SirStone
e0666a562d
MEASURED against the real DrussGT, one frozen binary built from clean HEAD, rack knobs only (no source edits), 5 arms x 7 runs x 7 rounds, 8 concurrent battles, judged ONLY on server-side real hit rate from the events sidecar, exact two-sided permutation test on per-run rates. arm runs shots hits real % dmg/run p vs full full (shipped) 7 3898 270 6.93 159 -- onlyPattern 7 4582 494 10.78 287 0.0012 <- BETTER onlyKNN 7 4033 207 5.13 119 0.1340 onlyLinear 7 3215 105 3.27 65 0.0082 onlyGF 7 3193 72 2.25 45 0.0012 Firing Pattern ALONE gives +3.85pp pooled hit rate and +80% damage per run, and it fires MORE shots (4582 vs 3898) - it dominates on rate and volume. This is not "any single gun wins" (full beats Linear, GF and KNN); it is specifically "Pattern alone beats the rack". WHY - the virtual fitness signal mis-ranks guns against real outcomes: - HeadOn is massively over-selected: 31.4% of ticks, the most real shots (1070), but only 4.5% REAL. It alone drags the rack down. - Pattern has the best virtual rank and near-best real rate (11.9%, rank 2), yet is selected only 22.6% of the time. - Linear's apparent strength was SELECTION BIAS: conditional on being selected it looked like 15.2% (n=33), but its UNCONDITIONAL rate (onlyLinear) is 3.27%. Every earlier per-gun "real rate" in this repo is conditional on selection and is therefore confounded. This experiment is the clean measurement. NOT YET SETTLED (do not overclaim): - ONE ADVERSARY. All of this is vs DrussGT. Pattern must be re-checked against other bots before it becomes the default on this evidence alone. - Whether a SMALL rack of good guns beats Pattern alone. The selector is negative value on the CURRENT bloated rack; that does not prove it is negative value on a rack of only good guns. That is the next experiment and it decides whether the selection apparatus is fixed or disabled. - The user's standing directive is to KEEP virtual-fitness selection. This measurement conflicts with it, so the next step tests the selector on a small good rack rather than assuming either answer. Context - three prior selection-side attempts all failed: hysteresis (7.02% -> 5.10%, p=0.002), commitment (7.17% -> 4.44%, p=0.0012), arrival-accuracy tie-break (7.08%, p=0.88 null). The per-tick random draw is load-bearing on three independent measurements. This experiment locates the real problem one level up: which guns are in the rack, and that the virtual signal ranks them wrongly. Preserves the reusable harness (tools/ab/which_gun_run_one.sh, which_gun_arm_env.sh, which_gun_analyze.py) and the full writeup (docs/selector_negative_value.md).
65 lines
3.6 KiB
Markdown
65 lines
3.6 KiB
Markdown
# The gun selector is currently NEGATIVE value
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**Measured. The best single gun beats the full rack, and not by a little.**
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| arm | runs | shots | hits | real % | dmg/run | per-run range | exact two-sided p vs `full` |
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|---|---:|---:|---:|---:|---:|---|---:|
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| `full` (shipped rack) | 7 | 3898 | 270 | **6.93** | 159 | 2.54–10.37 | — |
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| **`onlyPattern`** | 7 | 4582 | 494 | **10.78** | **287** | 9.14–11.85 | **0.0012** |
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| `onlyKNN` | 7 | 4033 | 207 | 5.13 | 119 | 4.15–6.47 | 0.1340 |
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| `onlyLinear` | 7 | 3215 | 105 | 3.27 | 65 | 1.85–4.69 | 0.0082 |
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| `onlyGF` | 7 | 3193 | 72 | 2.25 | 45 | 1.52–3.47 | 0.0012 |
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**Firing `Pattern` alone: +3.85 pp pooled hit rate, +80% damage per run, p = 0.0012.**
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It also fires MORE shots (4582 vs 3898), so it dominates on rate *and* volume.
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This is **not** "any single gun wins" — `full` beats `onlyLinear`, `onlyGF` and `onlyKNN`.
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It is specifically **"Pattern alone beats the rack"**.
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## Method
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One frozen binary built from clean `HEAD` via `git archive` (other agents had `common_libs/guns/*`
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dirty), sha256 `f02481d8…`. Arms selected with the rack knobs only (`TR_RACK_<GUN>=off`), no source
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edits. 5 arms × 7 runs × 7 rounds, 8 concurrent bridge battles, real DrussGT, judged ONLY on
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server-side real hit rate from the events sidecar. Exact two-sided permutation test on per-run rates
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(C(14,7)=3432 splits). Each arm's liveness verified from the selected-gun mix.
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Harness preserved at `tools/ab/which_gun_*.sh` and `tools/ab/which_gun_analyze.py`.
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## Why: the virtual fitness signal mis-ranks guns vs real outcomes
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From the `full` arm's own selection mix and per-gun real rates:
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- **`HeadOn` is massively over-selected** — **31.4% of ticks**, the most real shots (1070), but only
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**4.5% real**. It alone drags the rack down.
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- **`Pattern`** has the best *virtual* rank and near-best *real* rate (11.9%, real rank 2), yet is
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selected only **22.6%** of the time.
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- **`Linear`'s apparent strength was SELECTION BIAS.** Conditional on being selected it looked like
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15.2% (n=33); its *unconditional* rate (`onlyLinear`) is **3.27%**. Every earlier per-gun "real
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rate" in this repo is conditional on selection and is therefore confounded — this experiment is
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the clean measurement.
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## What this does NOT yet settle
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- **One adversary.** Everything here is vs DrussGT. `Pattern` should be re-checked against other
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bots before it becomes the default on the strength of this alone. (Supporting evidence: an offline
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audit found `Pattern` is the only gun competitive in *every* distance/speed bucket.)
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- **Whether a SMALL good rack beats `Pattern` alone.** The selector is negative value on the current
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bloated rack; that does not prove it is negative value on a rack of only good guns. That is the
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next experiment, and it decides whether the selection apparatus gets fixed or disabled.
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- **The user's directive was to KEEP the virtual-fitness selection mechanism.** This measurement
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conflicts with that directive, so the next step is to test the selector on a small, good rack
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rather than to assume either answer.
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## Prior context: three failed selection-side attempts
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| attempt | result |
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|---|---|
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| hysteresis (commit to incumbent) | 7.02% → 5.10%, p=0.002 |
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| commitment (remove the random draw) | 7.17% → 4.44%, p=0.0012 |
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| arrival-accuracy tie-break (rank by path, narrow by point) | 7.08%, p=0.88 — null |
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So the per-tick random draw is load-bearing on three independent measurements, and no attempt to
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"smarten" the tied band has helped. This experiment shows the problem is one level up: **which guns
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are in the rack, and the fact that the virtual signal ranks them wrongly.**
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