In this paper, we perform an in - depth investigation of relative merits of two adaptive learning algorithms with constant gain, Recursive Least Squares (RLS) and Stochastic Gradient (SG), using the Phelps model of monetary policy as a testing ground. The behavior of the two learning algorithms...

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...

This paper investigates opinion dynamics and social influence in directed communication networks. We study the theoretical properties of a boundedly rational model of opinion formation in which individuals aggregate the information they receive from their neighbors by using weights that are a...

Adaptive learning introduces persistence in the evolution of agents' beliefs over time. For applied purposes this is a convenient feature to help explain why economies present sluggish adjustments towards equilibrium. The pace of learning is directly determined by the gain parameter, which...

In this paper we investigate how cognitive ability and character skills influence behavior, success and the evolution of play towards Nash equilibrium in repeated strategic interactions. We study behavior in a p-beauty contest experiment and find striking differences according to cognitive...