License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2022.3
URN: urn:nbn:de:0030-drops-165269
URL: https://drops.dagstuhl.de/opus/volltexte/2022/16526/
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Ahmadi, Saba ; Beyhaghi, Hedyeh ; Blum, Avrim ; Naggita, Keziah

On Classification of Strategic Agents Who Can Both Game and Improve

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LIPIcs-FORC-2022-3.pdf (1.0 MB)


Abstract

In this work, we consider classification of agents who can both game and improve. For example, people wishing to get a loan may be able to take some actions that increase their perceived credit-worthiness and others that also increase their true credit-worthiness. A decision-maker would like to define a classification rule with few false-positives (does not give out many bad loans) while yielding many true positives (giving out many good loans), which includes encouraging agents to improve to become true positives if possible. We consider two models for this problem, a general discrete model and a linear model, and prove algorithmic, learning, and hardness results for each.
For the general discrete model, we give an efficient algorithm for the problem of maximizing the number of true positives subject to no false positives, and show how to extend this to a partial-information learning setting. We also show hardness for the problem of maximizing the number of true positives subject to a nonzero bound on the number of false positives, and that this hardness holds even for a finite-point version of our linear model. We also show that maximizing the number of true positives subject to no false positive is NP-hard in our full linear model. We additionally provide an algorithm that determines whether there exists a linear classifier that classifies all agents accurately and causes all improvable agents to become qualified, and give additional results for low-dimensional data.

BibTeX - Entry

@InProceedings{ahmadi_et_al:LIPIcs.FORC.2022.3,
  author =	{Ahmadi, Saba and Beyhaghi, Hedyeh and Blum, Avrim and Naggita, Keziah},
  title =	{{On Classification of Strategic Agents Who Can Both Game and Improve}},
  booktitle =	{3rd Symposium on Foundations of Responsible Computing (FORC 2022)},
  pages =	{3:1--3:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-226-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{218},
  editor =	{Celis, L. Elisa},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/16526},
  URN =		{urn:nbn:de:0030-drops-165269},
  doi =		{10.4230/LIPIcs.FORC.2022.3},
  annote =	{Keywords: Strategic Classification, Social Welfare, Learning}
}

Keywords: Strategic Classification, Social Welfare, Learning
Collection: 3rd Symposium on Foundations of Responsible Computing (FORC 2022)
Issue Date: 2022
Date of publication: 15.07.2022


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