Bayesian Theory of Truth (Poster Translation)
Tagline: Predictive ability is the only test of whether you understand the truth. The more accurate your predictions, the closer you are to truth.
Core Thesis
Truth is not just "seeing is believing" — it is the ability to make accurate predictions about the future based on evidence.
The Formula
P(truth | evidence)
Dynamically update beliefs: When new evidence (e.g., explosive news) appears, don't reject it based on "kind" intuition — instead, use Bayes' rule to compute the probability that it is true.
> Posterior probability = (Likelihood × Prior probability) / Normalizing factor
Kindness vs. Truth
- The trap of kindness: When facing unbelievable news, our *priors* are often constrained by social morality and wishful thinking. Because we don't want to believe the world is so dark and complicated, we assign a very low subjective probability to "cruel truths."
- The Bayesian antidote: If a piece of news is shocking but has extremely high *likelihood* (i.e., it perfectly explains a series of previously unexplainable events), then even with an extremely low prior, we must substantially increase our confidence in it.
A Four-Step Method for Judging Truth
1. Set a prior — Acknowledge subjective bias and honestly list your initial probability judgment about the event. 2. Evaluate the evidence — Compute how differently the news would appear under the hypothesis that it is true versus false. 3. Bayesian update — Calculate the posterior probability and adjust your beliefs, no matter how surprising the result. 4. Validate by prediction — Make predictions about the future based on your new beliefs. If they come true, you have touched the truth.
Closing Quote
> "If you cannot predict, you do not understand. If you refuse to update, you have drifted away from the truth."
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*The original post is an HTML/CSS poster (720×960) built with CSS Grid, custom properties, and a deep-blue/amber palette, attributed to "Bayesian statistics theory and cognitive psychology."*