# Strategic Ranking

arXiv: https://arxiv.org/abs/2109.08240 · HTML: https://mlsanity.com/paper/2109.08240/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 55 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 26 of 26)

- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [The Social Cost of Strategic Classification](https://mlsanity.com/paper/1808.08460/) (2018)
- [Strategic Classification is Causal Modeling in Disguise](https://mlsanity.com/paper/1910.10362/) (2019)
- [How Do Classifiers Induce Agents To Invest Effort Strategically?](https://mlsanity.com/paper/1807.05307/) (2018)
- [The Disparate Effects of Strategic Manipulation](https://mlsanity.com/paper/1808.08646/) (2018)
- [Causal Strategic Linear Regression](https://mlsanity.com/paper/2002.10066/) (2020)
- [Maximizing Welfare with Incentive-Aware Evaluation Mechanisms](https://mlsanity.com/paper/2011.01956/) (2020)
- [A Short-term Intervention for Long-term Fairness in the Labor Market](https://mlsanity.com/paper/1712.00064/) (2017)
- [The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally](https://mlsanity.com/paper/1910.04123/) (2019)
- [The Role of Randomness and Noise in Strategic Classification](https://mlsanity.com/paper/2005.08377/) (2020)
- [From Fair Decision Making to Social Equality](https://mlsanity.com/paper/1812.02952/) (2018)
- [Downstream Effects of Affirmative Action](https://mlsanity.com/paper/1808.09004/) (2018)
- [Active Fairness in Algorithmic Decision Making](https://mlsanity.com/paper/1810.00031/) (2018)
- [FairSearch: A Tool For Fairness in Ranked Search Results](https://mlsanity.com/paper/1905.13134/) (2019)
- [Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning](https://mlsanity.com/paper/2003.06740/) (2020)
- [Fair Allocation through Selective Information Acquisition](https://mlsanity.com/paper/1911.02715/) (2019)
- [Mechanism Design for Social Good](https://mlsanity.com/paper/1810.09832/) (2018)
- [Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps](https://mlsanity.com/paper/2107.08922/) (2021)
- [Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions](https://mlsanity.com/paper/1905.09947/) (2019)
- [Developments in Multi-Agent Fair Allocation](https://mlsanity.com/paper/1911.09852/) (2019)
- [Access to Population-Level Signaling as a Source of Inequality](https://mlsanity.com/paper/1809.04224/) (2018)
- [Best vs. All: Equity and Accuracy of Standardized Test Score Reporting](https://mlsanity.com/paper/2102.07809/) (2021)

## Built on by (most cited first, 21 of 21)

- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [Generalized Strategic Classification and the Case of Aligned Incentives](https://mlsanity.com/paper/2202.04357/) (2022)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models](https://mlsanity.com/paper/2308.04399/) (2023)
- [Strategic Classification with Graph Neural Networks](https://mlsanity.com/paper/2205.15765/) (2022)
- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [Performative Recommendation: Diversifying Content via Strategic Incentives](https://mlsanity.com/paper/2302.04336/) (2023)
- [Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments](https://mlsanity.com/paper/2208.11040/) (2022)
- [Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions](https://mlsanity.com/paper/2405.08027/) (2024)
- [Learning under Imitative Strategic Behavior with Unforeseeable Outcomes](https://mlsanity.com/paper/2405.01797/) (2024)
- [Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems](https://mlsanity.com/paper/2305.17428/) (2023)
- [Algorithmic Decision-Making under Agents with Persistent Improvement](https://mlsanity.com/paper/2405.01807/) (2024)
- [Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets](https://mlsanity.com/paper/2404.13240/) (2024)
- [Learning Classifiers That Induce Markets](https://mlsanity.com/paper/2502.20012/) (2025)
- [The Backfiring Effect of Weak AI Safety Regulation](https://mlsanity.com/paper/2503.20848/) (2025)
- [Leaderboard Incentives: Model Rankings under Strategic Post-Training](https://mlsanity.com/paper/2603.08371/) (2026)
- [Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap](https://mlsanity.com/paper/2502.11981/) (2025)
- [Power and Limitations of Aggregation in Compound AI Systems](https://mlsanity.com/paper/2602.21556/) (2026)
- [Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift](https://mlsanity.com/paper/2606.25152/) (2026)
- [Strategic Evaluation: Subjects, Evaluators, and Society](https://mlsanity.com/paper/2310.03655/) (2023)

## Abstract (arXiv)

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.