The price of dark traits: strategic exploitation and its limitations in repeated interactions

Abstract Trust and cooperation are fundamental to human social interaction, with personality traits significantly influencing economic decision making. This study introduces a novel approach using adaptive computational agents to provide the first examination of how the dark factor of personality (D-factor)—the unifying core of malevolent traits—affects trustworthiness and strategic exploitation in dynamic, repeated trust games. After pre-screening 1243 participants, we selected individuals with high and low D-factor scores. Participants (n = 183) played two 25-round trust games as trustees against programmed hidden Markov model (HMM) investors derived from human data but differing in strategic reactivity: one ‘human-like’ HMM exhibited a characteristic ‘sticky’ mid-trust state (potentially vulnerable to exploitation), while a ‘responsive’ HMM reacted more decisively and immediately to trustee returns. Consistent with the D-factor concept, high-D participants returned lower proportions, particularly later in the interactions. High-D individuals also demonstrated sophisticated strategic adaptation, significantly decreasing reciprocity over time only when receiving high investments from the more passively exploitable ‘human-like’ opponent. Crucially, this exploitative strategy proved self-limiting: high-D participants did not achieve higher total payoffs overall, as the ‘responsive’ HMM adaptively reduced investments. These findings uniquely demonstrate how D-factor manifests strategically in dynamic exchanges and reveal the critical role of opponent responsiveness in constraining exploitation, highlighting both the sophistication and the boundaries of dark personality expression in social interactions.