Abstract: Lael Schooler will introduce the simple heuristics research program that studies decision strategies that can make effective decisions in an uncertain world, even when limited time and information are available. These heuristics will be illustrated with examples that demonstrate how it can pay to ignore information. He will start by describing Δ‑Inference, a simple heuristic for paired comparison that ranks alternatives (e.g., banks) according to a criterion of interest (e.g., risk of failure) based on cues (e.g., loan to capital ratio). It stops cue search when the difference between the cue values of the two alternatives exceeds an aspiration level Δ, deciding in favor of the alternative with the cue value associated with the higher ranking. In simulated and real task environments, Δ‑Inference achieves a high level of prediction accuracy. A methodological challenge is to recover the strategy (e.g., Δ‑Inference) a decision maker used to make a choice. He will describe a machine learning approach that recovers the strategy a person used by considering not only their final choice but also the steps taken on the path to that choice. Finally, he shows how the ACT-R theory of cognition, which supports the development of computer simulations of human behavior, can be used to quantify the cognitive demands associated with using a particular strategy.