Learning Correlated Equilibria in Extensive Form Games


We formalize an efficient class of counterfactual regret minimization algorithms exploiting the “sequence form” to compute ‘‘$\Phi$-equilibria’’ – a generalized class of equilibrium concepts defined within general-sum extensive form games of imperfect information. We develop increasingly strong notions of no-regret, mapping those notions directly onto concepts of interest, such as agent- and extensive-form correlated and coarse correlated equilibria.

Working Paper

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Ph.D. Student in Economics and Statistics

I am an Economics Ph.D. candidate at MIT, and former Senior Research Analyst on the dynamic stochastic general equilibrium (DSGE) team in the Macroeconomic and Monetary Studies function at the NY Fed. Views expressed are my own.