超越黑箱：人类随机化失败的可解释模型
Beyond the Black Box: Interpretable Models of Human Randomisation Failures
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- Alpen-Adria-University of Klagenfurt（克拉根福阿尔卑斯-亚得里亚大学）
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中文总结 AI 辅助
本文以奥尼尔零和纸牌游戏为研究场景，通过对比各类模型，探究能否用可解释模型替代LSTM等黑箱序列模型预测人类随机化失败行为，发现玩家对自身近期行动历史的管理是关键可解释信号。
中文摘要 AI 辅助
混合策略均衡预测独立同分布博弈：过去行动不应帮助预测未来决策。然而人类玩家系统地偏离这一基准，在奥尼尔零和纸牌游戏中，这些偏离可通过LSTM等黑箱序列模型预测。本文探究透明替代模型能否在实现该预测能力的同时揭示其背后的行为结构。利用来自2802对玩家的84060次决策，分析先将朴素模型、行为模型与可解释机器学习及深度学习模型进行基准测试，再将前人研究的修正EWA模型与这些基准对比，并用LASSO诊断法提出进一步嵌套频率追踪扩展。结果显示，重复或规避行为（尤其是玩家对自身近期行动历史的管理）构成了大部分可解释且具策略可利用性的信号，而频率追踪在样本外预测中贡献甚微。
英文摘要
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 predicted by black box sequence models such as LSTMs. This paper asks whether that predictive power can be achieved by transparent alternatives that also reveal the behavioural structure behind it. Using 84,060 decisions from 2,802 pairs, the analysis first benchmarks naive and behavioral models against interpretable machine learning and deep learning models, then evaluates the modified EWA specifications of prior work against these benchmarks and uses the LASSO diagnostics to motivate a further nested frequency tracking extension. The results show that repeat or avoid behavior, especially players' management of their own recent action histories, accounts for most of the interpretable and strategically exploitable signal, while frequency tracking adds little out of sample.
