محو شدن یادگیری حافظه در یک کلاس از مدل های با نگاه به آینده با کاربرد پویایی ابرتورم

We analyzed a class of non-linear deterministic forward-looking economic models (the state today is affected by today’s and tomorrow’s expectation) under bounded rationality learning. The learning mechanism proposed in this paper defines the expected state as a geometric average of past observations. We show that the memory of the learning process plays a stabilizing role: it enlarges the local stability parameters region of the perfect foresight stationary equilibria and it eliminates non-perfect foresight cycles–attractors generated through local bifurcations. In a hyperinflation economy with two stationary equilibria we show that only one of the two equilibria can be stable under bounded rationality learning provided that agents have a ‘long memory’. We can also have convergence towards non-perfect foresight cycles and even chaotic dynamics. If the debt financing quota is small enough then no restriction on the agent’s memory is needed. The equilibrium with the higher inflation rate is stable under bounded rationality for a small (and not really significant) set of economies.