Many problems both in theory and in the real world require us to search a large (or unlimited) space for a solution. In fields that yield to an analytic method there is a clear path - just follow the steps and you are guaranteed an answer. Under the right conditions, not only are you guaranteed an answer, but you can also forecast the timeto find it.

Then there are the other fields: those that require going beyond our knowledge horizon. By definition, in these domains we don't know where this could take us nor how big a task it may be. Not to be dismayed, however, there are still methods at our disposal. The difference is that they center on interests and resources rather than endpoints and plans.

Iterative Deepening (ID) is a method to search the unknown for the unknown. In a way it is a type of planned serendipity. It's not guaranteed to find the optimal solution (or even any solution), yet importantly it does let the discovered knowledge feed back to the process itself.

Iterative Deepening is a search strategy loosely based on an eponymous method in computer science. The process itself consists of small searches into areas of interest under strict resource limitations. These limits can be of time, money, people, or all three. Each search round ends when the limit is hit. The searchers re-group, share their knowledge, improve their tactical search methods, and set out again. It is the process of evolving the search tactics and sharing team learning that gives ID its power.