Collective dynamics of strategic classification

• URL: http://arxiv.org/abs/2508.09340v1
• Date: Tue, 12 Aug 2025 20:57:17 GMT
• Title: Collective dynamics of strategic classification
• Authors: Marta C. Couto, Flavia Barsotti, Fernando P. Santos,
• Abstract summary: We apply evolutionary game theory to the problem of feedback loops between collectives of users and institutions.<n>We show that increased detection capabilities reduce social costs and could lead to users' improvement.<n>The speed at which the institutions re-adapt to the user's population plays a role in the final outcome.
• Score: 49.14942990735098
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classification algorithms based on Artificial Intelligence (AI) are nowadays applied in high-stakes decisions in finance, healthcare, criminal justice, or education. Individuals can strategically adapt to the information gathered about classifiers, which in turn may require algorithms to be re-trained. Which collective dynamics will result from users' adaptation and algorithms' retraining? We apply evolutionary game theory to address this question. Our framework provides a mathematically rigorous way of treating the problem of feedback loops between collectives of users and institutions, allowing to test interventions to mitigate the adverse effects of strategic adaptation. As a case study, we consider institutions deploying algorithms for credit lending. We consider several scenarios, each representing different interaction paradigms. When algorithms are not robust against strategic manipulation, we are able to capture previous challenges discussed in the strategic classification literature, whereby users either pay excessive costs to meet the institutions' expectations (leading to high social costs) or game the algorithm (e.g., provide fake information). From this baseline setting, we test the role of improving gaming detection and providing algorithmic recourse. We show that increased detection capabilities reduce social costs and could lead to users' improvement; when perfect classifiers are not feasible (likely to occur in practice), algorithmic recourse can steer the dynamics towards high users' improvement rates. The speed at which the institutions re-adapt to the user's population plays a role in the final outcome. Finally, we explore a scenario where strict institutions provide actionable recourse to their unsuccessful users and observe cycling dynamics so far unnoticed in the literature.

Related papers

• When AI Democratizes Exploitation: LLM-Assisted Strategic Manipulation of Fair Division Algorithms [1.0365487946214889]
  This paper demonstrates how Large Language Models (LLMs) can dismantle protective barriers by democratizing access to strategic expertise.<n>We show that users can obtain actionable manipulation strategies via simple conversational queries to AI assistants.<n>Our experiments reveal that LLMs can explain algorithmic mechanics, identify profitable deviations, and generate specific numerical inputs for coordinated preference misreporting--capabilities previously requiring deep technical knowledge.
  arXiv  Detail & Related papers  (2025-11-18T18:09:02Z)
• Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
  We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
  arXiv  Detail & Related papers  (2025-10-07T17:49:24Z)
• Anticipating Gaming to Incentivize Improvement: Guiding Agents in (Fair) Strategic Classification [6.660458629649826]
  We explore individuals' choice between genuinely improving their qualifications ('improvement'') vs. attempting to deceive the algorithm.<n>We formulate these interactions as a Stackelberg game, where a firm deploys a (fair) classifier, and individuals strategically respond.
  arXiv  Detail & Related papers  (2025-05-08T18:47:23Z)
• Learnability Gaps of Strategic Classification [68.726857356532]
  We focus on addressing a fundamental question: the learnability gaps between strategic classification and standard learning. We provide nearly tight sample complexity and regret bounds, offering significant improvements over prior results. Notably, our algorithm in this setting is of independent interest and can be applied to other problems such as multi-label learning.
  arXiv  Detail & Related papers  (2024-02-29T16:09:19Z)
• Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control [44.77500987121531]
  Hierarchical Imitation Learning (HIL) has been proposed to recover highly-complex behaviors in long-horizon tasks from expert demonstrations. We develop a novel HIL algorithm based on Adversarial Inverse Reinforcement Learning. We also propose a Variational Autoencoder framework for learning with our objectives in an end-to-end fashion.
  arXiv  Detail & Related papers  (2022-10-05T00:28:26Z)
• Learning in Stackelberg Games with Non-myopic Agents [60.927889817803745]
  We study Stackelberg games where a principal repeatedly interacts with a non-myopic long-lived agent, without knowing the agent's payoff function.<n>We provide a general framework that reduces learning in presence of non-myopic agents to robust bandit optimization in the presence of myopic agents.
  arXiv  Detail & Related papers  (2022-08-19T15:49:30Z)
• Robust Reinforcement Learning via Genetic Curriculum [5.421464476555662]
  Genetic curriculum is an algorithm that automatically identifies scenarios in which the agent currently fails and generates an associated curriculum. Our empirical studies show improvement in robustness over the existing state of the art algorithms, providing training curricula that result in agents being 2 - 8x times less likely to fail.
  arXiv  Detail & Related papers  (2022-02-17T01:14:20Z)
• MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning [65.52675802289775]
  We show that an uncertainty aware classifier can solve challenging reinforcement learning problems. We propose a novel method for computing the normalized maximum likelihood (NML) distribution. We show that the resulting algorithm has a number of intriguing connections to both count-based exploration methods and prior algorithms for learning reward functions.
  arXiv  Detail & Related papers  (2021-07-15T08:19:57Z)
• A black-box adversarial attack for poisoning clustering [78.19784577498031]
  We propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. We show that our attacks are transferable even against supervised algorithms such as SVMs, random forests, and neural networks.
  arXiv  Detail & Related papers  (2020-09-09T18:19:31Z)
• Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey [1.8782750537161614]
  This paper studies strategies to implement adversary robustly trained algorithms towards guaranteeing safety in machine learning algorithms. We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting, and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses.
  arXiv  Detail & Related papers  (2020-07-01T21:00:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.