78 Pages Posted: 31 Jan 2026 Université de Nice Sophia Antipolis - Groupe de Recherche en Droit, Economie et Gestion (GREDEG) Mohammed VI Polytechnic University (UM6P) - Mohammed VI Polytechnic University GREDEG CNRS Date Written: January 15, 2026 Prior work on algorithmic aversion shows that people are less forgiving of algorithmic than human errors. However, little is known about how the magnitude and frequency of errors shape delegation. We run a laboratory energy-forecasting experiment in which participants make repeated predictions and can delegate subsequent forecasts to any subset of up to nine agents whose past performance is displayed. Agent type (human vs. algorithm) and the structure of expected payoffs across agents (varying vs. fixed) are manipulated between subjects. Across treatments, delegation is primarily explained by expected payoffs; conditional on payoffs, error magnitude and frequency add little predictive power. Consistent with this, participants prefer agents who make few large errors over those who make many small errors when both yield similar expected payoffs. This result does not hold when agents' payoffs are fixed. This asymmetry reflects a comparative evaluation effect, whereby algorithms benefit more than humans from relative performance comparisons. Keywords: JEL Codes: C92, D63 Delegation, Algorithmic Errors, Expected Payoffs, Laboratory Experiment Declaration of Interest Conflicts of interest: The authors declare that they have no conflict of interest. Funder Statement This work was financially supported by Digital Systems for Humans (DS4H). JEL Classification: C92, D63 Suggested Citation: Suggested Citation 250, rue Albert Einstein Valbonne, 06560 France 250 RUE ALBERT EINSTEIN SOPHIA ANTIPOLIS, 06560 France Behavioral & Experimental Economics eJournal Subscribe to this fee journal for more curated articles on this topic
Chevrier et al. (Thu,) studied this question.