Behavior and Dynamics of Systems

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• Lead Author:
• Gary Smith

• Contributing Author:
• Gary Smith

SEBoK v. 2.13, released 17 November 2025

Acknowledgement

This article is part of The Nature of Systems area (KA). The article reflects established knowledge from systems science and systems engineering, organized and collated for the SEBoK. Drafting support was provided by OpenAI’s ChatGPT, with all content reviewed and finalized by the author, who retains full responsibility.

Introduction

Behaviour and dynamics describe how systems act and change over time. Behaviour refers to the externally observable responses or outputs of a system, while dynamics refers to the internal processes and interactions that give rise to these behaviours. Together, they provide an internal perspective on Form, complementing concepts such as identity and togetherness, and distinct from the temporal rhythms described in Cycles and Phases of Systems.

Understanding behaviour and dynamics is critical for systems engineers. Internal dynamics such as feedback, thresholds, and attractors shape how systems behave under stress, adapt to change, or collapse into dysfunction. These mechanisms underpin, but are not the same as, the recurring temporal patterns of Function described in Cycles and Phases.

Definitions and Perspectives

• Behaviour: The observable outcomes of a system as it interacts with its environment.
• Dynamics: The internal mechanisms, feedback, flows, state transitions, that drive and constrain behaviour.

Key perspectives include:

• Boulding (1956): levels of system behaviour from simple reactive to goal-seeking and learning.
• Forrester (1961): system dynamics, focusing on stocks, flows, and feedback loops.
• Troncale (1978, 1985): systems isomorphies such as Feedback, Oscillation, and Regulation.
• Rosen (1985): anticipatory systems, where internal models shape behaviour.
• Mobus & Kalton (2015): principles of systemic dynamics including stability, chaos, and adaptation.

Transversal Context

Examples across domains illustrate how dynamics generate behavior:

• Physical systems: oscillations of a pendulum, turbulence, critical transitions in fluids.
• Biological systems: homeostasis, neural firing, adaptation to stimuli.
• Ecological systems: predator–prey growth and decline, resilience, sudden regime shifts.
• Social systems: reinforcing loops in markets, diffusion of innovations, collective behaviours.
• Engineered systems: feedback control in aircraft, congestion dynamics in networks, emergent effects in software.

These cases show that while behaviour is what we observe, it is dynamics that explain why systems act as they do.

Patterns and Archetypes of System Behaviour and Dynamics

Systems exhibit recurring dynamic modes generated by internal mechanisms such as feedback, nonlinearity, and state dependence. These archetypes form the mechanistic basis for the cycles and phases described in the companion article Cycles and Phases of Systems.

Stability and Equilibrium

• Description: A system resists change and maintains its state through balancing feedback.
• System example: The body’s temperature regulation through sweating and shivering.
• Engineering example: An aircraft autopilot holding altitude by adjusting control surfaces.
• Lesson for SE: Stability depends on effective balancing feedback and control; poorly tuned loops can cause drift or oscillation.

Oscillation and Feedback Loops

• Description: Interactions of reinforcing and balancing loops generate cycles around an equilibrium.
• System example: Predator–prey population swings in ecosystems.
• Engineering example: Underdamped suspension systems producing oscillations in vehicles.
• Lesson for SE: Oscillations can be beneficial (rhythmic processes) or harmful (instability); understanding feedback strengths is critical.

Attractors and Dynamic Regimes

• Description: Systems converge toward stable points, cycles, or chaotic attractors that define long-term behaviour.
• System example: The Lorenz “butterfly” attractor in weather models.
• Engineering example: Internet traffic stabilizing at a particular level of congestion.
• Lesson for SE: Attractors help anticipate possible end states; design should account for which regime the system may settle into.

Nonlinear Growth and Saturation

• Description: Reinforcing feedback leads to exponential growth until constraints produce saturation.
• System example: Logistic growth of bacteria in a nutrient-rich medium.
• Engineering example: Adoption of new technologies following an S-curve.
• Lesson for SE: Anticipating resource constraints and saturation points avoids overdesign and unexpected decline in performance.

Criticality and Tipping Points

• Description: Small changes can push systems across thresholds, causing large regime shifts.
• System example: Sudden collapse of fisheries once population drops below a critical threshold.
• Engineering example: Cascading failures in a power grid triggered by a single line outage.
• Lesson for SE: Early warning indicators of criticality (stress, load, redundancy loss) are essential for risk management.

