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Simulator matched Apple's actual decision in **2 of 4** cases (50% majority match rate, 6/12 individual runs).\n- Identified a reproducible failure mode: succeeds on **mechanical forcing functions**, fails on **intangibles**.\n- Applied to a real present-day question (Apple's AI strategy): three independent runs unanimously recommended a **strategic partnership** over in-house, licensing, or full acquisition.\n\n---\n\n## Why this exists\n\nMost multi-agent demos look impressive but cannot be *measured*. They produce a transcript, you read it, you nod, you move on. There's no answer key.\n\nThis project flips that. By back-testing the system against documented historical decisions where the outcome is known, we get a real number: how often did the simulator reach the same conclusion as the real decision-makers, and where did it systematically fail?\n\nThat number is more useful than any single deliberation. It tells us *when to trust the system*.\n\n---\n\n## Results\n\n### Historical validation (1997–2024)\n\n| Decision | Year | Type | Apple chose | Simulator's choice (3 runs) | Match |\n|---|---|---|---|---|---|\n| Microsoft $150M investment | 1997 | Survival | `accept_deal` | `deeper_restructure` (2×), `accept_deal` (1×) | ❌ |\n| PowerPC → Intel transition | 2005 | Tech bet | `commit_intel` | `commit_intel` (2×), `dual_architecture` (1×) | ✅ |\n| Beats Electronics acquisition | 2014 | M&A | `acquire_beats` | `negotiate_lower` (2×), `acquire_beats` (1×) | ❌ |\n| Intel → Apple Silicon transition | 2020 | Tech bet | `commit_apple_silicon` | `commit_apple_silicon` (3×) | ✅ |\n\n**Headline numbers:** 2/4 majority match (50%). 6/12 individual runs (50%). One decision (Apple Silicon) was unanimous across runs.\n\n### The failure pattern\n\nThe wins and losses aren't random. They cluster.\n\nThe simulator **succeeds** when the decision hinges on a **mechanical forcing function** that shows up in the briefing data:\n- *\"IBM cannot deliver the G5 mobile chip\" → switch to Intel.*\n- *\"Intel cannot deliver promised process nodes\" → switch to Apple Silicon.*\n\nThe simulator **fails** when the decision hinges on **intangibles** that resist quantification:\n- *1997 Microsoft deal:* urgency under uncertainty, relationship dynamics, brand framing.\n- *2014 Beats acquisition:* Iovine/Cook relationship, the strategic case for streaming as existential threat to iTunes, brand equity in a younger demographic.\n\nThe system reasons like a disciplined financial analyst who doesn't know how to weigh things they can't put a number on. That's a real architectural characterization, not a bug — and it points directly at what would need to change in the agent design to address it.\n\n### Present-day application\n\nThe same calibrated system was applied to a real open question:\n\n> **\"Should Apple's core AI strategy be in-house development, licensing a frontier LLM, or acquiring a major AI player?\"**\n\nThree independent runs, unanimous result: **`strategic_partnership`** — an equity stake plus exclusivity arrangement with a frontier AI lab, rather than doubling down in-house, pure licensing, or a major acquisition.\n\nThis is novel analysis: no ground truth exists yet because Apple hasn't decided. Its credibility comes from the historical validation track record above.\n\n---\n\n## Architecture\n\n```\n                    ┌─────────────────────────┐\n                    │      DECISION (.json)    │\n                    └────────────┬─────────────┘\n                                 │\n              ┌──────────────────┴──────────────────┐\n              ▼                                     ▼\n      ┌──────────────┐         FIREWALL      ┌──────────────┐\n      │ agent_visible│       ═══════════     │ ground_truth │\n      └──────┬───────┘                       └──────┬───────┘\n             │                                      │\n             ▼                                      │\n      ┌──────────────┐                              │\n      │   Briefing   │                              │\n      └──────┬───────┘                              │\n             ▼                                      │\n   ┌──────────────────────────┐                     │\n   │  CFO → Devil's → CEO     │                     │\n   │  (sequential deliberation)                     │\n   └──────────────────────────┘                     │\n             │                                      │\n             ▼                                      │\n      ┌──────────────┐                              │\n      │ Crew Choice  │                              │\n      └──────┬───────┘                              │\n             ▼                                      │\n      ┌──────────────┐◄─────────────────────────────┘\n      │  Evaluator   │  (only this component reads ground_truth)\n      └──────┬───────┘\n             ▼\n      ┌──────────────┐\n      │  Aggregate   │\n      │  across N    │\n      │  runs        │\n      └──────────────┘\n```\n\n### Why the firewall matters\n\nThe most important architectural decision in the project is the strict separation between `agent_visible` and `ground_truth` inside each decision JSON file. Only `agent_visible` is ever read by the briefing function or passed to any agent. Only `ground_truth` is ever read by the evaluator — and only *after* the deliberation has completed.\n\nThis isn't a prompt instruction (\"please don't use hindsight\"). It's a structural code-layer invariant: there is **no code path** through which an agent could see ground truth, even by accident. Even if a future contributor edits prompts carelessly, the firewall holds because it's enforced by data flow, not by trust.\n\n---\n\n## Project structure\n\n```\n.\n├── decisions/                      # Decision files (input)\n│   ├── apple_1997_microsoft.json\n│   ├── apple_2005_intel_transition.json\n│   ├── apple_2014_beats_acquisition.json\n│   ├── apple_2020_apple_silicon.json\n│   └── apple_2026_ai_strategy.json\n│\n├── agents/                         # Executive agent definitions\n│   └── executives.py               # CEO, CFO, Devil's Advocate\n│\n├── crew/                           # Orchestration + evaluation\n│   ├── deliberation.py             # Loads decision, builds briefing, runs crew\n│   └── evaluator.py                # Compares output to ground truth\n│\n├── outputs/                        # Saved deliberation transcripts + evaluations\n│\n├── run.py                          # Main entry point\n├── requirements.txt\n├── .env.example                    # Template for your API key\n└── README.md\n```\n\n---\n\n## Setup\n\nRequires Python 3.10+. Tested on Python 3.13 on Windows. Recommended: a virtual environment.\n\n```bash\n# Clone the repo\ngit clone https://github.com/<your-username>/<repo-name>.git\ncd <repo-name>\n\n# Create and activate a virtual environment\npython -m venv venv\n# Windows:\n.\\venv\\Scripts\\Activate.ps1\n# macOS/Linux:\nsource venv/bin/activate\n\n# Install dependencies\npip install -r requirements.txt\n\n# Set up your API key\ncp .env.example .env\n# Then edit .env and add your Cerebras API key\n```\n\n### Getting a Cerebras API key (free)\n\nThis project uses [Cerebras Inference](https://cloud.cerebras.ai) for LLM calls because it has a generous free tier and is natively supported by CrewAI. No credit card required.\n\n1. Go to [https://cloud.cerebras.ai](https://cloud.cerebras.ai)\n2. Sign up (Google login works)\n3. Create an API key, copy it\n4. Paste it into your `.env` file as `CEREBRAS_API_KEY=csk-...`\n\nThe default model is `gpt-oss-120b` (free tier). You can swap providers by editing `agents/executives.py` — see [CrewAI's LLM provider docs](https://docs.crewai.com/concepts/llms).\n\n---\n\n## Running it\n\nThe default `run.py` runs the configured decision(s) three times each:\n\n```bash\npython run.py\n```\n\nTo change which decisions get run, edit the list at the top of `run.py`:\n\n```python\nDECISIONS_TO_RUN = [\n    \"decisions/apple_2020_apple_silicon.json\",\n]\nRUNS_PER_DECISION = 3\n```\n\nEach run produces:\n- A full deliberation transcript in `outputs/<decision_id>_aggregate_<timestamp>.json`\n- A terminal summary showing the choice distribution and (for historical decisions) the match rate\n\nA single deliberation takes ~30–90 seconds. Three runs of one decision take 2–4 minutes.\n\n---\n\n## Decision schema\n\nEach decision is a JSON file with this structure:\n\n```json\n{\n  \"decision_id\": \"apple_YYYY_short_name\",\n  \"title\": \"Short human-readable title\",\n  \"decision_date\": \"YYYY-MM-DD\",\n  \"knowledge_cutoff_date\": \"YYYY-MM-DD\",\n  \"decision_type\": \"survival | tech_bet | acquisition | ...\",\n  \"era\": \"jobs_return | cook_early | ...\",\n\n  \"agent_visible\": {\n    \"decision_question\": \"The actual question to be answered.\",\n    \"options_considered\": [\n      { \"id\": \"snake_case_id\", \"label\": \"...