Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting
2026-07-02 • Computers and Society
Computers and SocietyArtificial Intelligence
AI summaryⓘ
The authors looked at how people and AI work together when predicting outcomes on a real-money prediction market. They found that most people either just copied the AI's guesses or ignored useful AI advice, but a small group combined their thinking with the AI in a way that made their predictions more accurate than either alone. This success was linked to traits like being open-minded and curious rather than just intelligence or how good the AI was. Their findings are early but reliable and they plan to test this again with more data.
human-AI collaborationprediction marketPolymarketforecastingintellectual humilitycuriositycomplementary reasoningcognitive abilityhybrid performance
Authors
Vivienne Ming
Abstract
Whether pairing people with AI helps or hurts is usually reported as a single average effect. Using a real-money prediction market (Polymarket) as an objective, externally resolved benchmark, this pilot shows that the value of human-AI collaboration depends on a specific, measurable form of human capital. Analyzed at the level of the individual forecaster, hybrid performance is trimodal: most people either deferred to the model (matching it) or used it to rubber-stamp a prior guess (performing worse than the model alone), while a minority engaged in genuine complementary reasoning and reached accuracy matching or even exceeding (i.e., lower error than) the market itself. Collaborative traits (perspective-taking, intellectual humility, and curiosity) rather than raw cognitive ability or model benchmarks, distinguished who reached that mode. The results are preliminary but statistically robust, and motivate a pre-registered replication now in preparation.