Predictions

This page summarizes the falsifiable predictions derived from Cohesion Dynamics.

Each prediction is stated with fixed mathematical form and clear falsification criteria. No free parameters may be tuned to save the theory. These predictions exist to be tested, not to explain data.

Purpose of Predictions

The P-series exists to make Cohesion Dynamics falsifiable.

Each prediction:

States what must be observed if Cohesion Dynamics is correct
Fixes the mathematical form (no parameter fitting)
Specifies clear falsification criteria
Makes no empirical claims or data analysis
Stands or falls as stated

If any prediction is robustly falsified, the entire framework must be reconsidered.

How to Test These Predictions

For each prediction, we provide:

P-[X] — The prediction paper stating the claim
R-PRED-[X] — A non-normative testing interface specifying how observables should be extracted and compared

Testing should:

Reference predictions exactly as stated
Use consistent methodology across datasets
Report results transparently
Not retrofit parameters after seeing data

Summary Table

Prediction	Description	Status	Paper	Interface
P-DM1	Dark matter halo core radius scales as $r_{\text{core}} \propto M_{\text{halo}}^{1/2}$ with constant central surface density	Ready for testing	P-DM1	R-PRED-DM1

Future Predictions

Additional predictions will be added as derivations are completed in B-series (Derived Physics), G-series (Gravity and Geometry), and other theoretical work.

Planned prediction areas:

Quantum mechanics — Predictions from substrate-based quantum recovery
Cosmology — Large-scale structure and CMB predictions
Strong gravity — Predictions in extreme gravitational regimes

Why These Predictions Matter

Standard approaches to dark matter and quantum mechanics rely on:

Phenomenological models with tunable parameters
Astrophysical factors that vary by system
Effective theories without substrate-level justification

Cohesion Dynamics makes parameter-free predictions derived from substrate mechanics.

This means:

Success would be surprising if the framework were wrong
Failure would falsify the framework, not an auxiliary hypothesis
Testing is unambiguous — no room for post-hoc adjustments

How to Engage

If you are interested in testing these predictions:

Read the prediction paper (P-[X]) carefully
Review the testing interface (R-PRED-[X]) for implementation details
Apply consistent methodology across your dataset
Report results transparently, whether confirming or falsifying

We welcome independent testing and will acknowledge all results fairly, whether they support or falsify the framework.