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Cognition, in whatever form we find it, evolved. Understanding cognition therefore requires placing it in the context of its ecology and evolutionary history. Many of the methods traditionally used by experimental cognitive scientists, however, are inadequate for drawing inferences about those histories. Yet all is not lost. We can build models. The world is complicated. Models simplify. Real-world systems are often too large, too small, too fast, too slow, or have too many parts to study directly. Models allow us to study a real-world system by studying something else, by studying something that is like the system but simpler and fully describable. By explicitly stating all of our assumptions in the language of mathematics or algorithms, we make it clear what aspects of our system we are or aren’t talking about, which aids theory development and the falsifiability of hypotheses (Smaldino, 2017). Good models allow us to draw conclusions about real-world system X from the behavior of the model Y. A model decomposes the world into parts, with properties and behaviors, and relationships between those parts (Kauffman, 1971). In general, there is no one correct way to decompose any particular system. Rather, because complex systems can be understood at multiple levels of organization, understanding them requires a collection of models, with decompositions across those levels (Page, 2018). This special issue contains two articles modeling the evolution of strategies for learning and decision making. Each model tackles a slightly different problem, and each does so with a different decomposition of the system of learners and their environment. As such, each model sheds light on a different aspect of the evolution of learning and decision making. The first, by Kvam and Hintze (2018), tackles the evolution of individual decision making strategies. Evolutionary theorists often assume that natural selection optimizes decision processes to maximize fitness. However, optimal Correspondence concerning this article should be addressed to Paul E. Smaldino, Cognitive and Information Sciences, School of Social Sciences, Humanities, and Arts, University of California, Merced, 5200 North Lake Road, Merced, CA 95343. E-mail: [email protected] T hi s do cu m en t is co py ri gh te d by th e A m er ic an Ps yc ho lo gi ca l A ss oc ia tio n or on e of its al lie d pu bl is he rs .