Stuart Armstrong's explanation of the 5-and-10 problem is:

The five-and-ten problem (sometimes known as the heavy ghost problem) is a problem in certain types of [updateless decision theory]-like decision theories, when the fact that a counterfactual is known to be false makes the algorithm implement it.

Specifically, let there be a decision problem which involves the choice between $5 and $10, a utility function that values the $10 more than the $5, and an algorithm A that reasons something like:

"Look at all proposition of the type '(A decides to do X) implies (Utility=y)', and find the X that maximises y, then do X."

When faced with the above problem, certain types of algorithm can reason:

"The utility of $10 is greater than the utility of $5. Therefore I will never decide to choose $5. Therefor (A decides to do 'choose $5') is a false statement. Since a false statement implies anything, (A decides to do 'choose $5') implies (Utility=y) for any, arbitrarily high, value of y. Therefore this is the utility maximising decision, and I should choose $5."

That is the informal, natural language statement of the problem. Whether the algorithm is actually vulnerable to the 5-and-10 problem depends on the details of what the algorithm is allowed to deduce about itself.

However, some think Drescher's explanation is more accurate. Somebody should write a short paper on the problem so I can cite that instead. :)