Aprendizaje con información incompleta en modelos de consumo con múltiples atributos

This paper deals with an intertemporal model of optimization, which is based on multiple attribute utility functions (MUAT). The model assumes that consumers do not know a priori the optimal mixture of attributes which would maximize their utility from consumption. By using a MUAT lineal model, we state that the resulting consumption paths for four “extreme cases” are associated with several learning processes. In particular, we show that optimal equilibria in consumption of goods can be reached before the consumer exhausts her/his budget, a kind of equilibrium situation not analyzed in traditional utility functions.