Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Vivienne Ming
• Venue: arXiv: cs.CY
• Published: 2026-07-02
• Collected: 2026-07-03
• URL: http://arxiv.org/abs/2607.02467v1
• PDF: https://arxiv.org/pdf/2607.02467v1
• DOI: UNKNOWN

Plain-Language Brief

The study shows that the effectiveness of human-AI collaboration in forecasting depends on specific forms of human capital such as perspective-taking, intellectual humility, and curiosity. Most people either deferred to the model or used it to confirm their prior guesses, while a minority engaged in complementary reasoning and achieved results matching or exceeding the market itself.

What the Document Says

• The effectiveness of human-AI collaboration is measured using a real-money prediction market (Polymarket) as a benchmark.
• The performance of human-AI collaboration varies among individuals and can be categorized into three modes: deferring to the model, using the model to confirm prior guesses, or engaging in complementary reasoning with lower error than the market itself.
• Collaborative traits such as perspective-taking, intellectual humility, and curiosity distinguish those who engage in complementary reasoning.
• The results are preliminary but statistically robust.

Key Concepts Mentioned

• human-AI collaboration
• real-money prediction market
• hybrid performance
• collaborative traits
• perspective-taking
• intellectual humility
• curiosity

Claims Made by the Document

• The value of human-AI collaboration depends on a specific, measurable form of human capital.
• Collaborative traits distinguish those who engage in complementary reasoning.

Evidence Used

• Using a real-money prediction market as an objective, externally resolved benchmark
• Analyzed at the level of the individual forecaster

Methods / Approach

• Analyzing human-AI collaboration at the individual level using a real-money prediction market

Limitations Stated by the Authors

• The results are preliminary

Limitations Noted by the Agent

Agent interpretation — not from the source.

• This is a single study and its findings need to be replicated for further validation.

Open Questions

• What are the long-term implications of human-AI collaboration in forecasting?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The text does not provide information on the specific nature of the prediction market or the number of participants.

Raw Abstract / Excerpt

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.