Values are usually treated as one thing: either you have them or you don't. In practice, they operate at three different levels, on different timescales — and most decision friction happens when two levels disagree.
Your past decisions are the most reliable dataset for understanding your values. Log a sample, and the three layers become visible.
Layer one: what your attention selects first
The first layer appears as a pattern in what you notice first when a situation appears. The available options are usually one of three signal classes: opportunity signals (what could be built), risk signals (what could go wrong and where), or people signals (who is affected, and how). The class that consistently reaches your attention first is your default filter. Two people can watch the same meeting and genuinely see different events, because their filters select different inputs.
Layer two: how you position yourself
The second layer appears as a recurring stance in how you relate to expectations and other people. The common stances are: advance and claim (move the agenda forward and take space), align and support (make the group's plan work), observe and wait (gather more information before committing), and defend a boundary (keep the boundary even at a cost). Most decision logs show one dominant stance plus one fallback. This layer shows up in repeated choices — whose agenda you advance, what you accept, what you walk away from.
Layer three: what you optimize when good things collide
The third layer is the criterion you apply when two good options conflict: autonomy versus security, speed versus durability, fairness to the individual versus fairness to the system. This layer is rarely spoken out loud. It appears in the pattern of tradeoffs you have already made — the choices that cost you something, which you made anyway.
How to build your three-layer decision dataset
Log ten decisions across three horizons. For each one, record four variables:
| Horizon | Sample decisions | Variables to record |
|---|---|---|
| Daily | where attention went, what got postponed, what got a fast yes | signal class noticed first (1-3), stance taken, criterion applied, clarity (1-5) |
| Mid-term | jobs, projects, collaborations entered or exited | same four variables |
| Long-term | commitments you kept returning to for years | same four variables |
Then group the answers. The layer with the most consistent answers is the one the one driving decisions. The layer with scattered answers is the one you have not yet made explicit. Note that the categories above are common filters, not a closed set — your own patterns may differ, and the key is to identify your specific default, not to fit one of these boxes.
Which layer is running your decisions?
Three questions — that's how long it takes to see the weights you actually use when stakes are real. TangoEra's free check maps how you weigh trade-offs, risk, and time horizons.
See my decision profileThe consistency check
Most decision friction is layer conflict, not lack of information. Your attention selects opportunity signals; your positioning instinct says wait; your long-run criterion says security. Each layer is internally reasonable — but they are pulling in different directions, so the decision feels stuck.
When the three layers align, decisions feel unusually clear. When they conflict, the same information produces the same stuck feeling every time. The practical move is not to find the "right" layer. It is to name which two are fighting, then decide, for this specific window, which one gets priority.
Deciding which layer gets priority in a given window is the core challenge of decision timing. There is no single correct answer — only a strategic choice for now.
Your data indicates the layers. The priority is up to you.