(e sub j) <-- (e sub j) + beta(|P - (p sub j)|/*payoff-range* - (e sub j))

i.e., the absolute difference between P and (p sub j) is normalized by dividing by the payoff range (1000). This makes the error a quantity between 0 and 1, as it is treated in the rest of the paper.

A question is whether the program could be written to run the
same way *without* normalizing the error. It appears that the
answer is no, given the way (e sub 0) is used as a threshold in
the fitness calculation of Section 3.4. In other words, to change
the accuracy formula so that error can be expressed in payoff
units, you would have to express (e sub 0) in payoff units, which
would imply knowing the payoff range.

However, in a fitness calculation that did *not* use (e sub 0) as
a threshold--but used it only as part of the exponential argument--
I think the relative accuracies, and thus the fitnesses, would
come out unaffected by expressing the error in payoff units.
So, with a different fitness calculation, the system might not
ever need to know the payoff range. I just don't know at this point.

*
Subsequent descriptions of the way the error (e sub j) is used
suggest that the value should lie between zero and one,
though I could find no explicit statement confirming that. If
this is the case, then shouldn't the adjustment of (e sub j)
include a normalization of the difference between P and (p sub j) ?
*