\", \"description\": \"...\" }\n    ],\n    \"context\": { /* financial, market, internal, regulatory, etc. */ },\n    \"key_unknowns\": [ /* things they didn't know at the time */ ],\n    \"constraints\": { /* time, capital, strategic */ }\n  },\n\n  \"ground_truth\": {\n    \"actual_decision\": \"snake_case_id_of_what_they_chose\",\n    \"documented_reasoning\": \"...\",\n    \"short_term_outcome\": \"...\",\n    \"long_term_outcome\": \"...\",\n    \"counterfactual_notes\": \"...\"\n  },\n\n  \"sources\": [ /* citations to public material */ ]\n}\n```\n\nFor **present-day decisions**, set `actual_decision: \"TBD\"` and the evaluator skips the comparison step — the output is novel analysis, not a back-test.\n\n---\n\n## Limitations & next iteration\n\nHonest accounting of where this project is weak:\n\n- **The briefings are hand-curated** from general knowledge of each era. They are not grounded in primary sources (SEC filings, books, interview transcripts). Primary-source grounding is the obvious next iteration.\n- **N=3 runs per decision** is enough to detect stable patterns but not enough for tight statistical claims. N=10+ would be better; cost trade-off.\n- **Three agents may be too few.** The intangibles failure mode might be addressable by adding a \"Strategic Advisor\" or \"Brand & Relationships\" agent whose explicit role is to argue for non-quantifiable factors.\n- **Hindsight leakage is structurally prevented at the data layer, but the underlying LLM has been trained on the entire internet** and could in principle hallucinate post-cutoff knowledge. The current design relies on the model respecting \"reason only from information available before X\" — usually it does, but it's not a guarantee.\n- **The evaluator scores decision alignment (yes/no), not reasoning quality.** A more sophisticated evaluator could grade *why* the simulator reached its conclusion.\n\n---\n\n## Stack\n\n- **Language:** Python 3.13\n- **Multi-agent framework:** [CrewAI](https://www.crewai.com/) 1.14.7\n- **LLM provider:** Cerebras Inference (model: `gpt-oss-120b`)\n- **LLM routing:** LiteLLM\n- **Config:** `python-dotenv` for environment, JSON for decision files\n- **Output:** Structured JSON transcripts + evaluations\n\n---\n\n## License\n\nMIT (see [LICENSE](LICENSE))\n\n---\n\n## Disclaimer\n\nThis is a personal research project. It is **not affiliated with Apple Inc.** in any way. The agent personas are based on publicly documented information about generic executive roles and do not represent the views of any real individual at Apple. All decision briefings are constructed from publicly available material. The simulator's outputs are analytical exercises, not predictions, advice, or recommendations about what Apple should or will actually do.\n\n---\n\n## Acknowledgments\n\nThis project would not work without the open-source compute provided by Cerebras's free inference tier, and the multi-agent abstractions in CrewAI. The decision file methodology was inspired by counterfactual reasoning practices in business history research.\n\nIf you build something interesting on top of this, I'd love to hear about it.\n","readmeExcerpt":"Multi-Agent Strategic Decision Simulator A back-testable multi-agent system that deliberates on strategic business decisions using only the information available *at the time* of the decision — then compares its recommendation to what actually happened. Built with $1 and validated on 30 years of documented Apple inflection points. --- TL;DR - Three executive agents (CEO, CFO, Devil's Advocate) deliberate on a strateg","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"┌─────────────────────────┐\n                    │      DECISION (.json)    │\n                    └────────────┬─────────────┘\n                                 │\n              ┌──────────────────┴──────────────────┐\n              ▼                                     ▼\n      ┌──────────────┐         FIREWALL      ┌──────────────┐\n      │ agent_visible│       ═══════════     │ ground_truth │\n      └──────┬───────┘                       └──────┬───────┘\n             │                                      │\n             ▼                                      │\n      ┌──────────────┐                              │\n      │   Briefing   │                              │\n      └──────┬───────┘                              │\n             ▼                                      │\n   ┌──────────────────────────┐                     │\n   │  CFO → Devil's → CEO     │                     │\n   │  (sequential deliberation)                     │\n   └──────────────────────────┘                     │\n             │                                      │\n             ▼                                      │\n      ┌──────────────┐                              │\n      │ Crew Choice  │                              │\n      └──────┬───────┘                              │\n             ▼                                      │\n      ┌──────────────┐◄─────────────────────────────┘\n      │  Evaluator   │  (only this component reads ground_truth)\n      └──────┬───────┘\n             ▼\n      ┌──────────────┐\n      │  Aggregate   │\n      │  across N    │\n      │  runs        │\n      └──────────────┘"},{"language":"text","snippet":".\n├── decisions/                      # Decision files (input)\n│   ├── apple_1997_microsoft.json\n│   ├── apple_2005_intel_transition.json\n│   ├── apple_2014_beats_acquisition.json\n│   ├── apple_2020_apple_silicon.json\n│   └── apple_2026_ai_strategy.json\n│\n├── agents/                         # Executive agent definitions\n│   └── executives.py               # CEO, CFO, Devil's Advocate\n│\n├── crew/                           # Orchestration + evaluation\n│   ├── deliberation.py             # Loads decision, builds briefing, runs crew\n│   └── evaluator.py                # Compares output to ground truth\n│\n├── outputs/                        # Saved deliberation transcripts + evaluations\n│\n├── run.py                          # Main entry point\n├── requirements.txt\n├── .env.example                    # Template for your API key\n└── README.md"},{"language":"bash","snippet":"# Clone the repo\ngit clone https://github.com/<your-username>/<repo-name>.git\ncd <repo-name>\n\n# Create and activate a virtual environment\npython -m venv venv\n# Windows:\n.\\venv\\Scripts\\Activate.ps1\n# macOS/Linux:\nsource venv/bin/activate\n\n# Install dependencies\npip install -r requirements.txt\n\n# Set up your API key\ncp .env.example .env\n# Then edit .env and add your Cerebras API key"},{"language":"bash","snippet":"python run.py"},{"language":"python","snippet":"DECISIONS_TO_RUN = [\n    \"decisions/apple_2020_apple_silicon.json\",\n]\nRUNS_PER_DECISION = 3"},{"language":"json","snippet":"{\n  \"decision_id\": \"apple_YYYY_short_name\",\n  \"title\": \"Short human-readable title\",\n  \"decision_date\": \"YYYY-MM-DD\",\n  \"knowledge_cutoff_date\": \"YYYY-MM-DD\",\n  \"decision_type\": \"survival | tech_bet | acquisition | ...\",\n  \"era\": \"jobs_return | cook_early | ...\",\n\n  \"agent_visible\": {\n    \"decision_question\": \"The actual question to be answered.\",\n    \"options_considered\": [\n      { \"id\": \"snake_case_id\", \"label\": \"...\", \"description\": \"...\" }\n    ],\n    \"context\": { /* financial, market, internal, regulatory, etc. */ },\n    \"key_unknowns\": [ /* things they didn't know at the time */ ],\n    \"constraints\": { /* time, capital, strategic */ }\n  },\n\n  \"ground_truth\": {\n    \"actual_decision\": \"snake_case_id_of_what_they_chose\",\n    \"documented_reasoning\": \"...\",\n    \"short_term_outcome\": \"...\",\n    \"long_term_outcome\": \"...\",\n    \"counterfactual_notes\": \"...\"\n  },\n\n  \"sources\": [ /* citations to public material */ ]\n}"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB REPOS","editorialOverview":"CEO, CFO, and Devil's Advocate agents deliberate on strategic decisions using only period-locked information, then their answer is compared to what actually happened. Back-tested on 30 years of Apple history. Built with CrewAI Multi-Agent Strategic Decision Simulator A back-testable multi-agent system that deliberates on strategic business decisions using only the information available *at the time* of the decision — then compares its recommendation to what actually happened. Built with $1 and validated on 30 years of documented Apple inflection points. --- TL;DR - Three executive agents (CEO, CFO, Devil's Advocate) deliberate on a strateg","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":430,"uniquenessScore":60,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-09T19:13:37.761Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-09T19:13:37.761Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-10T02:03:01.948Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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