Institute of Integrative and Interdisciplinary Research

Evolutionary Dynamics of Adaptive Systems

This program investigates the evolutionary pressures, structural constraints, and selection dynamics that shape the architecture of adaptive systems — biological, cognitive, and synthetic. It examines why certain organizational architectures are evolutionarily inevitable, how resource constraints drive the emergence of specific cognitive and perceptual structures, how memory systems evolved under physical limitations, and what formal principles govern resilience, autonomy, and hierarchy in multi-agent systems. The framework unifies insights from evolutionary theory, dynamical systems theory, and information theory to explain why adaptive systems converge on particular structural solutions.

The research questions below outline the current frontier problems pursued within this framework. The accompanying reference collection integrates key literature with ongoing contributions developed at the Institute.

Research Questions: Evolutionary Dynamics of Adaptive Systems

  • Why do adaptive systems converge on predictive processing architectures under physical constraints?
  • What evolutionary pressures necessitate representational isolation in complex cognitive systems?
  • How do resource constraints shape the temporal structure of memory storage and retrieval?
  • What are the formal conditions for structural resilience in discrete-state dynamical systems?
  • How does autonomy suppression emerge and stabilize in hierarchical multi-agent systems?
  • Why does differentiation serve as a necessary precondition for systemic actualization?
  • What evolutionary and information-theoretic arguments establish that direct perception was never viable for complex organisms?
  • How do bounded adaptive systems generate and maintain goal structures in post-scarcity environments?
  • What structural principles govern the formation and dissolution of coherent systems?

Publications

Ahmad, M. A., Baryannis, G., & Hill, R. (2024). Defining complex adaptive systems: An algorithmic approach. Systems, 12(2), 45. https://doi.org/10.3390/systems12020045

Ay, N., Bertschinger, N., Der, R., Güttler, F., & Olbrich, E. (2008). Predictive information and explorative behavior of autonomous robots. The European Physical Journal B, 63(3), 329–339. https://doi.org/10.1140/epjb/e2008-00175-0

Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477

Flack, J. C. (2017). Coarse-graining as a downward causation mechanism. Philosophical Transactions of the Royal Society A, 375(2109), 20160338. https://doi.org/10.1098/rsta.2016.0338

Frank, S. A. (2012). Natural selection. V. How to read the fundamental equations of evolutionary change in terms of information theory. Journal of Evolutionary Biology, 25(12), 2377–2396. https://doi.org/10.1111/jeb.12010

Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787

Kauffman, S. A. (1993). The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press.

Krakauer, D. C. (2011). Darwinian demons, evolutionary complexity, and information maximization. Chaos, 21(3), 037110. https://doi.org/10.1063/1.3643064

Kriger, B. (2019). Evolutionary Selection for Atemporal Memory Storage: Why Three Convergent Pressures Favor Architectures Where Time Belongs to Retrieval, Not to Storage. Zenodo. https://doi.org/10.5281/zenodo.18381880

Kriger, B. (2019). Formalization of Structural Resilience in Discrete-State Dynamical Systems. https://doi.org/10.5281/zenodo.18351470

Kriger, B. (2021). The Evolutionary Architecture of Human Consciousness: Cognitive Contradictions, Biological Duality, and the Illusion of Time. Zenodo. https://doi.org/10.5281/zenodo.18384277

Kriger, B. (2022). Evolutionary Theory of Credence: A Conceptual Framework with Formal Analogies for Understanding Generative Modeling as a Resource-Theoretic Consequence of Complexity. Zenodo. https://doi.org/10.5281/zenodo.18379476

Kriger, B. (2025). Atemporality of Mental Memory Space: A Structural Hypothesis Grounded in Resource Constraints, Cyclical Closure, and Reconstructive Retrieval. Zenodo. https://doi.org/10.5281/zenodo.18381912

Kriger, B. (2026). Autonomy Suppression in Hierarchical Multi-Agent Systems: A Unifying Systems-Theoretic Framework. Zenodo. https://doi.org/10.5281/zenodo.18520653

Kriger, B. (2026). Coherent systems emerge through defining differentiation [Preprint].

Kriger, B. (2026). Differentiation as the ontological condition of actualization. https://doi.org/10.5281/ZENODO.18268520

Kriger, B. (2026). Evolutionary and Information-Theoretic Argument for the Necessity of Representational Isolation: Why Direct Perception Was Never an Option for Complex Systems. https://doi.org/10.5281/zenodo.18331202

Kriger, B. (2026). On the possibility of self-sufficient systems: Fixed points and cyclical closure. https://doi.org/10.5281/ZENODO.18256776

Kriger, B. (2026). The Comparative Asymmetry Principle: Relational Disequilibrium in Multi-Agent Environments. Zenodo. https://doi.org/10.5281/zenodo.18462518

Kriger, B. (2026). The Eruptive Manifestation of Model–Reality Mismatch: A Unified Structural Framework for High-Activation Episodes in Bounded Adaptive Systems. Zenodo. https://doi.org/10.5281/zenodo.18474532

Kriger, B. (2026). The Evolutionary Inevitability of Predictive Processing: A Physical Constraint Argument. https://doi.org/10.5281/zenodo.18324374

Kriger, B. (2026). The Stimulus Problem: A Formal Theory of Goal Generation in Post-Scarcity Information Environments. Zenodo. https://doi.org/10.5281/zenodo.18511908

Kriger, B. (2026). The principle of structural non-neutrality in coherent systems. https://doi.org/10.5281/ZENODO.18213503

Kriger, B. (2026). Structural Viability of Dyadic Systems: A Dynamical Account of Romantic Dissolution. Zenodo. https://doi.org/10.5281/zenodo.18632250

Levin, M. (2019). The computational boundary of a “self”: Developmental bioelectricity drives multicellularity and scale-free cognition. Frontiers in Psychology, 10, 2688. https://doi.org/10.3389/fpsyg.2019.02688

Moore, S. A., Mann, B. P., & Chen, B. (2025). Automated global analysis of experimental dynamics through low-dimensional linear embeddings. npj Complexity, 2(1), Article 62. https://doi.org/10.1038/s44260-025-00062-y

Solé, R. V., & Valverde, S. (2004). Information theory of complex networks: On evolution and architectural constraints. In Complex Networks, 189–207. Springer. https://doi.org/10.1007/978-3-540-44485-5_9

Watson, R. A., & Szathmáry, E. (2016). How can evolution learn? Trends in Ecology & Evolution, 31(2), 147–157. https://doi.org/10.1016/j.tree.2015.11.009

West, G. B., Brown, J. H., & Enquist, B. J. (1997). A general model for the origin of allometric scaling laws in biology. Science, 276(5309), 122–126. https://doi.org/10.1126/science.276.5309.122

Yuan, B., Zhang, J., Lyu, A., Wu, J., Wang, Z., Yang, M., Liu, K., Mou, M., & Cui, P. (2024). Emergence and causality in complex systems: A survey of causal emergence and related quantitative studies. Entropy, 26(2), 108. https://doi.org/10.3390/e26020108