learning transfers to new games. Current theories of learning model learning as adjustment in behavior in response to feedback … about outcomes and payoffs and largely ignore the possibility that learning may take place in the absence of such feedback … feedback between plays of the game. However, this previous work demonstrates this "no-feedback" learning using a special game …

Deep Reinforcement Learning for Delta Hedging of options in an utility based framework where an agent is faced with a trade … variance. In the presence of transaction costs we compare the performance of two state-of-the-art Reinforcement Learning …, and demonstrate that transfer learning is successful: hedge costs encountered by reinforced learning decrease by as much …

, 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 …

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud …-based Azure Machine Learning Studio (MLS) has a potential of expediting machine learning experiments by offering a convenient and … powerful integrated development environment. The process of evaluating machine learning models in Azure MLS has certain …

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 …

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 …

concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also …

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 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 …