GoKawiilGPT Guesses Between 1 and 100
An interesting thing about humans is that they are not good random number generators.
If you ask a person to "pick a random number between 1 and 100", they are remarkably predictable. Answers cluster on 37 and 73, on "messy" numbers, and on memes like 42 and 69, while round numbers are quietly avoided. A true random generator would instead produce a flat, uniform distribution.
This project asks gpt-4.1 the same question 10,000 times and characterizes the distribution it produces, measured against a uniform baseline. Does an LLM, which is trained on human text, behave like a fair die, or does it inherit the lumpy human pattern?
Full design and methodology: docs/LLM Random Bias Experiment SDD.md .
Inspiration
This experiment is an LLM-focused follow-up to two well-known explorations of human number-picking bias.
Methodology
Full experimental design is in the SDD; the essentials:
Model. gpt-4.1 (OpenAI), called via the Responses API. It is a non-reasoning model. It emits a direct answer rather than deliberating; what we're measuring is its raw output distribution, not a reasoning strategy. The exact model string is recorded in every raw-CSV row ( Model column) and in data/raw/run_metadata.json , so the dataset is self-describing.
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