  For gambling on horses, a one-parameter family of utility functions is
proposed, which contains Kelly's logarithmic criterion and the expected-return
criterion as special cases. The strategies that maximize the utility function
are derived, and the connection to the R\'enyi divergence is shown. Optimal
strategies are also derived when the gambler has some side information; this
setting leads to a novel conditional R\'enyi divergence.
