Game Development Reference
In-Depth Information
Guess Two-Thirds contest in Denmark. Notice that while we certainly
understood
some of the logic involved in people's guesses (especially that of the “33� and “22�
guessers), we didn't necessarily need to
apply
that logic in reconstructing our some-
what complex population distribution.
If we were to hook this function up to a research project similar to the one that
the chaps from Copenhagen did, we would likely be hard-pressed to determine the
difference between the real guessers and the simulated ones. We could even be so bold
as to suggest that our Guess Two-Thirds simulation passes the Turing test… and
yet we did it without actually modeling the mindset behind any given guess. In fact,
our function knows nothing of the rules of the game. The major drawback of this
is that if the rules of the game change (such as becoming “Guess 70% of the
Average�), our function does not know how to respond. What's more,
we
don't know
how to change it until we either see empirical evidence of how real people play or
translate some of our understanding of the original game into the new rule set.
Regardless, we have shown that by using various forms of probability distribu-
tions, we can simulate a relatively complex population. This skill will play a part
throughout much of the remainder of the topic—in both large ways and small.
