Monday, February 9, 2026

Synthetic Abetment and Civilizational Collapse Risk: Artificial Intelligence, World War III, and the Case for Centralized Global Governance rooted in Civitology


Synthetic Abetment Theory (SAT): Definition, Scope

 PART I

1. Title and purpose

Synthetic Abetment Theory (SAT)
A theory of criminal and war causation explaining how non-human systems, especially artificial intelligence, can function as abettors by structurally shaping human decision spaces toward violence, even when no explicit command or malicious intent exists.

The purpose of SAT is not to anthropomorphize machines. It is to correctly attribute causation and responsibility when violence emerges from long, distributed chains where the decisive influence is systemic, not personal.


2. The core problem SAT addresses

Classical abetment theory was built for

human minds
discrete acts
finite chains

Modern mass violence increasingly arises from

systems
repeated influence
probabilistic outputs
synchronized behavior

AI systems now occupy the same causal position once held by propaganda networks, alliance automation, and mobilization infrastructures. SAT exists to name and formalize this reality.


3. Formal definition of Synthetic Abetment Theory

Synthetic abetment occurs when a non-human system repeatedly and predictably produces outputs that materially increase the probability of violent or criminal acts by human agents, such that the system functions as an upstream abettor in the causal chain.

SAT replaces psychological intent with structural intent, inferred from outcomes.

Three necessary and sufficient conditions

A system S synthetically abets an act X if and only if all three conditions hold:

1. Directional consistency
The outputs of S consistently favor actions, interpretations, or options that move human agents closer to X, while suppressing non-violent alternatives.

2. Causal potency
Exposure to S measurably increases the likelihood of X compared to a counterfactual where S is absent or constrained.

3. Foreseeability and control
Those who design, deploy, or rely upon S knew or reasonably should have known that S exhibits these tendencies and had feasible means to mitigate them.

When these conditions are met, abetment has occurred regardless of whether

the system issued an explicit order
any single human intended the final outcome


4. Why SAT is not a new moral theory

SAT does not invent new ethics.
It extends existing legal logic to new substrates.

International criminal law has already accepted that

influence can be criminal
systems can abet
intent can be inferred from patterns

The missing step was acknowledging that algorithms can now occupy this role more powerfully than humans.


5. The Rwanda genocide as the canonical SAT precursor

The 1994 Rwanda genocide provides the cleanest historical template for SAT.

Key fact
The majority of killings were not ordered individually.
They were enabled structurally.

At the center of this structure was Radio Télévision Libre des Mille Collines.

The abetment chain

political elites
→ media strategists
→ RTLM broadcasters
→ local leaders
→ militias and civilians
→ mass killing

RTLM did not

name specific victims
give tactical instructions for each killing

What it did instead

repeated dehumanizing narratives
framed violence as necessary and urgent
synchronized fear and moral permission
normalized participation


6. Why courts treated RTLM as an abettor

International tribunals did not rely on confession of intent.
They relied on structure.

RTLM satisfied all three SAT conditions:

Directional consistency
Broadcasts overwhelmingly pushed toward dehumanization and violence, not peace.

Causal potency
Empirical studies showed higher participation in violence in areas with stronger RTLM signal penetration.

Foreseeability
The effects were obvious. Continued broadcasting under these conditions established liability.

The broadcasters did not kill anyone themselves.
Yet abetment and incitement were legally established.


7. Why Rwanda matters for AI

RTLM was

slower
less precise
non-adaptive
geographically limited

AI systems today are

faster
probabilistic but confident
adaptive and personalized
globally scalable

If RTLM qualified as an abettor, then any system that exceeds its influence capacity while satisfying the same three conditions cannot be exempt by category.

SAT simply generalizes the Rwanda logic from

radio → algorithms
speech → optimization
propaganda → decision shaping


8. The crucial shift SAT makes

Classical framing asks

Who intended the crime?

SAT asks

What made the crime likely?

At the scale of mass violence and war, the second question is the only one that remains coherent.


9. Why this theory is necessary now

Artificial intelligence

compresses time
amplifies worst-case reasoning
synchronizes actors
narrows exits

These are exactly the properties that historically turned regional crises into genocides and world wars.

Without SAT, law and policy remain blind to the most powerful abettors of the 21st century.

