Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon’s notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the authors held a competition between the satisficing “Take The Best ” algorithm and various “rational ” inference procedures (e.g., multiple regression). The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. Organisms make inductive inferences. Darwin (1872/1965) observed that people use facial cues, such as eyes that waver and lids that hang low, to infer a person’s guilt. Male toads, roaming through swamps at night, use the pitch of a rival’s croak to infer its size when deciding whether to fight (Krebs & Davies, 1987). Stock brokers must make fast decisions about which of several stocks to trade or invest when only limited information is available. The list goes on. Inductive

Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a hypothesis in hand. The author reviews evidence of such a bias in a variety of guises and gives examples of its operation in several practical contexts. Possible explanations are considered, and the question of its utility or disutility is discussed. When men wish to construct or support a theory, how they torture facts into their service! (Mackay, 1852/ 1932, p. 552) Confirmation bias is perhaps the best known and most widely accepted notion of inferential error to come out of the literature on human reasoning. (Evans, 1989, p. 41) If one were to attempt to identify a single problematic aspect of human reasoning that deserves attention above all others, the confirmation bias would have to be among the candidates for consideration. Many have written about this bias, and it appears to be sufficiently strong and pervasive that one is led to wonder whether the bias, by itself, might account for a significant fraction of the disputes, altercations, and misunderstandings that occur among individuals, groups, and nations. Confirmation bias has been used in the psychological literature to refer to a variety of phenomena. Here I take the term to represent a generic concept that subsumes several more specific ideas that connote the inappropriate bolstering of hypotheses or beliefs whose truth is in question.

People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion—an illusion of explanatory depth. The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures or narratives. The illusion for explanatory knowledge is most robust where the environment supports real-time explanations with visible mechanisms. We demonstrate the illusion of depth with explanatory knowledge in Studies 1–6. Then we show differences in overconfidence about knowledge across different knowledge domains in Studies 7–10. Finally, we explore the mechanisms behind the initial confidence and behind overconfidence in Studies 11 and 12, and discuss the implications of our findings for the roles of intuitive theories in concepts and cognition.

Please do not quote or cite without permission of the authors Overconfidence in interval estimates 2 Many studies over the last several decades have found that people are generally overconfident about the accuracy of their knowledge. This generalization has been overturned by a number of recent, carefully controlled studies. These studies show little or no overall bias when judges express confidence in a choice between two alternative answers to a question. Apparent overconfidence is due primarily to unsystematic error in judgments, combined with an unrepresentative selection of task items. However, Klayman et al. (1999), found that substantial overconfidence persisted under equivalently controlled conditions with a different type of confidence judgment. When judges are asked to provide intervals such that they are x % sure the correct answer is within the interval, the answer falls inside their interval much less than x % of the time. The present paper shows that, although unsystematic judgmental error may contribute to overconfidence, subjective confidence intervals are indeed systematically too narrow—

The average probability estimate of J> 1 judges is generally better than its components. Two studies test 3 predictions regarding averaging that follow from theorems based on a cognitive model of the judges and idealizations of the judgment situation. Prediction 1 is that the average of conditionally pairwise independent estimates will be highly diagnostic, and Prediction 2 is that the average of dependent estimates (differing only by independent error terms) may be well calibrated. Prediction 3 contrasts between- and within-subject averaging. Results demonstrate the predictions ' robustness by showing the extent to which they hold as the information conditions depart from the ideal and as J increases. Practical consequences are that (a) substantial improvement can be obtained with as few as 2- 6 judges and (b) the decision maker can estimate the nature of the expected improvement by considering the information conditions. On many occasions, experts are required to provide decision makers or policymakers with subjective probability estimates of uncertain events (Morgan & Henrion, 1990). The extensive literature (e.g., Harvey, 1997; McClelland & Bolger, 1994) on the topic shows that in general, but with clear exceptions, subjective

We experimentally test overconfidence in investment decisions by offering participants the possibility to substitute their own for alternative investment choices.

Under review at the Journal of

We present an experimental design that allows us to investigate the representations and processes used in human multiple -cue judgment. We compare three ideal models of how knowledge is stored and applied in a judgment: A linear additive model (LAM), a heuristic model, Take-the-best (TTB) and a generic exemplar-based model (EBM). The results show that people adaptively change processing depending on what information is present in the learning phase and whether or not the learning situation is compatible with the test. Feedback on a continuous variable provides information sufficient to estimate a LAM that can be used both when learning is and is not compatible with the test. When only dichotomous feedback is provided, the processes differ depending on the learning-test compatibility. At high compatibility, the processing is best described by EBM, but at low compatibility heuristic processes such as TTB become more frequent alternatives to LAM.

Excess entry refers to the high failure rate of new entrepreneurial ventures. Economic explanations suggest “hit and run ” entrants and risk-seeking behavior. A psychological explanation is that people (entrepreneurs) are overconfident in their abilities (Camerer & Lovallo, 1999). Characterizing entry decisions as ambiguous gambles, we alternatively suggest – following Heath and Tversky (1991) – that people seek ambiguity when the source of uncertainty is related to their competence. Overconfidence, as such, plays no role. This hypothesis is confirmed in an experimental study that also documents the phenomenon of reference group neglect. Finally, we emphasize the utility that people gain from engaging in activities that contribute to a sense of competence. This is an important force in economic activity that deserves more explicit attention.

Confidence in decision making is an important dimension of managerial behavior. However, what is the relation between confidence, on the one hand, and the fact of receiving or expecting to receive feedback on decisions taken, on the other hand? To explore this and related issues in the context of everyday decision making, use was made of the ESM (Experience Sampling Method) to sample decisions taken by undergraduates and business executives. For several days, participants received 4 or 5 SMS messages daily (on their mobile telephones) at random moments at which point they completed brief questionnaires about their current decision making activities. Issues considered here include differences between the types of decisions faced by the two groups, their structure, feedback (received and expected), and confidence in decisions taken as well as in the validity of feedback. No relation was found between confidence in decisions and whether participants received or expected to receive feedback on those decisions. In addition, although participants are clearly aware that feedback can provide both “confirming ” and “disconfirming ” evidence, their ability to specify appropriate feedback is imperfect. Finally, difficulties experienced in using the ESM are discussed as are possibilities for further research using this methodology.