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This study investigates the impact of human capital on the effectiveness of human-AI collaboration in forecasting, using a real-money prediction market as a benchmark. The findings reveal a trimodal distribution of performance among forecasters, where most either matched the AI or performed worse, while a minority exhibited complementary reasoning that led to superior accuracy. Notably, collaborative traits such as perspective-taking and intellectual humility were found to be more predictive of success than cognitive ability or model benchmarks.
Human-AI collaboration thrives on collaborative traits, not just cognitive skills, with only a minority of forecasters achieving superior accuracy through genuine engagement.
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.
