Bank choice, bank runs, and coordination in the presence of two banks

We investigate learning in a repeated bank choice game, where agents first choose a bank to deposit in and then decide to withdraw that deposit or not. This game has a single Nash equilibrium in pure strategies, characterized by all agents depositing in the bank that offers the highest return, even though it may be more vulnerable to bankruptcy if some agents withdraw early. We use an individual evolutionary learning algorithm to model under which circumstances and with which beliefs agents can learn the Nash equilibrium in the repeated game and compare the results to an experiment. We find participants coordinating on the Nash equilibrium in the presence of low or medium vulnerability banks, but efficient coordination fails when both banks are highly vulnerable (irrespective of whether participants have full or only partial information).