  Many socioeconomic phenomena, such as technology adoption, collaborative
problem-solving, and content engagement, involve a collection of agents
coordinating to take a common action, aligning their decisions to maximize
their individual goals. We consider a model for networked interactions where
agents learn to coordinate their binary actions under a strict bound on their
rationality. We first prove that our model is a potential game and that the
optimal action profile is always to achieve perfect alignment at one of the two
possible actions, regardless of the network structure. Using a stochastic
learning algorithm known as Log Linear Learning, where agents have the same
finite rationality parameter, we show that the probability of agents
successfully agreeing on the correct decision is monotonically increasing in
the number of network links. Therefore, more connectivity improves the accuracy
of collective decision-making, as predicted by the phenomenon known as Wisdom
of Crowds. Finally, we show that for a fixed number of links, a regular network
maximizes the probability of success. We conclude that when using a network of
irrational agents, promoting more homogeneous connectivity improves the
accuracy of collective decision-making.
