Cyber Psychosis Cascade Model

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Last edited:2025-12-27 (11 days ago)

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LLM Summary:Model analyzes how AI-generated content triggers psychological harm cascades through three pathways (targeted campaigns, mass confusion, dependency), estimating 1-3% of population as highly vulnerable with 5-10x increased risk multipliers. Provides structured framework with vulnerability distributions and cascade timelines but lacks external citations or quantified intervention effectiveness.

Model

Cyber Psychosis Cascade Model

Importance45

Model TypePopulation Risk Model

Target RiskMental Health Impacts

Key InsightAI-generated content can trigger cascading psychological effects in vulnerable populations

Related

Risks

• Deepfakes
• AI Disinformation

Model Quality

Novelty

4

Rigor

3

Actionability

3

Completeness

4

Overview

The proliferation of AI-generated content creates novel vectors for psychological harm at population scale. This model examines how synthetic media can trigger, exacerbate, or weaponize psychological vulnerabilities in susceptible individuals, potentially creating cascading effects that propagate through social networks and destabilize collective sense-making. The core insight is that psychological harm from AI-generated content operates through cascade dynamics: individual distress can amplify through social channels, erode institutional trust, and ultimately impair society’s ability to coordinate around shared reality.

Understanding these cascade mechanisms matters because they represent a category of AI harm that is poorly addressed by current safety frameworks focused on individual model behavior. A model that generates convincing synthetic content may pass all standard evaluations while enabling attacks on mental health at scale. The population-level effects—mass confusion events, dependency cascades, and collective reality fragmentation—are emergent properties that cannot be predicted from individual content generation. This creates a gap between safety research (focused on individual model outputs) and harm prevention (which requires understanding population dynamics).

The model distinguishes three primary cascade pathways: targeted individual campaigns that exploit personal vulnerabilities, mass confusion events that overwhelm collective sense-making, and gradual dependency cascades that erode human social capacity over time. Each pathway has different attack vectors, vulnerable populations, and intervention points. The critical uncertainty is whether defensive measures (detection technology, authentication infrastructure, mental health support) can scale faster than offensive capabilities (synthetic content generation, personalized targeting, social amplification). Current evidence suggests defense is losing this race, making proactive intervention increasingly urgent.

Conceptual Framework

Cascade Stages

Stage	Examples	Key Transition
Triggers	Deepfakes, reality confusion, personalized harassment, parasocial manipulation	Vulnerable exposure
Individual Effects	Acute distress, identity disruption, trust erosion, social withdrawal	Social sharing
Cascade Amplification	Social media spread, community impact, institutional failure, collective uncertainty	Threshold crossing
Population Outcomes	Mass confusion, dependency epidemics, democratic dysfunction, mental health crisis	Systemic harm

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Individual trigger mechanisms propagate through cascade amplification to produce population-level outcomes. The severity increases at each stage as effects compound.

Mechanisms of Harm

Direct Exposure Effects

Vector	Mechanism	Vulnerable Population	Severity	Emergence Timeline
Deepfake targeting	Synthetic content depicting individual in harmful scenarios	Specific targets with public profiles	High	Already occurring
Reality confusion	Inability to distinguish real from synthetic	Elderly, cognitive decline, psychosis-prone	Moderate-High	2024-2026
Personalized harassment	AI-generated content tailored to individual fears	Anxiety/PTSD sufferers	High	Already occurring
Parasocial manipulation	AI personas exploiting attachment needs	Lonely, socially isolated	Moderate	Emerging
Identity erosion	Synthetic content undermining self-concept	Adolescents, identity-formation stage	Moderate-High	2025-2027

Cascade Pathways

Three Primary Cascade Pathways

Pathway	Trigger	Progression	Terminal State	Timeline
1. Targeted Campaign	Deepfake created	Distribution → Social exposure → Relationship damage → Identity crisis	Long-term PTSD	Weeks-months
2. Mass Confusion	Synthetic evidence released	Conflicting “proof” → Authority questioned → Polarization	Factional conflict	Days-weeks
3. Dependency Cascade	AI companion adoption	Human interaction decline → Social skill atrophy → Isolation	Complete withdrawal	Months-years

