Teaching the Machine How You Decide

Teaching the Machine How You Decide

Section 5 asks questions that sound abstract until you answer them with real events: a decision you walked through, what counts as enough research, whether you favor speed or accuracy, your risk tolerance with examples, which decisions you never delegate, how you behave when wrong, and how hard the twin should push back. Together they teach the twin your decision process — the part of you that voice-mimicking AI can never fake.

The walked-through decision

Pick one real decision — a hire, a big purchase, firing a difficult client — and narrate it on paper from start to finish. Not the outcome: the process. What information you gathered, when you had enough, what options you compared, what weighted what, how long you sat with it, and what you would change if you chose again today. This single exercise calibrates the twin better than any list of values, because it shows judgment in motion. When a similar decision arrives later, the profile instructs it to work the way the owner worked the recorded one — same evidence, same order, same patience.

Enough, speed, and risk — with numbers

Define your research sufficiency bar: for instance, two competing quotes, one conversation with whoever handled the problem last, and a 24-hour wait above a certain dollar amount. Without a defined bar, a language model drifts to either extreme — researching forever or stopping after the first plausible answer. Then write the speed-versus-accuracy rule: most owners are fast where errors are reversible, such as trying a software tool or reordering stock, and slow where they are not — people, contracts, and reputation. Write it as a small table by category; the twin reads tables happily.

Risk tolerance must come with examples, because the abstract word means nothing to a machine. Try three sentences: "I will risk five thousand dollars on a sixty-day experiment. I will never risk client data or trust. I will tolerate a slow month to land the right hire." That maps the board: here the twin may experiment, there it must escalate to you first, and never does it cross the data line.

Never-delegated, wrongness, and pushback

List the decisions that stay with you forever: pricing floors, firing, anything bearing your signature or a legal consequence. The twin may prepare the briefing pack; it never closes the loop. Then define what being wrong looks like: if the twin's recommendation fails, you want the plain sentence — "I was wrong, here is why, here is what changes" — the same words you would accept from your best employee. Language models drift toward confident excuses, and your profile forbids invented narratives outright.

Last, set the pushback strength and form. When an instruction collides with its profile-based judgment, does the twin argue once with evidence and then comply? Push hard twice, then comply? Raise a one-line concern and execute? Choose, and state the exact form: evidence first, tone level, on the record. This rule keeps the twin useful — a yes-machine tells you nothing new, while a rule-governed dissenter is worth a staff meeting.

Key Takeaways

• Section 5 encodes your decision process: walked example, research bar, speed-accuracy table, risk examples, never-delegate list, wrongness, pushback strength.
• The walked-through decision is the strongest calibration exercise — narrate the process, not the outcome.
• Give research sufficiency and risk tolerance concrete numbers and named examples.
• The twin must own mistakes in plain language; invented excuses are forbidden.
• Set pushback strength and form once: an evidence-first dissenter beats a sycophant.