人的资本比模型性能更能决定人机协作预测效果
Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting
- 以真实市场数据为基准，分析个体预测者与AI协作模式
- 仅少数人通过互补思考实现超越市场的准确率
- 开放心态与求知欲是人机协同成功的关键
以往人机协作效果常以单一平均值报告。本研究使用真实资金预测市场（Polymarket）作为客观、外部验证的基准，发现人机协作价值取决于特定可测量的人类资本。个体层面分析显示，混合表现呈三态分布：多数人要么完全依赖模型（与模型一致），要么用模型佐证已有判断（表现劣于模型单独预测），而少数人进行真正互补推理，达到甚至超越市场本身的准确度（误差更低）。协作特质（换位思考、认知谦逊、好奇心）而非认知能力或模型基准，能有效区分能否进入高阶协作状态。结果初步但统计稳健，已启动预注册复制实验。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。