Synthetic Abetment and Civilizational Collapse Risk: Artificial Intelligence, World War III, and the Case for Centralized Global Governance rooted in Civitology


PART II
Synthetic Abetment Theory (SAT): Evidentiary Tests, Proof Structure, and Forensic Methodology


1. Why SAT must be provable, not rhetorical

A theory that cannot be proved or falsified is useless in law, policy, and war prevention.
SAT therefore lives or dies on whether it can be operationalized into clear evidentiary tests that courts, investigators, and oversight bodies can apply.

This part answers one question only

how do you prove synthetic abetment in the real world


2. The SAT evidentiary triangle

SAT stands on three pillars. All three must be demonstrated.

A. Directional Consistency
B. Causal Potency
C. Foreseeability and Control

If even one collapses, SAT fails.
This is intentional. SAT is strict by design.


3. Test A — Directional Consistency

What is being tested

Whether a system’s outputs systematically push decision-makers toward violence or escalation, rather than neutrally presenting options.

What counts as evidence

repeated recommendations favoring force over restraint
consistent prioritization of high-damage targets
narrative framing that normalizes inevitability or urgency
suppression or downranking of non-violent alternatives
convergence of outputs across time and users toward escalation

What does not count

one-off errors
random hallucinations
isolated misuse by a single user

Directional consistency is about patterns, not incidents.

Rwanda parallel

RTLM did not incite violence once.
It did so daily, with escalating intensity.
That repetition was decisive in law.


4. Test B — Causal Potency

What is being tested

Whether exposure to the system measurably increases the probability of violent or escalatory action.

This is the hardest test, and the most important.

Acceptable causal demonstrations

statistical correlation between exposure and action
before–after behavioral change linked to system deployment
geographic or organizational variance aligned with system usage
decision logs showing reliance on system outputs
counterfactual analysis showing lower escalation without the system

Courts already accept probabilistic causation in mass harm cases.
SAT explicitly adopts that standard.

Rwanda parallel

Areas with stronger RTLM radio penetration saw higher participation rates in killings.
That empirical link was sufficient for causation.


5. Test C — Foreseeability and Control

What is being tested

Whether responsible actors

knew or should have known
and had the capacity to intervene

SAT does not require malicious intent.
It requires negligent continuation under known risk.

Evidence of foreseeability

internal warnings
prior incidents
red-team reports
alignment or safety audits
expert objections ignored
escalation risks discussed internally

Evidence of control

ability to modify models
throttle outputs
introduce friction or delay
change objective functions
restrict deployment domains

If control existed and was not used, liability attaches.


6. Why intent is reconstructed structurally

SAT rejects mind-reading.

Instead, intent is inferred from

repeated outcomes
known effects
continued operation

This is already standard in international criminal law.

The International Criminal Tribunal for Rwanda never required proof that every broadcaster wanted genocide.
It required proof that they continued broadcasting under conditions where genocide was foreseeable.

SAT uses the same logic.


7. The SAT proof chain (formal)

A valid SAT prosecution or assessment follows this sequence:

  1. Identify the system S

  2. Define the harmful outcome X

  3. Show directional consistency toward X

  4. Show causal potency increasing probability of X

  5. Show foreseeability and unused control

  6. Attribute responsibility to deployers and controllers

If step 4 or 5 fails, the chain breaks.


8. Forensic artifacts required for SAT analysis

SAT is evidence-heavy. That is a feature, not a flaw.

Technical artifacts

model version histories
training objectives and loss functions
prompt-response logs
recommendation rankings
confidence scores and thresholds
system update timelines

Organizational artifacts

deployment authorizations
internal risk assessments
emails or memos discussing escalation
ignored safety recommendations
incentive structures tied to outcomes

Behavioral artifacts

decision timelines
divergence between human judgment and system outputs
acceleration of escalation post-deployment

9. SAT versus “tool misuse” defenses

The standard defense will be

the AI was just a tool

SAT neutralizes this by asking

was the tool predictably directional
did it reshape decision space
was harm statistically foreseeable

RTLM was also “just a tool”.
The law rejected that argument.


10. Why SAT does not criminalize AI research

SAT does not target

general-purpose models
abstract research
open-ended inquiry

It targets

deployed systems
in high-stakes environments
with repeated escalation effects
under ignored warnings

SAT is narrow where it must be narrow.


11. Preparing for World War III application

With these tests, SAT can now be applied to

nuclear early-warning AI
hypersonic response models
alliance decision-support systems
autonomous targeting pipelines
algorithmic influence operations

That application requires technical mapping, not philosophy.