Path Dependence and Hysteresis

• Description: The trajectory of system behavior depends on past states, and recovery does not follow the dame path as decline.
• System example: Ecosystems degraded by deforestation may not recover even if regrowth occurs.
• Engineering example: Legacy IT systems locking organizations into architectures that are difficult to replace.
• Lesson for SE: History matters; decisions create lock-in, so architects should anticipate long-term dependencies.

Adaptive Dynamics

• Description: Systems may reorganize their internal structures and feedback processes in response to disturbances, producing new modes of behavior.
• System example: An ecosystem reorganizing after a disturbance, shifting species dominance but maintaining function.
• Engineering example: Resilient infrastructure rerouting flows after a component failure.
• Lesson for SE: Adaptive dynamics increase resilience by enabling systems to absorb shocks and reconfigure, but can also introduce unpredictability and unintended consequences.

Figure 1. Archetypes of System Behaviour and Dynamics.

This figure illustrates seven archetypal modes of system dynamics: stability, oscillation, attractors, growth, criticality, path dependence, and adaptive dynamics. The Watt flyball governor, shown at the centre, symbolizes the internal mechanisms of feedback and regulation that generate such behaviours.

The governor demonstrates in mechanical form how centrifugal force, feedback, and thresholds can stabilize a system against perturbations, or, if mis-tuned, lead to oscillation and collapse. Similarly, across natural, social, and engineered systems, internal processes such as balancing and reinforcing feedback, attractors, and nonlinear thresholds drive recurrent dynamic modes.

These archetypes provide analogies for systems engineering practice:

• Stability in control loops and architectural balance.
• Oscillation in under-damped systems or cyclical market behaviours.
• Attractors in enterprise dynamics and traffic flow equilibria.
• Growth and saturation in technology adoption.
• Criticality in cascading failures such as power grids.
• Path dependence in legacy architectures.
• Adaptive reorganization in resilient infrastructures.

By focusing on dynamics as the engine of behaviour, the figure highlights how internal processes shape observable system outcomes, and how understanding these mechanisms helps systems engineers anticipate, design for, and manage complex behaviour.

Theories and Frameworks

• System Dynamics (Forrester, Meadows): models of stocks, flows, and feedback.
• Control Theory and Cybernetics: regulation, stability, and homeostasis.
• Complex Adaptive Systems: nonlinear interactions, emergence, and co-evolution.
• Chaos Theory: sensitivity to initial conditions and unpredictability.
• Troncale’s Systems Processes: cross-domain isomorphies of feedback and oscillation.

Implications for Systems Engineering

System dynamics have direct consequences across the lifecycle:

• Requirements and SoI Definition: Anticipating desired behaviors requires attention to dynamic mechanisms, not just static functions. Poorly specified dynamics can produce instability or unintended responses.
• Architecture and Design: Togetherness of parts must be structured through feedback and control. Examples include avoiding oscillations in flight control or ensuring damping in mechanical systems.
• Modelling and Simulation: Dynamic models such as system dynamics, MBSE, agent-based simulation, are vital tools to test how internal processes shape external behaviour.
• Integration, Verification, and Validation: Traceability depends on ensuring that the integrated dynamics generate the intended behaviours across conditions.
• Risk and Safety: Understanding reinforcing loops, tipping points, and collapse dynamics is essential to preventing failure cascades (e.g., power grid blackouts).
• Enterprise and SoS Engineering: Emergent dynamics arise from interactions among independently managed systems; governance and coordination are needed to maintain coherence.
• Sustainability: Designing with feedback from natural and social systems in mind helps create resilient and adaptive engineered systems.

References

Works Cited

• Boulding, K. (1956). “General systems theory: The skeleton of science.” Management Science, 2(3), 197–208.
• Forrester, J. (1961). Industrial Dynamics. MIT Press.
• Meadows, D. (2008). Thinking in Systems. Chelsea Green.
• Rosen, R. (1985). Anticipatory Systems. Pergamon.
• Troncale, L. R. (1978, 1985). Systems isomorphies on Feedback, Oscillations, Regulation.

Primary References

• Holling, C. S. (1973). “Resilience and stability of ecological systems.” Annual Review of Ecology and Systematics, 4, 1–23.
• Mobus, G. E., & Kalton, M. C. (2015). Principles of Systems Science. Springer.
• Taleb, N. N. (2012). Antifragile: Things That Gain from Disorder. Random House.
• ISO/IEC/IEEE. (2023). ISO/IEC/IEEE 15288:2023 Systems and Software Engineering — System Life Cycle Processes. ISO/IEC/IEEE.
• INCOSE. (2022). Systems Engineering Vision 2035: Engineering Solutions for a Better World. INCOSE.

Additional References

None