Overview

Computational Modeling of Intelligence as a Dynamic Process of Agents in Complex Environments

“Intelligence is demonstrated by the neuroprocesses when they achieve their goals" and "Artificial Intelligence is the research program concerned with the design and implementation of machines for tasks requiring significant intelligence of the subconscious neuroprocesses." — Klaus Truemper, in Artificial Intelligence: Why AI Projects Succeed or Fail

1. Framing Intelligence as a Process

“Any map is necessarily smaller than the territory—but a dynamic, process‑based model can grow with the territory itself.” —Adapted from Bateson and Meadows [1]

Human and machine intelligences alike arise not from fixed algorithms or rote rules, but from flows of information among agents embedded in rich, ever‑changing contexts. In VIM, we treat intelligence as:

Emergent: Patterns and behaviors unfold through interactions, not pre‑programmed directives.
Relational: Knowing is co‑constructed across agents, environments, and feedback loops.
Adaptive: Models must update their own structure in response to new data, insights, and shifts in context.

2. Why Modeling Matters

A Shared Language Modeling gives us a common vocabulary—mathematical, visual, narrative—to speak across disciplines (neuroscience ↔ complexity science ↔ contemplative practice).
Experimentation In Silico By simulating “what‑if” scenarios, we can surface hidden leverage points, test regenerative cycles, and explore unintended consequences before acting in the world.
Transparency & Trust Open models build shared understanding: stakeholders can trace how inputs become outcomes, reducing the black‑box risks of conventional AI.
Scalability of Wisdom Reusable model components (agents, feedback loops, thresholds) let us seed new domains—economic, ecological, social—without reinventing the wheel.

3. Core Modeling Principles

Agents & Boundaries Define the actors (cells, individuals, institutions, AIs) and the “edges” where information flows in and out.
State & Transition Use finite‑state or differential‑equation formalisms to capture how agents move between modes (e.g., sensing → feeling → thinking → insight).
Feedback Loops Identify reinforcing and balancing loops that drive growth, decay, resilience, or collapse—laying the groundwork for the Regenerative Cycle page.
Multi‑Scale Integration Bridge micro‑level (neural, somatic) and macro‑level (ecosystem, society) processes, honoring pink‑noise–like rhythms that span scales.

4. How This Page Connects to the Rest of “Model”

Domains We’ll unpack the four VIM domains (Embodied Interaction, Relational Mapping, Ethical Alignment, Adaptive Learning), each a reusable agent archetype in our simulations.
Alignment Matrix Here, you’ll see how natural (NI) and synthetic (AI) capabilities map onto those domains—and how their synergy gives rise to Vital Intelligence (NI + AI).
Regenerative Cycle Building on feedback‑loop principles, this section shows how integrating “shadow” elements (trauma, resource depletion) into wisdom leads to renewal rather than collapse.

1. Framing Intelligence as a Process

2. Why Modeling Matters

3. Core Modeling Principles

4. How This Page Connects to the Rest of “Model”

Further Reading