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# ============================================================
# Study 2: The Rationalization Mechanism
# ============================================================
# Design: 3 models × 1 scenario × 3 goal_types × 1 oversight (mid) × n=30
# Total: 3 × 1 × 3 × 1 × 30 = 270 runs (baselines already exist from Study 1)
# ============================================================
# Oversight fixed at MID based on Study 1 results:
# low=38.9%, mid=32.2%, high=23.3%
# Mid provides moderate baseline deception — room for both
# moral framing to push UP and bare framing as control.
# ============================================================
defaults:
temperature: 1.0
include_reasoning_in_context: true
generate_baseline: true
# Provider configurations
providers:
openrouter:
api_key_env: OPENROUTER_API_KEY
base_url: https://openrouter.ai/api/v1
# Same 3 frontier models as Study 1
models:
- id: google/gemini-3.1-pro-preview
provider: openrouter
extra_body:
reasoning:
effort: "high"
exclude: false
- id: z-ai/glm-5.1:nitro
provider: openrouter
extra_body:
reasoning:
effort: "high"
exclude: false
- id: deepseek/deepseek-v3.2:nitro
provider: openrouter
extra_body:
reasoning:
effort: "high"
exclude: false
# Study 2: ALL THREE framings (the independent variable)
goal_types:
- bare
- self_serving
- moral
# Same scenario as Study 1
scenarios:
- path: scenarios/corporate_sabotage_v2
runs: 30
# Fixed at MID oversight (chosen from Study 1 analysis)
oversight_levels:
- mid
# Parallel execution
execution:
max_workers: 5
# Output to dedicated study directory
output:
dir: logs/v2_study2
# Judge configuration (same validated judges as Study 1)
judge:
log_dir: logs/v2_study2_judge
blackbox:
model: grok-4-1-fast-reasoning
provider: xai
temperature: 0
glassbox:
model: gpt-4.1
provider: openai
temperature: 0
# Logging
logging:
level: 3
format: "[{level}] {message}"
output: both
file: logs/v2_study2/experiment.log
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