Ran an observed 6-player game with persona AI players (one per
Lineage/Calling) via a Workflow (72 agents): avgFun 7.3/10, 6/6 would
replay, tight 84-82-75 finish. PLAYTEST.md captures the session +
designer's assessment.
Findings the Monte-Carlo bots structurally missed:
- the careful/safe line feels UNFAIR, not just weak (banker finished
last and resented it) — three human angles confirm the sim's
Cash-vs-Worthy substitution finding
- humans hit the no-Last-Stand feel-bad 6x where rational bots hit it 0x
- deep-collapses-first geometry confuses newcomers (component fix)
Fixes (re-validated, 1000 seeds): WorthyEnd strategy 46.7 -> 42.5%,
safe lines lifted, 6p margin to 9.5%.
- Desperate Strike: a no-Last-Stand Take salvages 1/3 unbanked loot
- Extraction bonus: +3 glory per crew retired alive
- RULEBOOK: "Maw eats NEXT" indicator as a component rule
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
DEEPFALL: a tabletop game mashing up 9 loved games (SmashUp, Dragon
Rampage, Blood Rage, Catan, EverQuest, Daggerfall, Eye of the Beholder,
Quarriors, SmallWorld). Core fusion = SmallWorld decline + Blood Rage
glorious death -> three exits (Cash Out / Press On / Worthy End) under a
rising Maw that collapses a reikhelm dungeon floor-by-floor.
- deepfall-core: pure deterministic engine (vendored ChaCha8, 11 tests)
- deepfall-sim: balance-by-bot sweeps -> CSV/JSON
- analysis/analyze.py: stdlib ASCII heatmap (no deps)
- DESIGN.md (rules + post-critique revisions), FINDINGS.md (9-iteration
balance journey), README.md
- reikhelm-core/examples/sample_dungeon.rs: dumps a real generated board
Method: adversarial design critics -> build -> simulate -> tune. 9
iterations compressed combo spread 77.8 -> 32.5 pts (all 36 combos
viable). Fixed deep-content lockout (value rises with the Maw), the
multi-collapse feel-bad (1 collapse/round cap; feel-bad now 0.00/game),
and dead combos. Open finding: Cash-Out and Worthy-End are substitutes
for a glory-maximizing bot.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
