Findings in recent research on the `conjunction fallacy ' have been taken as evidence that our minds are not designed to work by the rules of probability. This conclusion springs from the idea that norms should be content-blind Ð in the present case, the assumption that sound reasoning requires following the conjunction rule of probability theory. But content-blind norms overlook some of the intelligent ways in which humans deal with uncertainty, for instance, when drawing semantic and pragmatic inferences. In a series of studies, we ®rst show that people infer nonmathematical meanings of the polysemous term `probability' in the classic Linda conjunction problem. We then demonstrate that one can design contexts in which people infer mathematical meanings of the term and are therefore more likely to conform to the conjunction rule. Finally, we report evidence that the term `frequency ' narrows the spectrum of possible interpretations of `probability ' down to its mathematical meanings, and that this fact Ð rather than the presence or absence of `extensional cues ' Ð accounts for the low proportion of violations of the conjunction rule when people are asked for

Abstract. A swarm algorithm framework (SWAF), realized by agent-based modeling, is presented to solve numerical optimization problems. Each agent is a bare bones cognitive architecture, which learns knowledge by appropriately deploying a set of simple rules in fast and frugal heuristics. Two essential categories of rules, the generate-and-test and the problem-formulation rules, are implemented, and both of the macro rules by simple combination and subsymbolic deploying of multiple rules among them are also studied. Experimental results on benchmark problems are presented, and performance comparison between SWAF and other existing algorithms indicates that it is efficiently. 1

Our theoretical understanding individual differences can be used as a tool to test and refine theory. Individual differences are useful because judgments, including philosophically relevant intuitions, are the predictable products of the fit between adaptive psychological mechanisms (e.g., heuristics, traits, skills, capacities) and task constraints. As an illustration of this method and its potential implications, our target article used a canonical, representative, and affectively charged judgment task to reveal a relationship between the heritable personality trait extraversion and compatabilist judgments. In the current comment we further clarify major theoretical implications of these data and outline potential opportunities and obstacles for this methodology. Discussion focuses on (1) the need for theoretically grounded a priori predictions; (2) the use of precise process level data and theory; (3) the possibility of convergent validity as personality is known to predict life experiences and outcomes; and (4) the fundamentally adaptive nature of cognition. Adaptive Judgment and Philosophical Intuition 3 Adaptive Variation in Judgment and Philosophical Intuition How can we develop a broad theoretical understanding of the dynamics of folk judgment