Five Time-to-Human Mistakes
The input errors that flip GO and STAY.
Time-to-Human lies to you when you lie to the inputs. Here are the five common lies.
The tool is honest; most mistakes happen at the input layer. Each of the five below reliably flips a STAY into a GO or vice versa. Fixing them costs nothing and brings the score back into contact with reality.
Quick answer
Time-to-Human lies to you when you lie to the inputs. Here are the five common lies.
Key points
- ▸ Inflating connection intensity to "justify" a yes. If you score every plan 8, the tool becomes a yes-machine. Calibrate honestly — reserve 9-10 for the handful of people who actually fill the battery.
- ▸ Under-counting transit. Door-to-door time includes getting ready, parking, walking from the lot, return trip. A 20-minute drive is a 60-minute outing just to get there and back.
- ▸ Ignoring recovery cost. Some outings cost you the next morning. If Saturday brunch destroys your Sunday, Saturday's transit number needs to reflect that — add the recovery hours.
- ▸ Running it for plans you already feel good about. The tool is for ambiguity. Using it on clearly-yes plans invites fake numbers that erode your trust in the score.
- ▸ Running it too late. If the decision needs to be made by 2pm and you ran the tool at 6pm, you are just rationalizing the choice you already made. Trigger the tool at invite time, not at departure time.
Examples
- Inflated connection scoreEvery friend rated 8-9. Output always says GO. Recalibrate: reserve 9+ for the top-3 list. Suddenly STAYs appear where they should.
- Transit undercountEntered 15 min (the drive). Real door-to-door: 55 min (getting ready, parking, walk, return). Score drops from 1.4 to 0.6 — a clear STAY the first number hid.
- Post-hoc runArrived at restaurant, ran the tool, got STAY. Useless. The tool is input-at-invite, output-at-departure.
When to use which tool
- Time to HumanEnter honest numbers at the moment of the invite — not after committing.Weighed social ROI — does the connection payoff beat the transit, cost, and energy tax of leaving the house?
- Social LatencyIf the verbal verdict is pulling you toward dishonest inputs, switch to Social Latency for pure-number output with no narrative.Compare the benefit of a social plan against the time, energy, and money it costs.
Related
- Time to HumanWeighed social ROI — does the connection payoff beat the transit, cost, and energy tax of leaving the house?
- Social LatencyCompare the benefit of a social plan against the time, energy, and money it costs.
- What Time-to-Human CalculatesThe single score that tells you whether leaving the house is worth it tonight.
- When to Use Time-to-HumanFive moments when social math actually resolves the loop.
- Four Social Latency MistakesThe errors that make UPLINK and STANDBY unreliable.
Frequently asked questions
› What if my energy estimates are always wrong? Troubleshooting
Calibrate for a week: rate your energy morning-of, note what actually happened. Most people over-rate morning energy by 2 points because they confuse caffeine with baseline capacity.
› Should I log results? Trust & accuracy
Optional but useful. After a month of entries + outcomes, you learn which inputs you systematically miscalibrate. That is the real value — not any single decision but the calibration trail.
› How should I use a decision framework in real life? How-to
Use a decision framework to expose the tradeoff, not to outsource the decision. Write down the inputs, compare the output with your constraints, then ask what would change the answer. The strongest use is scenario testing: base case, conservative case, and failure case.
› Is this financial, legal, or tax advice? Trust & accuracy
No, this is not legal, financial, tax, medical, or professional advice unless the page explicitly says that use case is supported. It organizes assumptions so you can inspect them. Verify high-stakes choices with qualified people who can review facts, contracts, regulations, and downside risk.
› What assumption matters most in a decision model? Edge case
The most important assumption is usually the one you are least certain about and most emotionally attached to. Change that input first. If the recommendation flips after a small change, the decision is fragile and needs more evidence before you treat the model as useful.