GPT Guesses Between 1 and 100

Summary

GPT 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. This experiment is an LLM-focused follow-up to two well-known explorations of human number-picking bias. 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. Sample size. N = 10,000 independent calls — enough for a chi-square goodness-of-fit test and per-number proportions stable to ~±0.5 pp. Sampling. temperature = 1.0, so the model exercises its full sampling distribution. This is the experiment: at low temperature it would just repeat one number. Prompt. A fixed system prompt instructs the model to output only one integer between 1 and 100; the user prompt requests the number and carries a unique uuid4. (The UUID is request-tracing hygiene, not cache-busting — at temperature 1.0 every call should sample independently regardless.) Baseline. The result is compared against a uniform distribution — what a fair generator would produce — not against human data (see Assumptions). Pipeline. Four stag...