缓解住房市场冲击:一种基于代理的强化学习方法,对实时决策支持有影响
Sedar Olmez1,2, Alison Heppenstall3, Jiaqi Ge1
1School of Geography, University of Leeds, Leeds, UK.
概括
本研究将强化学习纳入住房市场动态的基于代理的模型. 代理商学会适应经济冲击,比如调整房价和抵押贷款利率,表现出新兴的保守行为.
科学领域:
- 计算经济学计算经济学
- 基于代理的建模模型
- 机器学习在经济学中的应用
背景情况:
- 基于代理的模型 (ABM) 越来越多地用于住房市场动态,因为数据可用.
- 现有的ABM往往缺乏适应经济冲击的适应性决策能力.
- 机器学习 (ML) 为ABM框架提供了潜在的改进.
研究的目的:
- 通过使用ABM来调查外源冲击对英国房地产市场的影响.
- 在ABM中整合强化学习 (RL) 进行适应性代理行为.
- 展示应对市场波动的新兴适应性策略.
主要方法:
- 为英国房地产市场开发基于代理的模型 (ABM).
- 整合强化学习 (RL) 代理来控制抵押贷款利率.
- 模拟外源性冲击,观察市场动态和代理反应.
主要成果:
- 代理商成功地学习了实时的市场趋势,并适应了决策,以应对经济冲击.
- 该模型实现了诸如调整中位数房价的目标,而没有预先定义的规则.
- 中央银行代理人在敏感场景中表现出新兴的保守行为,与先前的研究保持一致.
结论:
- 强化学习有效地提高了ABM对住房市场冲击的适应能力.
- 开发的模型展示了新出现的代理行为和政策调整.
- 该方法可用于其他复杂的住房市场模拟.
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