unchanged over centuries; the field of collective intelligence seeks to challenge and alter these paradigms.9
KEY TAKEAWAYS
Solving the world's biggest challenges will require input from large numbers of people with diverse experience and expertise, all of whom must be able to work collaboratively. The field of Collective Intelligence (CI) seeks to understand the theory and practice of human cooperation to make this a reality. Thanks to digital technology, Large-scale collaboration can now be used to solve scientific challenges, innovate new ideas and even revitalise democracy. But organisations and institutions need to be redesigned to help put these ideas into action. Insights into group dynamics and new tools are helping create Smarter teams in a time when working practices are in flux. Increasingly powerful AI agents could soon play a vital supporting role and may eventually become team members themselves. But continuing progress is reliant on better models of Collective cognition — how groups “think” as a unit. The theoretical underpinnings of CI are fragmented, which is holding back efforts to understand and enhance these processes. Attempts to use technology to boost CI also require advances in Human-computer interaction research. Our understanding of how people perceive and use decision-making tools remains piecemeal and AI is a long way from exhibiting the kind of social intelligence required to operate seamlessly alongside human teammates.
Three fundamental questions guide GESDA’s mission and drive its work: Who are we, as humans? How can we all live together? How can we ensure the well-being of humankind and the sustainable future of our planet? We asked researchers from the field to anticipate what impact future breakthroughs could have on each of these dimensions. This wheel summarises their opinions when considering each of these questions, with a higher score indicating high anticipated impact, and vice versa.
New tools for gathering and visualising data, and open-source repositories of information and tools, are helping enhance the Collective Intelligence (CI) of large groups. AI is also playing a growing role in facilitating CI by filtering complex data, organising human knowledge and optimising deliberative processes.10 Powerful large language models (LLMs) trained on reams of internet data both encapsulate the CI of millions and can actively support CI by making it easier for people to engage with deliberative and collaborative processes.11,12 Caution is needed because overuse could also lead to a homogenisation of perspectives and illusions of consensus, but there have been notable successes such as the Habermas Machine from Google DeepMind,13 which started with participants submitting written opinions on a question, used the AI to generate group statements and then asked participants to rate and critique them. The machine then refined the statements until the group was satisfied. Another example is SocraSynth, which combines LLMs to help groups think, synthesise and develop more complex solutions to problems.14
While CI has shown promise for generating novel solutions, existing decision-making processes are often too slow and inflexible to take full advantage. This is prompting calls to apply the principles of CI to overhaul corporate structure and culture,15 and to reimagine democratic processes.16,17 Efforts are already under way to design organisations and institutions that actively promote and harness CI to help with multiple aspects of, and approaches to, thought at scale — including observation, interpretation, memory, prediction and creativity.18 Decentralisation technologies like blockchain and quadratic voting also open up avenues for enhancing CI by helping run organisations in a distributed and non-hierarchical fashion.19
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
This involves solving several challenges, including goal alignment, task prioritisation, progress tracking, maintaining attention, distribution of responsibilities and ensuring that deliberative processes are efficient and equitable. This is becoming increasingly complicated, as more fluid organisational structures mean team composition and structure is often dynamic20. Globalisation and the rise of hybrid work also means teams frequently work remotely and asynchronously, raising new challenges for group cohesion.21
Nonetheless, research into group dynamics is providing simple yet powerful insights into how to improve the CI of small groups. These include intermittent breaks in collaboration,22 boosting the variety of solutions explored23 and ensuring the right balance of cognitive diversity and gender.24,25 It is also laying the foundations for new tools to enhance team performance. These include real-time visualisations of how much effort members are putting in,26 information dashboards that summarise people's skills27 and chatbots that help teams allocate work based on members’ expertise.28
Recent breakthroughs in LLMs are also opening up the prospect of AI agents working alongside humans in hybrid teams.29 These agents could be a crucial tool for asynchronous collaboration, acting as a “shared brain” for the group and keeping team dialogue flowing even when members can’t speak directly. They could ultimately act as moderators, steering group deliberations to boost CI. Adding AI to teams isn’t a silver bullet, though, and there’s evidence it can degrade performance as often as improve it.30,31 This is driving researchers to think more deeply about how the CI of human-AI groups differs and how to harness it.32,33
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
Research has shown that collective cognition is not uniform and is shaped by the social structures in which the groups operate.36 Groups in the real world also have to navigate numerous, disparate and ever-changing challenges, which means they are in a constant process of adaption.37 One particularly understudied aspect of collective cognition is the role of narrative. Stories are central to the way groups coalesce around particular goals or shared visions of reality, which is an essential precursor to any form of collective reasoning.38 Increasing use of technology to enhance Collective Intelligence (CI) also necessitates the development of new metrics of collective cognition that can be used by digital platforms or AI to improve group collaboration.
While there has been progress on defining and even measuring CI itself,39 decisions about what characterises “intelligent” group behaviour often remain subjective. Establishing what we mean by “intelligence” may be a precursor to being able to explain and improve CI.4041 Taking a multidisciplinary approach that pools knowledge from biology, computer science and the social sciences will be crucial for developing a holistic view of CI.42 The life sciences in particular are providing exciting insights into the role CI plays in organising everything from cells to entire animal populations.43,44
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
There is considerable excitement around LLMs, which have opened up an intuitive new way for humans to interface with computers through natural language. But AI struggles with certain challenging aspects of human interaction. Human decision-making is highly context-dependent, for instance, and that context is not static. Group interactions are also governed by subtle cues and complex social conventions.
AI researchers have made some progress in understanding context46 and gesture recognition,47 and there is growing evidence that LLMs have a rudimentary ability to model human mental states (known as theory of mind).48 Multimodal AI models that can interpret more than just text present a promising direction for developing more context-aware AI.49 But imbuing machines with true social intelligence remains a distant goal and will require a multidisciplinary effort spanning the social, behavioural and natural sciences.50,51 A concerted effort is also needed to improve our understanding of how humans perceive and interact with conversational AI.52
In addition, AI chatbots raise significant privacy and security concerns.53 Also, their propensity for sycophancy54 and their tendency to confidently assert incorrect information, or “hallucinate”, makes it essential to ensure people understand the technology’s limits so that they don’t become overly trusting of it.55 As technology increasingly mediates group interactions it will also be important to measure whether it is enhancing human capabilities or causing them to atrophy.
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