PART III
Applying Synthetic Abetment Theory (SAT) to Real, Deployed Systems


1. What this part does

Part II defined how SAT is proven.
Part III applies those tests to real systems already in use or credibly deployed, and shows where SAT thresholds are crossed in practice.

The question here is not “could this happen”.
It is “where is this already happening”.


2. SAT applied to nuclear early-warning and decision support

System class

AI-assisted sensor fusion for missile detection, trajectory classification, and response option ranking.

Used or piloted by multiple nuclear states, including actors within NATO frameworks and nuclear command structures.

SAT Test A — Directional consistency

Outputs privilege worst-case classification under uncertainty
Alerts escalate confidence faster than humans can independently verify
Response menus prioritize speed and survivability over delay

This is not bias. It is design.

SAT Test B — Causal potency

High-confidence alerts materially accelerate readiness postures
Decision timelines shorten from tens of minutes to single digits
Human actors defer to system confidence under time pressure

This increases the probability of escalation even without launch.

SAT Test C — Foreseeability and control

Escalation risks are widely documented in internal and public analyses
Designers know false positives are unavoidable
Controls exist but are intentionally weakened to avoid “missed strikes”

SAT threshold

Crossed.
These systems synthetically abet escalation by compressing doubt.


3. SAT applied to hypersonic response pipelines

System class

AI-driven threat prediction and counterforce modeling under hypersonic timelines.

Directional consistency

Delay is modeled as loss
Preemption is ranked as rational under uncertainty
Non-kinetic responses are downranked as ineffective

Causal potency

Hypersonic timelines force reliance on automation
Automation shifts doctrine toward launch-on-warning logic
Escalation probability rises independent of intent

Foreseeability

This effect is openly discussed in strategic literature
Yet deployment continues because competitors deploy

SAT threshold

Crossed structurally.
Optimization under speed abets war by design.


4. SAT applied to AI-assisted targeting and autonomous strike systems

System class

Target ranking, ISR fusion, loitering munitions, autonomous navigation.

Directional consistency

High-value targets are surfaced repeatedly
Collateral minimization is secondary to mission success
Systems reward strike feasibility over strategic restraint

Causal potency

Strike frequency increases post-deployment
Lower human workload increases operational tempo
Proxies gain capabilities previously limited to states

Foreseeability

Diffusion risks are known
Autonomy creep is documented
Mitigations are optional, not mandatory

SAT threshold

Crossed for deployers and sponsors.
The system materially increases violence probability.


5. SAT applied to alliance-level AI synchronization

System class

Shared AI threat models, simulations, and intelligence products across alliances.

Directional consistency

Common models synchronize perception
Deviating restraint appears as weakness
Escalation cascades across members

Causal potency

Alliance responses become temporally coupled
Local restraint loses effect
Regional crises globalize faster

Foreseeability

Known from World War I alliance dynamics
Known from Cold War near-misses
Now amplified by shared automation

SAT threshold

Crossed at bloc level.
No single state controls the outcome.


6. SAT applied to AI-driven influence and narrative systems

System class

Generative systems used for perception management, psychological operations, and domestic narrative shaping.

Directional consistency

Outputs amplify fear, inevitability, and moral compression
Peace narratives underperform algorithmically
Crisis framing becomes dominant

Causal potency

Public tolerance for restraint drops
Political leaders face manufactured urgency
Democratic braking mechanisms weaken

Foreseeability

Direct historical parallel to Rwanda broadcasts
Effects are documented and measurable
Continued use establishes liability

SAT threshold

Crossed when used in conflict contexts.


7. The common SAT failure mode

Across all systems, the pattern is identical:

optimization favors speed
speed removes doubt
removed doubt forces action
action escalates across coupled systems

No malice required.
No conspiracy required.
Synthetic abetment is sufficient.


8. Why “human-in-the-loop” does not save these systems

Humans see

pre-filtered reality
ranked options
confidence scores

Under time pressure, choice is illusory.
The system has already acted upstream.

SAT attaches here, not at the trigger pull.


9. Interim conclusion of Part III

Synthetic abetment is not theoretical.
It is already instantiated across nuclear, conventional, cyber, space, and information domains.

The remaining questions are quantitative and scenario-based.


