Beyond the Population Prior: Reliable and Amortizable Person Representations in Models of Human Decision-Making

Most learned models of human decision-making represent a population, not a person. We ask whether a person's departures from such a model persist, improve prediction, and can be inferred quickly. Chess is our testbed: 2.6 million real-game decisions at positions whose difficulty other people rated; person fits use 545 players with at least one hundred each. A model over a frozen population reference infers each person's tempo, dispersion and ability beyond rating. They replicate across date-split halves (length-corrected tempo r = 0.79); a year apart agreement is lower; at equal decision counts, tempo and ability show no detectable drop. Per decision the person adds little: the held-out think-time gain, 0.010 nats, misses our 0.02-nat margin; in-game clock history beats static person offsets on timing; ability beyond rating adds accuracy, below margin. An amortized encoder infers held-out players from few decisions, 0.9% short of a fit on all their decisions against a 0.5% margin. Instrumented by the time control, extra time's estimated effect on accuracy is positive for players whose think time it changes, opposite to the within-person association (exclusion restriction checked, not proved). The traits are reliable; their value depends on the baseline; timing is partly amortizable, ability not yet.