The bias bias

AbstractIn marketing and finance, surprisingly simple models sometimes predict more accurately than more complex, sophisticated models. Here, we address the question of when and why simple models succeed — or fail — by framing the forecasting problem in terms of the bias–variance dilemma. Controllable error in forecasting consists of two components, the “bias” and the “variance”. We argue that the benefits of simplicity are often overlooked because of a pervasive “bias bias”: the importance of the bias component of prediction error is inflated, and the variance component of prediction error, which reflects an oversensitivity of a model to different samples from the same population, is neglected. Using the study of cognitive heuristics, we discuss how to reduce variance by ignoring weights, attributes, and dependencies between attributes, and thus make better decisions. Bias and variance, we argue, offer a more insightful perspective on the benefits of simplicity than Occam’'s razor

Against the Tide. A Critical Review by Scientists of How Physics and Astronomy Get Done

Nobody should have a monopoly of the truth in this universe. The censorship and suppression of challenging ideas against the tide of mainstream research, the blacklisting of scientists, for instance, is neither the best way to do and filter science, nor to promote progress in the human knowledge. The removal of good and novel ideas from the scientific stage is very detrimental to the pursuit of the truth. There are instances in which a mere unqualified belief can occasionally be converted into a generally accepted scientific theory through the screening action of refereed literature and meetings planned by the scientific organizing committees and through the distribution of funds controlled by "club opinions". It leads to unitary paradigms and unitary thinking not necessarily associated to the unique truth. This is the topic of this book: to critically analyze the problems of the official (and sometimes illicit) mechanisms under which current science (physics and astronomy in particular) is being administered and filtered today, along with the onerous consequences these mechanisms have on all of us.\ud \ud The authors, all of them professional researchers, reveal a pessimistic view of the miseries of the actual system, while a glimmer of hope remains in the "leitmotiv" claim towards the freedom in doing research and attaining an acceptable level of ethics in science

Getting into sync:Data-driven analyses reveal patterns of neural coupling that distinguish among different social exchanges

In social interactions, each individual's brain drives an action that, in turn, elicits systematic neural responses in their partner that drive a reaction. Consequently, the brain responses of both interactants become temporally contingent upon one another through the actions they generate, and different interaction dynamics will be underpinned by distinct forms of between-brain coupling. In this study, we investigated this by “performing functional magnetic resonance imaging on two individuals simultaneously (dual-fMRI) while they competed or cooperated with one another in a turn-based or concurrent fashion.” To assess whether distinct patterns of neural coupling were associated with these different interactions, we combined two data-driven, model-free analytical techniques: group-independent component analysis and inter-subject correlation. This revealed four distinct patterns of brain responses that were temporally aligned between interactants: one emerged during co-operative exchanges and encompassed brain regions involved in social cognitive processing, such as the temporo-parietal cortex. The other three were associated with competitive exchanges and comprised brain systems implicated in visuo-motor processing and social decision-making, including the cerebellum and anterior cingulate cortex. Interestingly, neural coupling was significantly stronger in concurrent relative to turn-based exchanges. These results demonstrate the utility of data-driven approaches applied to “dual-fMRI” data in elucidating the interpersonal neural processes that give rise to the two-in-one dynamic characterizing social interaction