strategy

• MOO1 Optimal Strategy
• BattleValue
• BattleTech Simulator
• MOO1 Opening Optimizer
• Diplomacy: 7 AI Models
• D&D Spell Damage Model
• MOO1 MIRR Analysis
• Master of Magic — Economic Analysis
• MoM Tier System and MIRR
• MIRR — 4X Strategy as Capital Allocation — the cross-game 4X capital-allocation thesis (MoO + MoM)
• Capability Without Leverage — a paid capability is worthless without downstream leverage; cross-game (Monopoly/MoM/Catan)
• Randomness as Termination (N≥3) — randomness is the kill switch on the gang-up equilibrium; calibration = design quality
• The Dominance-Frontier Lens — map cost vs effect, frontier curve, asymmetric counters; cross-domain (games + markets)
• NA1 — Game-Design Crucible — index tying the Nobunaga RE repo to the theses it sparked
• The Hollow Opponent — how to actually beat NA1 (never be the weakest reachable neighbor) + where the residual strategy lives
• The Dead-Verb Test — slots are the binding constraint, so one-shot verbs are dominated by compounding develop; Pact is redundant with don’t-be-a-target; mechanism viability + “does KOEI learn?”
• Civilization Revolution — The City-Builder That Plays as a Rush — wide beats tall on the MIRR hurdle (positive ROI ≠ growth rate); Republic 1-pop-settler + per-city first-bonuses × geometric cities = unbraked snowball; gold as liquid tempo; the efficient frontier collapses to one line in 1v1
• CoM Anti-Colossus Counter Analysis
• CoM Spell Counter-Graph
• Catan — 47,000 Games of Empirical Findings — city-engine vs road-engine preference, Monopoly timing, trajectory-trades
• Catan 50-Game Validation — small-N proof-of-method; refined theory of “universal city-engine bias + layered archetype effect”
• Efficient Frontier Trade Theory — Using EPT frontier curves to solve the 3+ player trade valuation problem — reducing the permutation space through structural truths about development velocity and game horizon.
• Subgraph Trade Engine — Implementation Spec — Combining the build frontier with subgraph-driven trade search to create a complete Monopoly decision engine.
• Battleship — 30 Billion Boards — greedy guessing (center-out, spread, post-hit direction heuristic) beats a typical human 86%; placement barely beats random
• Yahtzee — 259 Trillion → 405 Million — practical keepers: the +35 top bonus is the real controllable lever (63 = three of each), zero out four-of-a-kind early / yahtzee late, keep small straight alive longest
• D&D Monster Tournament — Exact Markov Chains — the written cast/target policy is the model, so it’s the experimental protocol; running mobility off vs on quantifies the melee bias
• Risk — The Attrition Constant — a Risk battle solved exactly as an absorbing Markov chain; the 3-vs-2 dice cap fixes the engagement frontage regardless of stack size, so attrition is Lanchester-LINEAR and concentration of force buys nothing
• Hangman — Solving Both Sides — the alphabet tier list: the compression built to be human-readable is also the most model-error-robust policy, surviving frequency re-weighting and British spelling where the exact tree does not
• Civilization Revolution — Project Hub