We are seeking further contributions for the Complex Systems Toolkit. Please register your interest in developing a resource by 12th July 2026. NOTE: This deadline has been extended to 26th July.

 

Background

In November 2025 the EPC, with support from Quanser, launched a new Complex Systems Toolkit, aimed at providing accessible, practical resources for embedding complex systems concepts into engineering education.

The Toolkit launched with an abundance of resources, allowing educators and industry professionals to dive into the ‘what’ and ‘how’ of complex systems with knowledge and guidance articles, discover ready-to-use teaching resources including case studies and other classroom activities, and hear directly from the creators and partners who helped shape the Toolkit with a well-attended launch webinar (now available to watch on demand).

These resources have been well used in their first six months, but we’re not stopping there. We want to add further resources, on topics that are emerging as being of vital importance to students as they graduate and seek work. The first of the topics that we want to cover is intelligent robotics.

 

What and why?

Intelligent robotics, and the more recent applications to physical AI, generally refers to artificial intelligence systems that are embedded in and interact directly with the physical world, rather than operating purely in digital environments. This includes technologies like robots, autonomous vehicles, and drones that can perceive their surroundings through sensors, process that information using AI models, and take real-world actions. Unlike traditional software-based AI, intelligent robotics applications deal with real-time constraints, uncertainty, and complex environments, requiring tight integration between hardware (like sensors and actuators) and decision-making algorithms.

For engineering students, learning about intelligent robotics and physical AI workflows matters because it sits at the intersection of software, hardware, and real-world problem solving. It forces students to grapple with uncertainty, noisy sensor data, timing constraints, and safety considerations, which are unavoidable in real systems like robots or autonomous vehicles. That experience builds practical intuition about how algorithms behave outside ideal conditions. Engineers who understand this are better equipped to design systems that are robust, adaptive, and resilient. Industries are moving rapidly toward automation, robotics, and intelligent infrastructure, so familiarity with intelligent robotics and physical AI workflows opens doors in fields like manufacturing, healthcare technology, and transportation. It helps engineers think holistically: not just “does the code work?” but “does the system behave safely and effectively in the real world?”.

 

Contributors sought to develop resources on Intelligent Robotics for inclusion in the toolkit

We are seeking experts in intelligent robotics, from academia, industry, and engineering organisations, to develop resources on this topic for publication in the Complex Systems Toolkit. These resources will inform, guide and aid educators to embed teaching on intelligent robotics into their engineering lessons, modules or courses.

We invite contributors to develop resources in three areas:

We’re also looking for experts in intelligent robotics and physical AI to join us as reviewers and working group members.

 

We are seeking content on the following topics

Resources should reference the topic’s relationship to complex systems and engineering education/graduate skills. We are particularly interested in resources that help engineering educators teach these topics effectively.

 

 

Read more about the specific content we are looking for (click on the arrows to expand the sections)

Submit a knowledge article

Submit a knowledge article

As well as choosing a topic, you will need to choose an angle for your resource.

For knowledge articles. contributors might consider one of the following:

  • What it is: explaining the topic and its relation to complex systems.
  • Why educators should teach it / students should learn it.
  • Why it should be integrated into engineering education.
  • An angle of your own choosing.

These articles should connect the why (why must teaching about the topic be present in engineering education?) to the how (how can this be done efficiently and effectively?). Through these tools, we aim to help upskill UK engineering educators so that they feel capable of and confident in integrating complex systems concepts and intelligent robotics topics into their engineering teaching.

 

Step 1: Read the guidance for submitting a knowledge article

Research:

Knowledge articles are resources that users can access to improve their knowledge or find more information. These are intended to provide theoretical and practical background on complex systems concepts and tools such as modelling or decision-making approaches. While guidance articles focus on “how”, knowledge articles focus on “what”.

Before you begin, you should review existing Complex Systems Toolkit knowledge articles, since we hope that contributions will be fairly consistent in length, style, and tone.

Knowledge articles are meant to be overviews that a reader with no prior knowledge of the topic could refer to in order to develop a baseline understanding and learn where to look for additional information (they can reference other sources). They should be understandable to students as well: imagine that an educator might excerpt content from the article to provide their students context on a project or learning activity.

They should be approximately 500-1000 words (although they can be more in depth if necessary) and reference relevant online open-source resources.

Overview:

The articles are meant to be able to stand on their own as a piece of knowledge on a topic; they are also meant to work alongside other articles so that taken together they form a sort of complex systems in engineering handbook.

Purpose:

Each article should inform, explain, and provide knowledge on the topic. Put yourself in the perspective of an engineering educator who is new to the topic.

Content:

The content of the article should be organised and well developed. That is, it should be presented in a logical way and thoroughly explained.

References and resources:

Where additional explanation could be given, it might point to other resources, and where information is presented from another source, it needs to be properly referenced using Harvard referencing.

