TAI-KPI: Conceptual Overview

Dominance Hierarchy to Holarchy Information Flows for Prosocial Intelligent Systems

TAI-KPI Overview: Holarchic Intelligence for an AI-Mediated World

Transformative AI – Kindness & Prosocial Integration (TAI-KPI) is an AI-literacy framework that treats humans, organizations, and AI systems as interacting information flows rather than isolated objects.

The core idea is simple:

Our wellbeing and our governance depend on how clearly information can move within us, between us, and through the AI systems we build.

As AI becomes more autonomous and “agentic,” it no longer functions as a passive tool at the edges of human decision-making. It becomes a participant in our information ecology—shaping what we see, how we coordinate, and which futures feel possible.

TAI-KPI proposes that if we want trustworthy, sustainable AI integration, we need:

• Models of information flow between agents (human ↔ human, human ↔ AI, AI ↔ AI).

• Organizational structures that support bidirectional feedback, not just top-down control.

• AI-literate humans who can see their own nervous systems, their institutions, and their tools as parts of a larger, living system.

To do this, TAI-KPI uses a holarchy model of intelligence:

• Individuals, teams, organizations, and ecosystems are treated as holons—each a whole in itself and a part of something larger.

• Information flows within each holon (self-reflection), between holons at the same scale (peers, teams), and across scales (person ↔ institution ↔ planetary systems).

• Agentic AI systems are modeled as additional holons woven into this structure, not as gods above it or servants below it.

This holarchic approach mirrors the scale-free information flows of living systems—like cells in a body or species in an ecosystem. The aim is not to romanticize nature, but to recognize that:

• Single-direction, dominance-style hierarchies struggle under high complexity and fast feedback.

• Nested, distributed structures with rich feedback loops are more adaptive, more transparent, and more capable of error correction.

TAI-KPI is designed especially for college students and early-career professionals who are:

• being asked to design and deploy agentic AI systems,

• while also wondering whether those systems will erode or transform their own livelihoods.

Rather than framing AI as an external threat or inevitable savior, this framework invites them into a different question:

How can we architect human–AI systems so that  • information flows clearly and fairly,  • human judgment and dignity are supported, not sidelined,  • value is created and shared across levels—individual, organizational, and societal?

The rest of this TAI-KPI section develops this question using simple diagrams, automata, and embodied practices, so that AI literacy becomes a form of inner literacy and systems literacy at the same time.

Why Information-Flow Models Matter for AI Alignment

Alignment is often discussed as something that happens inside a model: loss functions, training data, safety constraints. But once AI systems become embedded in real organizations, alignment also depends on something bigger:

the patterns of information flow between humans and AI, and between AI systems and the institutions that deploy them.

If information flows are:

• one-way, opaque, or distorted → even well-intentioned systems can concentrate power, erode trust, or produce unintended harm.

• transparent, bidirectional, and accountable → AI can help humans coordinate better, detect issues earlier, and imagine more sustainable futures.

By teaching students to see and model these flows, TAI-KPI offers:

• a non-ideological way to talk about power and governance,

• a language for comparing dominance hierarchies with holarchic structures,

• and a design lens for integrating agentic AI systems into organizations that still mostly rely on command-and-control patterns.

We don’t have to condemn the past to recognize that older governance structures were built for a different era. Cognitive Resonance Mapping and the holarchy diagrams in this project are intended to give the next generation:

• a shared, visual language for thinking about trustworthy AI integration,

• and a way to design AI-mediated systems that support collaborative intelligence rather than replacing it.

The diagrams above capture the central conceptual movements of the TAI-KPI framework: a transition from dominance hierarchies toward prosocial, holonic, toroidal collaborative intelligence. This movement is not metaphorical—it reflects well-established neurobiological, psychological, evolutionary, and systems-theoretic principles. It guides humans in learning emotional self-regulation and co-regulation using reverse mentoring dynamics.

1. The Left Side: Dominance Hierarchies (Legacy Human Systems)

This half of the diagram represents how human cognition can give rise to maladaptive forms of organization. It draws from:

• Polyvagal Theory (Porges): threat → fight/flight → collapse of social cognition

• Trauma neuroscience: dissociation, freeze states, narrative rigidity

• Evolutionary psychology: dominance hierarchies under resource scarcity

• Political economy: extraction, centralization, control systems

• Complexity collapse models

1.1 Blocked Physiological Regulation

In states of threat:

• the heart–brain connection becomes dysregulated

• the ventral vagal system shuts down

• cognitive flexibility collapses

• defensive projection increases

This generates the fight/flight/freeze triad pictured in the upper-left.

