Integrative Quantum Probability Models for Explaining and Predicting Delegation Behaviors

Humr et al. (2025) propose using quantum probability theory (QPT) to better capture the uncertainties and irrationalities in human cognition during human–AI interactions. Erlei et al. (2024) and Salimzadeh et al. (2024) both observe that human delegation choices often violate rational independence and are swayed by irrelevant context or error types—patterns that are hard to model with classical probability. This research would develop and empirically validate QPT-based models that can account for contextuality, order effects, and interference in delegation decisions. For example, it could predict why trust in AI for one task “bleeds over” to unrelated tasks, or why error types have non-linear effects on delegation. Such models could underpin more accurate simulations of human–AI collaboration and inform design interventions (e.g., targeted explanations, interface adjustments) to steer delegation towards optimal outcomes.

References:

1. Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making. Sara Salimzadeh, Gaole He, U. Gadiraju (2024). International Conference on Human Factors in Computing Systems.
2. A Quantum Probability Approach to Improving Human–AI Decision Making. Scott A. Humr, M. Canan, Mustafa Demir (2025). Entropy.
3. Understanding Choice Independence and Error Types in Human-AI Collaboration. Alexander Erlei, Abhinav Sharma, U. Gadiraju (2024). International Conference on Human Factors in Computing Systems.