Looking at practices (in the sense of Practice Theory) through the lens of constraint regimes (or constraint architectures) adds a dimension to the analysis.
Practices themselves are the integration of parts (material, knowledge, skills, social configurations, etc.) and/or other practices. The practice of driving a car requires mastery of other practices, like steering, breaking, checking mirrors, understanding the rules of the road and being able to interpret the topography, as well as the actions of drivers, cyclists and pedestrians. Some of these competences can only be acquired through practice themselves.
Practices also materialise, for example in the creation of tools, which facilitate competences that the tool maker acquired for others to use without having to learn said competences.
A practice can be seen as an enabling constraint. In order to successfully integrate the constituent parts of driving, I will need to be able to close feedback loops between the underlying practices. I can maybe feel that the car is not fully adhering to my steering on an icy road, which means I need to change how I apply the brake.
The driving itself can be seen as a top-down context sensitive constraint that helps stabilise how I integrate the underlying (enabling) practices. If I’m challenged on time, I might be trying to optimise my integration to achieve speed (with the speed limit as a context-insensitive top-down constraint), but if I have enough time, I might optimise my integration to have a relaxed, stress free journey.
Analysis of complexity-informed constraints and their interactions can reveal qualitative aspects and explain how practice, as performed, can be better understood.