9 papers
Artificial intelligences and human scientists exhibit complementary strengths in theory building
Ke Li, Spyros I. Zoumpoulis, Phanish Puranam +80
We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social sci…
LLMAdBench: A Human Preference Benchmark for Advertising in LLM Responses
Rui Ai, Yuqing Liu, Sitao Qiu +14
Inserting advertisements (ads) into consumer-facing LLM output is emerging as a new business model, but there is little shared evidence on how such ad insertion should be evaluated…
Minimax-Optimal Online Contract Design with Unrestricted Bounded Contracts
Rui Ai, David Simchi-Levi, Han Zhong
We study repeated contract design when a principal observes outcomes but not the actions that generate them. The principal may use any bounded outcome-contingent payment vector, an…
ShapE-GRPO: Shapley-Enhanced Reward Allocation for Multi-Candidate LLM Training
Rui Ai, Yu Pan, David Simchi-Levi +1
In user-agent interaction scenarios such as recommendation, brainstorming, and code suggestion, Large Language Models (LLMs) often generate sets of candidate recommendations where…
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
Rui Ai, David Simchi-Levi, Feng Zhu
We study the pricing behavior of third-party platforms facing strategic agents. Assuming the platform is a revenue maximizer, it observes market features that generally affect dema…
GenAI vs. Human Creators: Procurement Mechanism Design in Two-/Three-Layer Markets
Rui Ai, David Simchi-Levi, Haifeng Xu
With the rapid advancement of generative AI (GenAI), mechanism design adapted to its unique characteristics poses new theoretical and practical challenges. Unlike traditional goods…
Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
Rui Ai, Yuqi Pan, David Simchi-Levi +2
With the rapid progress of multi-agent large language model (LLM) reasoning, how to effectively aggregate answers from multiple LLMs has emerged as a fundamental challenge. Standar…
Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving
Rui Ai, Hugo De Oliveira Barbalho, Sirui Li +3
A common challenge in real-time operations is deciding whether to re-solve an optimization problem or continue using an existing solution. While modern data platforms may collect i…
Contextual Online Decision Making with Infinite-Dimensional Functional Regression
Haichen Hu, Rui Ai, Stephen Bates +1
Contextual sequential decision-making problems play a crucial role in machine learning, encompassing a wide range of downstream applications such as bandits, sequential hypothesis…