Reposting here -
A picture is worth a thousand words – my layman spin on it :)
Our brains build models to interpret (translate) everything.
We use languages to communicate and share things.
When brains share the same models communication is simple. It’s simple to talk about day, night and light with anyone and, similarly, well formed teams (or couples) need few words to communicate even complex ideas because they have shared models.
When models differ the language needs to be adjusted or learned. Identical problem/solutions may sound different in different domains.
However when the model doesn’t exist in someone elses brain to communicate, the model needs to be built. Building models (learning) takes time.
Natural language evolved for natural things. Hence, it’s not very efficient for abstract things that require accuracy. Math (and code) is an accurate language, and (often) effective to communicate models that require accurate replication.
For example. It takes time to build the intuition for some things (e.g. fluid dynamics). Once you build the intuition, the easiest way to explain it is through the formulas. Someone can read the formulas, understand what the model communicates and then build intuition accurately.
You watch and learn to build a mental model, you download the mental model, someone uploads it and builds the model.
Our brains build models to interpret (translate) everything.
We use languages to communicate and share things.
When brains share the same models communication is simple. It’s simple to talk about day, night and light with anyone and, similarly, well formed teams (or couples) need few words to communicate even complex ideas because they have shared models.
When models differ the language needs to be adjusted or learned. Identical problem/solutions may sound different in different domains.
However when the model doesn’t exist in someone elses brain to communicate, the model needs to be built. Building models (learning) takes time.
Natural language evolved for natural things. Hence, it’s not very efficient for abstract things that require accuracy. Math (and code) is an accurate language, and (often) effective to communicate models that require accurate replication.
For example. It takes time to build the intuition for some things (e.g. fluid dynamics). Once you build the intuition, the easiest way to explain it is through the formulas. Someone can read the formulas, understand what the model communicates and then build intuition accurately.
You watch and learn to build a mental model, you download the mental model, someone uploads it and builds the model.
This is my mental model for communicating models