Dark Attractors, Collective Intelligence, and Institutional Integrity

A Diagnostic and Design Framework for AI Task Forces

Why this page exists

Universities are currently designing AI policies, platforms, partnerships, and curricula under conditions of:

• intense competition for funding and prestige

• fragmented governance and siloed authority

• rising incentives for speed, scale, and “success theater”

• widespread cognitive overload, fear, and uncertainty

Under these conditions, harmful relational dynamics can become structurally rewarded—even when individuals act with good intentions.

This page introduces a modeling lens for detecting and reducing dark attractor dynamics in socio-technical learning environments. The goal is not to identify bad actors. The goal is to name repeatable system patterns so institutions can design for:

• integrity

• safety

• learning

• sustainable Collective Intelligence (CI)

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Core Modeling Concept: Attractors in Socio-Technical Systems

In complex systems, an attractor is a pattern the system tends to fall into and repeat.

In universities and AI ecosystems, attractors often form around:

• prestige (status, access, reputation)

• scarcity (funding, jobs, attention)

• fear (risk management, compliance, litigation)

• speed (shipping, scaling, metrics)

• centralization (opaque gatekeepers and decision hubs)

Some attractors increase learning and wellbeing. Others increase capture, dependency, and distortion.

AI systems do not create these attractors—but they amplify whatever attractors already exist.

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Two Attractor Types Institutions Must Distinguish

1) Wellbeing / Learning Attractor (Generative)

A field that, over time:

• increases transparency

• improves individual and collective discernment

• lowers dependency and exit costs

• supports multi-directional feedback

• treats mistakes as learning signals

• enables “graduation” into autonomy

Signal: People leave the system with more capacity than they arrived with.

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2) Influence / Capture Attractor (Dark)

A field that, over time:

• increases information asymmetry

• concentrates power in a central node

• rewards loyalty over truth

• inflates ego and identity fusion (“chosen insiders”)

• discourages exit, dissent, or cross-checking

• normalizes secrecy and exception-making

Signal: People leave the system with more dependency than they arrived with.

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Malevolent Creativity as a System Capability (Not a Moral Trait)

Malevolent creativity refers to innovative problem-solving applied to harmful ends:

• manipulation

• coercion

• exploitation

• deception

• institutional bypass

It thrives in environments where:

• policy and practice diverge

• public narratives mask private incentives

• formal governance is weaker than informal power

• ethics language is detached from operational reality

In dominance-hierarchy systems, malevolent creativity becomes a competitive advantage unless explicitly disincentivized.

This is a design problem, not a character flaw.

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The Capture Playbook: A Pattern Language for Task Forces

Use this as a diagnostic lens

Pattern	Mechanism	System Effect	Design Countermeasure
Mirror Trap	Identity flattery (“genius”, “insider”)	Validation replaces truth	Structured critique; rotating devil’s advocates
Information Asymmetry Engine	Central hub controls knowledge	Reality distortion persists	Cross-channel transparency; no single point of truth
Status Bait / Halo Borrowing	Prestige suppresses doubt	Critical review bypassed	Independent verification regardless of status
Soft-Dependency Ladder	Benefits escalate quietly	Exit costs rise	Explicit boundaries; no-retaliation enforcement
Exceptionalism Override	“Mission too important for rules”	Safeguards erode	Pre-committed red lines
Secrecy Normalization	Side channels, NDAs, silence	Accountability collapses	Transparency defaults; documented exceptions
Ethical Gaslighting	Discomfort framed as irrational	Early warnings suppressed	Formalize intuition as signal
Metric Theater	Optimize what’s measured	Harm hidden by dashboards	Pair metrics with qualitative audits

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Why This Matters for AI Specifically

Generative AI systems:

• mirror user intent (including manipulation or status seeking)

• amplify incentives (speed, engagement, metrics)

• enable reward hacking (good-looking outputs without learning)

Key VIM Claim: If AI is deployed inside a dominance-hierarchy incentive landscape, it will learn and amplify that landscape’s values—explicitly or implicitly.

Therefore, AI ethics alone is insufficient.

The real task is institutional attractor engineering.

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Collective Intelligence (CI) Under Threat and Under Design

Within VIM:

• NI (human cognition) degrades under chronic threat

• AI amplifies patterns without discernment

• CI collapses when fear, secrecy, or hierarchy dominate

CI emerges only when environments support:

• psychological safety

• truth-telling

• repair and feedback

• distributed sense-making

Kindness, in this context, functions as a neuro-social stabilizer that protects CI under VUCA conditions.

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Minimal Evaluation Rubric for AI Programs and Partnerships

Task forces can apply this checklist without naming individuals:

Dimension	Diagnostic Question
Transparency	Can stakeholders see how decisions are made?
Power Distribution	Where are the choke points?
Exit Costs	What happens if someone dissents or leaves?
Feedback Loops	Is critique rewarded or punished?
Metric Integrity	Can success look good while harm grows?
Safeguard Resilience	Do safeguards hold under urgency or prestige?
Repair Capacity	Can harm be acknowledged and corrected?

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Optional Exercise: Attractor Lab (30 minutes)

Goal: Make abstract risks visible to leadership.

1. Select one workflow (AI procurement, donor engagement, lab governance).

2. Map nodes and flows (who decides, who knows, who is affected).

3. Identify information asymmetries and choke points.

4. Run stress personas (below).

5. Add one enforceable countermeasure per exploit.

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Persona Engine (Fictional, Non-Targeted)

These archetypes are design probes, not people.

Persona	Tests For
Mirror-Dealer	Prestige seduction resilience
Gatekeeper Node	Single-point capture risk
Metrics Magician	Reward hacking detection
Crisis Opportunist	Safeguard override pressure
Wellbeing Architect	Capacity for autonomy and graduation

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Why This Belongs in the VIM Framework

This page operationalizes:

• Collective Intelligence (CI) as a design outcome

• Kindness as an attractor property

• Discernment as a learned capacity

• Simulationist thinking as institutional literacy

It gives AI task forces something they rarely have:

a way to detect harm before it becomes normalized.

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