Robust priors for regularized regression

Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for a more general model, yielding a principled way to interpolate between models of differing complexity. We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data. Models with robust priors had excellent worst-case performance. Solutions followed from the form of the heuristic that was used to derive the prior. These new algorithms can serve applications in data analysis and machine learning, as well as help in understanding how people transition from novice to expert performance

Information filters : learn from personal and social experience, or how subjectivity explains "the" learning mechanism

People learn and make decisions by selectively gathering, processing and communicating information – a complex chain of actions prone to bias. In doing so, people do not only interact directly with the environment to gather information, but also with other individuals. Indeed, social context allow more efficient learning and decision-making by mitigating individual limitations. In this thesis, personal and social learning are viewed as two complementary levels of information filtering – a recursive process, the base case of which being individual (subjective) experience via sampling. Identity representing subjectivity in a learning process is a filtering parameter on both levels. A well-established phenomenon in risky decision-making – Description-Experience Gap – was used to explore the difference of personal and social learning. Firstly, newly developed decisions-from-observation and decisions-from-description experimental paradigms showed that decision-making by social and personal experience are similar learning processes, with communication tending to decrease personal experience bias by over-reporting it. Secondly, decisions-from-description paradigm with either present or absent social source showed that people assume social source even when it is absent. Lastly, identity (mis)alignment between social source and receiver showed to affect evaluation of the source, but not the evaluation of information they delivered. Overall, by comparing and combining personal and social learning strategies, and by performing Bayesian meta-analysis, this thesis shows that personal and social levels of information filtering are closely related with identity playing a mediating role. The experimental results also suggest that personal and social information filters are two different perspectives constituting a single complementary process necessary for efficient decision-making