Backward induction: from theory to empirics
- Konrad Grabiszewski, Instructional Professor, Applied Mathematics and Computational Sciences
KAUST
Backward induction, the cornerstone of dynamic game theory, is the classical algorithm applied to solve finite dynamic games with perfect and complete information. While theoretically sound and beautiful in its simplicity, backward induction does not perform so well when it comes to predicting human behavior. The objective of this seminar is twofold. First, we will understand what backward induction is and how to apply it on game-theoretic trees. Second, we will answer the question of whether backward induction is a good model of how people make choices in dynamic interactions.
Overview
Abstract
Backward induction, the cornerstone of dynamic game theory, is the classical algorithm applied to solve finite dynamic games with perfect and complete information. While theoretically sound and beautiful in its simplicity, backward induction does not perform so well when it comes to predicting human behavior. The objective of this seminar is twofold. First, we will understand what backward induction is and how to apply it on game-theoretic trees. Second, we will answer the question of whether backward induction is a good model of how people make choices in dynamic interactions.
Brief Biography
Konrad Grabiszewski is Courtesy Instructional Professor of Applied Mathematics and Computational Science at KAUST. He studied mathematics at the Courant Institute at New York University (MSc in 2006) and economics at the Stern School of Business at New York University (PhD in 2008). He was a faculty member at ITAM, Mexico (2008 – 2015), University of Miami, USA (2015 – 2018), and MBSC in Saudi Arabia (2018 – now). His research focuses on decision theory and game theory – both from mathematical and experimental points of view.