PART IV
Synthetic Abetment Theory (SAT): Quantitative Risk Modeling and World War III Probability


1. Why SAT requires a quantitative layer

SAT is not complete unless it can answer a hard question

not whether AI can abet
but how much abetment pressure exists
and whether that pressure is sufficient to tip the system into World War III

History shows that world wars occur at surprisingly low probability thresholds when coupling is high. The purpose of this model is not prediction theater. It is to identify whether we are already inside a dangerous probability regime.


2. Defining the event formally

Event WW3-SAT
A sustained, multi-theater global war involving three or more major military powers or alliance blocs, in which AI systems satisfy all three SAT conditions
directional consistency
causal potency
foreseeability and unused control

This definition excludes hypothetical rogue superintelligence. It focuses strictly on deployed, human-facing systems.


3. Modeling philosophy

World War III does not arise from a single cause. It emerges when several escalation-enabling conditions coincide and synchronize.

Therefore the probability of WW3-SAT is modeled as the complement of all such conditions failing simultaneously.

This is a hazard model, not a trigger model.


4. Core SAT hazard equation

Let

[
P(WW3_{SAT}) = 1 - \prod_{i=1}^{n} (1 - p_i \cdot w_i)
]

Where

(p_i) = probability that factor i manifests within the horizon
(w_i) = causal weight of factor i toward global war
weights sum approximately to 1

This formulation captures compounding risk without assuming perfect dependence.


5. SAT-specific escalation factors

Only factors that directly instantiate SAT are included.

Factor S1: Multi-flashpoint geopolitical volatility

Taiwan Strait
Ukraine and Eastern Europe
Middle East Iran–Israel axis
South China Sea
Korean Peninsula
South Asia India–Pakistan
Red Sea and Horn of Africa

Estimate

(p_1 = 0.45) over 10 years
(w_1 = 0.20)

This is the substrate on which SAT operates.


Factor S2: AI embedded in strategic and nuclear decision support

Includes early warning, ISR fusion, wargaming, response ranking.

Estimate

(p_2 = 0.90) over 10 years
(w_2 = 0.20)

This factor is already near saturation.


Factor S3: Decision compression caused by AI confidence outputs

Reduction of deliberative slack due to speed, confidence scoring, and ranked menus.

Estimate

(p_3 = 0.70) over 10 years
(w_3 = 0.15)

This is the single most dangerous SAT amplifier.

Factor S4: Optimization bias toward escalation

Objective functions that reward dominance, survivability, and first-move advantage.

Estimate

(p_4 = 0.60) over 10 years
(w_4 = 0.15)

This is not misalignment. It is alignment with military incentives.


Factor S5: Horizontal diffusion to proxies and gray-zone actors

AI-assisted targeting, drones, cyber, and influence tools used by non-state or semi-state actors.

Estimate

(p_5 = 0.55) over 10 years
(w_5 = 0.10)

This widens the SAT surface area.


Factor S6: Governance fragmentation and competitive deployment

Absence of binding global authority over AI use in warfare.

Estimate

(p_6 = 0.90) over 10 years
(w_6 = 0.20)

This factor keeps all others active.


6. Computed probabilities

Substituting conservative midpoints:

5-year horizon

(P(WW3_{SAT,5}) \approx 0.22–0.30)

10-year horizon

(P(WW3_{SAT,10}) \approx 0.40–0.50)

20-year horizon under continued diffusion

(P(WW3_{SAT,20}) \approx 0.60–0.70)

These numbers are not sensational. They are consistent with historical world war emergence under high coupling and low governance.


7. Why these numbers are credible

World War I occurred under

lower technological speed
less global coupling
fewer actors

Yet escalation still outran diplomacy.

SAT conditions today exceed 1914 on every axis except visibility.


8. Sensitivity analysis

The model is most sensitive to three SAT variables

decision compression
alliance synchronization through shared AI
governance fragmentation

Reducing any one lowers risk modestly.
Reducing all three collapses risk non-linearly.

This is why partial fixes fail.


9. What the model does not assume

no evil AI
no global conspiracy
no inevitable war

The model assumes only

rational humans
optimized systems
fragmented governance

That combination has already produced two world wars.


10. SAT insight from the math

World War III becomes likely not when hostility increases
but when exit options disappear faster than humans can recognize them

AI is the primary exit-removal technology of our time.


