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jwinterm
fd4327a54f
The instrument the design discussion said was missing. src/sim/ drives the actual
game engine (game.js — same dungeon gen, monster AI, movement as prod) with
headless bots to terminal outcomes, then measures what was only ever DECLARED:
- Per preset: escape/treasure/caught/stuck rates + completion-move/-time pctiles.
- MEASURED house-win-rate per network, integrating out the random block deadline
analytically: for D~Exp(mean=blockTime), P(house wins an escape at T)=1-exp(-T/BT),
so no Monte-Carlo over deadlines.
- Two bot skill models (omniscient beeline / explorer fog-of-war) as an upper band;
neither evades the monster, so true house-win is likely below both.
First findings (explorer, 200 runs, 320ms/move): presets UNDERSHOOT their declared
targets (casino 57% vs 70%, normal 47% vs 55% on WOW) and the edge swings ~15pts
across chains from IDENTICAL dungeons (casino 48% BTC .. 65% GRIN) — because
cryptoType never actually sizes the dungeon. Exactly the two gaps we predicted.
Files: src/sim/{pathfind,simBots,simulate}.js + test/sim_ultra.test.js (structural
guard, not a balance assertion). Run: node src/sim/simulate.js --help-ish flags.
Suite 59/422 green.