Economic markets are an example of a complex system, in which are variety of interdependent parts engage in interaction. This yields emergent patterns. They are also exceptionally dynamic, so that in any current investigation, data is unfolding and evolving quickly, making most analyses of data observational artifacts. In addition to this, there are examples of self-similarity across different scales or levels of economic markets, which is another characteristic of a complex system.
Viruses can be used to apply to many different things, and in contemporary speech, the terms virus and viral have literally spread to an application that is far wider than the original senses of the word. To gather more data on the contemporary applications to both terms, I analyzed collocates surrounding these node words in the Corpus of Contemporary American English (COCA), which is an excellent tool with many different sources of language data.
Sociolinguistics, especially within North America tends to focus on describing language variation in terms of different rule systems, often of a specific speech community. Labov pioneered this work specifically as system approaches to both external and internal mechanisms affecting and motivating language change (Principles of Linguistic Change, 1994, 2001). The goal is emphasized towards local settings for language, which is essentially a complex system and can be understood through this to make thorough and scientific assessments of language data at hand.
Historical linguistics and complexity science would make a great pair. CS predicts the variation that is an inherent part of language and the history of a language. Both regional and specific populations within a group could and do develop different characteristics. Even the vocabulary that is used in a given language is a result of the interaction in the complex system.
Discourse analysis is a subfield of linguistics that is focused on conversation and the interaction of speech and large amounts of language. This is quite different from a focus on formalized, abstract grammars like generative syntax. Complexity science can provide more options in the researcher’s toolbox for analyzing language in use in terms of both the fractal properties of grammar, showing prevalent usage and also how this works in terms of the discourse analyst view of language as a social practice.
Most scholars from North America who study syntax adhere to Universal Grammar and generativism, first promoted by linguist Noam Chomsky. Universal Grammar argues that language is innate rather than learned. Generative grammarians work to list out the most succinct set of rules possible for producing grammatical structures in a given language, while structuralists prefer including a wider set of rules for anything that could be considered grammatical by a native speaker of a language.
Complex systems is a natural fit with the science of language. Because nonlinear asymptotic hyperbolic curves (A-curves) are found in any situation of use and across social, geographical, and textual spaces, they can be used and analyzed in a variety of contexts and across different subfields of linguistics for various research purposes for the data at hand. A-curves can be used in linguistics subfields to model all aspects of the data and variation found there.
Complexity Science can be used by researchers and students from a variety of backgrounds and disciplines. It is already applied in evolutionary biology, engineering, economics, and computer science, but students in the humanities can also utilize it to benefit their studies. The underlying principles of complexity science include many moving parts characterized by activity and interaction, responding to contingencies in the environment, which create emergent patterns.