Institute of Integrative and Interdisciplinary Research

Information and Complexity

This program explores the information-theoretic foundations of complex adaptive systems, principles of self-organization and emergence, and the structural constraints governing cognition and consciousness. It investigates formal definitions of complexity, the necessary conditions for consciousness, causal emergence quantification, the role of uncertainty in complex worlds, and lessons from complexity science for understanding cognitive and biological systems. The framework bridges formal models of complexity with practical implications for consciousness, perception, and the organization of the universe.

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.

Institute Position on Integrated Information Theory (IIT)

Institute Position on Global Neuronal Workspace Theory (GNWT)

Institute Position on the Predictive Processing Framework

Research Questions: Information, Complexity, and Artificial Intelligence Systems

  • Research Questions: Information and Complexity
  • 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?
  • 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 can causal emergence be quantified and identified in complex systems?
  • Does any complex world require uncertainty?

Publications

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

Cea, I., & Signorelli, C. M. (2025). How to be an integrated information theorist without losing your body. Frontiers in Computational Neuroscience, 18, 1510066. https://doi.org/10.3389/fncom.2024.1510066

Cogitate Consortium, Melloni, L., Mudrik, L., Pitts, M., Bendtz, K., Ferrante, O., … Dehaene, S. (2025). Adversarial testing of global neuronal workspace and integrated information theories of consciousness. Nature, 642(8066), 133–142. https://doi.org/10.1038/s41586-025-08888-1

Gershenson, C. (2025). Self-organizing systems: What, how, and why? npj Complexity, 2, Article 31. https://doi.org/10.1038/s44260-025-00031-5

Hoel, E. P. (2025). Causal emergence 2.0: Quantifying emergent complexity. arXiv preprint arXiv:2503.13395. https://doi.org/10.48550/arXiv.2503.13395

Hodson, R., Mehta, M., & Smith, R. (2024). The empirical status of predictive coding and active inference. Neuroscience & Biobehavioral Reviews, 157, 105473. https://doi.org/10.1016/j.neubiorev.2023.105473

Hohwy, J. (2025). A metaphysics for predictive processing. Synthese, 206, 54. https://doi.org/10.1007/s11229-025-05169-2

Kriger, B. (2022). An Informational Framework for Understanding Population-Scale Viral Dynamics. https://doi.org/10.5281/zenodo.18392769

Kriger, B. (2024). The Functional Sufficiency Framework: Toward Empirical Criteria for Explanatory Redundancy in Models of Consciousness. https://doi.org/10.5281/zenodo.18319884

Kriger, B. (2024). Toward operational terminology in integrated information theory: A methodological consideration. https://doi.org/10.5281/ZENODO.18307674

Kriger, B. (2025). The law of imperative uncertainty: Why any complex world requires uncertainty. https://doi.org/10.5281/ZENODO.18101601

Kriger, B. (2026). Formalization of Mental Disintegration Phenomena Through Dynamical Systems Theory: With Applications to DSM-5-TR Diagnostic Categories. Zenodo. https://doi.org/10.5281/zenodo.18556979

Kriger, B. (2026). The Informational Preconditions of Meaning: A Structural Tendency Theorem on Civilizational Trade-offs Between Progress and Human Well-Being. https://doi.org/10.5281/zenodo.18292636

Kriger, B. (2026). The Predictive Mind and Its Myths: Metaphor, Narrative, and Ritual as Structural Necessities of Scientific Cognition. Zenodo. https://doi.org/10.5281/zenodo.18490146

Kriger, B. (2026). The Structural Distortion Principle: A Closed-Loop Model of Perception, Attention, and World-Maintenance in Bounded Cognitive Systems. https://doi.org/10.5281/zenodo.18452700

Ladyman, J., Lambert, J., & Wiesner, K. (2024). What is a complex system? European Journal for Philosophy of Science, 14, 49. https://doi.org/10.1007/s13194-024-00620-0

Melloni, L., Mudrik, L., Pitts, M., Bendtz, K., Ferrante, O., Gorska, U., … Koch, C. (2023). An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLOS ONE, 18(2), e0268577. https://doi.org/10.1371/journal.pone.0268577

Naccache, L., Sergent, C., Dehaene, S., Wang, X.-J., Farisco, M., & Changeux, J.-P. (2025). GNW theoretical framework and the adversarial testing of global neuronal workspace and integrated information theories of consciousness. Neuroscience of Consciousness, 2025(1), niaf037. https://doi.org/10.1093/nc/niaf037

Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2024). Generating meaning: Active inference and the scope and limits of passive AI. Trends in Cognitive Sciences, 28(2), 97–112. https://doi.org/10.1016/j.tics.2023.10.002

Rosas, F. E., Mediano, P. A. M., Jensen, H. J., Seth, A. K., Barrett, A. B., Carhart-Harris, R. L., & Bor, D. (2020). Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data. PLOS Computational Biology, 16(12), e1008289. https://doi.org/10.1371/journal.pcbi.1008289

Sas, M. I., Mediano, P. A. M., Rosas, F. E., & Barrett, A. B. (2025). Improved estimators of causal emergence for large systems. arXiv preprint arXiv:2601.00013. https://doi.org/10.48550/arXiv.2601.00013

Seth, A. K., & Bayne, T. (2022). Theories of consciousness. Nature Reviews Neuroscience, 23(7), 439–452. https://doi.org/10.1038/s41583-022-00587-4

Sprevak, M., & Smith, R. (2023). An introduction to predictive processing models of perception and decision-making. Topics in Cognitive Science, 16(2), 189–217. https://doi.org/10.1111/tops.12704

Tononi, G., & Boly, M. (2025). Integrated information theory: A consciousness-first approach to what exists. arXiv preprint arXiv:2510.25998. https://doi.org/10.48550/arXiv.2510.25998

Yang, M., Wang, Z., Liu, K., Rong, Y., Yuan, B., & Zhang, J. (2025). Finding emergence in data by maximizing effective information. National Science Review, 12(1), nwae279. https://doi.org/10.1093/nsr/nwae279

Zacks, O., & Jablonka, E. (2023). The evolutionary origins of the Global Neuronal Workspace in vertebrates. Neuroscience of Consciousness, 2023(1), niad020. https://doi.org/10.1093/nc/niad020

Zhang, J., Tao, R., Leong, K. H., Yang, M., & Yuan, B. (2025). Dynamical reversibility and a new theory of causal emergence based on SVD. npj Complexity, 2(1), 3. https://doi.org/10.1038/s44260-024-00014-0

Kriger, B. (2026). Decentralised Urbanism, Autonomous Habitation, and Adaptive Population Dynamics: A Systems-Theoretic Framework. Zenodo. https://doi.org/10.5281/zenodo.18708642

Kriger, B. (2026). Response to Automation-Driven Labour Displacement: Adaptive Universal Transaction Tax and Request-Based Universal Basic Income. Zenodo. https://doi.org/10.5281/zenodo.18706752