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.

DoubleVerify is a leading software platform for digital media measurement, data and analytics. DV's mission is to be the definitive source of transparency and data-driven insights into the quality and effectiveness of digital advertising for the world's largest brands, publishers and digital ad platforms. DV's technology platform provides advertisers with consistent and unbiased data and analytics that can be used to optimize the quality and return on their digital ad investments. Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best in class solutions across the digital advertising ecosystem, helping to build a better industry. As a Data Engineer Intern, you will work with DoubleVerify's versatile Engineering and Product teams on mission-critical systems that process billions of real-time transactions a day and solve/address complex challenges with Big-Data processing and analytics at scale.