Confidence biases and learning among intuitive Bayesians

We design a double-or-quits game to compare the speed of learning one’s specific ability with the speed of rising confidence as the task gets increasingly difficult. We find that people on average learn to be overconfident faster than they learn their true ability and we present an intuitive-Bayesian model of confidence which integrates confidence biases and learning. Uncertainty about one’s true ability to perform a task in isolation can be responsible for large and stable confidence biases, namely limited discrimination, the hard–easy effect, the Dunning–Kruger effect, conservative learning from experience and the overprecision phenomenon (without underprecision) if subjects act as Bayesian learners who rely only on sequentially perceived performance cues and contrarian illusory signals induced by doubt. Moreover, these biases are likely to persist since the Bayesian aggregation of past information consolidates the accumulation of errors and the perception of contrarian illusory signals generates conservatism and under-reaction to events. Taken together, these two features may explain why intuitive Bayesians make systematically wrong predictions of their own performance.     

Migration expectancy revisited: Results for the 1970s, 1980s and 1990s

Planners in a variety of situations require an improved understanding of migration trends if services and products that adequately meet constituent needs are to be provided. This note focuses on changes in migration expectancy over three decades in the context of the planning function. Using the CPS one-year migration question for the periods 1975–1976, 1980–1981, and 1987–1991, and the work of Wilber (1963) and Long (1973) as historical benchmarks, migration expectancy is found to have fallen since the earlier studies. Longer-distance migration (between counties and between states) has remained relatively constant over the same period so that these types of moves now account for a larger proportion of total residential mobility. The results are discussed in the context of their value to individuals and organizations who seek a better understanding of demographically-driven demand from services and products.This article is based on a paper presented at the Southern Demographic Association annual meeting, New Orleans, Louisiana, 22 October 1993.     

PREFERENCES FOR REDISTRIBUTION AND PERCEPTION OF FAIRNESS: AN EXPERIMENTAL STUDY

We conduct a laboratory experiment to study how demand for redistribution of income depends on self‐interest, insurance motives, and social concerns relating to inequality and efficiency. Our choice environments feature large groups of subjects and real‐world framing, and differ with respect to the source of inequality (earned or arbitrary), the cost of taxation to the decision maker, the dead‐weight loss of taxation, uncertainty about own pretax income, and whether the decision maker is affected by redistribution. We estimate utility weights for the different sources of demand for redistribution, with the potential to inform modeling in macroeconomics and political economy     

Clarifying Types of Uncertainty: When Are Models Accurate,and Uncertainties Small?

Professor Aven has recently noted the importance of clarifying the meaning of terms such as "scientific uncertainty" for use in risk management and policy decisions, such as when to trigger application of the precautionary principle. This comment examines some fundamental conceptual challenges for efforts to define "accurate" models and "small" input uncertainties by showing that increasing uncertainty in model inputs may reduce uncertainty in model outputs; that even correct models with "small" input uncertainties need not yield accurate or useful predictions for quantities of interest in risk management (such as the duration of an epidemic); and that accurate predictive models need not be accurate causal models.     

Application of commercial software to the classification of suicide cases: a brief report

Mosaic Profiler software was used to classify suicide and open verdict cases during 1996 to 1998 in England and within England, for the London and the North West regions. The classification system was based on the socioeconomic characteristics of the last place of residence of the cases at the level of postcode. The results highlighted that deprived areas and areas that contain elderly population or those areas that suffer from lack of social cohesion are overrepresented, whereas affluent areas are underrepresented. All of these, although in the larger scale, seem to support the results of other studies. Nevertheless, more studies would be required before one can fully evaluate the application of the Mosaic Profiler in the field of spatial epidemiology.     

Collective Action—and Then What?

Two aspects of the social psychology of collective action are of particular interest to social movement organizers and activists: how to motivate people to engage in collective action, and how to use collective action to create social change. The second question remains almost untouched within social psychology. The present article delineates research from political science and sociology concerning variables that moderate the effectiveness of collective action and maps these variables against intergroup research. Within intergroup social psychology, there is a theoretical literature on what needs to be done to achieve change (e.g., changing identification, social norms, or perceptions of legitimacy, stability, permeability). The article considers possible testable hypotheses concerning the outcomes of collective action which can be derived from intergroup research and from the synthesis of the three disciplines. For theoreticians and practitioners alike, a program of research which addresses the social-psychological outcomes of collective action and links these to identities, norms, intentions, and support for social change in bystanders, protagonists, and opponents has a great deal of interest.     

Gino Germant, 1911–1979: Sociologist from the other America

This personal statement was prepared in response to a series of questions put forth to me by Ana Germani. She is the daughter of Gino and a good friend in her own right. However, she is not responsible for any part of this narrative. Ana is preparing a full-scale biography of Gino, in which I am sure the shortcomings in this brief memoir will be properly corrected. - ILH Irving Louis Horowitz is Hannah Arendt distinguished professor emeritus of sociology and political science at Rutgers University, where he also serves as chairman of the board of Transaction Publishers. His most recent work is Behemoth: The History and Theory of Political Sociology.     

Quantifying Potential Human Health Impacts of Animal Antibiotic Use: Enrofloxacin and Macrolides in Chickens

Use of similar or identical antibiotics in both human and veterinary medicine has come under increasing scrutiny by regulators concerned that bacteria resistant to animal antibiotics will infect people and resist treatment with similar human antibiotics, leading to excess illnesses and deaths. Scientists, regulators, and interest groups in the United States and Europe have urged bans on nontherapeutic and some therapeutic uses of animal antibiotics to protect human health. Many regulators and public health experts have also expressed dissatisfaction with the perceived limitations of quantitative risk assessment and have proposed alternative qualitative and judgmental approaches ranging from "attributable fraction" estimates to risk management recommendations based on the precautionary principle or on expert judgments about the importance of classes of compounds in human medicine. This article presents a more traditional quantitative risk assessment of the likely human health impacts of continuing versus withdrawing use of fluoroquinolones and macrolides in production of broiler chickens in the United States. An analytic framework is developed and applied to available data. It indicates that withdrawing animal antibiotics can cause far more human illness-days than it would prevent: the estimated human BENEFIT:RISK health ratio for human health impacts of continued animal antibiotic use exceeds 1,000:1 in many cases. This conclusion is driven by a hypothesized causal sequence in which withdrawing animal antibiotic use increases illnesses rates in animals, microbial loads in servings from the affected animals, and hence human health risks. This potentially important aspect of human health risk assessment for animal antibiotics has not previously been quantified.