Optimistic beliefs have been criticized by philosophers as being irrational or epistemically deficient. This paper argues for the possibility of a rational optimism. We propose a novel four-fold taxonomy of optimistic beliefs and argue that people may hold optimistic beliefs rationally for at least two of the four types (resourced optimism and agentive optimism). These forms of rational optimism are grounded in facts about one’s resources and agency and may be epistemically justified under certain conditions. We argue that the fourth (...) type of optimism in our taxonomy (perspectival optimism) is not subject to epistemic scrutiny in the same way. It is better evaluated on practical and moral grounds. This paper advances the discussion of optimism within both the philosophical and psychological literatures by providing a compelling and philosophically rich taxonomy of optimism that clarifies the sometimes-competing forms of optimism identified by psychologists. This advances the field by putting forward cases of epistemically justified optimism, in contrast with unrealistic optimism, that is sometimes justified for its instrumental or adaptive characteristics, and also by highlighting a form of optimism, perspectival optimism, that is not being considered in the mainstream optimism literature in psychology. The paper concludes by suggesting several avenues for future empirical and philosophical research. (shrink)

Do people tend to be overconfident? Many think so. They’ve run studies on whether people are calibrated: whether their average confidence in their opinions matches the proportion of those opinions that are true. Under certain conditions, people are systematically ‘over-calibrated’—for example, of the opinions they’re 80% confident in, only 60% are true. From this empirical over-calibration, it’s inferred that people are irrationally overconfident. My question: When and why is this inference warranted? Answering it requires articulating a general connection between being (...) rational and being right—something extant studies have not done. I show how to do so using the notion of deference. This provides a theoretical foundation for calibration research, but also reveals a flaw: the connection between being rational and being right is much weaker than is standardly assumed, since rational people can often be expected to be miscalibrated. Thus we can’t test whether people are overconfident by simply testing whether they are over-calibrated; instead, we must try to predict the rational deviations from calibration, and then compare those predictions to people’s performance. I show how this can be done—and that doing so complicates the interpretation of robust empirical effects. (shrink)

One of the most fascinating topics of current investigation in the literature on judgment and decision-making concerns the exploration of foreign language effects (henceforth, FLE). Specifically, recent research suggests that presenting information in a foreign language helps reasoners make better choices. However, this piece aims at making scholars aware of a blind spot in this stream of research. In particular, research on FLE has imported only one view of judgment and decision-making, in which the heuristics that people use are seen (...) as conducive to biases and, in turn, to costly mistakes. But heuristics are not necessarily a liability, and this article indicates two routes to push forward research on FLE in judgment and decision-making. First, research on FLE should be expanded to explore also classes of fast and frugal heuristics, which have been shown to lead to accurate predictions in several contexts characterized by uncertainty. Second, research on FLE should be open to challenge the interpretations given to previous FLE findings, since alternative accounts are plausible and not ruled out by evidence. (shrink)

On appelle « biais de confirmation » la tendance supposée des humains à sélectionner les informations qui vont dans le sens de ce qu’ils croient (ou veulent croire) et à interpréter celles dont ils disposent en faveur de leurs hypothèses favorites. Cet article vise à porter un regard critique sur certains usages de cette notion, et plus généralement sur le recours aux « biais cognitifs » pour expliquer tout un ensemble de phénomènes sociaux interprétés comme les marques d’une forme d’irrationalité. (...) Remettant en question le caractère probant des résultats expérimentaux destinés à mettre en évidence une déviation qui nous conduirait systématiquement à des représentations inexactes, je suggère que l’approche bayésienne en psychologie du raisonnement permet de rendre compte de certains de ces résultats en faisant l’hypothèse que nos inférences se conforment à une telle norme. (shrink)

The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression, k-dependence Bayesian, and Random Forest. The Bayesian inference and classification methods allow (...) an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts, performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing. (shrink)

The statistics of small samples are often quite different from those of large samples, and this needs to be taken into account in assessing the rationality of human behavior. Specifically, in evaluating human responses to environmental statistics, it is the effective environment that matters; that is, the environment actually experienced by the agent needs to be considered, not simply long‐run frequencies. Significant deviations from long‐run statistics may arise through experiential limitations of the agent that stem from resource constraints and/or information‐processing (...) bounds. The article draws together recent work from a number of areas in judgment and decision making ranging from randomness perception (Hahn & Warren, ), information sampling (Hertwig & Pleskac, ; Kareev et al., ), and consequences of choice for exploration or exploitation (e.g., Denrell, ) to demonstrate how proper consideration of these deviations leads to reevaluation of behaviors that are otherwise deemed irrational. (shrink)

Humans generally exhibit a pervasive future bias in favour of optimism. We overestimate the likelihood of success in work, relationships and financial investments. Similarly, we underestimate the probability of experiencing negative events such as, serious illness or financial ruin. The optimism bias is widely considered as one of the most reproducible, prevalent and robust cognitive biases observed in psychology and behavioural economics. The catastrophic impact of the recent economic collapse has laid this cognitive bias bare. In this introductory overview, current (...) understanding of the neural basis of the optimism bias is explored. Topics considered include: converse negative biases in depressive illnesses, the role of dopamine in optimism bias generation and modulation and evidence from functional neuroimaging studies. Research on the optimism bias has afforded us a unique window into decision-making, reward-processing and the potential for systematic irrationality in the human mind. (shrink)