Learning-to-forecast experiment. A simulation approach with genetic algorithm

Abstract

In this work, A Genetic Algorithm (GA) is used to study the behavior in a Learning to Forecast Experiment in which short-term expectations have been elicited. In particular, by using the results from a previous experiment with human subjects, the same market is simulated implementing GA. After the training process, the simulation with GAs is able to produce similar results compared to the experiment with human subjects in markets with both negative and positive feedbacks. In addition, simulations in the long-run, i.e. considering 100 and 1000 periods, show a market convergence to the fundamental price of the market and the stability of the GA agents’ predictions. We tested how the simulated price reacts by introducing 3 shocks. Finally, the algorithm is also tested in market shocks. The sudden change in the market conditions shows the capability of the algorithm to rapidly adapt and answer to this changes in order to return to the equilibrium conditions.