We report two information cascade game experiments that directly test the impact of altruism on observational learning … opportunities to learn about the strategies played by observed which amplifies the impact of altruism on observational learning …

The paper investigates social-learning when the information structure is not commonly known. Individuals repeatedly … interact in social-learning settings with distinct information structures. In each round of interaction, they use their … behavior in the long-run if and only if individuals distinguish social-learning settings and receive rich feedback after each …

This paper introduces a model of boundedly rational observational learning, which is rationally founded and applicable …

Observational learning theories often assume that people's actions can be observed. However, in many naturally … learning under optional disclosure conditions. We further examine empirically how individuals decide whether to reveal …

We analyse the dynamics resulting from social learning in a simple general equilibrium (GE) model, whose structure is … social learning. We discuss the robustness of the symmetric optimal equilibrium of this GE model, by testing the ability of … agents to coordinate on it through this learning scheme in the absence of substantive rationality and perfect information …

We introduce learning in a dynamic game of international pollution, with ecological uncertainty. We characterize and … ecological uncertainty but they gain information (learn) about it. We then compare our learning model with the benchmark model of … anticipative learning induces a decrease in total emissions, but not necessarily in individual emissions. Further, the effect of …

Observational learning is typically examined when agents have precise information about their position in the sequence … individuals and receive a private signal about the state of the world. We show that social learning is robust to position … uncertainty. Under any sampling rule satisfying a stationarity assumption, learning is complete if signal strength is unbounded …

While payoff-based learning models are almost exclusively devised for finite action games, where players can test every … action, it is harder to design such learning processes for continuous games. We construct a stochastic learning rule …

We establish convergence of beliefs and actions in a class of one-dimensional learning settings in which the agent …. Applications of our framework include learning by a person who has an incorrect model of a technology she uses or is overconfident … about herself, learning by a representative agent who may misunderstand macroeconomic outcomes, as well as learning by a …