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Population Vulnerability Model

Causal Structure

Cascade probability depends on interacting factors that influence each other in a network structure rather than combining independently. The key factors and their relationships:

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Why simple multiplication fails: A naive model like P(cascade) = P(trigger) × P(exposure) × P(amplification) × P(institutional failure) treats these as independent dice rolls. In reality:

• Weak institutional response correlates with higher amplification (same underlying causes: underfunding, low priority)
• High vulnerable exposure makes amplification more likely (more nodes to propagate through)
• Certain trigger types amplify more easily than others (not independent events)

Factor Estimates (with correlation caveat)

Factor	Low	Central	High	Correlated With
Trigger likelihood	0.80	0.90	0.98	Institutional detection capacity
Vulnerable exposure	0.15	0.22	0.30	Amplification potential (+)
Amplification potential	0.30	0.45	0.60	Institutional response (-), Vulnerability (+)
Institutional failure	0.20	0.35	0.50	Amplification (+), Trigger detection (-)

Rough cascade probability range: 1-10%, but this range reflects uncertainty about correlation structure more than parameter uncertainty. If factors are highly correlated (institutions that fail to detect also fail to respond), the true probability is higher than naive multiplication suggests.

Vulnerability Distribution

Segment	% of Population	Primary Vulnerability	Risk Multiplier	Intervention Priority
Pre-existing psychosis	1-3%	Reality testing deficits	5-10x	Critical
Anxiety disorders	15-20%	Threat hypervigilance	2-3x	High
High institutional distrust	30-40%	Conspiracy susceptibility	1.5-2x	Medium-High
Information overload	50-60%	Decision paralysis	1.2-1.5x	Medium
Baseline resilient	20-30%	Standard vulnerability	1x	Low

Specific Scenario Analysis

Scenario 1: Targeted Individual Campaign

An individual becomes the subject of a coordinated AI-generated synthetic content campaign designed to destroy their reputation, relationships, and psychological wellbeing.

Attack Components:

Component	Technical Feasibility	Detectability	Harm Severity
Deepfake videos in compromising scenarios	High (available today)	Low-Medium	Very High
AI voice clones for fabricated statements	High (available today)	Low	High
Synthetic social media histories	Medium-High	Low	High
Coordinated cross-platform distribution	High	Medium	High
Personalized psychological targeting	Medium	Very Low	Very High

Psychological Impact Timeline:

Phase	Duration	Primary Effect	Intervention Window	Recovery Probability
Acute shock	Hours-days	Panic, disbelief, social paralysis	Immediate support critical	80-90% if addressed
Social erosion	Days-weeks	Relationship damage, social isolation	Reputation management	60-75%
Identity crisis	Weeks-months	Self-concept disruption, depression	Mental health treatment	40-60%
Chronic effects	Months-years	PTSD, anxiety disorders, permanent trauma	Long-term therapy	20-40%

Scenario 2: Mass Confusion Event

Large-scale release of synthetic content creates collective uncertainty about a major public event (election, crisis, terrorist attack).

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Historical Analogs and Scaling:

Historical Case	Mechanism	Scale	AI Enhancement Potential
Rwanda radio genocide	Information weaponization	National	10-100x reach, personalization
COVID misinformation	Health behavior cascades	Global	Real-time adaptation, targeting
Election interference	Democratic legitimacy erosion	National	Synthetic evidence, deepfakes
QAnon phenomenon	Collective delusion formation	Multi-national	Personalized recruitment, AI leaders

Scenario 3: AI Companion Dependency Cascade

Widespread reliance on AI companions leads to social skill atrophy and isolation at population scale.

