Dynamic Systems Theory

Definition:

Dynamic Systems Theory (DST) — also known as Complexity Theory or Complex Dynamic Systems Theory (CDST) in SLA contexts — is a theoretical framework borrowed from mathematics and natural sciences that treats language and language development as complex dynamic systems. Rather than viewing acquisition as a linear progression through fixed stages toward a target state, DST models language as an ever-changing adaptive system in which multiple components interact, small changes can have large effects (sensitivity to initial conditions), and the system can reorganize into qualitatively new states (emergence). In SLA, DST was championed particularly by Diane Larsen-Freeman, who used the term complexity theory and argued that even the grammatical system of a single learner is best understood dynamically.

Core Principles from Dynamic Systems Theory

1. Dynamic and ever-changing:

The language system of a learner is never static — it is constantly being modified by use, experience, and interaction with the linguistic environment. Proficiency does not plateau; it fluctuates. What looks like regression may be system reorganization before a new stage of development.

2. Complex (many interacting subsystems):

Language is not a monolithic system but an ensemble of interacting sub-systems (phonology, morphosyntax, vocabulary, pragmatics, discourse) that interact with each other and with non-linguistic systems (working memory, motivation, identity). Changes in one sub-system ripple through others.

3. Sensitive to initial conditions:

Small differences in starting points — initial L1, early input quality, learning context, motivational state — can produce dramatically different developmental trajectories over time. This explains why two learners in identical classroom conditions can end up at very different places.

4. Self-organizing (emergent structure):

Patterns and structures in the developing language system emerge from the interaction of components without being explicitly taught or innately specified. Grammar, in this view, is an emergent property of the learner-input interaction system.

5. Non-linear development:

Development is not smooth and monotonic. The DST framework predicts (and empirical research confirms) that learners show periods of relative stability, then rapid reorganization (stage transitions), sometimes through apparent regression, then a new stable state.

6. Attractor states:

Dynamic systems tend toward certain stable configurations (attractor states) that are more robust and attract the system’s behavior over time. In SLA, certain linguistic forms or structures may become attractor states — the system gravitates toward them regardless of starting point. Fossilized errors can be understood as attractor states that the system settles into and resists leaving.

DST and Variability

One of the most important DST contributions to SLA is its treatment of variability as principled and informative rather than noise to be averaged out. A learner who uses past tense correctly 70% of the time, then drops to 50% when task complexity increases, then returns to 80% with reduced cognitive load is showing the dynamic behavior DST predicts:

The form is not simply “acquired” or “not acquired” — it exists at a dynamic level in the system
Cognitive load, anxiety, topic familiarity, and interlocutor status all modulate system behavior
Variability patterns reveal information about where the system is in its development

This sharply contrasts with Krashen’s binary acquired/unacquired view or stage-based developmental sequence models.

Diane Larsen-Freeman and Language-as-Complex-System

Larsen-Freeman (1997) introduced the term “complexity” for language itself — not just acquisition — arguing that language itself is a complex adaptive system that changes through use. This has implications for teaching:

Grammar is not a fixed, static set of rules but an adaptive, usage-influenced system
Every use of language is a change in the system — language is not separate from use
Teachers and learners are both participants in the language system, not merely users of a pre-existing code

History

1960s–1970s: Systems theory and cybernetics (Norbert Wiener) develop; chaos theory in mathematics (Edward Lorenz) establishes the concept of sensitive dependence on initial conditions — the “butterfly effect.”
1989: Gleick’s popular science book Chaos brings complex systems thinking to wide audiences.
1994: Diane Larsen-Freeman publishes “On the complexity of second language development” and begins applying complexity/DST to language acquisition.
1997: Larsen-Freeman’s pivotal paper “Chaos/Complexity Science and Second Language Acquisition” makes the most explicit and systematic case for DST in SLA; published in Applied Linguistics.
2004: De Bot, Lowie, and Verspoor apply DST to specific SLA phenomena (development over time, variability, sensitive periods) in published research.
2010: Larsen-Freeman and Cameron publish Complex Systems and Applied Linguistics (Oxford), providing the most comprehensive treatment of DST for SLA/applied linguistics audiences.
2010s–present: Longitudinal case study methods designed for DST analysis become more common; researchers like Lowie, Verspoor, and Bollen develop statistical methods appropriate for dynamic systems data.

Common Misconceptions

“DST means language acquisition is random and unpredictable.”

DST does not claim that acquisition is random — it claims that acquisition is non-linear and sensitive to initial conditions. The system follows lawful dynamics, but those dynamics are complex enough that simple stage-based predictions are insufficient.

