This chapter provides a survey of the recent work on learning in the context of
macroeconomics. Learning has several roles. First, it provides a boundedly rational model of …

We postulate a continuous‐time heterogeneous agent model with a financial sector and
households to study the nonlinear linkages between aggregate and financial variables. In …

Inspired by recent developments in science studies, this book offers an innovative type of
analysis of the recent history of rational expectations economics. In the course of exploring …

Neural Networks–originally inspired from Neuroscience–provide powerful models for
statistical data analysis. Their most prominent feature is their ability to “learn” dependencies …

Expectations play a major role in macroeconomic dynamics, especially regarding the
conduct of monetary policy. Yet, modeling the interplay between communication …

I propose a method for inference in dynamic discrete choice models (DDCM) that utilizes
Markov chain Monte Carlo (MCMC) and artificial neural networks (ANNs). MCMC is …

We present a neural network methodology for learning game-playing rules in general.
Existing research suggests learning to find a Nash equilibrium in a new game is too difficult …

The birthday of a Central Bank provides a suitable occasion to describe our recent attempts
to analyze the acquisition of macroeconomic credibility. Central banks care about how …

The electoral cycle literature has developed in two clearly distinct phases. The first one,
which took place in the mid-1970s, considered the existence of non-rational (naive) voters …

This article models the learning process of a population of randomly rematched tabula rasa
neural network agents playing randomly generated 3× 3 normal form games of all strategic …