Progression Model:

Stage	Timeframe	Characteristics	Affected Population (%)	Reversibility
Initial adoption	0-6 months	Supplementary use, human preference maintained	15-25%	High
Primary preference	6-18 months	Human interaction actively avoided	5-10%	Moderate
Functional dependency	18-36 months	Atrophied social skills, AI required	2-5%	Low
Complete isolation	3+ years	Near-complete social withdrawal	0.5-2%	Very low

Epidemiological Projection:

$$\text{Dependency Rate}(t) = D_0 \times e^{rt} \times \left(1 - \frac{D(t)}{K}\right)$$

Where:

• $D_0$ = initial dependency rate (~0.1% in 2024)
• $r$ = growth rate (~0.3-0.5 annually)
• $K$ = carrying capacity (~5-15% of population)

Year	Projected Dependency Rate	Confidence Interval	Key Assumptions
2025	0.5-1.0%	Medium	Current trajectory
2027	1.5-3.0%	Medium	No major intervention
2030	3.0-6.0%	Low	Social norm shifts
2035	5.0-12.0%	Very Low	Generational effects

System-Level Effects

Trust Erosion Dynamics

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Mental Health System Impact

Effect	Timeline	Severity	Current Preparedness	Required Response
Increased caseload	1-3 years	Moderate (20-30% increase)	Low	Capacity expansion
Novel presentation types	2-5 years	Moderate	Very Low	Training, research
Treatment complexity	3-7 years	High	Very Low	New protocols
System overwhelm	5-10 years	High (potential 2-3x demand)	Minimal	Structural reform

Scenario Probability Analysis

The following scenarios represent probability-weighted paths for cyber-psychosis cascade evolution:

Scenario	Probability	2027 Harm Level	2035 Harm Level	Key Characteristics
A: Rapid Cascade	15%	Very High	Critical	Defense overwhelmed, mass confusion
B: Gradual Accumulation	40%	Elevated	High	Slow-building but persistent harm
C: Effective Defense	25%	Moderate	Low-Moderate	Detection and intervention succeed
D: Adaptation	20%	Moderate	Low	Population develops resilience

Scenario A: Rapid Cascade (15% probability)

Defensive measures fail to keep pace with synthetic content capabilities. A major mass confusion event occurs between 2025-2027, creating lasting damage to institutional trust and social cohesion. Mental health systems are overwhelmed by novel presentations. AI companion dependency grows faster than projected as people retreat from confusing reality. Democratic processes are significantly impaired by inability to establish shared facts.

Scenario B: Gradual Accumulation (40% probability)

No single catastrophic event, but steady accumulation of harms. Targeted campaigns become routine, affecting thousands of individuals annually. Trust erosion proceeds slowly but persistently. Mental health burden increases gradually, allowing partial adaptation. Society functions but with degraded epistemic capacity and elevated background anxiety. This is the most likely trajectory absent major intervention.

Scenario C: Effective Defense (25% probability)

Detection technology and authentication infrastructure develop fast enough to maintain reasonable content verification. Platform interventions are strengthened through regulation. Mental health support expands. Public awareness programs increase resilience. Harm remains at manageable levels through sustained investment in defensive measures.

Scenario D: Population Adaptation (20% probability)

Humans and societies prove more resilient than expected. New epistemological norms emerge (verification as default, skepticism as healthy). Social institutions adapt to synthetic content environment. Mental health impacts are real but manageable. This scenario requires no major intervention but also no major escalation by adversarial actors.

Expected Harm Calculation

$$E[\text{Harm}_{2030}] = \sum_{s} P(s) \times H_s(2030)$$

Scenario	P(s)	Harm₂₀₃₀ (0-10)	Contribution
A: Rapid Cascade	0.15	8.5	1.28
B: Gradual Accumulation	0.40	5.5	2.20
C: Effective Defense	0.25	3.0	0.75
D: Adaptation	0.20	2.5	0.50
Expected Value			4.73

This expected harm level of 4.73/10 by 2030 indicates “moderate-elevated” concern, with significant probability mass on more severe outcomes.

