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Contact

jrwright@microsoft.com

"That's your game theory? Rock Paper Scissors with statistics?"
— Peter Watts, Blindsight

James R. Wright

Hello, I'm James Wright. I am a postdoctoral researcher at Microsoft Research in New York City. I completed my Ph.D. at UBC in 2016, advised by Kevin Leyton-Brown.

Research

I am interested in problems at the intersection of behavioral game theory and computer science, with a focus on applying both machine learning techniques and models derived from experimental and behavioral economics to the prediction of human behavior in strategic settings. I am also interested in the implications of behavioral game theoretic models on multiagent systems and mechanisms.

Curriculum Vitae

My academic CV is available as both an HTML page and a PDF document. I also have a public Google Scholar citations page.

Publications

  1. Learning in the Repeated Secretary Problem.
    Daniel G. Goldstein, R. Preston McAfee, Siddarth Suri, and James R. Wright.
    ACM Conference on Economics and Computation (ACM-EC), 2017.
  2. Deep Learning for Human Strategic Modeling.
    Jason Hartford, James R. Wright, and Kevin Leyton-Brown.
    NIPS 2016: Thirtieth Annual Conference on Neural Information Processing Systems, 2016.
    Oral presentation.
  3. Incentivizing Evaluation via Limited Access to Ground Truth: Peer-Prediction Makes Things Worse.
    Xi Alice Gao, James R. Wright, and Kevin Leyton-Brown.
    Workshop on Algorithmic Game Theory and Data Science at ACM Conference on Economics and Computation, 2016.
  4. Mechanical TA: Partially Automated High-Stakes Peer Grading.
    James R. Wright, Chris Thornton, Kevin Leyton-Brown.
    ACM Technical Symposium on Computer Science Education (ACM-SIGCSE), 2015
  5. Level-0 Meta-Models for Predicting Human Behavior in Games.
    James R. Wright and Kevin Leyton-Brown.
    ACM Conference on Economics and Computation (ACM-EC), 2014.
  6. Behavioral Game-Theoretic Models: A Bayesian Framework For Parameter Analysis.
    James R. Wright and Kevin Leyton-Brown.
    Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012), pages 921–928, 2012.
    Best student paper (runner up).
  7. Linear solvers for nonlinear games: using pivoting algorithms to find Nash equilibria in n-player games.
    James R. Wright, Albert Xin Jiang, and Kevin Leyton-Brown.
    SIGecom Exchanges, volume 10, number 1, pages 9–12, 2011.
  8. Beyond Equilibrium: Predicting Human Behavior in Normal Form Games.
    James R. Wright and Kevin Leyton-Brown.
    Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), pages 901–907, 2010.

Working Papers

  1. Models of Level-0 Behavior for Predicting Human Behavior in Games.
    James R. Wright and Kevin Leyton-Brown.
    Under review at Journal of Artificial Intelligence Research.
    (supersedes Wright & Leyton-Brown [2014])
  2. Incentivizing Evaluation via Limited Access to Ground Truth:
    Peer-Prediction Makes Things Worse.

    Xi Alice Gao, James R. Wright, and Kevin Leyton-Brown.
    Under review by Artificial Intelligence Journal.
    (supersedes Gao, Wright, and Leyton-Brown [2016])
  3. Predicting Human Behavior in Unrepeated, Simultaneous-Move Games.
    James R. Wright and Kevin Leyton-Brown.
    Requested revisions under review by Games and Economic Behavior.
    (supersedes Wright & Leyton-Brown [2010, 2012])

Last update: Jun 16/2017