A starting point for discussion
The idea
The study compares each proposal’s win rate in a sample with its win rate among all study participants. It then uses R², a score for how closely two patterns match, to measure how closely the sample reproduces the pattern for all participants.
Place in the reasoning
AI assessment · Connected to reasoning
Defines the sample-match measure as similarity between proposal win rates in a sample and the full participant data, explicitly not a direct test of external-population accuracy.
Consistent study results may not reflect the wider public
Specifies what the reported sample-match measure captures and limits it to reproducing the study’s participant data, not population representation.
Different prediction checks reveal different limits
Clarifies that a match on aggregate proposal win rates does not establish individual-level accuracy or broader reliability.
What this means and why it matters
What this means
A proposal’s win rate is how often participants chose it over another proposal. R² is a score the authors use to summarize how closely win rates from a smaller or expanded sample follow the pattern for all study participants.
Why it matters
This explains what the reported accuracy gains mean: they concern how closely the sample reproduces the pattern of preferences recorded in the study.
Conditions & assumptions
Source coverage: The full readable text of the 37-page research draft, including references and appendices. Figures are not independently interpreted. This measures similarity to the full study sample; it does not directly test whether the preferences are correct for people outside the study. Addresses: The study’s measure of how closely a sample matches the full study
Questions this connects to
AI-suggested relationships. Authors have not necessarily responded to one another.