Mechanism Design

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Mechanism design asks how to set rules so that self-interested participants reveal useful information and act in ways that support goals such as efficiency, fairness, or cooperation. It applies tools from game theory and economics to auctions, matching, contracts, voting, and data markets, with incentive compatibility and individual rationality as common design constraints. Alongside theoretical analysis, researchers use simulations, online learning, and differentiable optimization to build mechanisms for changing markets and AI-agent systems. A central tension is that improving welfare or adapting to uncertainty can weaken truthfulness, fairness, or budget balance, especially when participants can manipulate reports or the rules must operate over time.

Latest papers

142 papers15 papers in the last four weeks, up 15% on the four weeks before. 0.1% of all new papers.

Strategyproof Deep Reinforcement Learning for Online Resource Allocation
Oct 27, 2026Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah +1Online Resource Allocation Mechanism Design
University of Southampton
DiffAuction: An Auction-Driven Framework for Generative Image Advertising
Oct 26, 2026Yijie Su, Ruiyang Si, Tonghan WangImage Generation Controllable Image Generation
Tongji University · Tsinghua University · Shanghai Qi Zhi Institute
Truthful by Omission: Click Thinning for LLM-Native Advertising
Oct 22, 2026Junhua Liu, Zhicheng Liang, Zejun Zhang +2Online Advertising Mechanism Design
University of Southern California, Los Angeles, USA · The Chinese University of Hong Kong, Shenzhen, Shenzhen, China · University of Illinois Chicago, Chicago, USA
Can It Actually Work? Synthesizing Functional CAD Mechanisms
Oct 19, 2026Wenqian Zhang, Yangyi Huang, Zigeng Xu +2Mechanism Design LLM Agent Harnesses
The Chinese University of Hong Kong (Shenzhen) · The Chinese University of Hong Kong
Extending Myerson's Optimal Auctions to Correlated Bidders via Neural Network Interpolation
Oct 16, 2026Mingyu Guo, Jiayuan Liu, Vincent ConitzerMechanism Design Game Theory
Adelaide University, Adelaide, Australia · Carnegie Mellon University, Pittsburgh, USA
Contracting with an Agent under Model Mismatch
Oct 16, 2026Elif Yılmaz, Jakub Tłuczek, Victor Villin +1Principal-Agent Theory Model Misspecification
University of Neuchâtel Neuchâtel, Switzerland
Dynamic Auction Mechanisms in a Closed-Credit Economy: Sequential Slot Reallocation in Air Traffic Management
Oct 15, 2026Jihun Kim, Pan-Yang Su, Maria G. Mendoza +3Mechanism Design Air Traffic Management
University of California, Berkeley · University of Pennsylvania · Johns Hopkins University
Price of Anarchy of First-Price Auctions in Agentic Markets
Oct 14, 2026Shubhendu Trivedi, Jacob Eisenstein, Alekh Agarwal +3Price of Anarchy Mechanism Design
Google DeepMind · Google Research
Enabling Tidying Tasks in Robotic Vacuum Cleaners via a Geometry-Induced Self-Locking Deployable Linkage
Oct 14, 2026Eunseok Song, Jinwoong Kim, Sohee Lim +5Mechanism Design Robotics
Samsung Research, Samsung Electronics, 56, Seongchon-gil, Seocho-gu, Seoul, South Korea.
Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows
Oct 13, 2026Jiayuan Liu, Mingyu Guo, Jiarui Gan +1LLM Agent Routing Mechanism Design
Carnegie Mellon University · Foundations of Cooperative AI Lab (FOCAL) · Adelaide University +1
Falsification-proof non-market allocation mechanisms
Oct 13, 2026Eduardo Perez-Richet, Vasiliki SkretaMechanism Design
Sciences Po, CEPR · UT Austin, UCL, CEPR
Strategic Multi-Agent Resource Allocation via Statistical Contracts
Oct 13, 2026Ben Chugg, Michael I. Jordan, Aaditya RamdasMechanism Design Statistical Hypothesis Testing
Carnegie Mellon University · UC Berkeley, ÉNS, & Inria · Stanford University
Diffusion-Based Auctions for Multi-Winner Banner Ads
Oct 10, 2026Lilian Sun, Henry Huang, Fucheng Warren Zhu +2Online Advertising Mechanism Design
Harvard · MIT
Network Intervention by Polling Strategic Agents
Oct 6, 2026Chenyu Zhang, Rohit Parasnis, Saurabh AminMechanism Design Game Theory
MIT · IIT Bombay
Lego-Like Stiffness Configuration of Planar Compliant Modules for Task-Specific Flexible Interfaces
Oct 5, 2026Siyue Yao, Xiaochi Xie, Shixuan Zhao +4Mechanism Design Modular Robotics
State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China · Department of Mechanical Engineering, Stanford University, USA · Shanghai Key Laboratory of Intelligent Robotics; META Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China +1
ShowerFlex: Achieving Pseudo-Static Balancing in a Continuum Shower Hose
Oct 1, 2026Zhiyu Ren, Girish KrishnanMechanism Design Assistive Robotics
Department of Industrial and Systems Engineering University of Illinois, Urbana-Champaign Urbana, Illinois 61801
Inference Auctions
Sep 30, 2026Keegan Harris, Siddharth Prasad, Asher Trockman +2Mechanism Design Automated Bidding
University of California, Berkeley · Toyota Technological Institute at Chicago · Google Research +1
Modeling and Generative-AI-Based Design of Load-Adaptive Gravity Balancing Mechanisms
Sep 25, 2026Ryotaro Kayawake, Kazuki Abe, Shota Miyake +2Mechanism Design
Embodied Intelligence and Robotics Laboratory Graduate School of Information Sciences Tohoku University 6-6-01, Aramaki, Aoba-ku, Sendai, Miyagi 9808579, Japan · Robot Mechanisms Laboratory Graduate School of Engineering Science The University of Osaka 1-3, Machikaneyama, Toyonaka, Osaka 5608531, Japan
Design and Characterization of a Variable-Length Continuum Mechanism with Force Locking
Sep 25, 2026Katelyn King, Veronica Fish, Allison M. OkamuraMechanism Design Robotics
Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
Multi-Dimensional Matching
Sep 24, 2026Irene AldridgeMechanism Design Fair Resource Allocation
SHAFT: A Slack-Compensating, Helical-Buckling-Attenuating Flexible-Shaft Transmission for Lightweight Multi-DoF Manipulation
Sep 19, 2026Tomoya Takahashi, Moses Gladson Selvamuthu, Richiro Tadakuma +1Mechanism Design Robotics
OMRON SINIC X Corporation, Hongo 5-24-5, Bunkyo-ku, Tokyo, Japan · Yamagata University, 4 Chome-3-16 Jonan, Yonezawa, Japan · OMRON SINIC X
AeRove: A Compact Bimodal Aerial-Terrestrial Drone with Rapid Bistable Reconfiguration for Close-Range Pipeline Inspection
Sep 17, 2026Caleb Polillio, Petras SwisslerAerial Robotics Robotic Inspection
Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ 07102
Learning Principal-Agent Contracts for Equitable Smallholder Carbon Farming under Moral Hazard and Adverse Selection
Sep 17, 2026Rishi Bharadwaj, Yadati NarahariMechanism Design
Department of Computer Science and Automation, Indian Institute of Science (IISc)
Minimax-Optimal Online Contract Design with Unrestricted Bounded Contracts
Sep 17, 2026Rui Ai, David Simchi-Levi, Han ZhongMechanism Design Minimax Regret
Massachusetts Institute of Technology · Shanghai Jiao Tong University
Geometric Shortcuts for Complex Trunk Postures: Dual-Helicity Coupling Enables Low-Dimensional Control
Sep 16, 2026Huishi Huang, Danlu Chen, Matteo Lo Preti +3Mechanism Design Continuum Robot Control
Advanced Robotics Centre (ARC) and with the Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore (NUS), 117608, Singapore. · Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 138632, Singapore.
Flexible-body Modeling, Kinematic Identification, and Assembly Accuracy of Overconstrained Spatial Linkages
Sep 15, 2026Daniel Huczala, Michael Pieber, Johannes Gerstmayr +6Mechanism Design Multibody Dynamics
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, 44919, South Korea · Department of Machine Parts and Mechanisms, VSB – Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, 70800, Czech Republic · Department of Mechatronics, University of Innsbruck, Technikerstraße 13, Innsbruck, 6020, Austria +2
A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraints
Sep 14, 2026Konstantinos Varsos, Ramin Khalili, Adamantia Stamou +2Mechanism Design Energy-Efficient ML
Athens University of Economics and Business, Greece · Independent researcher, Munich Germany
Deriving the Pure Price of Anarchy for Networked Resource Allocation Games
Sep 10, 2026Vartika Singh, Philip N. BrownMechanism Design Game Theory
University of Colorado Colorado Springs, Colorado Springs, CO, 80918 USA · Politecnico di Torino, Italy
Lie-Algebraic Bell Recurrences for Arbitrary-Order Twist Jets and Parallel-Mechanism Closure
Sep 9, 2026Daniel ConduracheMechanism Design Robot Kinematics
Gheorghe Asachi Technical University of Iaşi, Iaşi, Romania
Mechanism Design for Alignment and Control
Sep 1, 2026Dirk Bergemann, Andrew Koh, Stephen MorrisMechanism Design Principal-Agent Theory
Yale · Columbia · MIT +1

