How to build a Collective Mind that speaks for humanity in real-time

Blog by Louis Rosenberg: “This begs the question — could large human groups deliberate in real-time with the efficiency of fish schools and quickly reach optimized decisions?

For years this goal seemed impossible. That’s because conversational deliberations have been shown to be most productive in small groups of 4 to 7 people and quickly degrade as groups grow larger. This is because the “airtime per person” gets progressively squeezed and the wait-time to respond to others steadily increases. By 12 to 15 people, the conversational dynamics change from thoughtful debate to a series of monologues that become increasingly disjointed. By 20 people, the dialog ceases to be a conversation at all. This problem seemed impenetrable until recent advances in Generative AI opened up new solutions.

The resulting technology is called Conversational Swarm Intelligence and it promises to allow groups of almost any size (200, 2000, or even 2 million people) to discuss complex problems in real-time and quickly converge on solutions with significantly amplified intelligence. The first step is to divide the population into small subgroups, each sized for thoughtful dialog. For example, a 1000-person group could be divided into 200 subgroups of 5, each routed into their own chat room or video conferencing session. Of course, this does not create a single unified conversation — it creates 200 parallel conversations…(More)”.

Using Artificial Intelligence to Accelerate Collective Intelligence

Paper by Róbert Bjarnason, Dane Gambrell and Joshua Lanthier-Welch: “In an era characterized by rapid societal changes and complex challenges, institutions’ traditional methods of problem-solving in the public sector are increasingly proving inadequate. In this study, we present an innovative and effective model for how institutions can use artificial intelligence to enable groups of people to generate effective solutions to urgent problems more efficiently. We describe a proven collective intelligence method, called Smarter Crowdsourcing, which is designed to channel the collective intelligence of those with expertise about a problem into actionable solutions through crowdsourcing. Then we introduce Policy Synth, an innovative toolkit which leverages AI to make the Smarter Crowdsourcing problem-solving approach both more scalable, more effective and more efficient. Policy Synth is crafted using a human-centric approach, recognizing that AI is a tool to enhance human intelligence and creativity, not replace it. Based on a real-world case study comparing the results of expert crowdsourcing alone with expert sourcing supported by Policy Synth AI agents, we conclude that Smarter Crowdsourcing with Policy Synth presents an effective model for integrating the collective wisdom of human experts and the computational power of AI to enhance and scale up public problem-solving processes.

The potential for artificial intelligence to enhance the performance of groups of people has been a topic of great interest among scholars of collective intelligence. Though many AI toolkits exist, they too often are not fitted to the needs of institutions and policymakers. While many existing approaches view AI as a tool to make crowdsourcing and deliberative processes better and more efficient, Policy Synth goes a step further, recognizing that AI can also be used to synthesize the findings from engagements together with research to develop evidence-based solutions and policies. This study contributes significantly to the fields of collective intelligence, public problem-solving, and AI. The study offers practical tools and insights for institutions looking to engage communities effectively in addressing urgent societal challenges…(More)”

Collective Intelligence in Open Policymaking

Book by Rafał Olszowski: “This book examines the nexus of collective intelligence (CI), a feature of online projects in which various types of communities solve problems intelligently, and open policymaking, as a trend of large groups of people shaping public policies.

While doing so, it presents the current state of theoretical knowledge for these concepts, many practical examples of successful and unsuccessful projects, as well as additional research and laboratory experiments. The book develops an analytical framework based on qualitative research, which is applied to the analysis of different projects in selected case studies, such as Decide Madrid; Better Reykjavik; Loomio; Deliberatorium; Civic Budget of the City of Kraków.

The book is structured into four chapters, addressing essential questions in the field: (1) Opening Policymaking; (2) Beyond the Individual: Understanding the Evolution of Collective Intelligence; (3) A Review of the Projects Using Collective Intelligence in Policymaking; (4) Online Public Debate. How Can We Make it More Intelligent?…(More)”.


About: “Twenty-first century collective intelligence- combining people’s knowledge and skills, new forms of data and increasingly, technology – has the untapped potential to transform the way we understand and act on climate change.

Collective intelligence for climate action in the Global South takes many forms: from crowdsourcing of indigenous knowledge to preserve biodiversity to participatory monitoring of extreme heat and farmer experiments adapting crops to weather variability.

This research analyzes 100+ climate case studies across 45 countries that tap into people’s participation and use new forms of data. This research illustrates the potential that exists in communities everywhere to contribute to climate adaptation and mitigation efforts. It also aims to shine a light on practical ways in which these initiatives could be designed and further developed so this potential can be fully unleashed…(More)”.

The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents

Paper by Yun-Shiuan Chuang et al: “Human groups are able to converge to more accurate beliefs through deliberation, even in the presence of polarization and partisan bias – a phenomenon known as the “wisdom of partisan crowds.” Large Language Models (LLMs) agents are increasingly being used to simulate human collective behavior, yet few benchmarks exist for evaluating their dynamics against the behavior of human groups. In this paper, we examine the extent to which the wisdom of partisan crowds emerges in groups of LLM-based agents that are prompted to role-play as partisan personas (e.g., Democrat or Republican). We find that they not only display human-like partisan biases, but also converge to more accurate beliefs through deliberation, as humans do. We then identify several factors that interfere with convergence, including the use of chain-of-thought prompting and lack of details in personas. Conversely, fine-tuning on human data appears to enhance convergence. These findings show the potential and limitations of LLM-based agents as a model of human collective intelligence…(More)”

AI-enhanced Collective Intelligence: The State of the Art and Prospects

Paper by Hao Cui and Taha Yasseri: “The current societal challenges exceed the capacity of human individual or collective effort alone. As AI evolves, its role within human collectives is poised to vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, when synergized, can achieve a level of collective intelligence that surpasses the collective capabilities of either humans or AI in isolation. However, the interactions in human-AI systems are inherently complex, involving intricate processes and interdependencies. This review incorporates perspectives from network science to conceptualize a multilayer representation of human-AI collective intelligence, comprising a cognition layer, a physical layer, and an information layer. Within this multilayer network, humans and AI agents exhibit varying characteristics; humans differ in diversity from surface-level to deep-level attributes, while AI agents range in degrees of functionality and anthropomorphism. The interplay among these agents shapes the overall structure and dynamics of the system. We explore how agents’ diversity and interactions influence the system’s collective intelligence. Furthermore, we present an analysis of real-world instances of AI-enhanced collective intelligence. We conclude by addressing the potential challenges in AI-enhanced collective intelligence and offer perspectives on future developments in this field…(More)”.

