Complexity Science and Modeling

How formal models bridge subconscious/neuroprocesses, AI paradigms, and living systems dynamics to guide a regenerative evolution.

Complexity Science: The study of how large networks of simple interacting agents give rise to emergent patterns, resilience, and self‑organization across scales.
Model: An abstract representation—mathematical, computational, or narrative—that captures key variables and relationships to explore “what‑if” scenarios.
Klaus Truemper’s Perspective: Intelligence arises between subconscious and conscious neuroprocesses, each constituting rich “models of the world” that human systems continuously update.
VIM’s Revolutions: Integrates NI’s embodied models with AI’s algorithmic frameworks to form a foundational intelligence model—countering resource‑intensive brute‑force approaches.

Emergence & Self‑Organization: Living systems hover at critical thresholds (SOC) where energy transforms into structure—analogous to pink‑noise resonances in neurodynamics.
Holarchies & Multi‑Scale Models: From quantum‑level resonance patterns to global networks, models connect energy transformations across nested layers.
Trauma & Model Dysregulation: Chronic stress implants maladaptive priors; without integrative models, human systems fracture under rapid information flows.
Abstract Power of Models: Models are the lingua franca across disciplines—CS, neuroscience, contemplative practice—giving form to otherwise invisible relationships.

Fishwick’s Simulation Model Design & Execution: Best practices for building, validating, and iterating agent‑based and discrete event models in interdisciplinary settings.
Dynamic System Modeling: Techniques for stocks and flows (Vensim, InsightMaker), capturing feedback loops that shape resource, emotional, and informational flows.
Agent‑Based Hybridization: Combining ABM, network science, and system dynamics to simulate socio‑ecological and techno‑social scenarios.

World 1 (Physical): Embodied agents and ecosystems—modeled via system dynamics.
World 2 (Mental): Neurocognitive models—predictive processing, Truemper’s subconscious/conscious loops.
World 3 (Abstract/Virtual): Models themselves—VIM’s domain for guiding AI design, trust architectures, and regenerative platforms.

VIM unites all three: using CS modeling languages to instantiate psychological and social phenomena in code, then feeding back to physical practice.

Resource Efficiency: Human brains (~20 W) vs. synthetic AI (kW–MW) highlight the need for resonance‑based computation rather than brute force.
AI Governance: Embedding VIM’s foundational models into AI development can steer AGI away from unchecked SOC loops that risk runaway behaviors.
Human‑AI Synergy: By codifying embodied, ethical, and relational models, LLMs become partners in articulating and refining the very frameworks that shape their own growth.

Fishwick, P. A. (2007). Simulation Model Design and Execution: Building Digital Worlds.
Fishwick, P. A. (1998). Dynamic System Modeling: Simulation and Control of Complex Systems.
Truemper, K. (2012). Applied Knowledge Modeling: Representations in AI and Cognitive Systems.
Penrose, R. (1994). Shadows of the Mind: A Search for the Missing Science of Consciousness.
Mitchell, M. (2009). Complexity: A Guided Tour.
Bak, P. (1996). How Nature Works: The Science of Self‑Organized Criticality.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems.

Last updated 13 days ago