Can an AI agent hit a moving target
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 according
to the agent's experience in the world.
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