Quantum-Like Cognition and Rationality: Biological and Artificial Intelligence Systems

This is a short introductory review on quantum-like modeling of cognition with applications to decision making and rationality. The aim of the review is twofold: a) to present briefly the apparatus of quantum information and probability theory useful for such modeling; b) to motivate applications of this apparatus in cognitive studies and artifical intelligence, psychology, decision making, social and political sciences. We define quantum rationality as decision making that is based on quantum information processing. Quantumly and classically rational agents behaves differently. A quantum-like agent can violate the Savage Sure Thing Principle, the Aumann theorem on impossibility of agreeing to disagree. Such an agent violates the basic laws of classical probability, e.g., the law of total probability and the Bayesian probability inference. In some contexts, “irrational behavior” (from the viewpoint of classical theory of rationality) can be profitable, especially for agents who are overloaded by a variety of information flows. Quantumly rational agents can save a lot of information processing resources. At the same time, this sort of rationality is the basis for quantumlike socio-political engineering, e.g., social laser. This rationality plays the important role in the process of decision making not only by biosystems, but even by AI-systems. The latter equipped with quantum(-like) information processors would behave irrationally, from the classical viewpoint. As for biosystems, quantum rational behavior of AI-systems has its advantages and disadvantages. Finally, we point out that quantum-like information processing in AI-systems can be based on classical physical devices, e.g., classical digital or analog computers. keywords: mathematical formalism; quantum mechanics; open quantum systems; quantum instruments; quantum Markov dynamics; psychological effects; cognition; decision making, classical rationality, quantum rationality, information overload. 1 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 15 March 2021 doi:10.20944/preprints202103.0395.v1 © 2021 by the author(s). Distributed under a Creative Commons CC BY license.