Quantum-Fuzzy Relational Dynamics in the Iterated Prisoner’s Dilemma: Competitive Prediction with Stronger Recovery, Coupling, and Resilience Effects - Salem, Murilo; Pontes, Daniel; Böhm, Luísa; Reis, Henrique dos; Ferrugem, Anderson Priebe (Anais do Simpósio Brasileiro de Computação e Comunicação Quânticas (SBCCQ), 2026)
Modeling socially interdependent decision processes remains a major challenge in computational intelligence, particularly when behavior is influenced by emotional ambiguity, inter-agent coupling, and recovery after disruption. This paper introduces a quantum-fuzzy benchmark for emotion-aware relational dynamics in the Iterated Prisoner’s Dilemma. Emotional variables are represented through fuzzy membership degrees and encoded into quantum-fuzzy relational states, enabling controlled analysis of cooperation, betrayal, recovery, coupling, and robustness under perturbation. We compare three settings: a classical fuzzy baseline, a quantum-fuzzy model, and a classifier-augmented quantum-fuzzy variant. On the predictive benchmark, the quantum-fuzzy model is competitive and slightly outperforms the classical baseline, achieving the best F1-score (0.8153 vs. 0.8039) and accuracy (0.7672 vs. 0.7610), while the augmented variant performs similarly. The main gains, however, appear in relational dynamics. Quantum-fuzzy models recover cooperation much faster after betrayal, reduce collapse rates, respond more strongly to inter-agent coupling, and show higher resilience under relational shocks, with recovery time dropping from 5.91 to 2.28 and 1.43, and resilience increasing from 0.0466 to 0.2256. Overall, the results suggest that quantum-fuzzy encoding contributes less through predictive gains and more through improved relational adaptation in emotionally interdependent environments.