Observational Learning with Position Uncertainty

Observational learning is typically examined when agents have precise information about their position in the sequence of play. We present a model in which agents are uncertain about their positions. Agents are allowed to have arbitrary ex-ante beliefs about their positions: they may observe their position perfectly, imperfectly, or not at all. Agents sample the decisions of past 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. In cases with bounded signal strength, we show that agents achieve what we define as constrained efficient learning: individuals do at least as well as the most informed agent would do in isolation.social learning; information aggregation; herds; position uncertainty; observational learning

Observational-Interventional Priors for Dose-Response Learning

Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treatment is not controlled by a known mechanism, is sometimes available. Under some strong assumptions, observational data allows for the estimation of dose-response curves. Estimating such curves nonparametrically is hard: sample sizes for controlled interventions may be small, while in the observational case a large number of measured confounders may need to be marginalized. In this paper, we introduce a hierarchical Gaussian process prior that constructs a distribution over the dose-response curve by learning from observational data, and reshapes the distribution with a nonparametric affine transform learned from controlled interventions. This function composition from different sources is shown to speed-up learning, which we demonstrate with a thorough sensitivity analysis and an application to modeling the effect of therapy on cognitive skills of premature infants

Learning By Not Doing: An Experimental Investigation of Observational Learning

In this paper we present experimental evidence suggesting that observational learning (i.e. learning-by-not-doing but by observing) may outperform learning-by-doing.LEARNING

Learning By Not Doing: An Experimental Investigation of Observational Learning

In this paper we present experimental evidence suggesting that observational learning (i.e. learning-by-not-doing but by observing) may outperform learning-by-doing.LEARNING

How does observational learning produce placebo effects? : a model integrating research findings

There is a growing body of evidence proving that observational learning, in addition to classical conditioning and verbal suggestions, may induce both placebo analgesia and nocebo hyperalgesia. However, much less is known about the mechanisms and factors influencing placebo effects induced by observational learning. The paper critically reviews the research findings in the field in the context of Bandura’s social learning theory. We apply Bandura’s taxonomy of the sources of social learning (behavioral, symbolic, and verbal modeling) and discuss the results of previous studies. Critical points in the placebo effects induced by observational learning are identified.We discuss aspects of behavior presented by the model (both verbal and nonverbal) involved in the formation of placebo effects induced by observational learning as well as the role of expectancies in this process. As a result, we propose a model that integrates the existing research findings. The model shows the main ways of transmission of painrelated information from the model to the observer. It highlights the role of expectancies and the individual characteristics of the observer in formation of placebo analgesia and nocebo hyperalgesia induced by observational learning. Finally, we propose future research directions based on our model