Learning with Expert Advice
构建了一个包含理性预期和适应性学习两类主体的均衡模型，发现即使参数设定不足的学习算法也能在与理性预期的竞争中存活，解释了通胀预测中前瞻与后顾元素并存的现象。
Surveys of inflation forecasts show that expectations combine forward-looking and backward-looking elements. This contradicts conventional wisdom: In the presence of rational agents, adaptive agents would be driven out of the market. In our paper, we rationalize this finding in an equilibrium framework. Our model has two types of agents, one having rational expectations and the other using adaptive learning. The proportion of these agents in the population evolves according to their past forecasting performance. We show that even an underparameterized learning algorithm survives competition with rational expectations.