Presenter:  Brian Stankiewicz
Presentation type:  Symposium
Presentation date/time:  7/27  10:30-10:55
 
Using Partially Observable Markov Decision Processes to Understand Human Sequential Decision Making with Uncertainty
 
Brian Stankiewicz, University of Texas, Austin
 
It appears that humans possess the remarkable ability to make good and rapid decisions when they have incomplete knowledge (uncertainty) and when they make these decisions in sequence. Although we know this anecdotally, in order to fully understand our ability to do this, one would like to compare human performance to the theoretical optimal performance. Our lab has conducted a series of studies investigating human sequential decision making with uncertainty using Partially Observable Markov Decision Processes (POMDP) as a benchmark for human behavior (Stankiewicz, Legge, Mansfield, and Schlicht, 2006; Stankiewicz, Under Review). We have used this benchmark to measure the decision efficiency (human performance relative to the optimal performance) for a variety of tasks. These studies have illuminated some interesting findings. First, in a variety of tasks we find that people are approximately 50% efficient when they have to make all of the calculations and decisions on their own. Furthermore, we find that providing a memory aid that makes explicit the participant's previous actions and observations does not significantly improve performance. However, information about the likelihood of each state being true improves participant's performance to approximately 95% efficiency. In this talk I will describe the POMDP framework and how it has been used to elucidate human the limitations in human sequential decision making performance in these studies.