This chapter outlines an emergentist approach to understanding why human languages have the particular properties that they do and how those properties are acquired by children. Drawing on a variety of examples, it illustrates the role of two factors in shaping language and its acquisition: limitations on the resources available for processing utterances in real time, and the role of input in facilitating the acquisition of particular words and patterns. Both these factors fit well with a key claim of linguistic emergentism, which is that the human language faculty is shaped by forces – cognition, perception, memory, computation, and experience – that are not themselves linguistic in character. The emergentist approach thus provides an alternative to theories that attribute the unique human ability to learn and use language to an inborn Universal Grammar. The second line of inquiry pursued investigates the emergence of particular features of Korean, especially reflexive pronouns and relative clauses, in child and adult heritage learners. The developmental profile associated with these phenomena fits well with the emergentist approach to language, and helps confirm that heritage languages are learned in essentially the same way as languages that are acquired in a monolingual setting.

Climate is an emergent system with many interacting processes and components. Complexity is essential to accurately model the system and make quantitative predictions. But this complexity obscures the different compensating errors inherent in climate models. The Anna Karenina principle, which assumes that these compensating errors are random, is introduced. By using models with different formulations for small-scale processes to make predictions and then averaging them, we can expect to cancel out the random errors. This multimodel averaging can increase the skill of climate predictions, provided the models are sufficiently diverse. Climate models tend to borrow formulations from each other, which can lead to “herd mentality” and reduce model diversity. The need to preserve the diversity of models works against the need for replicability of results from those models. A compromise between these two conflicting goals becomes essential.