Half the reason for POMDP's in the literature: You can pronounce the name!

Today, I noticed a new paper in Mathematics of Operations Research, which suggests a way to combine risk-sensitivity with POMDP's without having to use only exponential utility functions. I'm bothering to write this post because the title is infuriating to me. In my opinion, the title of the paper will probably largely contribute to why it will be forgotten – only to be referenced by other papers that find it while doing their post hoc due diligence. 

"Partially Observable Risk-Sensitive Markov Decision Processes"

by Bäauerle and Rieder

Mathematics of Operations Research (2017), Articles in advance

http://pubsonline.informs.org/doi/abs/10.1287/moor.2016.0844

The great success of POMDP as a framework used in the literature is in part because people like the name – "POM-D-P". If you're taking in a young graduate student, you can send them to the literature with a few quick words – "Go check out POMDP's". 

But what am I going to do with PORSMDP? "Poor-Sim-Dop"? "Po-Rism-Dip"? Even if one of those managed to roll of off the tongue, the relationship to POMDP wouldn't be obvious.

Why didn't anyone in the chain of custody of this manuscript suggest putting "Risk-Sensitive" up front, as in RSPOMDP (R-S-POM-D-P)? Or maybe at the end, as in POMDPRS ("POM-DiPpeRS")? The latter suggestion not only is memorable, but it sounds delicious.

Just some food for thought.

Finally, a date for the defense!

Finally, after lots of unexpected twists and turns, my MS thesis defense and PhD qualifier has arrived.

The acknowledgments (and abstract and vita) taken from the frontmatter of the document are available on-line. Details of the actual defense are available below. Of course, the public is welcome.

When: Tuesday, June 5, 2007, at 2:30pm

Where: Dreese Labs Room 260

Title: Optimal Foraging Theory Revisited

Abstract: Optimal foraging theory explains adaptation via natural selection through quantitative models. Behaviors that are most likely to be favored by natural selection can be predicted by maximizing functions representing Darwinian fitness. Optimization has natural applications in engineering, and so this approach can also be used to design behaviors of engineered agents. In this thesis, we generalize ideas from optimal foraging theory to allow for its easy application to engineering design. By extending standard models and suggesting new value functions of interest, we enhance the analytical efficacy of optimal foraging theory and suggest possible optimality reasons for previously unexplained behaviors observed in nature. Finally, we develop a procedure for maximizing a class of optimization functions relevant to our general model. As designing strategies to maximize returns in a stochastic environment is effectively an optimal portfolio problem, our methods are influenced by results from modern and post-modern portfolio theory. We suggest that optimal foraging theory could benefit by injecting updated concepts from these economic areas.