, Simpson, and Gelman(2018), to a setting where dynamic learning occurs about features of predictive densities of possibly … misspecified models. This improves the averaging process of good and bad model forecasts. We summarise how this learning is done in … connection with machine learning. We illustrate our suggestion using results from Ba̧stürk, Borowska, Grassi, Hoogerheide, and …

The adaptive learning approach has been fruitfully employed to model the formation of aggregate expectations at the … macroeconomic level, as an alternative to rational expectations. This paper uses adaptive learning to understand, instead, the … sentiment, defined as the degrees of excess optimism and pessimism that cannot be justified by the near-rational learning model …

Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading …

statistical structure in this scientific learning problem, techniques of model selection and meta-analysis are not directly useful …. Combining Bayesian and frequentist approaches, I derive simple, intuitive rules for updating beliefs. The theory incorporates … accuracy and general applicability. I establish necessary and sufficient conditions for the consistency of the learning …

The adaptive learning approach has been fruitfully employed to model the formation of aggregate expectations at the … macroeconomic level, as an alternative to rational expectations. This paper uses adaptive learning to understand, instead, the … sentiment, defined as the degrees of excess optimism and pessimism that cannot be justified by the near-rational learning model …

We study observational learning among agents with coordination motives. On a discrete time line, communities of agents … observations are perfectly uncorrelated, asymptotic learning becomes achievable. Herding can also be eliminated by making private …

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 …

We study learning in the Kydland and Prescott environment. Our policy maker evaluates its potential strategies … light of the experimental findings, our model is of particular interest as it is the only agent-based or adaptive learning …

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