PhD Student

My website.

Research Interests

• Macroeconomics
• Expectation formation
• Deep reinforcement learning algorithms and artificial intelligent techniques

Working Paper

• Learning to make consumption-saving decisions in an adaptive environment: an AI approach. Current version.

  Abstract: This exercise introduces AI algorithms to economic models highlighting how the two can be blended and produced innovative agents’ learning mechanism. In particular, this AI agent has limited or no information on the underlying economic structure and his own preference. It is able to learn in real time through constantly interacting with the environment and adjust its actions accordingly. I illustrate that the economic agent under deep reinforcement learning is adaptive to changes in the environment in real time. The AI agent’s learning characteristics, governed by parameters within the learning algorithms, can also be adjusted, which leads to sophisticated learning behaviours with welfare implications. The chosen economic structural can be generalised to other decision-making processes and economic models.

Selected Grants and Awards