# Non-linear Welfare-Aware Strategic Learning arXiv: https://arxiv.org/abs/2405.01810 ยท HTML: https://mlsanity.com/paper/2405.01810/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 47 references that are in the map. - First posted on arXiv 3 May 2024. ## Builds on (most cited first, 30 of 32) - [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021) - [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020) - [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015) - [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018) - [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019) - [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017) - [The Social Cost of Strategic Classification](https://mlsanity.com/paper/1808.08460/) (2018) - [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017) - [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) - [How to Learn when Data Reacts to Your Model: Performative Gradient Descent](https://mlsanity.com/paper/2102.07698/) (2021) - [Causal Strategic Linear Regression](https://mlsanity.com/paper/2002.10066/) (2020) - [How Do Fair Decisions Fare in Long-term Qualification?](https://mlsanity.com/paper/2010.11300/) (2020) - [Strategic Classification Made Practical](https://mlsanity.com/paper/2103.01826/) (2021) - [Performative Prediction: Past and Future](https://mlsanity.com/paper/2310.16608/) (2023) - [PAC-Learning for Strategic Classification](https://mlsanity.com/paper/2012.03310/) (2020) - [On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning](https://mlsanity.com/paper/1903.01209/) (2019) - [The Strategic Perceptron](https://mlsanity.com/paper/2008.01710/) (2020) - [Information Discrepancy in Strategic Learning](https://mlsanity.com/paper/2103.01028/) (2021) - [Alternative Microfoundations for Strategic Classification](https://mlsanity.com/paper/2106.12705/) (2021) - [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) - [Generalized Strategic Classification and the Case of Aligned Incentives](https://mlsanity.com/paper/2202.04357/) (2022) - [Stateful Strategic Regression](https://mlsanity.com/paper/2106.03827/) (2021) - [Causal Strategic Classification: A Tale of Two Shifts](https://mlsanity.com/paper/2302.06280/) (2023) - [On classification of strategic agents who can both game and improve](https://mlsanity.com/paper/2203.00124/) (2022) - [Strategic Classification under Unknown Personalized Manipulation](https://mlsanity.com/paper/2305.16501/) (2023) - [From Predictions to Decisions: Using Lookahead Regularization](https://mlsanity.com/paper/2006.11638/) (2020) - [Unintended Selection: Persistent Qualification Rate Disparities and Interventions](https://mlsanity.com/paper/2111.01201/) (2021) - [Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning](https://mlsanity.com/paper/2003.06740/) (2020) ## Built on by (most cited first, 12 of 12) - [Addressing Polarization and Unfairness in Performative Prediction](https://mlsanity.com/paper/2406.16756/) (2024) - [Strategic Classification With Externalities](https://mlsanity.com/paper/2410.08032/) (2024) - [How Strategic Agents Respond: Comparing Analytical Models with LLM-Generated Responses in Strategic Classification](https://mlsanity.com/paper/2501.16355/) (2025) - [Strategic Classification with Non-Linear Classifiers](https://mlsanity.com/paper/2505.23443/) (2025) - [ProFL: Performative Robust Optimal Federated Learning](https://mlsanity.com/paper/2410.18075/) (2024) - [Lookahead Counterfactual Fairness](https://mlsanity.com/paper/2412.01065/) (2024) - [Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responses](https://mlsanity.com/paper/2502.04058/) (2025) - [Beyond Rational Illusion: Behaviorally Realistic Strategic Classification](https://mlsanity.com/paper/2605.19674/) (2026) - [Individual Fairness In Strategic Classification](https://mlsanity.com/paper/2602.05084/) (2026) - [Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry](https://mlsanity.com/paper/2602.17787/) (2026) - [Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents](https://mlsanity.com/paper/2606.00826/) (2026) - [Off-Policy Evaluation with Strategic Agents via Local Disclosure](https://mlsanity.com/paper/2606.07308/) (2026) ## Abstract (arXiv) This paper studies algorithmic decision-making in the presence of strategic individual behaviors, where an ML model is used to make decisions about human agents and the latter can adapt their behavior strategically to improve their future data. Existing results on strategic learning have largely focused on the linear setting where agents with linear labeling functions best respond to a (noisy) linear decision policy. Instead, this work focuses on general non-linear settings where agents respond to the decision policy with only "local information" of the policy. Moreover, we simultaneously consider the objectives of maximizing decision-maker welfare (model prediction accuracy), social welfare (agent improvement caused by strategic behaviors), and agent welfare (the extent that ML underestimates the agents). We first generalize the agent best response model in previous works to the non-linear setting, then reveal the compatibility of welfare objectives. We show the three welfare can attain the optimum simultaneously only under restrictive conditions which are challenging to achieve in non-linear settings. The theoretical results imply that existing works solely maximizing the welfare of a subset of parties inevitably diminish the welfare of the others. We thus claim the necessity of balancing the welfare of each party in non-linear settings and propose an irreducible optimization algorithm suitable for general strategic learning. Experiments on synthetic and real data validate the proposed algorithm.