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Book
Improving Bayesian Reasoning: What Works and Why?
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Year: 2016 Publisher: Frontiers Media SA

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We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a normative approach to probabilistic belief revision and, as such, it is in need of no improvement. Rather, it is the typical individual whose reasoning and judgments often fall short of the Bayesian ideal who is the focus of improvement. What have we learnt from over a half-century of research and theory on this topic that could explain why people are often non-Bayesian? Can Bayesian reasoning be facilitated, and if so why? These are the questions that motivate this Frontiers in Psychology Research Topic. Bayes' theorem, named after English statistician, philosopher, and Presbyterian minister, Thomas Bayes, offers a method for updating one’s prior probability of an hypothesis H on the basis of new data D such that P(H|D) = P(D|H)P(H)/P(D). The first wave of psychological research, pioneered by Ward Edwards, revealed that people were overly conservative in updating their posterior probabilities (i.e., P(D|H)). A second wave, spearheaded by Daniel Kahneman and Amos Tversky, showed that people often ignored prior probabilities or base rates, where the priors had a frequentist interpretation, and hence were not Bayesians at all. In the 1990s, a third wave of research spurred by Leda Cosmides and John Tooby and by Gerd Gigerenzer and Ulrich Hoffrage showed that people can reason more like a Bayesian if only the information provided takes the form of (non-relativized) natural frequencies. Although Kahneman and Tversky had already noted the advantages of frequency representations, it was the third wave scholars who pushed the prescriptive agenda, arguing that there are feasible and effective methods for improving belief revision. Most scholars now agree that natural frequency representations do facilitate Bayesian reasoning. However, they do not agree on why this is so. The original third wave scholars favor an evolutionary account that posits human brain adaptation to natural frequency processing. But almost as soon as this view was proposed, other scholars challenged it, arguing that such evolutionary assumptions were not needed. The dominant opposing view has been that the benefit of natural frequencies is mainly due to the fact that such representations make the nested set relations perfectly transparent. Thus, people can more easily see what information they need to focus on and how to simply combine it. This Research Topic aims to take stock of where we are at present. Are we in a proto-fourth wave? If so, does it offer a synthesis of recent theoretical disagreements? The second part of the title orients the reader to the two main subtopics: what works and why? In terms of the first subtopic, we seek contributions that advance understanding of how to improve people’s abilities to revise their beliefs and to integrate probabilistic information effectively. The second subtopic centers on explaining why methods that improve non-Bayesian reasoning work as well as they do. In addressing that issue, we welcome both critical analyses of existing theories as well as fresh perspectives. For both subtopics, we welcome the full range of manuscript types.


Book
Identifying Urban Areas by Combining Human Judgment and Machine Learning : An Application to India
Authors: --- ---
Year: 2020 Publisher: Washington, D.C. : The World Bank,

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This paper proposes a methodology for identifying urban areas that combines subjective assessments with machine learning, and applies it to India, a country where several studies see the official urbanization rate as an under-estimate. For a representative sample of cities, towns and villages, as administratively defined, human judgment of Google images is used to determine whether they are urban or rural in practice. Judgments are collected across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. The judgment-based classification is then combined with data from the population census and from satellite imagery to predict the urban status of the sample. The Logit model, and LASSO and random forests methods, are applied. These approaches are then used to decide whether each of the out-of-sample administrative units in India is urban or rural in practice. The analysis does not find that India is substantially more urban than officially claimed. However, there are important differences at more disaggregated levels, with "other towns" and "census towns" being more rural, and some southern states more urban, than is officially claimed. The consistency of human judgment across assessors and protocols, the easy availability of crowd-sourcing, and the stability of predictions across approaches, suggest that the proposed methodology is a promising avenue for studying urban issues.


Book
The Birth of Modern Belief : Faith and Judgment from the Middle Ages to the Enlightenment
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ISBN: 0691184941 Year: 2019 Publisher: Princeton, NJ : Princeton University Press,

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An illuminating history of how religious belief lost its uncontested status in the WestThis landmark book traces the history of belief in the Christian West from the Middle Ages to the Enlightenment, revealing for the first time how a distinctively modern category of belief came into being. Ethan Shagan focuses not on what people believed, which is the normal concern of Reformation history, but on the more fundamental question of what people took belief to be.Shagan shows how religious belief enjoyed a special prestige in medieval Europe, one that set it apart from judgment, opinion, and the evidence of the senses. But with the outbreak of the Protestant Reformation, the question of just what kind of knowledge religious belief was-and how it related to more mundane ways of knowing-was forced into the open. As the warring churches fought over the answer, each claimed belief as their exclusive possession, insisting that their rivals were unbelievers. Shagan challenges the common notion that modern belief was a gift of the Reformation, showing how it was as much a reaction against Luther and Calvin as it was against the Council of Trent. He describes how dissidents on both sides came to regard religious belief as something that needed to be justified by individual judgment, evidence, and argument.Brilliantly illuminating, The Birth of Modern Belief demonstrates how belief came to occupy such an ambivalent place in the modern world, becoming the essential category by which we express our judgments about science, society, and the sacred, but at the expense of the unique status religion once enjoyed.

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