Will governments ever learn? A study of current provision and the key gaps

Paper by Geoff Mulgan: “The paper describes the history of training from ancient China onwards and the main forms it now takes. It suggests 10 areas where change may be needed and goes onto discuss how skills are learned, suggesting the need for more continuous learning and new approaches to capacity.

I hope anyone interested in this field will at least find it stimulating. I couldn’t find an overview of this kind available and so tried to fill the gap, if only with a personal view. This topic is particularly important for the UK which allowed its training system to collapse over the last decade. But the issues are relevant everywhere since the capacity of governments arguably has more impact on human wellbeing than anything else…(More)”.

How to improve economic forecasting

Article by Nicholas Gruen: “Today’s four-day weather forecasts are as accurate as one-day forecasts were 30 years ago. Economic forecasts, on the other hand, aren’t noticeably better. Former Federal Reserve chair Ben Bernanke should ponder this in his forthcoming review of the Bank of England’s forecasting.

There’s growing evidence that we can improve. But myopia and complacency get in the way. Myopia is an issue because economists think technical expertise is the essence of good forecasting when, actually, two things matter more: forecasters’ understanding of the limits of their expertise and their judgment in handling those limits.

Enter Philip Tetlock, whose 2005 book on geopolitical forecasting showed how little experts added to forecasting done by informed non-experts. To compare forecasts between the two groups, he forced participants to drop their vague weasel words — “probably”, “can’t be ruled out” — and specify exactly what they were forecasting and with what probability. 

That started sorting the sheep from the goats. The simple “point forecasts” provided by economists — such as “growth will be 3.0 per cent” — are doubly unhelpful in this regard. They’re silent about what success looks like. If I have forecast 3.0 per cent growth and actual growth comes in at 3.2 per cent — did I succeed or fail? Such predictions also don’t tell us how confident the forecaster is.

By contrast, “a 70 per cent chance of rain” specifies a clear event with a precise estimation of the weather forecaster’s confidence. Having rigorously specified the rules of the game, Tetlock has since shown how what he calls “superforecasting” is possible and how diverse teams of superforecasters do even better. 

What qualities does Tetlock see in superforecasters? As well as mastering necessary formal techniques, they’re open-minded, careful, curious and self-critical — in other words, they’re not complacent. Aware, like Socrates, of how little they know, they’re constantly seeking to learn — from unfolding events and from colleagues…(More)”.

Gamifying medical data labeling to advance AI

Article by Zach Winn: “…Duhaime began exploring ways to leverage collective intelligence to improve medical diagnoses. In one experiment, he trained groups of lay people and medical school students that he describes as “semiexperts” to classify skin conditions, finding that by combining the opinions of the highest performers he could outperform professional dermatologists. He also found that by combining algorithms trained to detect skin cancer with the opinions of experts, he could outperform either method on its own….The DiagnosUs app, which Duhaime developed with Centaur co-founders Zach Rausnitz and Tom Gellatly, is designed to help users test and improve their skills. Duhaime says about half of users are medical school students and the other half are mostly doctors, nurses, and other medical professionals…

The approach stands in sharp contrast to traditional data labeling and AI content moderation, which are typically outsourced to low-resource countries.

Centaur’s approach produces accurate results, too. In a paper with researchers from Brigham and Women’s Hospital, Massachusetts General Hospital (MGH), and Eindhoven University of Technology, Centaur showed its crowdsourced opinions labeled lung ultrasounds as reliably as experts did…

Centaur has found that the best performers come from surprising places. In 2021, to collect expert opinions on EEG patterns, researchers held a contest through the DiagnosUs app at a conference featuring about 50 epileptologists, each with more than 10 years of experience. The organizers made a custom shirt to give to the contest’s winner, who they assumed would be in attendance at the conference.

But when the results came in, a pair of medical students in Ghana, Jeffery Danquah and Andrews Gyabaah, had beaten everyone in attendance. The highest-ranked conference attendee had come in ninth…(More)”

Turning the Cacophony of the Internet’s Tower of Babel into a Coherent General Collective Intelligence

Paper by Andy E. Williams: “Increasing the number, diversity, or uniformity of opinions in a group does not necessarily imply that those opinions will converge into a single more “intelligent” one, if an objective definition of the term intelligent exists as it applies to opinions. However, a recently developed approach called human-centric functional modeling provides what might be the first general model for individual or collective intelligence. In the case of the collective intelligence of groups, this model suggests how a cacophony of incoherent opinions in a large group might be combined into coherent collective reasoning by a hypothetical platform called “general collective intelligence” (GCI). When applied to solving group problems, a GCI might be considered a system that leverages collective reasoning to increase the beneficial insights that might be derived from the information available to any group. This GCI model also suggests how the collective reasoning ability (intelligence) might be exponentially increased compared to the intelligence of any individual in a group, potentially resulting in what is predicted to be a collective superintelligence….(More)”