- Expectation formation
- Deep reinforcement learning algorithms and artificial intelligent techniques
Learning from Zero: How to Make Consumption-Saving Decisions in a Stochastic Environment with an AI Algorithm
Abstract: This exercise offers an innovative learning mechanism to model economic agent’s decision-making process using a deep reinforcement learning algorithm. In particular, this AI agent is born in an economic environment with no information on the underlying economic structure and its own preference. I model how the AI agent learns from square one in terms of how it collects and processes information. It is able to learn in real time through constantly interacting with the environment and adjusting its actions accordingly (i.e., online learning). I illustrate that the economic agent under deep reinforcement learning is adaptive to changes in a given environment in real time. AI agents differ in their ways of collecting and processing information, and this leads to different learning behaviours and welfare distinctions. The chosen economic structure can be generalised to other decision-making processes and economic models.
Selected Grants and Awards
- ESRC/ Rebuilding Macroeconomics Grant, 2019 (Co-investigator)
- Financial Economics: 3rd year BSc Economics module, Term 2 2019/20
- Intermediate Econometrics: 2nd year BSc Economics module, Term 2 2019/20
Deep Learning in a New Keynesian Model of Banking and Money Creation (July 2019 - July 2020)
Other partners: Mingli Chen, Andreas Joseph, Michael Kumhof, and Xinlei Pan.