竞争性定价使用基于模型的盗
Lukasz Sliwinski1, Tanut Treetanthiploet2,3, David Siska1
1Maxwell Institute for Mathematical Sciences, School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD UK.
概括
这项研究为学习算法引入了一个新的定价环境. 环境建模在静态环境中提高了性能,但在竞争激烈的非静态市场中却没有.
科学领域:
- 机器学习 机器学习
- 市场动态 市场动态
- 算法游戏理论 算法游戏理论
背景情况:
- 学习算法越来越多地用于自动调整价格.
- 标准算法假定无关联和静止的奖励分布,这在竞争性市场是不现实的.
- 这种假设限制在动态环境中阻碍了有效的价格调整策略.
研究的目的:
- 引入一个新的定价环境,更好地反映市场现实.
- 为了研究这种环境中独特的纳什平衡的条件.
- 开发和评估用于固定和非固定竞争市场的价格调整的盗算法.
主要方法:
- 引入一个新的定价环境模型.
- 对纳什平衡存在条件的分析.
- 开发一个包含环境结构的盗算法.
- 对非静止设置的算法的扩展.
- 在固定和竞争性定价场景中进行数值测试.
主要成果:
- 拟议的定价环境允许验证独特的纳什平衡条件.
- 开发了一个盗算法,该算法近似于定价环境的结构.
- 静止环境的建模在静止环境中增强了算法性能.
- 然而,这种结构建模在非静止的竞争性定价场景中没有带来好处.
结论:
- 将环境结构纳入学习算法可以提高固定市场的性能.
- 环境结构建模的好处并不延伸到非静止的竞争性定价环境.
- 对于在动态市场竞争中适应性学习算法的进一步研究是必要的.
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