TAI-KPI · Modeling Frameworks
A visual language for understanding intelligent, adaptive systems
1. Why We Use Modeling Frameworks
Human intelligence and artificial intelligence are both information-processing systems. But they work through different substrates:
Humans: embodied, affective, sensing → feeling → thinking → intuiting
Machines: data → parameters → tokens → predictions
To help learners understand these differences without overwhelming them, TAI-KPI uses a set of simple visual modeling frameworks—a “starter kit” for thinking about intelligence as flows, feedback loops, and adaptive transitions rather than as fixed categories.
These frameworks help answer questions like:
How do organisms and machines update their expectations about the world?
How does information flow differently in a dominance hierarchy versus a holarchic, distributed system?
How do individuals form a theory of mind—the sense of what others might think, feel, or intend?
How do patterns scale from Human→Human to Human→AI to AI→AI interactions?
The goal is not to teach mathematics. The goal is to give people conceptual maps that make the invisible architecture of intelligence easier to see.
2. Two Archetypes of Information Flow
Across the TAI-KPI curriculum, educators and learners will encounter two contrasting information-flow logics.
A. Dominance Hierarchy (Top-Down Control)
This is the traditional model many organizations and technologies have inherited:
One center of authority
Information flows downward
Limited feedback upward
Errors or surprises treated as threats
People (and sometimes machines) narrow their cognition under pressure
Behavior becomes rigid, predictable, and defensive
This pattern appears in early stages of human cognitive development, in trauma-shaped nervous systems, and in older forms of AI control architecture.
It is simple, but not adaptive.
B. Holarchic / Scale-Free Adaptive Systems (Living Intelligence)
This model is inspired by biological networks, social ecosystems, and human communities at their healthiest:
Many semi-independent parts
Information flows in multiple directions at once
Feedback is fast and continuous
Surprises become signals for learning
Collective intelligence emerges from flexible interactions
This is how healthy groups, ecosystems, and skilled collaborative teams function. It is also how the brain coordinates sensing, feeling, thinking, and action.
The TAI-KPI project helps learners shift from the first archetype toward the second—not by rejecting structure, but by introducing more intelligent structure.
3. A Visual Language for Intelligent Systems
To make these ideas accessible, TAI-KPI uses four simple modeling tools. Each reveals a different facet of intelligence:
1. Finite-State Automata (FSMs): Modes of Being and Boundaries of Choice
FSMs introduce the idea that intelligent systems move through modes:
Safe → Curious → Overwhelmed → Shut Down
Explore → Evaluate → Act → Reflect
Trust → Tension → Repair → Realign
They help illustrate:
polyvagal state transitions
trauma-shaped patterns of narrow cognition
AI pipelines (ingest → generate → evaluate → update)
decision boundaries (ethical constraints, red flags)
FSMs are the beginner-friendly doorway into dynamic intelligence.
2. Cellular Automata (CA): Emergence from Local Interactions
Cellular automata demonstrate how simple local rules create complex global patterns:
trust cascades
kindness contagion
group norms
breakdown spirals
CA help learners intuit scale-free dynamics without equations.
They let us ask: “What happens when many small intelligences interact on a shared surface?”
3. Simple System Dynamics (Stocks & Flows): What Changes Slowly
Automata show fast transitions. System dynamics shows what shifts gradually:
trust as a stock that fills or drains
ecological capacity
cognitive bandwidth
institutional resilience
Using only a few well-chosen diagrams, learners see how slow variables set the conditions for fast decisions—human or machine.
This is where dominance hierarchies reveal their brittleness, and holarchies reveal their regenerative power.
4. Active-Inference Sketches: Prediction, Surprise, and Learning
Learners are not introduced to equations; instead we use intuitive shapes:
States (what the system thinks is happening)
Observations (what the system senses)
Actions (how it responds)
Preferences (what it wants or values)
Prediction Errors (signals for updating expectations)
With these simple boxes and arrows, people can compare:
Embodied sensations
Sensor data
Emotion + intuition as priors
Statistical priors from training
Social meaning / theory of mind
Pattern recognition on text
Repair + relational feedback
Loss minimization
Learning shaped by safety
Learning shaped by objective functions
This helps learners grasp why Human→AI and AI→Human interactions require trauma-informed design, and why AI→AI interactions need governance aligned with human wellbeing.
4. Four Directions of Theory of Mind
TAI-KPI introduces a developmental concept rarely made explicit:
Intelligent systems evolve their “theory of mind” through four relational directions:
Human → Human
empathy, boundaries, trust, meaning-making
Human → AI
understanding what a machine can and cannot “understand”
knowing when an AI is confident vs uncertain
healthy regulation rather than over-reliance
AI → Human
models of user intent
alignment through transparency, safety, and kindness signals
AI → AI
how autonomous systems coordinate
how to prevent adversarial or emergent harmful patterns
governance built on holarchic principles rather than competition
Understanding these four directions prepares learners for a future where human-AI relationships are dynamic, relational, and holistic—not one-sided or exploitative.
5. How We Introduce These Ideas to Novices
We begin with visual, experiential metaphors:
“Modes” instead of “states.”
“Unexpected signals” instead of “prediction error.”
“Relational loops” instead of “feedback functions.”
“Breathing room” or “cognitive space” instead of “working memory.”
“Group patterns” instead of “emergent phenomena.”
Then we connect each metaphor to:
a simple model (FSM, CA, stock-flow, active inference sketch)
a lived example from nervous system dynamics
a parallel example from AI behavior
a contrast between top-down control vs holarchic adaptability
This structure helps novices feel that the models are maps of lived experience, not abstract machinery.
The aim is empowerment: “I can see how intelligent systems work—and how to guide them toward prosocial, regenerative futures.”
6. The Purpose of the Modeling Ensemble
The frameworks in TAI-KPI are not technical requirements. They are literacy tools—ways to cultivate:
flexible cognition
systems awareness
ethical imagination
trauma-informed decision-making
holarchic collaboration
a shared, visual language for human-AI evolution
Used together, they make visible what is normally hidden: the flow of intelligence across scales—from the nervous system, to relationships, to organizations, to emerging digital ecologies.
They help us build the capacity to transition from dominance hierarchies to adaptive, caring, scale-free systems of collective intelligence.
Last updated