通过遗憾驱动的神经网络预测人类互动学习
Davide Marchiori1, Massimo Warglien
1Interdepartmental Center for Research Training in Economics and Management (CIFREM), University of Trento, Italy.
概括
在游戏中模拟人类互动学习的神经网络准确预测行为. 基于遗憾的反显著提高了预测,在理解社会学习动态方面表现优于传统经济模型.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 游戏理论 游戏理论
背景情况:
- 人类在社会环境中的学习本质上是互动的,个人学习受到其他人的并发学习的影响.
- 游戏是交互式决策场景的标准模型.
- 了解和预测交互式学习在社会环境中至关重要.
研究的目的:
- 探索神经网络在模拟和预测在重复游戏环境中的人类互动学习中的有效性.
- 评估基于遗憾的反对这些模型的预测准确性的影响.
- 为了比较基于遗憾的神经网络模型与既有经济模型的性能.
主要方法:
- 利用设计用于模拟学习过程的简单神经网络.
- 将基于遗憾的反机制纳入学习网络.
- 在21个不同的游戏中测试了这些模型,其中包括独特的混合策略平衡.
- 与实验环境中观察到的人类行为的模型预测进行了比较.
主要成果:
- 即使是基础的神经网络与基于遗憾的反也准确地预测了重复游戏中的人类行为.
- 将遗憾纳入反机制大大提高了神经网络的预测性能.
- 与传统经济模型相比,基于遗憾的模型表现出优越的预测能力.
结论:
- 神经网络,特别是在结合基于遗憾的反时,为模拟人类交互式学习提供了强大的工具.
- 遗憾是社会学习的一个关键因素,可以通过计算模型有效地捕捉到.
- 这些发现表明,通过计算方法推进我们对交互式决策和社会学习的理解有希望的方向.
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