You Can't Know Someone's Prior
Bayesian models pathologize what they can't predict. The only honest response is safety.
Schizophrenia has been called "aberrant Bayesian inference." Autism has been called "deficient predictive processing." Both assume you know what someone's brain should expect. You don't. Nobody does. Nobody ever will.
The 5 Studies
Prior Sensitivity
Same data, 11 priors, 3 sample sizes. At n=25 (typical clinical study), prior bias reaches 40%. The prior controls the conclusion.
The Dead Salmon Problem
125,000 voxels of pure noise. The stronger your prior belief in activation, the more "activation" you find. In nothing.
Diagnosis Depends on the Prior
500 people, 3 Bayesian classifiers. Same person is "pathological" or "adaptive" depending on what the clinician believed before they walked in.
Computational Intractability
Exact Bayesian inference is NP-hard. The brain has 86 billion neurons. Even approximations disagree with each other.
Safety as the Only Honest Prior
200 agents, 500 timesteps. Safety-first: everyone converges. Punitive: everyone diverges. You don't need the prior. Make them safe.
Run It Yourself
You don't need a science degree. You need Python and 5 minutes.
Get the code
Download the ZIP from GitHub, or:
git clone https://github.com/webmasterproT/the-prior-problem.git cd the-prior-problem
Set up (one time)
bash setup.sh # Mac / Linux python3 setup_windows.py # Windows
Run all studies
bash run_all.sh # Mac / Linux python3 run_windows.py # Windows
Or run just one: bash run_one.sh 3 (diagnosis depends)
See the results
Each study's results/ folder has the output. results/figures/ has the charts. WHAT_THIS_PROVES.md explains it in plain language.