Applied a theory of information integration to decision making with probabilistic events. 10 undergraduates judged the subjective worth of duplex bets that included independent gain and lose components. The worth of each component was assumed to be the product of a subjective weight that reflected the probability of winning or losing, and the subjective worth of the money to be won or lost. The total worth of the bet was the sum of the worths of the 2 components. Thus, each (...) judgment required multiplying and adding operations. The multiplying model worked quite well in 4 experimental conditions. The adding model showed more serious discrepancies, though these were small in magnitude. The theory of functional measurement was applied to scale the subjective values of the probability and money stimuli. Subjective and objective values were nonlinearly related both for probability and for money. (shrink)

Three correctives can get researchers out of the trap of constructing unitary theories of “thinking”: (1) Strong inference methods largely avoid problems associated with universal prescriptive normativism; (2) theories must recognize that significant modularity of cognitive processes is antithetical to general accounts of thinking; and (3) consideration of the domain-specificity of rationality render many of the present article's issues moot.

Simple heuristics that make us smart offers an impressive compilation of work that demonstrates fast and frugal (one-reason) heuristics can be simple, adaptive, and accurate. However, many decision environments differ from those explored in the book. We conducted a Monte Carlo simulation that shows one-reason strategies are accurate in “friendly” environments, but less accurate in “unfriendly” environments characterized by negative cue intercorrelations, that is, tradeoffs.

Pothos & Busemeyer (P&B) argue that classical probability (CP) fails to describe human decision processes accurately and should be supplanted by quantum probability. We accept the premise, but reject P&B's conclusion. CP is a prescriptive framework that has inspired a great deal of valuable research. Also, because CP is used across the sciences, it is a cornerstone of interdisciplinary collaboration.