Learning Nonlinear Decision Making
Abstract
To advance the science of decision-making as it pertains to how people learn to make decisions and how this process can be captured computationally, we specifically addressed the challenge of how nonlinear decisions can be learned from data, experience, and even interactions with other decision-makers. Our goal was to research and develop a rigorous and comprehensive computation and cognitive framework to understanding and capturing how non-linear decision making occurs and how we can learn them.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 09, 2019
- Accession Number
- AD1096587
Entities
People
- Eugene Jr Santos