World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
73
Citations
22632
World Ranking
1582
National Ranking
825

Research.com Recognitions

  • 2005 - ACM Fellow For contributions to market-based and decentralized computation
  • 2001 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to decision theory, qualitative probabilistic and utilitarian reasoning, planning, multiagent systems, computational economics, electronic commerce, and editing the Journal of Artificial Intelligence Research.

Overview

Michael P. Wellman is affiliated with the University of Michigan-Ann Arbor in the United States and has a focused research career primarily in the field of computer science. Their work concentrates heavily on artificial intelligence, management science and operations research, and economics and econometrics, with additional contributions in sociology and political science as well as safety research.

Their research topics cover a range of areas including:

  • Reinforcement Learning in Robotics
  • Artificial Intelligence in Games
  • Sports Analytics and Performance
  • Auction Theory and Applications
  • Experimental Behavioral Economics Studies
  • Stock Market Forecasting Methods
  • Advanced Bandit Algorithms Research

Michael P. Wellman has authored numerous papers, several of which have appeared in prestigious venues. Notable recent publications include:

  • "Generating Realistic Stock Market Order Streams" (2020) in the Proceedings of the AAAI Conference on Artificial Intelligence
  • "Spoofing the Limit Order Book: A Strategic Agent-Based Analysis" (2021) in Games
  • "Empirical Game Theoretic Analysis: A Survey" (2025) in the Journal of Artificial Intelligence Research
  • "Evolution Strategies for Approximate Solution of Bayesian Games" (2021) in the Proceedings of the AAAI Conference on Artificial Intelligence
  • "Multi-Agent Risks from Advanced AI" (2025) in arXiv (Cornell University)

The scientist frequently collaborates with other researchers, with prominent coauthors including Mithun Chakraborty, Yongzhao Wang, Max Smith, Xintong Wang, and Yevgeniy Vorobeychik. Their collaborative work has contributed to multiple papers across various subfields.

Many of their works are published in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Autonomous Agents and Multi-Agent Systems
  • Journal of Artificial Intelligence Research
  • Games

Michael P. Wellman has been recognized by their professional community with honors including:

  • ACM Fellow (2005) for contributions to market-based and decentralized computation
  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) (2001) for significant contributions to decision theory, qualitative probabilistic and utilitarian reasoning, planning, multiagent systems, computational economics, electronic commerce, and editing the Journal of Artificial Intelligence Research

Best Publications

  • Nash q-learning for general-sum stochastic games

    Junling Hu;Michael P. Wellman

  • Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm

    Junling Hu;Michael P. Wellman

  • A market-oriented programming environment and its application to distributed multicommodity flow problems

    Michael P. Wellman

  • Planning and Control

    Thomas L. Dean;Michael P. Wellman

  • The Michigan Internet AuctionBot: a configurable auction server for human and software agents

    Peter R. Wurman;Michael P. Wellman;William E. Walsh

  • Towards the Science of Security and Privacy in Machine Learning

    Nicolas Papernot;Patrick D. McDaniel;Arunesh Sinha;Michael P. Wellman

  • Auction Protocols for Decentralized Scheduling

    Michael P. Wellman;William E. Walsh;Peter R. Wurman;Jeffrey K. MacKie-Mason

  • Real-world applications of Bayesian networks

    David Heckerman;Abe Mamdani;Michael P. Wellman

  • Fundamental concepts of qualitative probabilistic networks

    M. P. Wellman

  • Flexible double auctions for electionic commerce: theory and implementation

    Peter R. Wurman;William E. Walsh;Michael P. Wellman

  • A Parametrization of the Auction Design Space

    Peter R. Wurman;Michael P. Wellman;William E. Walsh

  • Bayesian networks

    David Heckerman;Michael P. Wellman

  • SoK: Security and Privacy in Machine Learning

    Nicolas Papernot;Patrick McDaniel;Arunesh Sinha;Michael P. Wellman

  • The WALRAS Algorithm: A Convergent Distributed Implementation of General Equilibrium Outcomes

    John Q. Cheng;Michael P. Wellman

  • From knowledge bases to decision models

    Michael P. Wellman;John S. Breese;Robert P. Goldman

  • A market protocol for decentralized task allocation

    W.E. Walsh;M.P. Wellman

  • Preferential semantics for goals

    Michael P. Wellman;Jon Doyle

  • Economic reasoning and artificial intelligence

    David C. Parkes;Michael P. Wellman

  • The 2001 Trading Agent Competition

    Michael P. Wellman;Amy Greenwald;Peter Stone;Peter R. Wurman

  • Market-oriented programming: some early lessons

    Michael P. Wellman

  • Combinatorial auctions for supply chain formation

    William E. Walsh;Michael P. Wellman;Fredrik Ygge

Frequent Co-Authors

Yevgeniy Vorobeychik
Yevgeniy Vorobeychik Washington University in St. Louis
Satinder Singh
Satinder Singh DeepMind (United Kingdom)
Christopher Kiekintveld
Christopher Kiekintveld The University of Texas at El Paso
Peter Stone
Peter Stone The University of Texas at Austin
Edmund H. Durfee
Edmund H. Durfee University of Michigan–Ann Arbor
David V. Pynadath
David V. Pynadath University of Southern California
Yoav Shoham
Yoav Shoham Stanford University
David Heckerman
David Heckerman Microsoft (United States)
David M. Pennock
David M. Pennock Rutgers, The State University of New Jersey
James A. Hendler
James A. Hendler Rensselaer Polytechnic Institute

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