taking an (irreversible) action. We define \textit{asymptotic learning} as the fraction of agents taking the correct action … converging to one as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under … ``information hubs'', which receive and distribute a large amount of information. Asymptotic learning therefore requires information …

This paper examines social learning when only one of the two types of decisions is observable. Because agents arrive …, as in the standard model, learning is complete if and only if signals are unbounded. If signals are bounded, cascades may …

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 …

Crowd-sourced recommender platforms organize social learning about products by recommending items based on information … study how market structure affects experimentation, social learning, and welfare. I present a model of competing platforms …

I experimentally investigate observational (social) learning in the simple two-armed bandit framework where the models …, but not in a random way. In addition, the Quantal Response Equilibrium is tested and the robustness of the theory …

. We find that the firm encourages social learning for a greater range of beliefs and has greater expected revenue when it …

I present a model of social learning over an exogenous, directed network that may be readily nested within broader …

Information-based reinforcement learning is effective for trading and price discovery in limit order markets. It helps …-run momentum of the informed market orders. The learning increases liquidity supply of uninformed and liquidity consumption of … price discovery. The results shed a light into the market practice of using machine learning in limit order markets …