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.

FIGURE 1—

Sources of policy resistance. Note. Arrows indicate causation, e.g., our actions alter the environment. Thin arrows show the basic feedback loop through which we seek to bring the state of the system in line with our goals. Policy resistance (thick arrows) arises when we fail to account for the so called “side effects” of our actions, the responses of other agents in the system (and the unanticipated consequences of these), the ways in which experience shapes our goals, and the time delays often present in these feedbacks.

FIGURE 2—

Learning is a feedback process. Note. The diagram shows the main impediments to learning. Arrows indicate causation.

FIGURE 3—

Idealized learning process. Note. Simulations create virtual worlds to speed and improve the generation of evidence. To be effective, inquiry skills for the interpretation of evidence must also improve. Arrows indicate causation.