This episode examines the evolution of financial artificial intelligence from classical models toward a more sophisticated framework based on quantum logic. The authors of the paper we discuss argue that traditional AI often fails to capture human-centric decision-making, particularly the "bounded rationality" and non-linear expectations observed in real-world investors. By utilizing quantum machine learning and neural networks, these systems can better simulate human cognitive processes like superposition and interference, which represent the simultaneous presence of multiple conflicting expectations. The text demonstrates how quantum probability theory accounts for market anomalies and order effects that classical Bayesian logic cannot explain. Ultimately, the researchers advocate for quantum-driven techniques to improve the accuracy, speed, and explainability of AI in complex areas like algorithmic trading and risk management. This shift represents a transition toward human-like artificial intelligence capable of navigating the inherent uncertainty of global financial environments.

Reference

From Classical Rationality to Contextual Reasoning: Quantum Logic as a New Frontier for Human-Centric AI in Finance

Fabio Bagarello, Francesco Gargano, Polina Khrennikova

https://doi.org/10.48550/arXiv.2510.05475

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