Dynamical System Theory

It can be confirmed and agreed that the world we live in is a nonlinear, interconnected, and spontaneous one. Such a dynamic system implies the system and the subsystem change over time (Thelen & Smith, 1996). Therefore, it requires dynamical system theory (DST) to model and describe how the system changes over time (Thelen & Smith, 1996). These features of the real world indicate that the use of DST presents several benefits to a system engineer or engineering psychologist. As Hooker (1997) commented, psychologists tend to hold their concepts in a simple, logical, and structured view. Most of the time in scientific studies, the utilization of controlled variables requires disregarding external validity, achieving accurate “experimental control” (Doyle, 1996). The scientific research methods can be helpful when the study is at a theoretical level. However, when applying findings to the real world as engineering psychologists and system engineers do, the consideration of nonlinearity, emergence, and interconnectedness needs to be considered. Hence, DST is useful as a framework when designing systems as it not only focuses on nonlinearity and interconnectedness, but also adds in the variable of time, allowing for prediction and comparison.

The biggest strength and most important variable mentioned in the DST is the variable of time. From a common understanding, time is constant and set in the real world; it is an independent variable that should be used to design systems and attempt to simulate and predict how a system will behave in real life. According to Thelen and Smith (1996), a dynamical system provides a framework to conceptualize, operationalize, and formalize the linkage of time, subsystems, and process. By treating time as an independent variable and a commonality between the theoretical and real-world, systems engineers can better examine the changes between subsystems and processes over time. This will also help with error prevention, which stems from behavioral, systemic or worker, prediction. When using time as an independent variable, the experiment can still be under “experimental control,” but visuals or evidence comparing and contrasting other variables in the system can be compelling (Gelfand, 2012).

Additionally, the use of experimental control commonly used by engineering psychologists implies creating a boundary, often a small one, for the issue identified. This is the only way to achieve experimental control, where all variables can stay controlled and constant, only maneuvering one variable at a time to avoid any unidentifiable compounding factors. Yet, the interactions within a nonlinear and dynamic system are often coupled. When modeling and designing a nonlinear system or a system that will be placed in a more extensive nonlinear system, it cannot be understood by components as each subsystem (Goldberger, 1996). The findings will not be an accurate representation of the real world. When using the DST approach, the DST broadens engineering psychologists’ view of the system and how one action change can lead to a cascade of events. Although two systems with identical initial states can have two differing results (Karwowski, 2012), when DST is used with simulations, it will help engineers map out the most common occurrences under those conditions and thus used for designing the system, along with embracing and accepting chaos as normal occurrences in the system (Karwowski, 2012). It can also be used to observe the self-organizing nature of a dynamical system.

References

Doyle, J. K. (1997). The cognitive psychology of systems thinking. System Dynamics Review, 13(3), 253–265. https://doi-org.ezproxy.libproxy.db.erau.edu/10.1002/(SICI)1099-1727(199723)13:3<253::AID-SDR129>3.0.CO;2-H

Gelfand, L. A., & Engelhart, S. (2012). Dynamical Systems Theory in Psychology: Assistance for the Lay Reader is Required. Frontiers in Psychology, 3, 1–3. https://doi.org/10.3389/fpsyg.2012.00382

Goldberger, A. (1996). Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. The Lancet, 347(9011), 1312–1314. https://doi.org/10.1016/s0140-6736(96)90948-4

Hooker, C. A. (1997). Dynamical systems in development: Review essay of Linda V. Smith & Esther Thelen (Eds)a dynamics systems approach to development: Applications. Philosophical Psychology, 10(1), 103–112. https://doi.org/10.1080/09515089708573209

Karwowski, W. (2012). A Review of Human Factors Challenges of Complex Adaptive Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(6), 983–995. https://doi.org/10.1177/0018720812467459

Thelen, E., & Smith, L. B. (1996). A Dynamic Systems Approach to the Development of Cognition and Action (Cognitive Psychology). A Bradford Book. https://cogdev.sitehost.iu.edu/labwork/handbook.pdf