Paper Detail

Labels

Mark Whitmeyer

arxiv Score 5.2

Published 2026-06-24 · First seen 2026-06-25

General AI

Abstract

Labels -- grades, credentials, scores, ratings, ranks -- do two things. They inform receivers, and they give agents something to chase. I study optimal classification when labels must be earned through costly self-selection. I show that exact certification is inefficiently fine: pooling a small bottom interval saves first-order signaling costs while losing only higher-order decision value. I provide sufficient conditions for lower censorship to maximize efficiency as well as for every optimal classification to use finitely many categories.

Workflow Status

Review status
pending
Role
unreviewed
Read priority
later
Vote
Not set.
Saved
no
Collections
Not filed yet.
Next action
Not filled yet.

Reading Brief

No structured notes yet. Add `summary_sections`, `why_relevant`, `claim_impact`, or `next_action` in `papers.jsonl` to enrich this view.

Why It Surfaced

No ranking explanation is available yet.

Tags

No tags.

BibTeX

@article{whitmeyer2026labels,
  title = {Labels},
  author = {Mark Whitmeyer},
  year = {2026},
  abstract = {Labels -- grades, credentials, scores, ratings, ranks -- do two things. They inform receivers, and they give agents something to chase. I study optimal classification when labels must be earned through costly self-selection. I show that exact certification is inefficiently fine: pooling a small bottom interval saves first-order signaling costs while losing only higher-order decision value. I provide sufficient conditions for lower censorship to maximize efficiency as well as for every optimal cl},
  url = {https://arxiv.org/abs/2606.26064},
  keywords = {econ.TH},
  eprint = {2606.26064},
  archiveprefix = {arXiv},
}

Metadata

{}