“DST is just a metaphor borrowed from physics.”

DST researchers argue it is more than a metaphor — it is a framework that generates specific empirical predictions (about variability patterns, sensitive periods, the relationship between subsystems) that can be tested. The debate about whether it is a genuinely scientific framework or a productive metaphor is ongoing.

“Dynamic Systems Theory replaces Krashen’s Input Hypothesis.”

DST is an overarching framework for thinking about acquisition processes — it is compatible with comprehensible input being a critical driver of development, but does not replace input-based accounts. It adds the dimension of how the system responds to input dynamically and non-linearly.

Criticisms

Operationalizability: Core DST constructs (attractors, self-organization, emergent properties) are difficult to operationalize in empirical research designs. Much DST “research” in SLA is descriptive case study rather than experimental test.
Predictive power: Does DST generate falsifiable predictions beyond “development is variable and non-linear” — predictions that distinguish it from other theories? Critics argue the framework is more descriptive than predictive.
Methodological demands: Testing DST properly requires dense longitudinal data from individual learners over extended time periods — data that is extremely expensive and difficult to collect.
Risk of explaining everything: Because DST explicitly accommodates variability, regression, and non-linear patterns, it risks being unfalsifiable — any observed pattern can be post-hoc “explained” by the framework.

Social Media Sentiment

DST is an academic theoretical framework with minimal direct presence in learner communities. However, themes it addresses resonate strongly:

“Plateau” frustration: Learners frequently report that progress suddenly stops or even feels like it regresses after long periods of improvement — exactly the attractor state and system reorganization dynamics DST describes.
“Sometimes I can, sometimes I can’t”: Variability in L2 performance under different conditions (“I speak great when I’m relaxed but freeze in formal situations”) is a DST prediction that learners experience personally.
r/LearnJapanese: “Why does my Japanese feel worse lately even though I’m studying more?” is a recurring thread — DST would explain this as a system reorganization phase that precedes a new level of development.

Last updated: 2026-04

Practical Application

Expect and accept variability. Your Japanese performance will fluctuate — not just as daily noise, but as a meaningful signal about where the system is in its development. Poor performance in demanding contexts (formal conversations, writing under time pressure) does not mean you haven’t “really” acquired a form.
Understand the plateau. Periods of plateau or apparent regression often precede acquisitional breakthrough. The system is reorganizing. DST predicts this; expect it; continue input exposure and use.
Optimize initial conditions. Because the system is sensitive to initial conditions, the quality and nature of early exposure matters more than quantity alone. Early high-quality comprehensible input with authentic materials establishes the foundation the system develops from.
Use multiple exposure contexts. Because the system has multiple interacting subsystems, varied contexts of input and output — different registers, skill modalities, topics — allow subsystems to develop and interact in ways single-context study does not.

Related Terms

Research

Larsen-Freeman, D. (1997). “Chaos/complexity science and second language acquisition.” Applied Linguistics, 18(2), 141–165. [Summary: The seminal paper applying DST/complexity theory to SLA; argues that language development exemplifies complex, dynamic, non-linear systems; introduces key concepts (sensitive dependence, attractors, emergence) to the SLA literature.]
Larsen-Freeman, D., & Cameron, L. (2008). Complex Systems and Applied Linguistics. Oxford University Press. [Summary: Comprehensive treatment of complexity theory for applied linguistics; covers research methods, theoretical implications for acquisition, and applications to classroom language learning — the primary reference text for DST in SLA.]
de Bot, K., Lowie, W., & Verspoor, M. (2007). “A dynamic systems theory approach to second language acquisition.” Bilingualism: Language and Cognition, 10(1), 7–21. [Summary: Provides the most methodologically concrete early application of DST to SLA; discusses operationalization challenges, proposes longitudinal individual-learner trajectory analysis as the appropriate research design, and illustrates DST predictions with empirical examples.]
Ellis, N. C., & Larsen-Freeman, D. (Eds.). (2009). Language as a Complex Adaptive System. Wiley-Blackwell. [Summary: Edited volume bringing together complexity, connectionism, and usage-based acquisition; positions Language as a Complex Adaptive System (LCAS) as a unified theoretical framework and provides empirical instantiations.]
Verspoor, M., de Bot, K., & Lowie, W. (Eds.). (2011). A Dynamic Approach to Second Language Development: Methods and Techniques. John Benjamins. [Summary: Methodologically oriented volume on how to study SLA from a DST perspective; includes specific statistical and analytical techniques for longitudinal data appropriate for dynamic systems research — important for understanding how DST predictions can be empirically tested.]

Mikey Does