Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computer Science and Game Theory

arXiv:2109.08240 (cs)
[Submitted on 16 Sep 2021 (v1), last revised 21 Feb 2022 (this version, v2)]

Title:Strategic Ranking

Authors:Lydia T. Liu, Nikhil Garg, Christian Borgs
View PDF
Abstract:Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals. However, the existing literature does not consider the effect of competition among individuals as induced by the algorithm design. Motivated by constrained allocation settings such as college admissions, we introduce strategic ranking, in which the (designed) individual reward depends on an applicant's post-effort rank in a measurement of interest. Our results illustrate how competition among applicants affects the resulting equilibria and model insights. We analyze how various ranking reward designs, belonging to a family of step functions, trade off applicant, school, and societal utility, as well as how ranking design counters inequities arising from disparate access to resources. In particular, we find that randomization in the reward design can mitigate two measures of disparate impact, welfare gap and access.
Comments: 30 pages. To appear in the conference proceedings of AISTATS 2022
Subjects: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
Cite as: arXiv:2109.08240 [cs.GT]
  (or arXiv:2109.08240v2 [cs.GT] for this version)
  https://doi.org/10.48550/arXiv.2109.08240

Submission history

From: Lydia T. Liu [view email]
[v1] Thu, 16 Sep 2021 22:04:24 UTC (1,443 KB)
[v2] Mon, 21 Feb 2022 20:30:06 UTC (1,820 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.GT
< prev   |   next >
new | recent | 2021-09
Change to browse by:
cs
cs.LG

References & Citations

DBLP - CS Bibliography

listing | bibtex
Lydia T. Liu
Nikhil Garg
Christian Borgs
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)