Digital Thinking Engine

Problem

When decisions involve probability, systems, ethics, or multi-party incentives, jumping to an answer can hide weak assumptions. mind-engine makes the reasoning process explicit: it diagnoses the question, selects analysis models, asks methodology-driven questions, generates multiple hypotheses, and verifies them before recommending options.

How It Works

• Seven-stage pipeline: starts with Problem Diagnosis, checking problem type, uncertainty, repeatability, stakeholder count, hidden assumptions, and cognitive biases.
• Model matching: maps problems to tools such as Bayesian Updating, Nash Equilibrium, First Principles, and Antifragility, while explaining why each model fits.
• Dialogue exploration: asks about goals, constraints, missing information, players, time, risk, and past similar cases, and states why each question matters.
• Hypothesis verification: produces at least 3 distinct paths and tests ergodicity, stepwise verification, skin in the game, recursive traps, worst case, and antifragility.
• Recommendation and consolidation: outputs multi-option advice with reasoning chains, then records preferences, constraints, and methodology limits.

Boundaries

Best for clarifying decisions, comparing options, and structuring tradeoffs, especially with multiple stakeholders, uncertainty, or long-term risk. It does not replace factual retrieval, code generation, or live data. When information is missing, the engine should keep asking rather than guessing. A custom knowledge base can replace or extend the default model-matching table.