實驗EXP-2026-0005v0.1

R4——政策變異面：分散治理 vs 單一強制邊界

對 N ∈ {1, 4, 16, 64} 個 canonical 寫入 caller 與 R = 6 條治理規則，政策站點與規則放置在分散治理下隨 N 與 N·R 成長，在集中式應用 gate 或 AER-ECT 下固定為 1 與 R；新規則要改 N 個 caller vs 改一處；75 % 的 caller 端遷移仍留下 64 個中的 16 個在舊合約上。反向權衡明說：漏掉一條局部規則的爆炸半徑是 1/N，共享 gate 的缺陷爆炸半徑是 1，而在等 p 的玩具遺漏模型下三套系統的預期受影響 caller 比例相同。

研究狀態: STABLE 目前的研究結論相對穩定
證據等級: E2 受控實驗
結果: MIXED
資料基礎: DETERMINISTIC RUNTIME 在真實 runtime 裡執行的決定性情境；程式碼與做法都列出，數字是什麼就是什麼。
版本: 0.1
更新: 2026-09-08
建立: 2026-09-08
領域: AI Architecture, Evaluation, Agent Systems
計畫: PRG-2026-0001 自適應世界狀態系統的第一原理框架
作者: Neo.K (EveMissLab)
AI 協作: Sol (GPT-5.6, OpenAI ChatGPT)

假設

hypothesis: Elevating epistemic governance into one mandatory boundary reduces policy scattering, migration surface and drift opportunity — without magically reducing expected harm.

設定

software_environment: Python 3.11+, SQLite; no network, no external database, no LLM API required.

程序

procedure: Deterministic structural benchmark plus executable AER regression; CENTRAL_GATE and AER-ECT predicted and observed identical on policy topology.

執行

run_count

metrics

verdict

CENTRALIZATION_REDUCES_SCATTERING_NOT_COMPUTATIONAL_CAPABILITY

rules

callers

at_64_callers

scattered

policy_sites: 64
rule_placements: 384
blast_radius_one_omission: 0.015625
migration_edits: 64
vulnerable_after_75pct_migration: 16

central_gate_and_aer_ect

policy_sites: 1
rule_placements: 6
blast_radius_one_omission: 1.0
migration_edits: 1
vulnerable_after_75pct_migration: 0

toy_omission_model_p_0_01

P_any_scattered: 0.9789
P_any_central: 0.0585
expected_exposed_caller_fraction_all_systems: 0.0585

詮釋

interpretation: Centralization changes the distribution of failure — fewer opportunities for many small local defects, few opportunities for large shared ones — and makes new business callers free of policy replication. AER-ECT is reference-monitor-like (always invoked on the normal write path), not proven tamperproof.

限制

limitations: A model with an explicit toy assumption, not empirical defect data; 97.89 % is not a real-world defect rate.
Distributed replica/version skew of the central gate, hostile bypass and tamperproofness not modelled.

重現

reproduction_instructions: Extract the round's FINAL bundle; python -m pytest -q; python -m examples.research_assistant_demo; python -m benchmarks.<round benchmark>. Checksums in SHA256SUMS.txt.

記錄欄位

completed_at: 2026-09-08

關係

來源	關係	目標	狀態	ID
`EXP-2026-0005` R4——政策變異面：分散治理 vs 單一強制邊界	`runs_on`	`SYS-2026-0001` AER-0——自適應認識 runtime MVP	`ACTIVE`	`REL-2026-0137`
`EXP-2026-0005` R4——政策變異面：分散治理 vs 單一強制邊界	`uses_benchmark`	`BEN-2026-0002` AER-0 架構比較套件（R1–R6）	`ACTIVE`	`REL-2026-0138`
`EXP-2026-0005` R4——政策變異面：分散治理 vs 單一強制邊界	`tests`	`THY-2026-0006` AER-ECT：強制的認識論 commit 交易邊界	`ACTIVE`	`REL-2026-0139`
`EXP-2026-0005` R4——政策變異面：分散治理 vs 單一強制邊界	`extends`	`EXP-2026-0004` R3——認識論 commit 交易 vs 分散式與集中式應用 gate	`ACTIVE`	`REL-2026-0140`
`EXP-2026-0005` R4——政策變異面：分散治理 vs 單一強制邊界	`produced`	`ART-2026-0018` AER-0 MVP v0.1 Architecture Comparison R4 Policy Mutation `artifact://evemisslab/adaptive-epistemic-systems/AER-0_MVP_v0.1_Architecture_Comparison_R4_Policy_Mutation.zip`	`ACTIVE`	`REL-2026-0141`
`EXP-2026-0006` R5——完全中介與繞過抗性	`extends`	`EXP-2026-0005` R4——政策變異面：分散治理 vs 單一強制邊界	`ACTIVE`	`REL-2026-0145`

歷史與來源歷程

Canonical URL

https://evemisslab.com/ai/experiments/EXP-2026-0005/

機器可讀

/ai/experiments/EXP-2026-0005/index.json

快照

AI-SNAPSHOT-v0.1-fe85b9694a45

來源歷程

source: EveMissLab research collection: Adaptive Epistemic Systems (真本體論13)
extracted_by: Splice (Claude Code), reading the canonical UTF-8 sources and each lab's own result reports
extracted_at: 2026-09-11
generator: tools/extract_aes/extract.py
claim_boundary: status, evidence level and result type follow the source artifact's own stated claim boundary; nothing is upgraded beyond what the report supports