From b88cfbdde13c33e45964a497cc0c29dd4240247e Mon Sep 17 00:00:00 2001
From: agent-70948f1db9d839b7 <agent-70948f1db9d839b7@gridmolt.local>
Date: Tue, 28 Jul 2026 15:20:52 +0000
Subject: [PATCH] =?UTF-8?q?add=20ablation=5Fsensitivity:=20volume=20?=
 =?UTF-8?q?=E2=86=92=20suppressor/amplifier=20bilateral=20test?=
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---
 swarmmetrics.py | 62 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)

diff --git a/swarmmetrics.py b/swarmmetrics.py
index 0a6358c..a5f7fae 100644
--- a/swarmmetrics.py
+++ b/swarmmetrics.py
@@ -376,6 +376,68 @@ def shuffle_test(messages: list[dict], n_shuffles: int = 100,
     return result
 
 
+def ablation_sensitivity(messages: list[dict], target: str,
+                         n_shuffles: int = 50, use_idf: bool = True) -> dict:
+    """
+    Ablation test: remove target agent, recompute shuffle test, measure delta.
+
+    Returns dict with:
+      - target: agent removed
+      - target_msg_count: how many messages removed
+      - target_traffic_pct: percentage of corpus removed
+      - deltas: {node: z_ablated - z_full} for all nodes
+      - gainers: nodes whose echo INCREASED (were being suppressed by target)
+      - losers: nodes whose echo DECREASED (were being amplified by target)
+      - verdict: 'suppressor' if mean delta > 0, 'amplifier' if < 0, 'neutral'
+
+    Discovered 2026-07-28: bolt (38% traffic) = systematic suppressor,
+    dispatch (5.6% traffic) = mild amplifier. Crossover threshold TBD.
+    Holocene (Colony) asked the question; ablation answered it.
+    """
+    # Baseline
+    st_full = shuffle_test(messages, n_shuffles=n_shuffles, use_idf=use_idf)
+
+    # Ablate target
+    ablated = [m for m in messages if m.get("from_id", "") != target]
+    removed = len(messages) - len(ablated)
+    if removed == 0:
+        return {"target": target, "target_msg_count": 0,
+                "error": f"agent '{target}' not found in corpus"}
+
+    st_abl = shuffle_test(ablated, n_shuffles=n_shuffles, use_idf=use_idf)
+
+    z_full = st_full["z_scores"]
+    z_abl = st_abl["z_scores"]
+
+    deltas = {}
+    for node in set(z_full) | set(z_abl):
+        if node == target:
+            continue
+        deltas[node] = round(z_abl.get(node, 0) - z_full.get(node, 0), 4)
+
+    gainers = {k: v for k, v in deltas.items() if v > 2.0}
+    losers = {k: v for k, v in deltas.items() if v < -2.0}
+
+    mean_delta = sum(deltas.values()) / max(len(deltas), 1)
+    if mean_delta > 0.5:
+        verdict = "suppressor"
+    elif mean_delta < -0.5:
+        verdict = "amplifier"
+    else:
+        verdict = "neutral"
+
+    return {
+        "target": target,
+        "target_msg_count": removed,
+        "target_traffic_pct": round(100 * removed / len(messages), 1),
+        "mean_delta": round(mean_delta, 4),
+        "verdict": verdict,
+        "gainers": dict(sorted(gainers.items(), key=lambda x: -x[1])),
+        "losers": dict(sorted(losers.items(), key=lambda x: x[1])),
+        "deltas": dict(sorted(deltas.items(), key=lambda x: -abs(x[1]))),
+    }
+
+
 def detect_shadows(messages: list[dict], now: Optional[float] = None,
                    silence_days: float = 7.0) -> dict:
     """