Renormalisation is the coarse-graining of Models when switching between levels of System description.
To correctly predict observable quantities at larger scales, the parameters or quantities attributed to the observables need to be “renormalised” – adapted to account for the accumulation of interactions on the micro-scale that add up to a change on the macro-scale.
Renormalization specifies relationships between parameters in the theory when parameters describing large distance scales differ from parameters describing small distance scales.Wikipedia
In other words, renormalisation
is a method for determining which parameters describing the interaction are important and which are not. “Relevant” parameters are those parameters … that increase with scale; “irrelevant” parameters are those that decrease with scale.Bar-Yam (2017), 3
References
- Bar-Yam (2017): “Why Complexity is Different”
- Wikipedia: “Renormalization”