风险偏好作为进化适应性学习机制的结果:在各种风险环境下的进化模拟
Shogo Homma1,2,3, Masanori Takezawa1,4,5
1Department of Behavioral Science, Graduate School of Humanities and Human Sciences, Hokkaido University, Sapporo, Hokkaido, Japan.
PloS one
|August 1, 2024
概括
这项研究表明,强化学习 (RL) 中的进化学习率导致灵活的风险偏好. 代理人发展了比负面学习率更高的积极学习率,在不同的奖励场景中实现了适应性行为.
科学领域:
- 认知科学 认知科学
- 行为经济学是一种行为经济学.
- 计算神经科学是一种神经科学.
背景情况:
- 认知和学习机制是复杂行为的基础.
- 强化学习 (RL) 模型对正和负奖励预测错误使用不同的规则.
- 风险偏好既表现出特定领域的特征,也表现出一般领域的特征.
研究的目的:
- 研究与风险偏好的学习偏差的演变.
- 探索不同的学习规则如何促进特定领域和领域一般的风险行为.
- 模拟风险下复杂决策的基础上的适应机制.
主要方法:
- 模拟了代理人的积极和消极学习率的演变.
- 暴露在不同风险环境中的代理人,具有不同的奖励结构.
- 分析了进化的学习率和由此产生的行为模式.
主要成果:
- 积极的学习率始终演变为高于负面的学习率.
- 进化的代理人表现出基于任务类型的灵活的奖励寻求和风险回避行为.
- 模拟代理人表现出与前景理论相一致的行为模式.
结论:
- 进化学习偏差可以解释风险偏好的双重性质 (特定领域和域一般).
- 较高的正比负的学习率促进了在各种风险环境中的适应性行为.
- 建议的内在学习约束框架为复杂的行为现象提供了洞察力.
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