Give each message six language scores
We score clarity, guidance, evidence, reassurance, choice, and cost language. You can review and change every score.
A synthetic message experiment
Which of three messages keeps winning?
Test three message ideas against a made-up audience. Then rebuild that audience 50 times to see whether the ranking holds.
Important
This is a thought experiment with invented inputs. It does not predict how real people will respond.
The whole test
Three steps.
One clear question.
Score all three against one made-up audience
Each fictional person values the six scores differently. A message earns a higher fit score when its language matches those invented needs.
Rebuild the audience and compare again
Monte Carlo repeats the whole comparison while the people and selected assumptions change. We count how often each message ranks first.
Set up the test
Three messages.
One made-up audience.
01
Three message ideas
1. Choose three messages to compare.
Each comparison tests three different message strategies. Every line comes with a matching, reviewable score profile so you can see exactly what the model is comparing.
ClarityIs the main idea easy to understand?
GuidanceDoes it offer a useful next step?
EvidenceDoes it sound specific or supported?
ReassuranceDoes it reduce worry or uncertainty?
ChoiceDoes it support control and options?
CostDoes it address price or predictability?
02
Made-up audience
2. Set who the messages are scored for.
The demo invents three audience types and a starting mix. Each type values different parts of a message. The names, shares, traits, and preferences below are assumptions—not measured people or a market estimate.
Daily burden How much friction a person is assumed to face.
Preference for control How strongly they are assumed to value choice.
Starting trust How open they are assumed to be to guidance.
03 / Run the comparison
3. Create many worlds and count the winners.
One world creates a fictional audience and scores all three messages once. Monte Carlo repeats that full step. The final result combines every world—not just the first one.
Stays fixed
The three message lines · the three audience types · the six score definitions · your replay code
Changes in every world
The fictional people · the mix of audience types · each message score within its shown range · how much each score matters
Result across every world
Which message ranked first most often?
Two different numbers
Fit score is a 0–100 model score. It is not a response rate. Worlds won is the share of repeated fictional worlds where that message ranked first.
Best fit for each made-up audience type
Across all worldsA closer look at world 1
One example, not the full resultWhen did each message tend to win?
Strongest pattern across these worldsWhy repeat the test?
That is the Monte Carlo idea.
One made-up world gives one answer. Repeating the same test with controlled changes shows whether that answer is stable or fragile.
Try the idea
More repeats make the pattern easier to see.
Each dot is one whole fictional world. Its color shows which of the three messages ranked first in that world.
A game of solitaire
Stanislaw Ulam considers estimating the chance of winning by playing repeatedly instead of calculating every possible game.
A job for computers
John von Neumann develops the approach for early computers and difficult neutron problems.
The method is published
Nicholas Metropolis and Ulam publish the method. Its casino-inspired name makes chance part of its identity.
Used wherever outcomes are uncertain
Monte Carlo is used in physics, engineering, finance, measurement, and other complex systems.
History: Los Alamos. Applications: NIST.
In plain English
What this demo does.
- Compare three message lines using six clearly defined language scores.
- Score them against a made-up audience with invented preferences.
- Rebuild that audience many times while selected assumptions change.
- Show which message ranks first most often—and when the ranking changes.
What it does not do: predict a response rate, represent a real population, or replace research with people.