autodecision

autodecision

A decision operating system for high-stakes choices — business, strategy, career.
Simulates disagreement, stress-tests assumptions, and converges on what actually holds up.

Quick Start · Why Decisions Fail · How It's Different · Examples · Roadmap

_{Applies Karpathy's autoresearch + LLM Council patterns to decisions.}

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Why decisions fail

Most bad decisions don't look bad upfront. They fail later — in second-order effects, edge cases, and under stress.

People routinely:

• Think in first-order effects only ("cut prices, sales go up")
• Ignore second-order effects ("…and then competitors match, margins compress, existing customers renegotiate, NRR drops")
• Underestimate worst-case scenarios ("what if we're wrong by 2x?")
• Miss black swan events ("what if visa risk and market risk are correlated?")

By the time these failure modes appear, the decision is already in motion and hard to reverse.

Autodecision exists because these failure modes are predictable — if you force yourself to look for them.

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What autodecision does differently

Most AI tells you what's likely to happen. Autodecision shows what happens next, what breaks, and what flips the outcome.

Dimension	What it means
Second-order effects	Not just the immediate consequence — the cascade that follows, with probabilities and timeframes
Worst-case scenarios	Treated as decision drivers, not footnotes. Every run includes an adversary red-team phase
Black swan stress tests	Models correlated risks, irrational actors, and rare events explicitly
Assumption sensitivity	Shows exactly which assumption, if wrong, flips the recommendation — with the threshold
Grounded in real data	Every analysis starts with a web search for market comps, benchmarks, precedents — no vacuum reasoning
Explicit disagreement	5 independent personas argue. The disagreement range IS the uncertainty signal
Mechanical convergence	A Judge measures stability across iterations. Stops when insights stabilize, not when the model runs out of things to say

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What it does

You give it a high-stakes decision. It:

1. Decomposes the decision into sub-questions

2. Grounds every analysis in real-world data — market comps, benchmarks, case studies, precedents

3. Reviews assumptions, personas, and data with you before simulating

4. Spawns 5 independent personas as parallel subagents — each with its own context window, genuinely unable to see the others:

   Persona	Sees	Blind Spot
   Growth Optimist	Upside, creative alternatives	Execution risk
   Risk Pessimist	Downside, failure modes	Opportunity cost of inaction
   Competitor Strategist	Market dynamics, game theory	Overestimates rationality
   Regulator	Legal, compliance, constraints	Overweights unlikely regulation
   Customer Advocate	User value, adoption, retention	Ignores unit economics

5. Simulates first-order AND second-order effects with probabilities and explicit assumption tracking

6. Critiques via anonymized peer review — personas rank each other without knowing who wrote what

7. Red-teams with worst-case scenarios, irrational actors, and black swans

8. Analyzes sensitivity — which assumption, if wrong, flips the conclusion?

9. Iterates until a Convergence Judge mechanically measures that insights have stabilized

10. Produces a Decision Brief — a structured strategy memo

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Best for

Autodecision is built for a specific class of decisions:

• Strategic business decisions — pricing, expansion, M&A, capital allocation, build-vs-buy, hiring senior roles
• Career decisions with asymmetric outcomes — role moves, relocation, founder vs operator, fundraising terms
• Situations with uncertainty AND second-order effects — where the obvious answer isn't the robust answer
• Irreversible or hard-to-reverse decisions — where you only get one shot and want to stress-test it first

It's less useful for:

• Simple factual questions (use a single LLM call)
• Low-stakes everyday decisions (the overhead isn't worth it)
• Decisions where you already have high conviction (this will just slow you down)

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Output: The Decision Brief

Structure is defined by brief-schema.json (v1.1). The writer emits all 16 H2 headers in order; the Phase 8.5 validator HARD_FAILs on deviation.