Improving the Performance of Satisficing Cognitive Algorithms

We investigate a family of cognitive algorithms that has been proposed recently by Gigerenzer and Goldstein (1996) to model a kind of human behavior - known as one-reason decision making - in the task of comparing two objects as which scores higher on a given criterion based on binary cue information. How should the cues be ranked in order to achieve the largest number of correct decisions? We provide a theoretical framework for studying this question by analyzing the approximation capabilities of satisficing cognitive algorithms. We introduce an algorithm that has not been considered before and show that it can be used to improve the performance of any cue-based algorithm in many cases. We also exhibit a relation between the comparison task and a class of problems that is studied in the area of machine learning.