图 书 馆
经验决策中的效用加权抽样Utility-weighted sampling in decisions from experience |
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| 课程网址: | http://videolectures.net/rldm2015_lieder_utility_weighted/ |
| 主讲教师: | Falk Lieder |
| 开课单位: | 加州大学伯克利分校 |
| 开课时间: | 2015-07-28 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | People overweight extreme events in decision-making and overestimate their frequency. Previous theoretical work has shown that this apparently irrational bias could result from utility-weighted sampling–a decision mechanism that makes rational use of limited computational resources (Lieder, Hsu, & Griffiths, 2014). Here, we show that utility-weighted sampling can emerge from a neurally plausible associative learning mechanism. Our model explains the over-weighting of extreme outcomes in repeated decisions from experience (Ludvig, Madan, & Spetch, 2014), as well as the overestimation of their frequency and the underlying memory biases (Madan, Ludvig, & Spetch, 2014). Our results support the conclusion that utility drives probability-weighting by biasing the neural simulation of potential consequences towards extreme values. |
| 关 键 词: | 联想学习; 资源; 加权抽样 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-14：yxd |
| 最后编审: | 2020-12-14：yxd |
| 阅读次数: | 52 |