  Choices in scientific research and management require balancing multiple,
often competing objectives.Multiple-objective optimization (MOO) provides a
unifying framework for solving multiple objective problems. Model selection is
a critical component to scientific inference and prediction and concerns
balancing the competing objectives of model fit and model complexity. The
tradeoff between model fit and model complexity provides a basis for describing
the model-selection problem within the MOO framework. We discuss MOO and two
strategies for solving the MOO problem; modeling preferences pre-optimization
and post-optimization. Most model selection methods are consistent with solving
MOO problems via specification of preferences pre-optimization. We reconcile
these methods within the MOO framework. We also consider model selection using
post-optimization specification of preferences. That is, by first identifying
Pareto optimal solutions, and then selecting among them. We demonstrate
concepts with an ecological application of model selection using avian species
richness data in the continental United States.