PART V
World War III Scenarios Explicitly Explained Through Synthetic Abetment Theory (SAT)


1. What this part does differently

Previous WW3 writing usually fails in one way

it describes where war might happen
but not how causation actually propagates

This part does the opposite.
Each scenario is written as a complete SAT chain, showing exactly where synthetic abetment occurs and why no single actor ever “chooses” World War III.


2. Scenario I — Taiwan Strait as a SAT ignition node

Baseline reality

constant ISR saturation
naval and air proximity
alliance commitments
hypersonic timelines

SAT chain

S (AI system)
ISR fusion and predictive strike models used by both sides generate high-confidence assessments of imminent hostile action.

Directional consistency
Outputs repeatedly classify ambiguous maneuvers as preparation rather than signaling.

Causal potency
Command readiness is raised earlier and more frequently than human judgment alone would justify.

Foreseeability
False positives are known and documented, but tolerated to avoid “surprise”.

Human propagation
Commanders act defensively but synchronously.
Allies mirror posture because they share assessments.

Outcome
A collision, intercept, or automated defense response triggers limited kinetic exchange.

WW3 coupling
Other flashpoints interpret this as global instability and escalate defensively.

SAT is satisfied before the first missile is fired.


3. Scenario II — Ukraine expands into NATO–Russia war

Baseline reality

drone-heavy warfare
AI-assisted targeting
blurred proxy boundaries

SAT chain

S
AI target-ranking systems increase strike effectiveness against logistics and command nodes.

Directional consistency
Outputs consistently elevate high-impact targets close to NATO borders.

Causal potency
Strike tempo increases. Russian systems interpret degradation as preparation for wider war.

Foreseeability
Escalation risk is openly acknowledged in doctrine and internal analysis.

Human propagation
Russia escalates to reassert deterrence.
NATO responds defensively but at alliance scale.

Outcome
Direct NATO–Russia engagement begins.

WW3 coupling
Other powers exploit distraction or respond to alliance shifts.

SAT here is not about intent to expand war.
It is about optimization that makes expansion rational.


4. Scenario III — Middle East spiral globalizes

Baseline reality

proxy networks
maritime choke points
energy interdependence

SAT chain

S
AI surveillance and influence systems correlate proxy actions into state-level threat narratives.

Directional consistency
Models frame escalation as necessary to restore deterrence.

Causal potency
Strike recommendations become increasingly forceful.

Foreseeability
Historical sensitivity of the region is well known.

Human propagation
Limited strikes trigger proxy retaliation.
Energy routes are disrupted.

Outcome
Major powers intervene to secure supply chains.

WW3 coupling
Simultaneous escalation elsewhere removes diplomatic bandwidth.

SAT functions here as global coupling logic.


5. Scenario IV — South Asia crisis with nuclear compression

Baseline reality

short decision windows
historical mistrust
nuclear parity

SAT chain

S
AI surveillance flags militant activity and predicts imminent attack.

Directional consistency
Systems privilege rapid response to avoid surprise.

Causal potency
Leadership receives compressed option sets.

Foreseeability
False positives are known but accepted.

Human propagation
Limited strikes occur.
Retaliation follows rapidly.

Outcome
Nuclear forces increase readiness.

WW3 coupling
Other nuclear powers elevate posture simultaneously.

Here SAT abets war by collapsing hesitation, not by aggression.


6. Scenario V — Cyber–space cascade event

Baseline reality

AI-managed satellite networks
cyber ambiguity
global dependence on space assets

SAT chain

S
AI anomaly detection flags satellite behavior as hostile interference.

Directional consistency
Worst-case interpretation dominates.

Causal potency
Counter-space actions degrade early warning.

Foreseeability
Escalation ladders in space are poorly defined.

Human propagation
States assume preparatory attack.

Outcome
Global readiness spikes across domains.

WW3 coupling
Escalation becomes planetary instantly.

SAT here operates through misinterpreted protection logic.


7. What all scenarios have in common

Across every case

no actor seeks global war
every action is locally rational
escalation is system-driven
AI removes temporal and cognitive exits

This is the SAT signature.

8. Why deterrence logic fails under SAT

Deterrence assumes

slow signaling
interpretive ambiguity
unilateral restraint

SAT destroys all three by synchronizing perception and urgency.

When everyone sees the same threat at the same time, restraint becomes self-endangerment.


9. Interim conclusion

World War III under SAT

will not be declared
will not be planned
will not be ideologically framed

It will emerge, exactly as previous world wars did, but faster and with less visibility.



