Learning from Evidence in a Complex World

Abstract

Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended "side effects." Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but self-confirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies. Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond.