Parameter Rosetta Stone

Every parameter has three names: physics notation for papers, Promise Names for humans, and a plain description for everyone. This page maps all three.

Core Parameters

#	Physics	Promise Name	Description
1	`k`	Commitment Shape	How predictable the timing is. The shape of the waiting-time distribution — whether commitments resolve quickly and predictably (k > 1) or spread out into a heavy tail (k < 1).
2	`γ_obs`	Watch Rate	How often someone checks. The rate at which commitments are observed or verified. High Watch Rate compresses resolution timing and creates the Verification Paradox.
3	`γ_dec`	Drift Rate	How fast unattended commitments decay. The rate at which commitment fidelity erodes when no one is watching. Zero in every non-cooperative system — the cooperation diagnostic.
4	`γ_prs`	Pressure Rate	How fast failures get corrected. The rate at which failed or degraded commitments are restored under external pressure or enforcement.
5	`mf`	Resolution Bias	What % of checked promises resolve positively. The fraction of observed commitments that resolve in the compliant direction, as opposed to failure or deferral.
6	`R²`	Fit Quality	How well the equation matches reality. The coefficient of determination — how much of the observed variance is explained by the Weibull survival model. Ranges from 0 to 1.
7	`Γ`	Noise Floor	Background chaos decoupling linked promises. Environmental noise that breaks correlations between structurally linked commitments, causing them to evolve independently.
8	`H`	Structural Coupling	Force that linked promises exert on each other. The Hamiltonian coupling strength between entangled commitment states — how strongly co-dependent commitments influence each other's evolution.
9	`γ_frac`	Autonomy Fraction	What % evolves independently (calibrated 75/25). The fraction of a commitment's evolution that is self-directed vs. structurally driven by linked promises. Empirically calibrated at 75% autonomous.

Derived Diagnostics

Physics	Promise Name	Description
`γ_dec > 0`	Cooperation Diagnostic	Drift Rate = 0 in every non-cooperative system — theater, not commitment. Drift Rate > 0 indicates active maintenance: someone is doing real work to keep promises alive.
`k(γ_obs → ∞)`	Verification Paradox	Commitment Shape jumps from ~0.37 to ~0.90 at the moment of verification. Checking makes networks look worse (more violations detected) while making them work better (violations resolved faster).

The Four Channels

The Lindblad master equation governing commitment dynamics operates through four dissipative channels, each corresponding to a physical process.

L₁Observation Channelγ_obs · σ₋

Measurement collapses the quantum superposition of commitment states. Observing a commitment forces it from ambiguous to resolved.

L₂Decay Channelγ_dec · σ₋

Unobserved commitments drift toward failure. Without active maintenance, promise fidelity erodes at rate γ_dec.

L₃Pressure Channelγ_prs · σ₊

External enforcement restores failed commitments. Regulatory pressure or accountability mechanisms push failed promises back toward compliance.

L₄Noise ChannelΓ · σ_z

Environmental turbulence decouples linked promises. Background noise breaks structural correlations between co-dependent commitments.

Commitment Shape Regimes

The Weibull shape parameter k (Commitment Shape) classifies the underlying dynamical regime. These are not grades — they are descriptions.

Compostingk < 1.0

Amorphous, spreading — heavy-tailed waiting times. Most commitments resolve quickly, but a long tail of promises take far longer than expected. Common in institutional settings where some conditions stall indefinitely.

Examples: MONA (k=0.667), Global Fund (k=0.820)

Steady Statek ≈ 1.0 (0.9–1.1)

Near-exponential — memoryless resolution timing. The hazard rate is roughly constant: a commitment is equally likely to resolve at any point in time, regardless of how long it's been active.

Examples: Freedom House (k=1.010), WGI (k=1.004), Mycelium (k=1.060)

Building1.1 ≤ k < 2.0

Increasing hazard — commitment strength grows over time. Older commitments are more likely to resolve than newer ones. The system has memory: survival so far predicts future survival.

Examples: NYC Taxi (k=1.394), ECHO EPA (k=1.196)

Crystallizingk ≥ 2.0

Deterministic-like — highly predictable, narrow timing distribution. Commitments cluster tightly around a characteristic resolution time. Near-mechanical precision.

Examples: Oregon CCO (k=2.019), IEG World Bank (k=2.283)

Cooperation Diagnostic

Drift Rate = 0 in every non-cooperative system.

The Drift Rate parameter (γ_dec) measures how fast unobserved commitments decay. In every non-cooperative system tested — from NYC Taxi trips to World Bank project outcomes — this value is exactly zero. In every cooperative system, it is strictly positive.

A Drift Rate of zero means the system is in Theater: commitment-shaped activity with no underlying maintenance dynamics. The math detects it automatically — no judgment required.

Quick Reference

k       → Commitment Shape      → How predictable is the timing?
g_obs   → Watch Rate            → How often does someone check?
g_dec   → Drift Rate            → How fast do promises erode?
g_prs   → Pressure Rate         → How fast do failures get fixed?
mf      → Resolution Bias       → What % resolve positively?
R2      → Fit Quality           → How well does the model fit?
G       → Noise Floor           → How much background chaos exists?
H       → Structural Coupling   → How strongly are promises linked?
g_frac  → Autonomy Fraction     → What % is self-directed?