Sept. 21–26, 2025
Frankfurt am Main, Germany
Atsushi Iriki and Andrei Khrennikov, Chairpersons
Program Advisory Committee
Atsushi Iriki: RIKEN Quantum, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
Andrei Khrennikov: Dept. of Mathematics, Linnaeus University, Sweden
Masanao Ozawa: Center for Mathematical Science and Artificial Intelligence, Chubu University, Kasugai-shi, Japan
Karl Svozil: TU Wien, Institut für Theoretische Physik, 1040 Vienna, Austria
Julia R. Lupp, Ernst Strüngmann Forum, Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
Context
The “scientific view of the world,” which has characterized human civilization throughout modern history, has been successful in objectifying and rationalizing (in mechanical terms) nature, humans, and society for reductive analysis to permit straightforward causation. This view, however, has been unable to account for the multilayered, possibly even inconsistent (in classical terms), causal structures that underlie actual phenomena (i.e., the absurdity). Given the ever-increasing complexity in our world, it has been extremely difficult to explain systematically the complexities of human cognitive traits based on classical “rational” reasoning.
Quantum-like methodology/modeling (QLM) provides an alternative entry point. By borrowing from quantum probability and information and applying quantum formalizations, it may be possible to rationalize seemingly “absurd” phenomena and to extend understanding beyond what has been obtained through the “simplicity” of straightforward formalisms. Importantly, QLM is distinguished from quantum biophysics as well as from quantum cognition and consciousness (in the spirit of Hameroff and Penrose). Whereas quantum biophysics and quantum cognition address physics processes of quantum mechanics in biological systems, QLM focuses on macroscopic biological, social, and, more recently, AI systems. Thus, the QLM approach describes information processing using quantum information and probability principles, but is not rooted in quantum mechanics.
Goals of the Forum
Just as physicists explored a new branch of mathematics, using the theory of operators in complex Hilbertian space to describe the quantum phenomena in an effective way, this Forum aims
Overarching Questions
Group 1: Living and Evolving Systems
Mechanisms of biological evolution include accumulative associations of coexisting “latent capabilities” combined and ordered in various ways. When the metaphor of “path integral” in quantum mechanics is applied, only one pathway, among a complex multitude of potential paths, is reinforced and realized, while other possible pathways may be weakened and disappear. To integrate currently expanding notions of biological evolution, guiding questions for this group include:
Group 2: Brain and Learning Systems
Neuro- and computer sciences have coevolved to understand and implement multi-layered network dynamics, in an effort to make brains and machines intelligent through learning. Deep learning, Bayesian inference, and variational free energy principle, among others, represent their peak within the range of classical probabilistic mathematics. Seeking to expand theories to QLM, this group will consider:
Group 3: Cognition and Decision Making
QLM has been successfully applied to reinterpret classical decision theory paradoxes (e.g., the Ellsberg paradox) and to model key cognitive psychology phenomena such as conjunction, disjunction, order, and response replicability effects. To broaden the use of QLM in cognition and decision-making research, this group will consider the following questions:
Group 4: Political, Social, and Economic Systems
Behind the explosive success of modern scientific civilization, complex causal relationships solemnly lurk, superimposed and layered in a probabilistic manner. These relationships are impossible for us to articulate because classical science has abstracted them through reductive approximation to predict and control our society. Aimed at designing a favorable future, this group will consider:
