A cognitively inspired heuristic for two-armed bandit problems: The loosely symmetric (LS) model
A cognitively inspired heuristic for two-armed bandit problems: The loosely symmetric (LS) model
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两臂老虎机问题的认知启发式启发式：松散对称（LS）模型
DOI：
10.1016/j.procs.2013.10.043
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
T.
中科院分区：
文献类型：
--
作者：
Oyo;K.;Takahashi;T.
我们研究了人类因果认知的模型，该模型通常偏离经典逻辑和概率论等规范系统。对于双臂老虎机问题，我们证明了松散对称模型（LS）的有效性及其对人类特有的两种认知偏差的实现：对称性和互斥性。具体来说，我们在强化学习的框架内使用LS作为简单的价值函数。由此产生的认知偏差估值准确地描述了人类的因果直觉。我们进一步表明，在最简单的贪婪策略下操作 LS 可以产生卓越的可靠性和鲁棒性，甚至能够克服通常的速度与精度权衡，并有效地消除参数调整的需要。
We examine a model of human causal cognition, which generally deviates from normative systems such as classical logic and probability theory. For two-armed bandit problems, we demonstrate the efficacy of our loosely symmetric model (LS) and its implementation of two cognitive biases peculiar to humans: symmetry and mutual exclusivity. Specifically, we useLSas a simple value function within the framework of reinforcement learning. The resulting cognitively biased valuations precisely describe human causal intuitions. We further show that operatingLSunder the simplest greedy policy yields superior reliability and robustness, even managing to overcome the usual speed-accuracy trade-off, and effectively removing the need for parameter tuning.