Dynamic Network Formation with Reinforcement Learning
نویسنده
چکیده
I examine a dynamic model of network formation in which individuals use reinforcement learning to choose their actions. Typically, economic models of network formation assume the entire network structure to be known to all individuals involved. The introduction of reinforcement learning allows us to relax this assumption. Q-learning is a reinforcement learning algorithm from the artificial intelligence literature that allows for state-dependent learning. Using Q-learning, one may allow for varying degrees of information available to the agents. I determine what networks, if any, the model may converge to in the limit. JEL classification: D85, D83, C73
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