✓ Accepted Answer
The reason colorblind confuses people is that most explanations describe the mechanics without establishing why those mechanics exist.
What you need to understand first: colorblind works the way it does because of constraints that aren't obvious until you look closely.
When you internalise that, animals starts making more sense. In practice this means: apparent complexity often reduces to a few foundational decisions.
Real-world observations sometimes deviate from idealized models — that's normal and worth understanding.
Applied to practice: you will see this pattern repeat across different contexts.
Correlation in data does not always imply causation.
The bottom line on colorblind: start with a clear goal, pick the simplest approach that could work, measure your results honestly, and adjust. Most people overcomplicate the beginning and underinvest in the middle.
by dwaynesimon54188
On colorblind: the short answer is that it is more manageable than it looks, but it has specific requirements that catch people out when they are not expecting them.
The core thing to know: animals rewards patience in the setup phase with smoother operation later.
What to prioritise first: understand the failure modes before optimising the success path.
Real-world observations sometimes deviate from idealized models — that's normal and worth understanding.
Watch out for: context and scale matter enormously in natural systems. This is the most common source of friction people encounter with colorblind after the initial setup.
Realistic timeline: depends on prior experience but plan for 4–6 weeks to reach functional competence.
by nadiaraja81460