James Wright
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
James Wright is a Canada CIFAR AI Chair at Amii and an assistant professor at the University of Alberta’s Department of Computing Science
Wright’s primary interest is in using data-driven machine learning models to predict human strategic behaviour – behaviour in interactions where each participant’s rewards depend partially on the actions of other participants. He conducts his research at the intersection of economics, machine learning, and behavioural modelling. His published research focuses on formalizing the boundary between strategic and nonstrategic reasoning.
Wright’s long-term research seeks to build a general theory for optimally designing algorithms for mediating interactions involving humans or other realistically bounded agents rather than idealized rational game theoretic agents.
Awards
- NSERC Alexander Graham Bell Canada Graduate Scholarship, 2012
- ACM SIGecom Doctoral Dissertation Award, Honourable Mention, 2017
Relevant Publications
Morrill, D., D’Orazio, R., Lanctot, M., Wright, J. R., Bowling, M., & Greenwald, A. (2021). Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games.
Albach, M., & Wright, J. R. (2021). The Role of Accuracy in Algorithmic Process Fairness Across Multiple Domains. In Proceedings of the 22nd ACM Conference on Economics and Computation (pp. 29-49).
Morrill, D., D’Orazio, R., Sarfati, R., Lanctot, M., Wright, J. R., Greenwald, A., & Bowling, M. (2020). Hindsight and Sequential Rationality of Correlated Play.
Gao, X. A., Wright, J. R., & Leyton-Brown, K. (2019). Incentivizing evaluation with peer prediction and limited access to ground truth. Artificial Intelligence, 275, 618-638.
Wright, J. R., & Leyton-Brown, K. (2020). A formal separation between strategic and nonstrategic behavior. In Proceedings of the 21st ACM Conference on Economics and Computation (pp. 535-536).
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