- Timezone: America/New_York
- Date: Dec 23 2018
- Time: 06:00 - 07:30
Rationally Inattentive Decision Making
Rationally Inattentive decision making extends general Bayesian statistical decision-making under
uncertainty to the case when the decision maker has several options for obtaining extra information about the environment, before making optimal action based on all his information. In contrast to classic models of Bayesian decision making and in this framework, perception and action get into a reciprocal interaction. The term was first coined by Noble laureate Christopher Sims to develop a model to justify sluggish Macroeconomic adjustments in economics. However, general problem goes beyond and includes various situations encountered in the study of networked control systems, artificially intelligent and automated systems, brain and cognitive sciences and behavioral economics.
Ehsan has earned a BSc in electrical engineering and a MSc in Economics from Sharif University of Technology, and a MSc and PhD in electrical engineering (Decision theory and stochastic systems) from university of Illinois at Urbana-Champaign. He is currently a senior data scientist at Walmart eCommerce and WalmartLabs. Utilizing tools from optimization, stochastic systems and machine learning, he constructs mathematical models and data driven methods for revenue optimization, inventory and assortment management at
different departments of Walmart.