4 citations · 4 across the 10 of their papers we have counts for
12 papers
Engineering Simplicity: Simple Mechanism Interfaces Steer LLM Agents
Kehang Zhu, Anand Shah, David Parkes
Can interaction formats and textual scaffolds help large language model (LLM) agents make better decisions, and do better decisions come with better explanations? We study these qu…
Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach
Nguyen Thach, Hau Chan, David Parkes +1
Crowdsourcing platforms coordinate large pools of online workers who strategically choose which contests to enter and how much effort to invest. This self-selection can leave impor…
Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learning
Yanchen Jiang, David C. Parkes, Tonghan Wang
Characterizing revenue-optimal auctions for multi-item, multi-bidder settings remains a fundamental open problem, with no known closed-form solution existing beyond restrictive bin…
LLM Active Alignment: A Nash Equilibrium Perspective
Tonghan Wang, Yuqi Pan, Xinyi Yang +3
We develop a game-theoretic framework for predicting and steering the behavior of populations of large language models (LLMs) through Nash equilibrium (NE) analysis. To avoid the i…
LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders
Anand Shah, Kehang Zhu, Yanchen Jiang +4
Training on vast amounts of human-generated data has motivated growing interest in using large language models (LLMs) to simulate human behavior. We ask which features of human beh…
BundleFlow: Deep Menus for Combinatorial Auctions by Diffusion-Based Optimization
Tonghan Wang, Yanchen Jiang, David C. Parkes
Differentiable economics -- the use of deep learning for auction design -- has driven progress in the automated design of multi-item auctions with additive or unit-demand valuation…
LLM-Powered Preference Elicitation in Combinatorial Assignment
Ermis Soumalias, Yanchen Jiang, Kehang Zhu +3
We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in combinatorial assignment. While traditional PE methods rely…
On Diffusion Models for Multi-Agent Partial Observability: Shared Attractors, Error Bounds, and Composite Flow
Tonghan Wang, Heng Dong, Yanchen Jiang +2
Multiagent systems grapple with partial observability (PO), and the decentralized POMDP (Dec-POMDP) model highlights the fundamental nature of this challenge. Whereas recent approa…
Deep Reinforcement Learning for Sequential Combinatorial Auctions
Sai Srivatsa Ravindranath, Zhe Feng, Di Wang +3
Revenue-optimal auction design is a challenging problem with significant theoretical and practical implications. Sequential auction mechanisms, known for their simplicity and stron…
Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts
Dima Ivanov, Paul Dütting, Inbal Talgam-Cohen +2
The increasing deployment of AI is shaping the future landscape of the internet, which is set to become an integrated ecosystem of AI agents. Orchestrating the interaction among AI…
GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning
Tonghan Wang, Yanchen Jiang, David C. Parkes
Automated mechanism design (AMD) uses computational methods for mechanism design. Differentiable economics is a form of AMD that uses deep learning to learn mechanism designs and h…
Data Market Design through Deep Learning
Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes
The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller,…