Earlier this year Max Post van der Burg and I published an article in Ecological Applications Integrating Info-gap Decision Theory With Robust Population Management: A Case Study Using The Mountain Plover" where we used a combination of methods borrowed from robust control theory and satisficing to understand the value of a particular management action to a threatened species. This was a piece of Max's dissertation, and as usual in such things, he did all the hard and important work!
The core idea of "satisficing" is to find a decision that performs good enough, but over the largest possible number of ways of being wrong. In contrast, optimisation focuses on maximizing performance assuming that the system is perfectly understood - i.e. all the parameters are known perfectly and the system model is exactly correct - circumstances that are never true even in the best of times. So an optimal decision will usually outperform a satisficing decision if one's knowledge of the system is perfect, the satisficing decision will continue to do well even if the system model and its parameters are incorrect.
Of course, it is possible that a satisficing strategy is also the optimal strategy, and then we're happiest, but this doesn't seem to happen very often.Max's contribution was to couple a matrix population model of Mountain Plover with the idea of satisficing to look at how well "nest marking" of Plovers performs as a conservation strategy. The upshot is that even if we are not sure about the life history of this species, nest marking increases the range of "wrongness" under which we will see positive population growth. What we didn't do was evaluate different types of actions against each other - this could easily be done, but was beyond the scope of what we wanted to achieve in the paper.