Reasoning And Choice

A major new theoretical explanation of how ordinary people decide what to favour and what to oppose politically.

Advances in the social sciences are used to uncover cognitive foundations of social decision making.

In democratic societies, opinion polls play a vital role. But it has been demonstrated that many people do not have an opinion about major issues--the "nonattitudes" problem. Also, the framing of questions in different ways can generate very different estimates of public opinion--the "framing" effect. Both dilemmas raise questions about the competence of ordinary citizens to play the role a democratic society ostensibly expects of them. Although the impact of some factors is well established, particularly political information and sophistication, much is yet to be understood. Building on and reaching beyond themes in the work of Philip Converse, one of the pioneers in the study of public opinion, Studies in Public Opinion brings together a group of leading American and European social scientists to explore a number of new factors, with a particular emphasis on the structure of political choices. In twelve chapters that reflect different perspectives on how people form political opinions and how these opinions are manipulated, this book offers an unparalleled view of the state-of-the-art research on these important questions as it has developed on two continents. The contributors include Matthew K. Berent, Jaak Billiet, George Y. Bizer, Paul R. Brewer, John Bullock, Danielle Bütschi, Michael Guge, Hanspeter Kriesi, Jon A. Krosnick, Milton Lodge, Michael F. Meffert, Peter Neijens, Willem E. Saris, Paul M. Sniderman, Marco R. Steenbergen, Marc Swyngedouw, Sean M. Theriault, William van der Veld, Penny S. Visser, Hans Waege, and John Zaller.

This work develops logical theories necessary to understand adaptable human reasoning & the design ofintelligent systems. It unifies lively & significant strands of research in logic, philosophy, economics & artificial intelligence.

The assumption that rewards and punishments influence our choices between different courses of action underlies economic, sociological, psychological, and legal thinking about human action. Hence, the notion of a reasoning criminal--one who employs the same sorts of cognitive strategies when contemplating offending as they and the rest of us use when making other decisions--might seem a small contribution to crime control. This conclusion would be mistaken. This volume develops an alternative approach, termed the "rational choice perspective," to explain criminal behavior. Instead of emphasizing the differences between criminals and non-criminals, it stresses some of the similarities. In particular, while the contributors do not deny the existence of irrational and pathological components in crimes, they suggest that the rational aspects of offending should be explored. An international group of researchers in criminology, psychology, and economics provide a comprehensive review of original research on the criminal offender as a reasoning decision maker. While recognizing the crucial influence of situational factors, the rational choice perspective provides a framework within which to incorporate and locate existing theories about crime. In doing so it also provides both a new agenda for research and sheds a fresh light on deterrent and prevention policies.

The first textbook to explain the principles of epistemic game theory.

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.

The work of Daniel Kahneman and Amos Tversky has transformed the study of judgment and decision-making, and penetrated related disciplines such as economics, finance, marketing, law and medicine. In recognition of these achievements, Kahneman was awarded the Nobel Prize for Economics in 2003. This special issue presents ongoing research inspired by both Kahneman and Tversky. It covers many of the central themes the heuristics and biases of judgment and prediction, framing effects, assessments and predictions of utility that made their work so innovative. The specially written papers illustrate the range and depth of this work, and emphasise its continued relevance to current research.