Institute of Integrative and Interdisciplinary Research

Information, Complexity, and Artificial Intelligence Systems

This program explores the information-theoretic foundations of complex adaptive systems, principles of self-organization and emergence, and the structural constraints governing algorithmic intelligence. It investigates thermodynamic costs of irreversible computation, entropy inversion mechanisms in large-scale AI, the reality of emergent abilities in language models, causal emergence quantification, and lessons from complexity science for effective AI governance. The framework bridges formal models of complexity with practical implications for consciousness, cognitive contributions to universal organization, and uncertainty in highly adaptive systems.

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: Information, Complexity, and Artificial Intelligence Systems

  • How can complex adaptive systems be formally defined and algorithmically identified?
  • What are the necessary and sufficient physical conditions for consciousness to emerge in a system?
  • How and why do complex systems self-organize?
  • What is the relationship between information theory and machine learning optimization?
  • Is chaos an intrinsic property of systems or relative to the framework of observation?
  • What is the contribution of cognitive systems to the total effective complexity of the universe?
  • How do large-scale AI systems produce local entropy inversion through algorithmic compression?
  • What is the thermodynamic cost of computation with irreversibility and stochastic processes?
  • Are emergent abilities of large language models genuine or measurement artifacts?
  • What lessons from complex systems science apply to AI governance?
  • How can causal emergence be quantified and identified in complex systems?
  • Does any complex world require uncertainty?

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
  • Albantakis, L., Barbosa, L., Findlay, G., Grasso, M., Haun, A. M., Marshall, W., Mayner, W. G. P., Zaeemzadeh, A., Boly, M., Juel, B. E., Sasai, S., Fujii, K., David, I., Hendren, J., Lang, J. P., & Tononi, G. (2023). Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms. PLOS Computational Biology, 19(10), e1011465. https://doi.org/10.1371/journal.pcbi.1011465
  • Gershenson, C. (2025a). Complexity, artificial life, and artificial intelligence. Artificial Life, 31(3), 289–303. https://doi.org/10.1162/artl_a_00462
  • Gershenson, C. (2025b). Self-organizing systems: What, how, and why? npj Complexity, 2, Article 31. https://doi.org/10.1038/s44260-025-00031-5
  • Ilcic, A., Fuentes, M., & Lawler, J. (2025). Artificial intelligence, complexity, and systemic resilience in global governance. Frontiers in Artificial Intelligence, 8, 1562095. https://doi.org/10.3389/frai.2025.1562095
  • Jaradat, Y., Masoud, M., Manasrah, A., Alia, M., Suwais, K., & Almanasra, S. (2025). Exploring the intersection of information theory and machine learning. The International Arab Journal of Information Technology, 22(5), 845–858. https://doi.org/10.34028/iajit/22/5/1
  • Kriger, B. (2026). The Informational Preconditions of Meaning: A Structural Tendency Theorem on Civilizational Trade-offs Between Progress and Human Well-Being . Zenodo. https://doi.org/10.5281/zenodo.18292636
  • Kriger, B. (2025c). The law of imperative uncertainty: Why any complex world requires uncertainty. Zenodo. https://doi.org/10.5281/ZENODO.18101601
  • Kriger, B. (2026b). Coherent systems emerge through defining differentiation [Preprint].
  • Kriger, B. (2026c). Differentiation as the ontological condition of actualization. Zenodo. https://doi.org/10.5281/ZENODO.18268520
  • Kriger, B. (2026d). Dynamics of information convergence: Empirical analysis of time density in the AI-centered infosphere. Zenodo. https://doi.org/10.5281/ZENODO.18256945
  • Kriger, B. (2026e). Estimation of the contribution of biospheric and synthetic cognitive systems to the total effective complexity of the universe. Zenodo. https://doi.org/10.5281/ZENODO.18261863
  • Kriger, B. (2026f). Local entropy inversion in large-scale AI systems: Thermodynamics of algorithmic compression. Zenodo. https://doi.org/10.5281/ZENODO.18262199
  • Kriger, B. (2026g). On the possibility of self-sufficient systems: Fixed points and cyclical closure. Zenodo. https://doi.org/10.5281/ZENODO.18256776
  • Kriger, B. (2026h). The principle of structural non-neutrality in coherent systems. Zenodo. https://doi.org/10.5281/ZENODO.18213503
  • Manzano, G., Kardeş, G., Roldán, É., & Wolpert, D. H. (2024). Thermodynamics of computations with absolute irreversibility, unidirectional transitions, and stochastic computation times. Physical Review X, 14, 021026. https://doi.org/10.1103/PhysRevX.14.021026
  • 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
  • Schaeffer, R., Miranda, B., & Koyejo, O. (2023). Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems, 36, 55565–55581.
  • Sepúlveda-Fontaine, S. A., & Amigó, J. M. (2024). Applications of entropy in data analysis and machine learning: A review. Entropy, 26(12), 1126. https://doi.org/10.3390/e26121126
  • Shur-Ofry, M., Abiri, E., Arbel-Raviv, R., Feder, M., Lavi, R., Sarfraz, M. U., & Tennenholtz, M. (2025). Lessons from complex systems science for AI governance. Patterns, 6(8), 101341. https://doi.org/10.1016/j.patter.2025.101341
  • Söderqvist, B., de Vries, H. P., & van der Vlist, F. (2024). Understanding the development of emerging complex intelligent systems. Journal of Engineering and Technology Management, 72, 101815. https://doi.org/10.1016/j.jengtecman.2024.101815
  • Wolpert, D. H. (2024). Is stochastic thermodynamics the key to understanding the energy costs of computation? Proceedings of the National Academy of Sciences, 121(45), e2321112121. https://doi.org/10.1073/pnas.2321112121
  • 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
  • Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., … Wen, J.-R. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223. https://doi.org/10.48550/arXiv.2303.18223