大语言模型的战略前瞻性：来自一个前瞻性创业竞赛的证据
The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament
AI总结：
大语言模型在战略前瞻性任务中超越人类，通过前瞻性创业竞赛验证其预测能力。
摘要：
人工智能能否在战略前瞻性——即在事件发生前准确判断不确定的高风险结果的能力上超越人类？我们通过一个完全前瞻性预测竞赛来回答这个问题，利用正在筹集资金的Kickstarter众筹项目。三十个美国技术初创企业，在所有研究模型的训练截止日期之后启动，在筹集资金仍在进行且结果未知的情况下进行评估。一套多样化的前沿和开放权重大语言模型（LLMs）完成了870对 pairwise 比较，产生了预测资金筹集成功的完整排名。我们这些预测与通过Prolific招募的346名经验丰富的管理者以及三个在受监控条件下工作的MBA培训投资者进行了基准测试。结果令人震惊：人类评估者与实际结果的排名相关性在0.04到0.45之间，而几个前沿LLMs超过了0.60，其中最好的（Gemini 2.5 Pro）达到了0.74——正确地将几乎每五对初创企业中的四对排序。这些差异在多种性能指标和稳健性检查中持续存在。群体智慧集合或人机混合团队并未超越最好的单一模型。
英文摘要：
Can artificial intelligence outperform humans at strategic foresight -- the capacity to form accurate judgments about uncertain, high-stakes outcomes before they unfold? We address this question through a fully prospective prediction tournament using live Kickstarter crowdfunding projects. Thirty U.S.-based technology ventures, launched after the training cutoffs of all models studied, were evaluated while fundraising remained in progress and outcomes were unknown. A diverse suite of frontier and open-weight large language models (LLMs) completed 870 pairwise comparisons, producing complete rankings of predicted fundraising success. We benchmarked these forecasts against 346 experienced managers recruited via Prolific and three MBA-trained investors working under monitored conditions. The results are striking: human evaluators achieved rank correlations with actual outcomes between 0.04 and 0.45, while several frontier LLMs exceeded 0.60, with the best (Gemini 2.5 Pro) reaching 0.74 -- correctly ordering nearly four of every five venture pairs. These differences persist across multiple performance metrics and robustness checks. Neither wisdom-of-the-crowd ensembles nor human-AI hybrid teams outperformed the best standalone model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