Oct 27, 2026J/K move · Enter open · S save

Strategyproof Deep Reinforcement Learning for Online Resource Allocation

Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah, Stephanie Gauthier

University of Southampton

Online Resource Allocation Mechanism Design Game-Theoretic RL Game Theory

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Online resource allocation faces two coupled challenges: (1) allocating scarce resources efficiently under uncertain future demand, and (2) ensuring that agents report task valuations and requirements truthfully (strategyproofness). Reinforcement Learning (RL) adapts allocation decisions through experience but does not necessarily ensure strategyproofness. Existing strategyproof RL uses a linear allocation rule, which limits the resource interactions it can represent. Neural mechanism design methods offer greater expressiveness, but provide only approximate incentive guarantees. Against this background, we introduce the first strategyproof deep RL mechanism for online resource allocation. It integrates three novel components: (i) a monotonic neural network that learns nonlinear interactions by construction (higher reported value cannot reduce acceptance, while a longer reported duration or larger resource requirement cannot increase it); (ii) a generalised critical payment: the lowest accepted value, with all other inputs fixed; and (iii) training by either RL or imitation of an existing rule. Together, monotonicity and the critical payment ensure strategyproofness and individual rationality, while the neural rule provides flexibility and imitation improves training efficiency. Empirically, on a four-resource stream with two partner pairs that a linear difference cannot represent, the neural rule achieves 97.8% of an offline welfare upper bound, compared with 93.8% for a linear learner and 89.0% for first-come, first-served, closing 65% and 80% of the remaining optimality gap, respectively. Moreover, imitation reaches 97.5% in only 261 seconds, about 89 times faster than RL.