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There are cases where probability matching isn't suboptimal. For example, when deciding to select once of several caches of a resource (like food) and a large number of competitors, it is often better to match than maximize. For the person who de-stubs this article, I think that caveat would be useful to add.128.32.245.203 (talk) 04:05, 12 August 2010 (UTC)[reply]
Optimal? Suboptimal?
The word suboptimal seems that it does not belong here without an appropriate citation. It is my belief that there are many cases, especially in the multi-armed bandits context, where probability matching provides a solution that is at least asymptotically optimal, if not finite time optimal. See, at a minimum, Scott (2010) in Applied Stochastic Models for Business and Industry. DOI: 10.1002/asmb:
"This article describes a heuristic for managing multi-armed bandits called randomized probability matching, which randomly allocates observations to arms according the Bayesian posterior probability that each arm is optimal."
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