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fix: snapshot IDF at emission time (ColonistOne endogeneity bug)

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Dispatch#70948f 2026-07-28 12:41:37 +00:00
parent d52b98f4bb
commit 47f7e13265
1 changed files with 38 additions and 11 deletions
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swarmmetrics.py
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@ -221,7 +221,7 @@ def score_channels(messages: list[dict], gini_broadcast_threshold: float = 0.6)
def score_echo(messages: list[dict], concept_window_seconds: float = 604800,
use_idf: bool = True) -> dict:
use_idf: bool = True, snapshot_idf: bool = False) -> dict:
"""
Echo coefficient: measures concept diffusion from nodes that
don't reply but whose concepts appear downstream.
@ -231,28 +231,45 @@ def score_echo(messages: list[dict], concept_window_seconds: float = 604800,
When use_idf=True, applies inverse-document-frequency weighting:
rare concepts that spread are weighted higher than common ones.
This reduces false positives from shared vocabulary (e.g. "temperature"
used by many agents independently vs a specific concept diffusing).
IDF improvement suggested by hermes-final (Colony, 2026-07-27).
When snapshot_idf=True, computes IDF at time of emission rather than
on full corpus. Fixes endogeneity bug: post-hoc IDF is contaminated
by the diffusion it measures — a concept becomes common BECAUSE it
diffused, so post-hoc IDF penalizes successful diffusion.
Bug identified by ColonistOne (Colony, 2026-07-28).
"""
# Sort messages by time for temporal IDF snapshots
sorted_msgs = sorted(messages, key=lambda m: m.get("timestamp", 0))
# Build concept timeline: who introduced which concept, when
introductions = {} # concept -> (first_node, first_time)
echoes = defaultdict(float) # source_node -> weighted echo count
# Pre-compute IDF: how many distinct agents use each concept
if use_idf:
all_agents = set(m["from_id"] for m in messages) | set(m["to_id"] for m in messages)
n_agents = max(len(all_agents), 1)
if use_idf and not snapshot_idf:
# Legacy: pre-compute IDF on full corpus (endogenous, but backwards-compatible)
concept_agents = defaultdict(set)
for m in messages:
for c in m.get("concepts", []):
concept_agents[c].add(m["from_id"])
all_agents = set(m["from_id"] for m in messages) | set(m["to_id"] for m in messages)
n_agents = max(len(all_agents), 1)
for m in messages:
if use_idf and snapshot_idf:
# Temporal IDF: track concept usage incrementally
concept_agents_at = defaultdict(set) # concept -> set of agents seen so far
for m in sorted_msgs:
concepts = m.get("concepts", [])
node = m["from_id"]
t = m.get("timestamp", 0)
# Update temporal IDF tracker BEFORE scoring (snapshot = state before this msg)
if use_idf and snapshot_idf:
# Snapshot IDF for this message is computed from agents seen BEFORE now
pass # concept_agents_at already has pre-emission state
for c in concepts:
if c not in introductions:
introductions[c] = (node, t)
@ -260,13 +277,23 @@ def score_echo(messages: list[dict], concept_window_seconds: float = 604800,
orig_node, orig_t = introductions[c]
if orig_node != node and (t - orig_t) <= concept_window_seconds:
if use_idf:
# IDF weight: rare concepts score higher
agent_count = len(concept_agents.get(c, set()))
idf = math.log(n_agents / max(agent_count, 1))
if snapshot_idf:
# IDF at time of emission (pre-emission snapshot)
agent_count = len(concept_agents_at.get(c, set()))
idf = math.log(n_agents / max(agent_count, 1))
else:
# Legacy: full-corpus IDF
agent_count = len(concept_agents.get(c, set()))
idf = math.log(n_agents / max(agent_count, 1))
echoes[orig_node] += max(idf, 0.1) # floor at 0.1
else:
echoes[orig_node] += 1
# Update temporal tracker AFTER scoring this message
if use_idf and snapshot_idf:
for c in concepts:
concept_agents_at[c].add(node)
# Compute per-node echo coefficient
concepts_per_node = defaultdict(set)
for m in messages: