Proceedings of the Annual Meeting of the Cognitive Science Society

Most approaches to modeling rational inference do not take inio account that in the real world, organisms make inferences under limited time and knowledge. In this tradition, the mind is treated as a calculating demon equipped with unlimited time, knowledge, and computational might. We propose a family of satisficing algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality, for example, they neither look up nor integrate all information. By computer simulation, we held a competition between the satisficing Take The Best algorithm and various more "optimal" decision procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. Most interesting was the flnding that the best algorithms in the competition, those which used a form of one-reason decision making, exhibited a startling "less-is-more" effect: they performed better with missing knowledge than with complete knowledge. We discuss the less-is-more effect and present evidence of it in human reasoning. This counter-intuitive effect demonstrates that the mind can satisfice and seize upon regularities in the environment to the extent that it can exploit even the absence of knowledge as knowledge.