Pith. sign in

REVIEW 1 cited by

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2310.12871 v1 pith:6OSWU2QX submitted 2023-10-19 stat.AP cs.CY

The origins of unpredictability in life trajectory prediction tasks

classification stat.AP cs.CY
keywords lifeunpredictabilitypredictionevidenceframeworkinterviewsoriginspredict
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Why are life trajectories difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multi-decade longitudinal study. Our sampling and interviewing process were informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative evidence we uncovered in these interviews combined with a well-known mathematical decomposition of prediction error helps us identify some origins of unpredictability and create a new conceptual framework. Our specific evidence and our more general framework suggest that unpredictability should be expected in many life trajectory prediction tasks, even in the presence of complex algorithms and large datasets. Our work also provides a foundation for future empirical and theoretical work on unpredictability in human lives.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Privacy, Prediction, and Allocation

    cs.CR 2026-04 unverdicted novelty 7.0

    Differentially private variants of individual and unit-level aid allocation strategies admit clean bounds on the tradeoffs between privacy, efficiency, and targeting precision across stochastic and distribution-free regimes.