There’s a comfort
in lines.
They promise clarity.
Predictability.
The hope that if we understand the parts,
we’ll understand the whole.
And so we build linear models—
neatly structured,
cleanly weighted,
each variable pulling its share,
each outcome marching in step.
We say:
“If X increases, Y should increase too.”
We say:
“Let’s assign numbers.
Let’s draw the relationship.
Let’s forecast the choice.”
But beneath the numbers,
beneath the clean geometry—
there is a question that hums:
Do people actually think this way?
The Elegance of the Model
Linear models are powerful.
They help us find patterns
where chaos once lived.
They force clarity:
What matters?
How much?
What changes what?
And when applied to judgment—
to how people make choices,
we ask:
Can the mind be modeled like this?
Can we reduce decision-making
to a weighted sum of cues?
A calculation,
systematic and consistent?
Sometimes—yes.
In specific contexts,
linear models predict better than people.
They catch the quiet logic
humans often miss.
But still—
we hesitate.
Because we know ourselves,
and we are rarely that simple.
The Messy Middle of Being Human
We are creatures of intuition,
not only integration.
We use rules of thumb.
We leap to conclusions.
We trust feelings
more than functions.
We notice one strong cue
and ignore the rest.
We overreact to rare events.
We change our minds
when the light shifts
or when someone smiles
or when fear speaks louder than reason.
We are not random.
But we are rarely linear.
And that doesn’t mean we are wrong.
It means we are complex.
When Models Explain Us Better Than We Explain Ourselves
Here is the paradox:
People often do not use linear models,
but linear models often predict their judgments better
than they can themselves.
Why?
Because we are noisy.
We are inconsistent.
We are swayed by what we cannot name.
Models are quiet.
They do not forget.
They do not flinch.
They follow their weights
even when emotions rise.
But that is not the same
as understanding.
A model can outperform the mind,
but it cannot replace the soul.
Between Linearity and Life
The real question is not
“Do people follow linear models?”
but
“When should they?”
And:
“When should we honor the curves,
the edges,
the non-linear truths of being alive?”
There is wisdom in the model.
But there is also wisdom
in knowing when life breaks the mold.
Some decisions need structure.
Others need softness.
And most—
need both.
A Closing Reflection
If you are building a model,
or watching one try to explain you,
pause.
Ask:
- Does this model describe how I actually think—
or only what I tend to do? - Where do my judgments follow structure,
and where do they follow story? - Can I let models guide me,
without letting them define me?
Because we are not bound to formulas.
But we can learn from them.
We are not always rational.
But we are always reaching for meaning.
And the truest model
is the one that leaves room
for both.
And in the end, asking whether people follow linear models
is not about dismissing the math—
it is about remembering the person.
That beneath every prediction
is a pulse.
That beneath every pattern
is a person with memory,
emotion,
intuition,
and contradiction.
And when we honor both the model
and the mystery,
we do not just explain judgment.
We begin to understand it—
not as a line,
but as a living path.