Lawrence Stone defined the elements of the optimal search problem in his 1986 book, Theory of Optimal Search.
This was paraphrased extremely well by Cooper, Frost and Robe in their 2003 report – Compatibility of Land SAR Procedures with Search Theory as quoted below;
A probability density distribution on search object location and state (so the probability of containment, POC (a.k.a. POA for “probability of area”), for any subset of the possible locations and states can be estimated),
A detection function relating the probability of detecting (POD) the object if it is in a searched area to the density of the searching effort expended there,
A known finite amount of available searching effort, and
An optimization criterion of maximizing probability of finding the object in a desirable state (probability of success or POS) subject to the constraint on effort availability.
I will endeavour to simplify further. In order to need and/or use the mathematics of search theory you require four essential elements.
- The ability to predict the likelihood that an object is in any particular search area or region. This might be done using sophisticated computer software working with the latest missing person behaviour statistics, or could be as simple as a coming up with a consensus within the search planning team.
- The ability to calculate the likelihood a given search resource will have of finding the object if it in the area being searched – unfortunately we can’t ask how many clues would you have found! [See my definition of POD for a brief explanation of why]
- A limited but known amount of search resource – when do you ever get too much search resource?
- A method of calculating the best way to use the search resource to maximise the chances of finding the search object as quickly as possible.
I’ll look in detail at each of these in further posts.
