We develop a framework for studying the introduction of a new payment method in a controlled laboratory environment, where consumers (buyers) and merchants (sellers) can learn to coordinate their adoption decisions over time. The underlying game exhibits network adoption effects as emphasized by the theoretical literature. We elicit players’ beliefs about the adoption decisions of the other side of the market so that we can directly test for network effects. We investigate how the additional fixed cost of adopting the new payment method, relative to its savings on per transaction costs, affects merchant's decisions to adopt the new payment method and how that in turn affects buyer's adoption decisions. We find that a low fixed cost favors quick adoption of the new payment method by all participants, while for a sufficiently high fixed cost, merchants gradually learn to reject the new payment method. We also find strong evidence of network effects and that the fixed costs are important for the strong response of seller acceptance decisions to buyer adoption decisions. An evolutionary learning model provides a good characterization of the dynamic adjustment paths found in our experimental data.

Correlated equilibrium (Aumann, 1974, 1987) is an important generalization of the Nash equilibrium concept for multiplayer non-cooperative games. In a correlated equilibrium, players rationally condition their strategies on realizations of a common external randomization device and, as a consequence, can achieve payoffs that Pareto dominate any of the game's Nash equilibria. In this paper we explore whether such correlated equilibria can be learned over time using an evolutionary learning model where agents do not start with any knowledge of the distribution of random draws made by the external randomization device. Furthermore, we validate our learning algorithm findings by comparing the end behavior of simulations of our algorithm with both the correlated equilibrium of the game and the behavior of human subjects that play that same game. Our results suggest that the evolutionary learning model is capable of learning the correlated equilibria of these games in a manner that approximates well the learning behavior of human subjects and that our findings are robust to changes in the specification and parameterization of the model.