Social Learning and Monetary Policy Rules

We analyse the effects of social learning in a monetary policy context. Social learning might be viewed as more descriptive of actual learning behaviour in complex market economies. In our model, Taylor Principle governs uniqueness and expectational stability of rational expectations equilibrium (REE) under homogeneous recursive algorithms. We find that the Taylor Principle is not necessary for convergence to REE minimum state variable (MSV) equilibrium under social learning. Sunspot equilibria exist in the indeterminate region. Our agents cannot co-ordinate on a sunspot equilibrium in general form specification, however, they can co-ordinate on common factor specification. We contribute to the use of genetic algorithm learning in stochastic environments.