Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We examine methods for evaluating interventions designed to improve decision-making quality when people misunderstand the consequences of their choices. In an experiment involving financial education, conventional outcome metrics (financial literacy and directional behavioral responses) imply that two interventions are equally beneficial even though only one reduces the average severity of errors. We trace these failures to violations of the assumptions embedded in the conventional metrics. We propose a simple, intuitive, and broadly applicable outcome metric that properly differentiates between the interventions, and is robustly interpretable as a measure of welfare loss from misunderstanding consequences even when additional biases distort choices.