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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. 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.
  2. 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.
  3. 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
  4. 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.
  5. 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).
  6. 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.
  7. 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