Binary Scoring Rules that Incentivize Precision

Eric Neyman, Georgy Noarov, S. Matthew Weinberg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

All proper scoring rules incentivize an expert to predict accurately (report their true estimate), but not all proper scoring rules equally incentivize precision. Rather than treating the expert's belief as exogenously given, we consider a model where a rational expert can endogenously refine their belief by repeatedly paying a fixed cost, and is incentivized to do so by a proper scoring rule. Specifically, our expert aims to predict the probability that a biased coin flipped tomorrow will land heads, and can flip the coin any number of times today at a cost of per flip. Our first main result defines an incentivization index for proper scoring rules, and proves that this index measures the expected error of the expert's estimate (where the number of flips today is chosen adaptively to maximize the predictor's expected payoff). Our second main result finds the unique scoring rule which optimizes the incentivization index over all proper scoring rules. We also consider extensions to minimizing the lh moment of error, and again provide an incentivization index and optimal proper scoring rule. In some cases, the resulting scoring rule is differentiable, but not infinitely differentiable. In these cases, we further prove that the optimum can be uniformly approximated by polynomial scoring rules. Finally, we compare common scoring rules via our measure, and include simulations confirming the relevance of our measure even in domains outside where it provably applies.

Original languageEnglish (US)
Title of host publicationEC 2021 - Proceedings of the 22nd ACM Conference on Economics and Computation
PublisherAssociation for Computing Machinery, Inc
Pages718-733
Number of pages16
ISBN (Electronic)9781450385541
DOIs
StatePublished - Jul 18 2021
Externally publishedYes
Event22nd ACM Conference on Economics and Computation, EC 2021 - Virtual, Online, Hungary
Duration: Jul 18 2021Jul 23 2021

Publication series

NameEC 2021 - Proceedings of the 22nd ACM Conference on Economics and Computation

Conference

Conference22nd ACM Conference on Economics and Computation, EC 2021
Country/TerritoryHungary
CityVirtual, Online
Period7/18/217/23/21

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Networks and Communications

Keywords

  • information elicitation
  • proper scoring rules

Fingerprint

Dive into the research topics of 'Binary Scoring Rules that Incentivize Precision'. Together they form a unique fingerprint.

Cite this