This paper describes a general framework for converting online game playing algorithms into constrained convex optimization algorithms. This framework allows us to convert the wel...
Abstract-- We study the convergence rate of average consensus algorithms in networks with stochastic communication failures. We show how the system dynamics can be modeled by a dis...
There is a large literature on the rate of convergence problem for general unconstrained stochastic approximations. Typically, one centers the iterate n about the limit point then...
Co-learning is a model involving agents from a large population, who interact by playing a fixed game and update their behaviour based on previous experience and the outcome of th...
Martin E. Dyer, Leslie Ann Goldberg, Catherine S. ...
We introduce a game setting called a joint process, where the history of actions determine the state, and the state and agent properties determine the payoff. This setting is a sp...