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Recommended Reading
Kühn, Reimer R., and Uta Horstmann (1997). “Random Matrix Approach to Glassy Physics: Low Temperatures and Beyond.” Physical Review Letters 78: 4067–4070. https://doi.org/10.1103/PhysRevLett.78.4067.
Kühn, Reimer (2008). “Spectra of Sparse Random Matrices.” Journal of Physics A 41: 295002. https://doi.org/10.1088/1751-8113/41/29/295002.
Anand, Kartik, Jonathan Khedair, and Reimer Kühn (2018). “A Structural Model for Fluctuations in Financial Markets.” Physical Review E 97: 052312. https://doi.org/10.1103/PhysRevE.97.052312.

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2025/2026
Reimer Kühn, Dr. rer. nat.
Professor of Statistical Physics (Emeritus)
King’s College London
Born in 1955 in Glückstadt, Germany
Diploma in Physics, Christian-Albrechts Universität zu Kiel and the University of Sussex, Dr. rer. nat., Christian-Albrechts Universität zu Kiel
Arbeitsvorhaben
From Neurons to Brains— from Societies of Brains to Brains of Societies
In my project, I will analyze collective information processing in and of societies. I will explore to which extent a neural network analogy can provide useful concepts and guiding principles for such an analysis, and whether it could help rationalize important structural and dynamic properties of societies. Key observations underlying the analogy are that neurons in a brain, like members of a society, communicate with each other, and that they evaluate received information to determine their (re-)action in a given communicative or situate context. Evaluations are, moreover, adapted in the process, entailing that responses and actions are not hard-wired. Neural networks, like societies, therefore constitute adaptive collective information processing systems, with information processing capabilities transcending those of their constituent elements. Key predictions of the analogy include (i) the stability of macroscopic properties of societies, thus a possible rationalization of the emergence of a stable spectrum of agreed norms, rituals, and dominant intellectual paradigms, which may, however, evolve on long time scales and even undergo rare spontaneous major disruptions analogous to phase transitions, (ii) the existence of fundamental limitations of information-processing capabilities, (iii) along with that, a degradation of the adaptability of societies as they increase the number and complexity of the processes they attempt to support, and (iv) a hierarchical de-differentiation of societies as generic response to major crises. The purpose of my fellowship at the Wissenschaftskolleg is to elaborate the predictions that suggest themselves through the neural network analogy and to try testing them against (i) historical studies of societies over longer time horizons, specifically concerning the dynamics and phenomenology of major transformations and crises, (ii) the history of law and economics, (iii) insights of evolutionary biology and anthropology, and (iv) existing sociological theories.Recommended Reading
Kühn, Reimer R., and Uta Horstmann (1997). “Random Matrix Approach to Glassy Physics: Low Temperatures and Beyond.” Physical Review Letters 78: 4067–4070. https://doi.org/10.1103/PhysRevLett.78.4067.
Kühn, Reimer (2008). “Spectra of Sparse Random Matrices.” Journal of Physics A 41: 295002. https://doi.org/10.1088/1751-8113/41/29/295002.
Anand, Kartik, Jonathan Khedair, and Reimer Kühn (2018). “A Structural Model for Fluctuations in Financial Markets.” Physical Review E 97: 052312. https://doi.org/10.1103/PhysRevE.97.052312.