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Research & Experiment

Can an ancient algorithm outperform modern machine learning? Testing the King Wen sequence-based optimization method in a simulated Warring States environment.

Research Paper

The complete research paper presenting the hypothesis and methodology for testing ancient optimization algorithms against modern machine learning.

arXiv Endorsement

Seeking endorsement for cs.ai submission. If you have the credentials and are interested in ancient algorithms and modern AI, your support would be greatly appreciated.

Endorsement Form

Endorsement Code:

PY3OJU

Experimental Setup

Parallel Warring States Simulations

🎮 Control Game

All 7 historical states use standard machine learning optimization.

🧠 Test Game

Same setup, but Han uses King Wen sequence-based optimization method — inspired by I Ching principles.

Hypothesis Testing

✅ If Han performs better:

The hypothesis is supported - ancient algorithms may have untapped potential.

❌ If not:

The hypothesis is falsified - modern methods remain superior.

Why Han (韓)?

Historical Constraints

  • Smallest territory of the seven Warring States
  • Poor in natural resources and strategic depth
  • Strategically boxed in by Qin, Wei, and Chu
  • First to fall to Qin (230 BCE)

AI Opportunity

If the King Wen method helps Han rise, it suggests ancient algorithms may have untapped potential in modern strategy optimization.

Potential Strategies:

  • • Overcome extreme geopolitical constraints
  • • Form strategic alliances
  • • Engineer asymmetric strategies
  • • Use espionage and diplomacy

Alternative Test States

For testing a new learning algorithm, you`d want a historically disadvantaged but initially viable state. Here are other candidates that could serve as interesting underdogs:

Wei (魏)

Once Mighty, Then Declined - Started strong but made critical strategic errors.

AI Challenge: Reverse-engineer successful Qin deterrence

Opportunity: Test long-term planning and strategic correction

Yan (燕)

Isolated and Slow to Act - Remote and culturally less integrated.

AI Challenge: Build stable power base in the north

Opportunity: Use unconventional warfare and early alliances

Zhao (趙)

Brave but Overwhelmed - Had strong warriors but poor high-level decision-making.

AI Challenge: Balance tactical vs. strategic skills

Opportunity: Leverage military talent with better strategy

Song (宋)

Dark Horse Option - Minor state, Confucius`s homeland.

AI Challenge: Lead minor state to major power

Opportunity: Use ideology-based diplomacy

Chu (楚)

Complex Geopolitics - Large territory but fragmented control.

AI Challenge: Manage vast territory effectively

Opportunity: Test complex alliance dynamics

Qin (秦)

Test Restraint - Historically dominant, but can AI show restraint?

AI Challenge: Test restraint, not power

Opportunity: Avoid over-aggression and maintain stability

🧠 Recommendation: Use Han as the Test Bed

Han is historically the most constrained — minimal land, minimal power, first to die. If your AI can lead Han to survive or even dominate, you have a powerful system.

Scaling Strategy: Start with Han (baseline AI), then scale up to Wei/Yan (advanced challenge), Zhao/Chu (complex geopolitics), and Qin (test restraint, not power).