Equilibria of a time-inconsistent Markov decision processes (Spring 2024)
This project focuses on a Markov decision process problem in infinite time horizon for a time-inconsistent setting, the underlying process is a controlled time-homogeneous Markov chain. The aim is to study the equilibrium policy, which is a type of time-homogeneous Markov control, for this problem. We first study the finite state case and try to characterize an equilibrium as a fixed point of a certain operator, and try to build algorithms that compute such fixed points. The next step is to generalize the results in finite state setting into infinite state setting.Â
For more information contact Zhenhua Wang (zhenhuaw@iastate.edu)
People:
Zhenhua Wang (Postdoc)
Pre-requisites:
Have taken courses: Probability, Mathematical Analysis, Random Processes, for undergraduate level.
Coding skills in python, R or Matlab are preferred.