Averaging plus learning models and their asymptotics

We develop original models to study interacting agents in financial markets and in social networks. Within these models randomness is vital as a form of shock or news that decays with time. Agents learn from their observations and learning ability to interpret news or private information in time-...

全面介紹

Saved in:
書目詳細資料
Main Authors: Popescu, Ionel, Vaidya, Tushar
其他作者: School of Physical and Mathematical Sciences
格式: Article
語言:English
出版: 2023
主題:
在線閱讀:https://hdl.handle.net/10356/171432
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
實物特徵
總結:We develop original models to study interacting agents in financial markets and in social networks. Within these models randomness is vital as a form of shock or news that decays with time. Agents learn from their observations and learning ability to interpret news or private information in time-varying networks. Under general assumption on the noise, a limit theorem is developed for the generalised DeGroot framework for certain type of conditions governing the learning. In this context, the agents beliefs (properly scaled) converge in distribution that is not necessarily normal. Fresh insights are gained not only from proposing a new setting for social learning models but also from using different techniques to study discrete time random linear dynamical systems.