Conversational Forecasting Across Large Human Groups Using A Swarm of Surrogate AI Agents
Louis Rosenberg, Hans Schumann, Ganesh Mani, Gregg Willcox

TL;DR
This study demonstrates that real-time conversational forecasting with Surrogate AI agents significantly improves group prediction accuracy on NBA game outcomes, outperforming Vegas odds and prediction markets.
Contribution
It introduces Hyperchat AI and Thinkscape platform for large human-AI collaborative forecasting, showing substantial accuracy improvements in sports predictions.
Findings
Teams achieved 62% accuracy on NBA forecasts using Hyperchat AI.
Forecast accuracy was positively correlated with conversation rate.
The approach outperformed Vegas odds and prediction markets in accuracy.
Abstract
Hyperchat AI is a communication and collaboration architecture that employs intervening AI agents to enable real-time conversational deliberations among networked human teams of unlimited size. Prior work has shown that teams as large as 250 people can hold productive real-time conversations by text, voice, or video using Hyperchat AI to discuss complex problems, brainstorm solutions, surface risks, assess alternatives, prioritize options, and converge on optimized results. Building on this prior work, this new study tasked groups of 25 to 30 basketball fans with conversationally forecasting NBA games (against the spread) over a 12-week period. Results show that when discussing and debating NBA games (for five minutes each) using a Hyperchat AI enabled platform called Thinkscape, human teams were 62% accurate across a set of 50 forecasted NBA games. This is an impressive result versus…
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