Expectations Formation and Stability of Large Socioeconomic Systems
在一个简化框架中探讨了自适应学习如何影响大型社会经济系统的稳定性与收敛性，发现当代理人对系统局部稳定性不确定时，学习动态会发散；反之则可能收敛。
This paper attemps to identify, in a framework deliberately stripped of unnecessary technical- ities, some of the basic reasons why adaptive learning may or may not lead to stability and convergence to self-fulfilling expectations in large socioeconomic systems where no agent, or collection of agents, can act to manipulate macroeconomic outcomes. It is shown that if agents are somewhat uncertain about the local stability of the system, and are accordingly ready to extrapolate a large range of regularities (trends) that may show up in past small deviations from equilibrium, including divergent ones, the learning dynamics is locally divergent. On the other hand, if agents are fairly sure of the local stability of the system, and extrapolate only convergent trends out of small past deviations from equilibrium, one may get local stability. This “uncertainty principle” does show up in a wide variety of contexts: smooth or discontin- uous, finite or infinite memory learning rules, error learning, recursive least squares, Bayesian learning.