论文标题
移动费用对社会隔离的影响：与RL和ABM的模拟
The impact of moving expenses on social segregation: a simulation with RL and ABM
论文作者
论文摘要
在过去的几十年中，诸如增强学习（RL）和基于代理的建模（ABM）之类的突破使经济模型的模拟可行。最近，人们对应用ABM来研究住宅偏好对Schelling种族隔离模型中邻里种族隔离的影响一直在越来越多。在本文中，将RL与ABM结合使用，以模拟修改的Schelling隔离模型，该模型将移动费用作为输入参数。特别是，深Q网络（DQN）被用作RL代理的学习算法，以模拟家庭的行为及其偏好。本文研究了移动费用对整体种族隔离模式及其在社会融合中的作用的影响。对隔离模型进行了更全面的模拟，旨在政策制定者预测其政策的潜在后果。
Over the past decades, breakthroughs such as Reinforcement Learning (RL) and Agent-based modeling (ABM) have made simulations of economic models feasible. Recently, there has been increasing interest in applying ABM to study the impact of residential preferences on neighborhood segregation in the Schelling Segregation Model. In this paper, RL is combined with ABM to simulate a modified Schelling Segregation model, which incorporates moving expenses as an input parameter. In particular, deep Q network (DQN) is adopted as RL agents' learning algorithm to simulate the behaviors of households and their preferences. This paper studies the impact of moving expenses on the overall segregation pattern and its role in social integration. A more comprehensive simulation of the segregation model is built for policymakers to forecast the potential consequences of their policies.
