Learning from Zero: How to Make Consumption-Saving Decisions in a Stochastic Environment with an AI Algorithm
Current version: arXiv 2105.10099 (February 2022)
Previous version: CESifo Working Paper version: No. 9255 (August 2021)
This exercise proposes a learning mechanism to model economic agent's decision-making process using an actor-critic structure in the literature of artificial intelligence. It is motivated by the psychology literature of learning through reinforcing good or bad decisions. In a model of an environment, to learn to make decisions, this AI agent needs to interact with its environment and make explorative actions. Each action in a given state brings a reward signal to the agent. These interactive experience is saved in the agent's memory, which is then used to update its subjective belief of the world. The agent's decision-making strategy is formed and adjusted based on this evolving subjective belief. This agent does not only take an action that it knows would bring a high reward, it also explores other possibilities. This is the process of taking explorative actions, and it ensures that the agent notices changes in its environment and adapt its subjective belief and decisions accordingly. Through a model of stochastic optimal growth, I illustrate that the economic agent under this proposed learning structure is adaptive to changes in an underlying stochastic process of the economy. AI agents can differ in their levels of exploration, which leads to different experience in the same environment. This reflects on to their different learning behaviours and welfare obtained. The chosen economic structure possesses the fundamental decision making problems of macroeconomic models, i.e., how to make consumption-saving decisions in a lifetime, and it can be generalised to other decision-making processes and economic models.
Can an AI agent hit a moving target?
Current version: October 2021.
As the economies we live in are evolving over time, it is imperative that economic agents in models form expectations that can adjust to changes in the environment. This exercise offers a plausible expectation formation model that connects to computer science, psychology and neural science research on learning and decision-making, and applies it to an economy with a policy regime change. Employing the actor-critic model of reinforcement learning, the agent born in a fresh environment learns through first interacting with the environment. This involves taking exploratory actions and observing the corresponding stimulus signals. This interactive experience is then used to update its subjective belief about the world. I show, through several simulation experiments, that the agent adjusts its subjective belief facing an increase of inflation target. Moreover, the subjective belief evolves based on this agent’s experience in the world.
Deep Reinforcement Learning in a Monetary Model
joint with Mingli Chen, Andreas Joseph, Michael Kumhof, Xinlei Pan, and Xuan Zhou.
April 2021. Rebuilding Macroeconomics Working Paper Series No.58
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