Strategies for hypothesis testing in scientific investigation and everyday reasoning have interested both psychologists and philosophers. A number of these scholars stress the importance of disconnrmation in reasoning and suggest that people are instead prone to a general deleterious "confirmation bias." In particular, it is suggested that people tend to test those cases that have the best chance of verifying current beliefs rather than those that have the best chance of falsifying them. We show, however; that many phenomena labeled "confirmation bias" are better understood in terms of a general positive test strategy. With this strategy, there is a tendency to test cases that are expected (or known) to have the property of interest rather than those expected (or known) to lack that property. This strategy is not equivalent to confirmation bias in the first sense; we show that the positive test strategy can be a very good heuristic for determining the truth or falsity of a hypothesis under realistic conditions. It can, however, lead to systematic errors or inefficiencies. The appropriateness of human hypothesis-testing strategies and prescriptions about optimal strategies must be understood in terms of the interaction between the strategy and the task at hand.

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.

this article, we have presented evidence that a computational model that instantiates approximate, local learning with graded transfer provides a good account of how subjects learn on-line from outcome feedback in the SPF, a simple dynamic task. We base this conclusion on the model's ability to predict subjects' performance during training and on two subsequent tests of their ability to generalize, the control questions and the transfer task. We now explore the limitations of our efforts and discuss two alternative approaches to understanding human performance before concluding on our own approach 's merits

Tested the hypothesis that perceived controllability and stringency of performance standards would affect self-regulatory mechanisms governing performance attainments of a simulated organization. Ss who managed the simulated organization under a cognitive set that organizations are not easily controllable displayed low perceived self-efficacy, even when standards were within easy reach, and lowered their organizational goals. Ss who operated under a cognitive set that organizations are controllable maintained a strong sense of self-efficacy, set increasingly challenging goals, and exhibited effective analytic thinking. The divergent changes in these self-regulatory factors were accompanied by large differences in organizational attainments. Path analyses revealed that perceived self-efficacy, which was affected by prior accomplishments, influenced subsequent organizational performance through its effects on analytic strategies. After further experience, the performance system was regulated more extensively and intricately by Ss ' self-conceptions of efficacy. Perceived self-efficacy affected subsequent organizational attainments both directly and indirectly through its influence on personal goal challenges. Personal goals, in turn, enhanced organizational attainments directly and through mediation of analytic strategies. Research on cognitive motivators and regulators of action has

Psychological evidence shows that probability theoryisnotaproper descriptive model of intuitive human judgment. Instead, some heuristics have been proposed as such a descriptive model. This paper argues that probability theory has limitations even as a normative model. A new normative model of judgment under uncertainty is designed under the assumption that the system's knowledge and resources are insu cient with respect to the questions that the system needs to answer. The proposed heuristics in human reasoning can also be observed inthis new model, and can be justi ed according to the assumption.

A formulation of learning in dynamic decision-making tasks is developed, building on the application of control theory to the study of human performance in dynamic decision making and a connectionist approach to motor control. The formulation is implemented as a connectionist model and compared with human subjects in learning a simulated dynamic decision-making task. When the model is pretrained with the prior knowledge that subjects are hypothesized to bring to the task, the model's performance is broadly similar to that of subjects. Furthermore, individual runs of the model show variability in learning much like individual subjects. Finally, the effects of various manipulations of the task representation on model performance are used to generate predictions for future empirical work. In this way, the model provides a platform for developing hypotheses on how to facilitate learning in dynamic decision-making tasks.

This article is concerned with the implications of the surprisingly different experimental practices in economics and in areas of psychology relevant to both economists and psychologists, such as behavioral decision making. We consider four features of experimentation in economics, namely, script enactment, repeated trials, performance-based monetary payments, and the proscription against deception, and compare them to experimental practices in psychology, primarily in the area of behavioral decision making. Whereas economists bring a precisely defined ìscriptî to experiments for

This section reviews a specialty within the field of decision-making known as dynamic decision-making. Dynamic decisions are characterized by a decision-maker choosing among various actions at different points in time in order to control and optimize performance of a dynamic stochastic system. Realistic examples include fighting fires, navigational control, battlefield decisions, medical emergencies, and so on. The section has four parts: The first reviews basic theory concerning optimal decision principles in a dynamic context; the second summarizes empirical approaches to the study of human performance on dynamic decision tasks; the third presents theoretical models that describe how humans learn to control dynamic systems; and the last discusses methodological issues arising from the study of complex decisions including differences between field versus laboratory research.

Overreaction to supply shortages can create havoc in supply chains, costing millions of dollars in excess inventory and manufacturing capacity. In an experiment with the Beer Distribution Game, we explore overreaction to shortages as a complementary behavioral cause of supply chain instability. As in previous studies, we find that players ignore the supply line. We find, however, that instead of overreacting to shortages, players limit the size of their order adjustment while aiming for higher than necessary inventory level; a policy that is more stable than the linear response suggested in previous studies. Since an ordering rule that fails to account for the supply line leads to higher than necessary costs and order amplification, our results suggest that players are not fully rational. However, evaluating the performance of the estimated policy we find that, given the information cues available, players show bounded rationality and develop a “satisficing ” replenishment decision rule that minimizes local cost at the expense of higher upstream cost. We explore the implications of these findings for the design of information and incentive systems for supply chain management.

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