Toolkit: Complex Systems Toolkit.
Author: Onyekachi Nwafor (KatexPower).
Topic: Emergence in complex systems.
Title: Understanding emergence in complex engineering systems.
Resource type: Knowledge article.
Relevant disciplines: Any.
Keywords: Available soon.
Licensing: This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Downloads: Available soon.
Who is this article for?: This article should be read by educators at all levels in higher education who are seeking to provide students with an overall perspective on complex systems in engineering.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking and Critical Thinking INCOSE competencies.
AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems).
Learning and teaching resources:
Premise:
Engineering systems today are increasingly complex, interconnected, and adaptive. To understand and manage them effectively, engineers must move beyond reductionist thinking where systems are broken into isolated parts and adopt systems thinking, which views systems as wholes made up of interacting components.
At the heart of this perspective lies emergence, a defining characteristic of complex systems. Emergence refers to properties or behaviours that arise from interactions among components but cannot be predicted or understood by examining those components in isolation. Appreciating emergence helps engineers anticipate how individual design decisions can produce system-level outcomes, sometimes beneficial, sometimes negative and unintended.
This article introduces the concept of emergence as one key characteristic of complex systems, situates it within systems thinking, and provides practical guidance for recognising and managing emergent behaviours in engineering practice.
1. What is a system?:
A system can be defined as “a set of interconnected elements organised to achieve a purpose” (Meadows, 2008). Systems possess structure (components), relationships (interactions), and purpose (function). Engineering systems such as aircraft, power grids, transport networks, or data infrastructures are composed of numerous subsystems that depend on each other.
Crucially, systems thinking emphasises interdependence and feedback. The behaviour of the whole cannot be fully explained by the behaviour of the parts alone. Properties such as resilience, adaptability, and emergence result from interactions within the system’s structure and environment. Recognising these relationships is essential to understanding how system-level behaviours arise.
Emergence describes the appearance of new patterns, properties, or behaviours at the system level that are not present in individual components. These properties are often irreducible: they cannot be explained solely by analysing each part separately (Holland, 2014).
Researchers distinguish between:
- Weak emergence – behaviours that are theoretically predictable if all component interactions were known but are practically impossible to compute due to complexity (e.g. traffic flow patterns).
- Strong emergence – properties that are fundamentally novel and irreducible to component-level descriptions (e.g., consciousness in biological systems).
In engineering, most emergent behaviours are weakly emergent: complex yet explainable with sufficient data and computational tools such as agent-based modelling or system dynamics.
A key caveat is that emergence depends on perspective and system boundaries. What seems emergent at one scale (e.g., the stability of a power grid) might appear straightforward when viewed at another. Therefore, engineers must define boundaries and assumptions clearly when analysing emergence.
3. Why emergence matters in engineering:
Emergence shapes how engineering systems behave, evolve, and sometimes fail. It can produce both desired outcomes (like adaptability or resilience) and undesired ones (like instability or cascading failure).
Understanding emergence enables engineers to:
- anticipate how local interactions scale up to global system behaviour;
- design feedback loops and architectures that promote stability; and
- identify potential points for intervention when emergent behaviour becomes undesirable.
For instance, in cyber-physical systems, emergent coordination can enhance efficiency, but it may also create unpredictable vulnerabilities if feedback loops reinforce errors. Engineers therefore must not only observe emergence but learn how to influence it through design and governance.
4. Recognising and managing emergent behaviour:
- Recognising emergence
Engineers can identify emergence by looking for:
-
- System-level patterns that do not trace directly to any single component (e.g. global traffic flow or collective oscillations in a power grid).
-
- Unexpected behaviours, such as new failure modes or self-organising phenomena.
-
- Scale-dependent properties, where behaviour changes qualitatively as the system grows or interacts with its environment.
-
- Adaptive or learning responses, where the system adjusts without explicit central control.
- Intervening in emergent systems
Not all emergence is beneficial. Engineers often need to mitigate unwanted emergent behaviours such as instability or inefficiency while reinforcing desirable ones. Effective approaches include:
-
- Redesigning interactions rather than individual components, focusing on how feedback and connectivity shape outcomes.
-
- Introducing constraints or buffers to dampen runaway feedback loops.
-
- Enhancing diversity and modularity so subsystems can adapt locally without propagating failures globally.
-
- Monitoring system states continuously, using sensors, data analytics, or digital twins to detect emergent behaviour early.
Managing emergence requires humility: complex systems cannot be fully controlled, only influenced. The goal is to guide system dynamics toward safe and productive outcomes.
5. Illustrative examples of emergence in engineering systems:
- Network systems
The Internet exemplifies emergence: billions of devices follow simple communication protocols, yet collectively create a resilient, adaptive global network. No single node dictates its performance; instead, routing efficiency and viral content propagation arise from local interactions among routers and users.
- Transportation systems
Urban traffic patterns such as congestion waves, spontaneous lane formation, and adaptive rerouting emerge from individual driver behaviour and infrastructural design. Traffic engineers use simulation models to study how simple decision rules generate complex city-wide flows.
- Energy systems
Electrical grids maintain frequency and voltage stability through distributed interactions among generators, loads, and controllers. Emergent synchronisation enables reliability, but loss of coordination can cause cascading blackouts showing both beneficial and harmful emergence.
- Manufacturing systems
In smart factories, machines and sensors collaborate autonomously, producing system-wide optimisation in scheduling and quality control. Adaptive algorithms and feedback loops create emergent flexibility beyond what central planning alone could achieve.
6. Practical guidance for engineers and educators:
For engineers, the key is to design with emergence in mind:
- focus on local rules that encourage desirable global behaviour;
- incorporate feedback and sensing to detect changes early; and
- use modular, diverse architectures to enhance resilience.
For educators, teaching emergence provides an opportunity to bridge theory and practice. Software such as NetLogo and Insight Maker allows students to visualise emergent behaviour through agent-based and system-dynamics models. Linking engineering examples to ecological, social, or digital systems helps learners appreciate the universality of emergence.
Conclusion:
Emergence is not an anomaly to be avoided but a natural attribute of complex systems. It challenges traditional engineering by revealing that system behaviour often arises from relationships, not components.
Understanding emergence equips engineers to recognise interdependencies, design adaptive solutions, and work with complexity rather than against it. By embracing systems thinking, engineers can create technologies that are not only functional but resilient, sustainable, and aligned with real-world dynamics.
References:
- Holland, J.H. (2014). Complexity: A Very Short Introduction. Oxford: Oxford University Press.
- Johnson, S. (2001). Emergence: The Connected Lives of Ants, Brains, Cities, and Software. New York: Scribner.
- Mitchell, M. (2009). Complexity: A Guided Tour. Oxford: Oxford University Press.
- Bar-Yam, Y. (2003). Dynamics of Complex Systems. Cambridge, MA: Perseus Publishing.
- Ottino, J.M. (2004). Engineering complex systems. Nature, 427(6973), 399.
- Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497(7447), 51-59.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
