Abstract
In this study, a café choice problem, which is an extension of Arthur's El Farol problem, is formulated, and is examined by an agent-based simulation. In the café choice problem, N agents predict a café's "congestion," and determine which café to visit. The N agents prefer less congested cafés because they are more relaxing. The agent is assumed to know the state of congestion of only the café he visits, and he learns to predict the café's congestion. The simulation results can be summarized as follows: (i) the agent is not necessarily acquiring the correct cognition for the café's congestion, even though sufficient time goes by; (ii) the agents are bifurcated into two groups which consist of agents with cognitions that the congestion of the café they do not visit is significantly more congested than the actual congestion; and (iii) the system does not necessarily converge to a Nash equilibrium, which is a theoretical solution, when the system's structure changes.
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