When a model speaks for a population
Language models answering as real people, scored once on survey answers nobody had read: a frontier model met 0 of 6, prompted voices overstated births threefold, and carrying the last shares forward beat a small model. Lives fitted to records came closer, a model's estimate counted as a prior meant fewer people to ask, and subgroup splits landed inside the survey's own noise in the United States and in 30 of 31 African countries.
Synthetic populations: where simulated people hold up, and where they fail
Census-built populations, agents grounded in interviews and language models speaking for whole groups have all advanced in 2025-26. The evidence now shows clearly which of them match real people, and the dividing line is what each simulated person is built from.
Families from lived records: children and siblings, counted before the answers were read
A population lived month by month in the United States from 1950 matched how many children Americans have had and how many brothers and sisters they report, closer than a frontier model answering from what it has read. The father's side, first counted through partnerships, missed, and became the next test.
Agents that remember, and people around a working machine
An agent that remembers what a person said predicted their answers four years later better than a persona, in every panel and on every question; an agent given only a life record did no better than a persona. And Living Worlds' crowd predicted how close real people came to a real robot in 7 of 8 recordings nobody had opened.