Mean dynamics govern convergence to rational expectations equilibria of self-referential systems under least squares learning. We highlight escape dynamics that propel away from a rational expectations equilibrium under fixed-gain recursive learning schemes. These learning schemes discount past...

Mean dynamics govern convergence to rational expectations equilibria of self-referential systems under least squares learning. We highlight escape dynamics that propel away from a rational expectations equilibrium under fixed-gain recursive learning schemes. These learning schemes discount past...

Mean dynamics govern convergence to rational expectations equilibria of self-referential systems under least squares learning. We highlight escape dynamics that propel away from a rational expectations equilibrium under fixed-gain recursive learning schemes. These learning schemes discount past...

An ordinary differential equation (ODE) gives the mean dynamics that govern the convergence to self-confirming equilibria of self-referential systems under discounted least squares learning. Another ODE governs escape dynamics that recurrently propel away from a selfconfirming equilibrium. In a...

This chapter describes feedforward neural networks as approximators and relates them to statistical discriminant functions, and explains the ways in which neural nets of varying complexity can represent equilibria in two repeated games and one dynamic economic model. Because linear strategies...