Friday, October 2, 2015

Ranking for simulation

This week I visited Georgia Tech and met with several faculty there. One of those people was Seong-Hee Kim who does work on a variety of topics including ranking and selection strategies for simulation. The idea behind selection via simulation is that we have a (manageable) set of alternatives and want to choose the best option. However, identifying some options which are not likely to be the best is much easier than actually identifying the best. Therefore, the goal is to get enough information about each alternative (in her example that was a few simulated outcomes) in order to identify some bad solutions. You then keep repeating the process (more simulated outcomes, eliminate more bad solutions) until you are left with only one option.

This method seems to be similar to satisficing (mentioned here) as a decision making strategy. With satisficing, we pick any alternative which matches our criteria. Said another way, we eliminate unacceptable alternatives until anything left is acceptable. There are certainly differences in the goals between the two problems... But if you paused either algorithm in the middle, you would probably have a similar looking set of alternatives. 

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