2026-05-26 ミシガン大学
＜関連情報＞
- https://news.umich.edu/ai-outperformed-humans-in-a-prediction-tournament-featuring-technology-ventures/
- https://arxiv.org/abs/2602.01684
LLMの戦略的先見性：完全将来を見据えたベンチャートーナメントからの証拠 The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament
Felipe A. Csaszar, Aticus Peterson, Daniel Wilde
arXiv Submitted on 2 Feb 2026
DOI:https://doi.org/10.48550/arXiv.2602.01684
Abstract
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.
