Testing the King Wen sequence-based optimization method—derived from the I Ching—against contemporary machine learning in a complex multi-agent strategy simulation.
Complete hypothesis and methodology for testing ancient optimization against modern machine learning approaches.
Seeking endorsement for cs.ai. If you have credentials and interest in ancient algorithms + modern AI, your support is appreciated—and I'm open to collaboration or feedback to strengthen the work and improve the chances of arXiv acceptance.
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All seven historical states use contemporary machine learning optimization techniques. This establishes the baseline for performance comparison.
Same setup, but Han (韓) uses the King Wen sequence-based optimization method—derived from I Ching principles.
The hypothesis is supported—ancient algorithms may have untapped potential for modern optimization problems.
The hypothesis is falsified—modern machine learning methods remain superior for this class of problems.
The ultimate underdog test case
If the King Wen method helps Han rise, it suggests ancient algorithms may have untapped potential in modern strategy optimization.
Potential Strategies
For testing a new learning algorithm, you want a historically disadvantaged but initially viable state. These candidates could serve as interesting underdogs:
Once Mighty, Then Declined
Challenge: Reverse-engineer successful Qin deterrence
Opportunity: Test long-term planning and strategic correction
Isolated and Slow to Act
Challenge: Build stable power base in the north
Opportunity: Use unconventional warfare and early alliances
Brave but Overwhelmed
Challenge: Balance tactical vs. strategic skills
Opportunity: Leverage military talent with better strategy
Dark Horse Option
Challenge: Lead minor state to major power
Opportunity: Use ideology-based diplomacy
Complex Geopolitics
Challenge: Manage vast territory effectively
Opportunity: Test complex alliance dynamics
Test Restraint
Challenge: Test restraint, not power
Opportunity: Avoid over-aggression and maintain stability
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 to Wei/Yan (advanced challenge), Zhao/Chu (complex geopolitics), and Qin (test restraint, not power).