Perspective (graphical)

Heuristics

Inference

Biology

Operating system

Epistemology

Machine learning

Logistic regression

Philosophy

Heuristic

Paleontology

Artificial intelligence

Management

Economics

Ecological rationality

Rationality

Context (archaeology)

Computer science

Task (project management)

Management science

Process (computing)

Bounded rationality

Selection (genetic algorithm)

Academy of Management Journal

Heuristics are often viewed as inferior to “rational” strategies that exhaustively search and process information. Introducing the theoretical perspective of ecological rationality, we challenge this view and argue that, under conditions of uncertainty common to managerial decision making, managers can actually make better decisions using fast-and-frugal heuristics. Within the context of personnel selection, we show that a heuristic called Δ-inference can more accurately predict which of two job applicants would perform better in the future than can logistic regression, a prototypical rational strategy. Using data from 236 applicants at an airline company, we demonstrate, in Study 1, that, despite searching less than half of the cues, Δ-inference leads to more accurate selection decisions than logistic regression. After this existence proof, we examine, in Study 2, the ecological conditions under which the heuristic predicts more accurately than logistic regression using 1,728 simulated task environments. Finally, in Study 3, we show in an experiment that participants adapted their strategies to the characteristics of a task—and increasingly so the greater their previous experience in selection decisions. The aim of this article is to propose ecological rationality as an alternative to current views about the nature of heuristics in managerial decisions.

Ecological Rationality: Fast-and-Frugal Heuristics for Managerial Decision Making under Uncertainty