The C4ISR Journal had a recent search theory article quoting me along with Larry Stone. I'm quite honored, like the British company in Dirk Gently's Holistic Detective Agency:
...was the only British software company that could be mentioned in the same sentence as ... Microsoft.... The sentence would probably run along the lines of ‘...unlike ... Microsoft...’ but it was a start.
It's a good article, covering the undeniably exciting historical origins hunting U-boats, and looking at what may be a modern renaissance. I think the article stretches to connect search theory with Big Data, but the author does note that when the data is visual, and you have humans scanning it for objects, there is a connection. With planning, it could have been used to prioritize the Amazon Mechanical Turk search for Jim Gray. (The resolution of the actual images in that search was probably too low regardless, but the core idea was sound.)
My colleagues just published a paper in Euro Journal on Decision Processes, for their special issue on risk management.
Karvetski, C.W, Olson, K.C., Gantz, D.T., Cross, G.A., "Structuring and analyzing competing hypotheses with Bayesian networks for intelligence analysis". EURO Journal on Decision Processes, Special Issue on Risk Management: http://link.springer.com/article/10.1007/s40070-013-0001-x
Alas, it's behind a paywall and the printed edition isn't due until Autumn. Here's an excerpt from the abstract:
The Ohio SAR Association conference has posted their 2013 talks online.
When searching for an image for this post, I came across several works by E.B. Banning applying search theory to archaeology:
- Sweep widths and the detection of artifacts in archaeological survey. (2011) [Science Direct]
- Detection functions for archaeological survey (2006). [JSTOR]
- Archaeological Survey (2002 book). [Google books]
Now what would archaeologists be doing with sweep widths? Looking for nails, shards, and other small objects in the soil. What they nicely call "small scatters of generally unobtrusive artifacts on the surface".
In WiSAR we call them clues.
An abstract just crossed my desk that I'd love to share. Briefly, adding meaningless math to your academic paper inordinately impresses humanities PhDs. The author does not say whether this also works in pickup lines, so there's room for follow-on research.
In the previous post, we began to build a theory of detection over time as the result of a very large number of independent glimpses. By assuming the environment to be fixed for awhile, we moved all the environmental factors into a constant (to be measured and tabulated), and simplified the function so it depended only on the range to the target.
In this post we simplify still further, introducing lateral range curves and the sweep width (also known as effective sweep width). We will follow Washburn's Search & Detection, Chapter 2. (So there's nothing new in this post. Just hopefully a clear and accessible presentation.)
We begin a four-part gentle introduction to search theory. Our topic is visual detection of targets by land searchers. Today we summarize Koopman Chapter 3, constructing the useful "inverse cube" detection model by starting from instantaneous glimpses with tiny detection probabilities.
The SARBayes MapScore server has been running for a month now at http://mapscore.sarbayes.org. It's a portal for scoring probability maps, so researchers like us can measure how well we are doing, and see which approaches work best for which situations. Take a look. (And if you have a model, register and start testing it!)