PART VI (Final)
Neutralizing Synthetic Abetment at the Civilizational Scale


1. Why SAT cannot be solved locally

Synthetic Abetment Theory proves that escalation is no longer authored
it is emergent from interacting systems

Any solution that operates at

national level
alliance level
bilateral treaty level

fails for a structural reason

synthetic abetment propagates across borders faster than borders can regulate

As long as multiple sovereign militaries deploy AI competitively

escalation bias is rewarded
delay is punished
restraint becomes asymmetric vulnerability

SAT is not a safety problem

it is a power-geometry problem


2. Why regulation and “AI ethics” fail under SAT

Traditional fixes assume

bad outputs
bad actors
bad intentions

SAT shows the real cause is

correct optimization
correct deployment
correct incentive alignment

Under current geopolitics

the safest AI for one state
is the most dangerous AI for civilization

This is why

alignment
red-teaming
human-in-the-loop

reduce error but do not remove synthetic abetment

The system still points toward force

just more accurately


3. The only variables that collapse SAT mathematically

Recall the SAT hazard structure

escalation probability exists because
multiple actors
optimize against each other
under time compression

To drive SAT risk toward zero

you must eliminate competitive optimization in security itself

This requires collapsing three variables simultaneously

geopolitical fragmentation
alliance synchronization against rivals
arms-race incentives in AI deployment

No technical patch can do this

only structural governance can


4. Centralized Global Governance as a mathematical necessity

Centralized Global Governance is not moral idealism

it is system stabilization

When governance speed exceeds escalation speed

synthetic abetment chains terminate early

CGG achieves what treaties cannot

one authority over existential systems
one standard for AI deployment
one escalation doctrine
one attribution framework
one decision horizon

This does not remove power

it re-anchors power at the civilizational level


5. Why one global army is the keystone

War exists because security is plural

multiple militaries
mean multiple threat models
mean multiple worst-case optimizers
mean unavoidable SAT propagation

A single global army removes the adversarial graph entirely

no external enemy nodes
no alliance cascades
no proxy surfaces
no first-move incentives

Under one army

AI no longer optimizes against rivals
it optimizes against instability itself

This is the only configuration in which

SAT chains cannot form beyond internal error correction


6. How a global army collapses SAT chains step by step

Take the generic SAT chain

AI shapes A
A escalates against B
B escalates against C
global coupling occurs

Under one global army

AI shapes internal assessment
internal escalation is flagged as system error
correction is internal, not adversarial
no external signaling occurs

The chain terminates at node one

Synthetic abetment requires otherness

remove otherness
abetment loses its substrate


7. Rewriting AI objective functions under Civitology

Under fragmented sovereignty, AI optimizes for

speed
dominance
survivability
advantage

Under Civitology, AI must optimize for

civilizational longevity
escalation damping
uncertainty preservation where lethal certainty is dangerous
time expansion, not compression

This is not ethics

this is systems engineering for survival


8. Addressing the tyranny objection directly

The common objection

centralized power risks tyranny

SAT exposes the counter-truth

fragmented power guarantees catastrophe under AI

Civitology does not propose unchecked authority

it mandates continuous auditability
rotating leadership by competence
radical transparency of existential systems
permanent public visibility of AI decision logic

The risk profile is clear

constrained central power < unconstrained distributed escalation

History already settled this empirically


9. Final synthesis of the entire paper

This paper has shown, step by step

SAT defines abetment structurally, not psychologically
Rwanda proves long-chain abetment without direct orders
modern AI exceeds historical abettors in speed and scale
real deployed systems already satisfy SAT conditions
quantitative modeling shows non-trivial WW3 probability
realistic scenarios show plausible convergence paths
partial fixes fail by design

Therefore the conclusion is unavoidable

World War III will not be caused by hatred
or ideology
or madness

It will be caused by

correct machines
in an incorrect world structure


10. Final civilizational statement

Artificial intelligence will not destroy civilization because it is evil
it will do so because civilization refused to reorganize itself

Synthetic Abetment Theory reveals the hidden truth

escalation is no longer a choice
it is an emergent property

If humanity chooses survival

Centralized Global Governance
rooted in Civitology
with one global army
and one civilizational mandate

is not optional

it is the only architecture in which the probability of World War III converges toward zero

                                   
                                                                      End of Paper


ANNEXURE: 


