Who Leads the Trade? Responsibility, Algorithmic Influence, and Regret in Financial Human‑Algorithm Collaboration

As financial decision‐making increasingly shifts toward algorithmic co‐pilot models, the psychological dynamics of human–algorithm collaboration remain insufficiently understood. This study examines defensive attribution mechanisms in financial trading, focusing on how individuals assign responsibility and experience regret under varying outcomes. A custom‐built trading simulator was used (N = 88; 1,320 incentivized trials), and behavior was analyzed using linear mixed‐effects models. The results reveal a robust self‐serving bias expressed through two distinct processes. Responsibility attribution was high for gains but declined sharply after losses, while perceived algorithm influence increased following failures, indicating retrospective inflation of the algorithm’s role. Regret exhibited a structural asymmetry: loss‐related regret was strongly dispositional, whereas gain‐related regret was situational. These patterns suggest that negative outcomes activate stable self‐evaluative tendencies, while positive outcomes elicit more context‐dependent responses. Crucially, restoring decision autonomy significantly reduced emotional distress after losses. Participants who chose to ignore the algorithm experienced lower regret, indicating that agency serves as a psychological buffer that protects self‐image more effectively than compliance with external advice. The findings imply that financial interfaces should avoid full automation and instead prioritize meaningful user engagement to preserve psychological ownership of decisions.