Intervention Analysis

Individual Level Interventions

Intervention	Effectiveness	Cost	Scalability	Implementation Status
Media literacy training	Moderate (30-40% harm reduction)	Low	High	Partial
Targeted mental health support	High (50-70% for affected)	High	Low	Minimal
Social connection programs	Moderate (25-35%)	Medium	Medium	Minimal
Early warning systems	Low-Moderate (20-30%)	Medium	Medium	Conceptual

Platform Level Interventions

Intervention	Effectiveness	Feasibility	Adoption Likelihood	Key Barriers
Synthetic content labeling	Moderate	High	Medium	Evasion, false negatives
Distribution velocity caps	Moderate	Medium	Low	Revenue impact
Proactive targeting detection	High	Medium	Low	Technical difficulty
Cross-platform coordination	High	Low	Very Low	Competition, legal

Institutional Level Interventions

Intervention	Effectiveness	Timeline	Cost	Current Progress
Rapid response capability	High	2-3 years	High	Low
Authentication infrastructure	High	3-5 years	Very High	Minimal
Research investment	Medium-High	Ongoing	Medium	Low
Regulatory frameworks	Variable	3-7 years	Medium	Very early

Strategic Importance

Magnitude Assessment

Dimension	Assessment	Quantitative Estimate
Potential severity	Population-level mental health crisis; democratic dysfunction	1-5% population experiencing significant harm by 2030
Probability-weighted importance	Medium-High - gradual accumulation most likely (40%)	Expected harm 4.73/10 by 2030 (see scenario analysis)
Comparative ranking	Top 20 AI risks; underweighted relative to technical alignment	Less attention than warranted given probability and scale
Timeline	Ongoing; critical escalation potential in 2-5 years	Targeted campaigns already occurring; mass confusion emerging

Resource Implications

Category	Current Investment	Recommended	Gap Assessment
Research on AI-induced psychological harm	$5-15M/year	$50-100M/year	5-10x underfunded
Detection infrastructure	$20-50M/year	$200-500M/year	5-10x underfunded
Mental health capacity expansion	Insufficient	+$1-5B/year (US)	Structural deficit
Authentication infrastructure	$50-100M/year	$500M-2B/year	10x+ underfunded
Platform safety requirements	Voluntary, limited	Regulatory mandates	Structural gap

Key Cruxes

1. Defense vs offense trajectory: Can detection and authentication scale faster than synthetic content generation? Current evidence suggests offense is winning by 2-5 years.
2. Population resilience: Will humans adapt epistemologically, or will vulnerability remain constant? Historical technology transitions suggest eventual adaptation, but 5-15 year lag.
3. Cascade threshold: What level of synthetic content saturation triggers mass confusion events? Unknown, but estimates range from 10-30% of encountered content.
4. Mental health system capacity: Can healthcare systems absorb 2-3x demand increase? Current trends suggest no; structural reform required.

Limitations

This model has significant limitations that affect confidence in its predictions:

Limited empirical data on population-scale effects. Large-scale AI-driven psychological harms are nascent phenomena with limited historical precedent. The model extrapolates from smaller-scale incidents and analogous cases, but population dynamics may differ qualitatively at scale. Cascade thresholds and amplification factors are particularly uncertain.

Cultural and demographic variation not captured. Vulnerability factors, social dynamics, and institutional trust vary significantly across populations. A model calibrated to Western democracies may not apply to other contexts. The framework does not adequately capture how different cultural contexts might produce different cascade dynamics.

Adversarial adaptation underestimated. The model treats attack capabilities as exogenous, but sophisticated adversaries will adapt to defenses. Each successful intervention may trigger counter-adaptation, creating arms race dynamics not captured in the static analysis. Adversarial creativity may exploit vulnerabilities the model does not anticipate.

Positive adaptation underestimated. Human and societal resilience may exceed expectations. Previous information technology transitions (printing press, broadcast media, internet) caused disruption but ultimately resulted in adaptation. The model may overweight harm scenarios relative to adaptation scenarios.

Technical evolution creates forecasting uncertainty. Both harmful capabilities (synthetic content quality, personalization, distribution) and defensive capabilities (detection, authentication, intervention) are rapidly evolving. Predictions beyond 2-3 years are highly uncertain, and the model cannot anticipate breakthrough developments on either side.

Intervention effectiveness poorly calibrated. Most proposed interventions have not been tested at scale. Effectiveness estimates are based on theoretical reasoning and limited pilots rather than rigorous evaluation. Actual effectiveness may differ substantially from projections.

Related Models

• Trust Cascade Failure Model - Institutional trust erosion dynamics

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