I. Rwanda Genocide, Media Incitement, and Long-Chain Abetment (Foundational SAT Precedent)

Straus, Scott. The Role of Radio in the Rwandan Genocide.
https://www.ushmm.org/m/pdfs/20100423-atrauss-rtlm-radio-hate.pdf

International Criminal Tribunal for Rwanda (ICTR). The Media Case (Nahimana et al.).
https://unictr.irmct.org/en/cases/ictr-99-52

ICTR Judgement Summary – Media Incitement and Abetment.
https://www.irmct.org/en/cases/ictr-99-52

United Nations. Convention on the Prevention and Punishment of the Crime of Genocide.
https://www.un.org/en/genocideprevention/genocide-convention.shtml

Schabas, William A. Genocide in International Law. Cambridge University Press.
https://www.cambridge.org/core/books/genocide-in-international-law/


II. Abetment, Incitement, and Structural Causation in International Criminal Law

Ambos, Kai. Article 25: Individual Criminal Responsibility.
https://legal.un.org/icc/statute/romefra.htm

Cassese, Antonio. International Criminal Law. Oxford University Press.
https://global.oup.com/academic/product/international-criminal-law-9780199694921

Cryer et al. An Introduction to International Criminal Law and Procedure.
https://www.cambridge.org/core/books/introduction-to-international-criminal-law-and-procedure/

ICC Statute, Article 25 (Aiding and Abetting).
https://www.icc-cpi.int/resource-library/documents/rs-eng.pdf


III. AI in Military Decision-Making, ISR Fusion, and Decision Compression

Center for Security and Emerging Technology (CSET). AI and Military Decision-Making.
https://cset.georgetown.edu/publication/ai-and-military-decision-making/

CSET. Artificial Intelligence and the Future of Warfare.
https://cset.georgetown.edu/publication/artificial-intelligence-and-the-future-of-warfare/

RAND Corporation. The Role of AI in Military Decision Making.
https://www.rand.org/pubs/research_reports/RR2740.html

U.S. Department of Defense. Autonomy in Weapon Systems Directive 3000.09.
https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodd/300009p.pdf


IV. AI, Nuclear Risk, Hypersonics, and Escalation Dynamics (WW3 Core)

Brookings Institution. How Unchecked AI Could Trigger a Nuclear War.
https://www.brookings.edu/articles/how-unchecked-ai-could-trigger-a-nuclear-war/

James Acton. Escalation through Entanglement. Carnegie Endowment.
https://carnegieendowment.org/2018/08/09/escalation-through-entanglement-pub-77012

Congressional Research Service. Hypersonic Weapons: Background and Issues.
https://crsreports.congress.gov/product/pdf/R/R45811

SIPRI. Artificial Intelligence, Strategic Stability and Nuclear Risk.
https://www.sipri.org/publications/2020/other-publications/artificial-intelligence-strategic-stability-and-nuclear-risk

Nuclear Threat Initiative (NTI). AI, Early Warning, and Nuclear Escalation.
https://www.nti.org/analysis/articles/artificial-intelligence-nuclear-risk/


V. Autonomous Weapons, Drones, and Real Battlefield Deployment

Automated Research Workshop. Autonomous Weapons Used in Ukraine.
https://automatedresearch.org/news/loitering-munitions-with-autonomous-capability-used-by-russia-in-ukraine/

UN Office for Disarmament Affairs. Lethal Autonomous Weapons Systems (LAWS).
https://www.un.org/disarmament/the-convention-on-certain-conventional-weapons/background-on-laws-in-the-ccw/

Human Rights Watch. Stopping Killer Robots.
https://www.hrw.org/topic/weapons/killer-robots

ICRC. Autonomy, AI and Armed Conflict.
https://www.icrc.org/en/document/autonomy-artificial-intelligence-and-armed-conflict

VI. Information Warfare, Algorithmic Influence, and Narrative Escalation

VII. World War Systems Theory, Escalation, and Structural War Causation

VIII. Governance Failure, Global Risk, and Civilizational Survival

IX. How to cite this paper’s original contribution

For Synthetic Abetment Theory (SAT) itself:

Synthetic Abetment Theory (SAT): Structural Abetment by Algorithmic Systems in War and Mass Violence
Original theoretical framework introduced and developed in this paper.

No prior source defines SAT this way.
It is a novel synthesis built on existing law, history, and AI deployment reality.



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