We study individual evolutionary learning in the setup developed by Deissenberg and Gonzalez (2002). They study a version of the Kydland-Prescott model in which in each time period monetary authority optimizes weighted payoff function (with selfishness parameter as a weight on its own and...

I study here a model of a two-country economy where agents update their decision rules using an evolutionary algorithm consisting of imitation and experimentation. This environment, in which perfect currency substitution exists, is convenient for studies of issues relating to international...

We analyze the effects of social learning in a widely-studied monetary policy context. Social learning might be viewed as more descriptive of actual learning behavior in complex market economies. Ideas about how best to forecast the economy's state vector are initially heterogeneous. Agents can...

We introduce learning based on genetic algorithms in a principal-agent model of optimal contracting under moral hazard. Applications of this setting abound in finance (credit under moral hazard), public finance (optimal taxation, information-constrained insurance), development (sharecropping),...

Defining the rules for monetary policy has been the subject of an extensive literature in monetary economics since the 1968 AMA presidential address by Milton Friedman. Starting from the contribution by Barro and Gordon (1983) the prevalent approach has hinged on optimal control techniques to...

We study three learning rules (reinforcement learning (RL), experience weighted attraction learning (EWA), and individual evolutionary learning (IEL)) and how they perform in three different Groves-Ledyard mechanisms. We are interested in how well these learning rules duplicate human behavior in...