English(EN) Beyond the Black Box: Interpretable Models of Human Randomisation Failures

可解释的AI模型揭示了游戏中人类的决策模式

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-10 04:00

研究人员探索了可解释的机器学习模型，以理解游戏中人类的决策，特别是偏离预测的独立同分布（i.i.d.）博弈行为。通过分析来自零和纸牌游戏的84,000多项决策，该研究将LSTMs等黑箱序列模型与透明模型进行了比较。研究结果表明，诸如重复或避免过去行为等可预测的行为，特别是关于玩家自身近期历史的管理，占了大部分具有战略意义的信号，而频率跟踪的预测价值很小。 AI

影响为人类行为建模提供了见解，有望提高AI预测和与人类决策互动的能力。

排序理由该集群包含一篇发表在arXiv上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Ngoc Linh Dao · 2026-08-10 04:00

超越黑箱：人类随机化故障的可解释模型

arXiv:2608.07220v1 Announce Type: new Abstract: Mixed strategy equilibrium predicts i.i.d play: past actions should not help predict future decisions. Human players, however, systematically depart from this benchmark, and in O'Neill's zero sum card game, these departures can be p…

本文如何被排名

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超越黑箱：人类随机化故障的可解释模型

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