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.

y be factored into the decision more heavily than is price. The execu- tive may choose to ride dow-ntown by taxi and then implement this decision by standing on line and taking a taxi to the hotel. To bring these sorts of decision situations into the laboratory, researchers commonly focused on the goal of obtaining money, which they assume is shared across people. In the prototypical task, subjects are given choice options that differ in probability and amount. The use of gambles enabled researchers to explore decision making under risk. Often, a number of different choices are made in a single experimental session, and the pattern of choices across sets is analyzed. For ample, people might be asked whether they prefer a 45% chance to win $200 or a 50% chance to win $150. Later in the same ses sion, they might be asked whether they prefer a 90% chance to win $200 or a 100% chance to win $150. At issue in studies like these is the consistency of people's choices. The anal- yses would in

Experiments in psychology, where subjects estimate confidence intervals to a series of factual questions, have shown that individuals report far too narrow intervals. This has been interpreted as evidence of overconfidence in the preciseness of knowledge, a potentially serious violation of the rationality assumption in economics. Following these results a growing literature in economics has incorporated overconfidence in models of, for instance, financial markets. In this paper we investigate the robustness of results from confidence interval estimation tasks with respect to a number of manipulations: frequency assessments, peer frequency assessments, iteration, and monetary incentives. Our results suggest that a large share of the overconfidence in interval estimation tasks is an artifact of the response format. Using frequencies and monetary incentives reduces the measured overconfidence in the confidence interval method by about 65%. The results are consistent with the notion that subjects have a deep aversion to setting broad confidence intervals, a reluctance that we attribute to a socially rational trade-off between informativeness and accuracy.

The study of decision making has been dominated by economic perspectives, which model people as rational agents who carefully weigh costs and benefits and try to maximize the utility of every choice, without consideration of issues such as cultural norms, religious beliefs and moral rules. However, psychological findings indicate that in many situations people are not rational decision makers as defined by the economic theories. One of the domains in which traditional cost-benefit models fail to predict human behavior is the domain of moral reasoning. This work presents the first computational model of recognitionbased moral decision making, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision making. MoralDM uses a natural language system to produce formal representations from psychological stimuli,