When we need to answer questions about the real world, we build models. Models are necessarily simplifications, which introduce departures from the real world. Over time we have developed a number modeling paradigms that have been found to work well for certain classes of questions. Each modeling paradigm's simplifying assumptions are appropriate for its class of questions but would not necessarily be appropriate for another class of questions. The simplifying assumptions of one modeling paradigm may be inconsistent with or even contradict the simplifying assumptions of another modeling paradigm. These contradictions are not present in the real world, only in our models. They are introduced by the simplifications. The gaps among the ontologies underlying different paradigms are not bridgeable by a unified ontology that enables addressing questions that cross complex physical, human, economic and social phenomena. We argue instead that the best strategy for addressing complex paradigm-spanning questions is a four-level formulation of optimize if you can; if not, adapt if you can; if not, hedge if you can; if not, accept the consequences.