Strategic Ranking
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation
LLM-based multi-agent systems are increasingly used for strategic decision-making tasks. In such settings, performance depends not only on individual model capabilities, but also on the policies by which agents interact …
Multi-agent Reinforcement LearningConvergence of Learning Dynamics in Information Retrieval Games
We consider a game-theoretic model of information retrieval with strategic authors. We examine two different utility schemes: authors who aim at maximizing exposure and authors who want to maximize active selection of th…
Information RetrievalRetrievalLLMsPark: A Benchmark for Evaluating Large Language Models in Strategic Gaming Contexts
As large language models (LLMs) advance across diverse tasks, the need for comprehensive evaluation beyond single metrics becomes increasingly important. To fully assess LLM intelligence, it is crucial to examine their i…
The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents
We study a game-theoretic information retrieval model in which strategic publishers aim to maximize their chances of being ranked first by the search engine while maintaining the integrity of their original documents. We…
Information RetrievalRetrievalPerformative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems
Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that could capture user interest. Despite the…
FairnessRecommendation SystemsRe-Ranking