James R. Wright is primarily interested in using data-driven machine learning models to predict human strategic behaviour

Predicting human strategic behaviour

James Wright is primarily interested 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. He has delivered invited talks on the subjects of multiagent systems and on the prediction and algorithmic modelling of human behaviour. His published research focuses on formalizing the boundary between strategic and nonstrategic reasoning. James’ long-term research agenda is to build a general theory for optimally designing algorithms for mediating interactions involving humans or other realistically bounded agents rather than idealized, perfectly rational game theoretic agents.

James is a Fellow and Canada CIFAR AI Chair at Amii and an Assistant Professor at the University of Alberta in the Department of Computing Science. Previously, he worked as a Postdoctoral Researcher at Microsoft Research. His papers have been published in top conferences and journals including the Journal of Artificial Intelligence Research (JAIR), Games and Economic Behaviour, the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), the ACM Conference on Economics and Computation, and the AAAI Conference on Artificial Intelligence.