在贝叶斯代理中,随机对对偏好趋同
Jordan Kemp1,2, Max-Olivier Hongler3, Olivier Gallay4
1Department of Physics, University of Chicago, Chicago, Illinois 60637, USA.
Physical review. E
|June 22, 2024
概括
这项研究模拟了代理偏好如何通过观察彼此来趋同. 学习时间影响偏好趋同和,其动态类似于随机过程.
科学领域:
- 基于代理人的建模.
- 计算经济学是计算机经济学.
- 机器学习是机器学习.
背景情况:
- 信念指导在复杂系统中的代理行为.
- 序列贝叶斯推理模型在动态条件下形成信念.
- 有限的理论存在于简单的代理相互作用的偏好进化.
研究的目的:
- 为了推导出高斯式,双向代理互动模型.
- 通过观察他人的行为来分析偏好趋同.
- 了解学习时间在偏好动态中的作用.
主要方法:
- 开发了一种高斯式,双向代理互动模型.
- 使用奥恩斯坦-乌伦贝克过程分析了收动态.
- 采用分析和计算技术.
主要成果:
- 偏好收动态模仿奥恩斯坦-乌伦贝克过程.
- 超先前的大小 (学习时间) 决定了收值和非对称偏好.
- 偏好的动态变异的特点是放松时间t*.
结论:
- 该模型增强了用于随机,交互式代理模型的工具.
- 正式化了代理互动的学习理论.
- 有助于建模主体-代理和市场理论挑战.
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