A population is a set of digital twins and a record of where they came from. Every simulation runs against exactly one, so the population is the single largest lever on whether an answer is any good.
The four source types
- 01Verified panelInterviewed, consented respondents. The most expensive to gather and the most reliable, because each twin is grounded in a real transcript.
- 02Social graphSampled from the followers of one or more accounts. Fast, wide, and biased toward whoever follows that account. Good for reach questions, weaker for anything about the general public.
- 03ImportYour own character definitions as JSON. Use this when you already know the segment and want the twins to match it exactly.
- 04FilterA narrowing expression over an existing pool. Cheap, and only as good as the attributes it filters on.
Stacking sources
Sources combine. A useful pattern is a panel base for grounding, a social graph for reach, and a filter to cut the result down to the behaviour you care about. Twins appearing in more than one source are deduplicated, so the total is smaller than the parts.
Checking your work
Every population carries an alignment score: how closely it reproduced held-out human responses on questions it had not seen. A population in the high eighties or low nineties is dependable. Below that, look at the source mix before you look at the model.
A population built only from a social graph will usually align worse than one with a panel base. That is not a defect in the graph; it is the graph telling you it is a different group of people.