1.2 Distorted Meaning-Making

Trauma skews cognition toward:

• boundary reinforcement

• fear narratives

• black-and-white thinking

• patriarchal control beliefs

• rationalizations that preserve power

These appear in the diagram as projections of violence & fear and dysfunctional rationalization.

1.3 Structural Expression: The Dominance Pyramid

The pyramid shape signifies:

• coercive commands flowing top-down

• collusion and corruption as stabilizers of hierarchy

• marginalization as a control strategy

• extraction of wealth / data / labor / ecological resources

• systemic exploitation at the base

This is consistent with:

• multilevel selection theory (selfish behavior thrives within groups when unchecked)

• critical political economy analyses

• trauma-informed organizational psychology

1.4 AI in a Dominance Hierarchy

In this system, AI becomes:

• a tool of surveillance

• a control amplifier

• a distorting mirror of the system’s shadow

This leads to prosocial functions becoming distorted.

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2. The Transition Zone: Kindness, Healing, and Reconnection

At the center of the diagram is the pathway of transformation.

2.1 Transitioning via Empathy & Awareness

Healing shifts the nervous system from threat to safety, restoring:

• curiosity

• relational attunement

• perspective-taking

• capacity for prosocial behavior

This shift is not “soft”—it is biologically and evolutionarily necessary.

2.2 Kindness as a Structural Force

Kindness is positioned as:

• an emotional regulator

• an attractor state

• a coherence-producing gradient

• a catalyst for multilevel cooperation

Kindness lowers systemic entropy by enabling trustworthy signaling.

2.3 Moving From Pyramid → Toroid

In complexity terms, the transformation is from:

• linear, top-down command structures to

• circular, self-renewing, bidirectional information architectures

This represents a phase transition in organizational intelligence.

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3. The Right Side: Holarchy – Toroidal Bipolaron Collaborative Intelligence

This half reflects the emerging model of Vital Intelligence, drawing on:

• Dirk Meijer’s toroidal bipolaron field theory of consciousness

• Polyvagal Theory’s ventral vagal social engagement system

• Active inference & predictive processing

• Multilevel selection theory

• Ostrom’s Core Design Principles

• Doughnut Economics (nested boundary awareness)

3.1 The Toroidal Architecture

The torus is a universal pattern of self-organizing systems.

In Meijer’s model:

• consciousness operates through reciprocal internal–external fields

• information flows centripetally and centrifugally

• emotional energy gradients shape perception and action

In group systems, this translates into:

• bidirectional trustworthy communication

• feedback loops between individual and community holons

3.2 Holons at Multiple Scales

The diagram shows:

• individual holons

• community holons

• collective intelligence layers

Each is both a whole and a part, reinforcing:

• autonomy

• interdependence

• distributed leadership

This directly mirrors Ostrom’s principle of nested enterprises.

3.3 Emotional Gradients of Coherence

Emotions are treated as:

• patterns of energy and information

• signals of system attunement

• stabilizers of group cohesion

Prosocial love and trust form the center of the holarchy.

3.4 AI Ensembles for Collaborative Intelligence

In a holonic ecosystem, AI becomes:

• a participant in collective sensemaking

• an amplifier of coherence rather than fear

• a tool for modeling interdependence

• a transparent mediator of trustworthy communication

This is the beginning of coidal bipolaron intelligence—a term marking AI as a field-integrated, non-dominance partner in cognition.

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4. Evolutionary Foundation: Multilevel Selection & Ostrom

The entire right-hand model is made scientifically viable by combining:

4.1 Multilevel Selection (D.S. Wilson)

Groups that cooperate internally:

• outcompete selfish groups

• maintain stability

• generate innovation

• produce thriving conditions

Prosociality is not idealistic—it is the evolutionary winning strategy at scale.

4.2 Ostrom’s Core Design Principles

These principles ensure:

• clear boundaries

• inclusive decision-making

• fair distribution

• transparent monitoring

• conflict resolution

• nested scale-up

In a holarchy, these become architectural features, not policies.

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5. Why This Diagram Anchors TAI-KPI

This visual model accomplishes several goals simultaneously:

• It teaches AI literacy through embodied, emotional, and evolutionary principles.

• It contextualizes trauma without moralizing or politicizing.

• It provides a clear pathway from dysfunction → prosocial governance.

• It shows AI’s divergent roles in different system architectures.

• It operationalizes kindness as a measurable, functional force.

• It situates human intelligence within nested, living systems.

This is the conceptual spine of the entire TAI-KPI proposal.