Read the equation
The objective adds the project’s surprise, uncertainty, and penalty terms and subtracts value. If the system minimizes Ω, it therefore favors smaller changes and penalties and larger value, subject to the chosen coefficients. The labels express the project’s intended roles; the total is a constructed scalar objective whose behavior depends on the separately defined terms.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| Ω | Composite objective |
| Surprise | Weighted state-change cost |
| Uncertainty | 1−b² |
| Value | Utility-weighted state score |
| Penalty | Squared input cost |
How it works
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Original context
Read the surrounding source passage
## 5.1 Complete Objective ``` Ω = Surprise + Uncertainty - Value + Penalty ``` **Explanation**: The system seeks to minimize Ω. This is analogous to free energy in variational inference - the system wants low surprise, low uncertainty, high value, and low penalty. ## 5.2 Surprise Component ``` Surprise = α₁ · ||ΔΨ||₁/16 + α₂ · ||ΔΨ||₂² ``` **Where**: - `||ΔΨ||₁` = L1 norm (sum of absolute differences) - `||ΔΨ||₂²` = squared L2 norm - `α₁, α₂` = weighting coefficients (default 0.5 each) **Explanation**: Surprise measures how much the state changed. L1 captures sparse, large changes; L2 captures distributed changes. ## 5.3 Uncertainty Component ```
Exact original notation
Ω = Surprise + Uncertainty - Value + Penalty