Format:

Knowledge articles should follow this format:

  • Premise;
  • Body of article, divided up into headed sections as necessary;
  • Conclusion (optional);
  • References: use Harvard referencing;
  • Resources (online and open source).

 

Step 2: Before you submit, review this checklist

  • Does the article both make sense as a single piece of content as well as fit in with the rest of the knowledge articles?
  • Would someone new to this complex systems topic understand the information presented and would it help them?
  • Do you need to expand on any ideas or reorganise them to make them clearer?
  • What additional resources or references have you included?
  • Are open resources or links to other toolkit materials included?
  • Are sources cited using Harvard referencing?

 

Step 3: Submitting your knowledge article:

Knowledge articles should be submitted in Word file format (.doc or .docx).

Also submit any additional resources such as spreadsheets, handouts etc., and ensure that they are in an editable format. Please clarify where in the resource these should be embedded or linked.

Any corresponding images should be submitted in either .jpeg, .jpg or .png format. We need these to be uploaded separately from the Word file, as we will be embedding them in a web page. Please ensure that they are of high resolution and adequate size (we suggest a minimum of 800 pixels wide); that you have the right or permission to use them (bearing in mind they will be published under a Creative Commons license); and that you have added any permissions, sources, credits or other details for them in the body of the document that you are submitting.

To ensure that everyone can use and adapt the Toolkit resources in a way that best fits their teaching or purpose, this work will be licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Under this licence users are free to share and adapt this material, under terms that they must give appropriate credit and attribution to the original material and indicate if any changes are made.

Download a copy of this guidance

Submit your knowledge article here

Submit a guidance article

Submit a guidance article

As well as choosing a topic, you will need to choose an angle for your resource.

For guidance articles, contributors might consider one of the following:

  • Guide to explaining the topic to students.
  • How to assess for skills / competencies on this topic.
  • An angle of your own choosing.

These articles should also connect the why (why must teaching about this topic be present in engineering education?) to the how (how can this be done efficiently and effectively?). Through these tools, we aim to help upskill UK engineering educators so that they feel capable of and confident in integrating complex systems concepts and intelligent robotics topics into their engineering teaching.

 

Step 1: Read the guidance for submitting a guidance article

Research:

Guidance articles are resources that users can access to learn how to do something. These are intended to provide practical advice on subjects such as how to explain complex systems to students, or how to assess for skills and competencies in complex systems. While knowledge articles focus on “what”, guidance articles should focus on “how.”

Before you begin, you should review existing Complex Systems Toolkit guidance articles, since we hope that contributions will be fairly consistent in length, style, and tone.

Guidance articles aim to help situate our teaching resources in an educational context and to signpost to additional research and resources on complex systems theory and tools.

They should be approximately 500-1000 words (although they can be more in depth if necessary) and reference relevant online open-source resources.

Overview:

Guidance articles are meant to be able to stand on their own as a piece of guidance on a topic; they are also meant to work alongside other articles so that taken together they form a sort of complex systems in engineering handbook.

Purpose:

Each article should inform, explain, and provide guidance on the topic. Put yourself in the perspective of an engineering educator who is new to the topic.

Content:

The content of the article should be organised and well developed. That is, it should be presented in a logical way and thoroughly explained.

References and resources:

Where additional explanation could be given, it might point to other resources, and where information is presented from another source, it needs to be properly referenced using Harvard referencing.

Format:

Guidance articles should follow this format:

  • Premise;
  • Body of article, divided up into headed sections as necessary;
  • Conclusion (optional);
  • References: use Harvard referencing;
  • Resources (online and open source).

 

Step 2: Before you submit, review this checklist

  • Does the article both make sense as a single piece of content as well as fit in with the rest of the guidance articles?
  • Would someone new to this complex systems topic understand the information presented and would it help them?
  • Is the explanation clear, logically structured and technically accurate?
  • Do you need to expand on any ideas or reorganise them to make them clearer?
  • What additional resources or references have you included?
  • Are open resources or links to other toolkit materials included?
  • Are sources cited using Harvard referencing?

 

Step 3: Submitting your guidance article

Guidance articles should be submitted in Word file format (.doc or .docx).

Also submit any additional resources such as spreadsheets, handouts etc., and ensure that they are in an editable format. Please clarify where in the resource these should be embedded or linked.

Any corresponding images should be submitted in either .jpeg, .jpg or .png format. We need these to be uploaded separately from the Word file, as we will be embedding them in a web page. Please ensure that they are of high resolution and adequate size (we suggest a minimum of 800 pixels wide); that you have the right or permission to use them (bearing in mind they will be published under a Creative Commons license); and that you have added any permissions, sources, credits or other details for them in the body of the document that you are submitting.

To ensure that everyone can use and adapt the Toolkit resources in a way that best fits their teaching or purpose, this work will be licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Under this licence users are free to share and adapt this material, under terms that they must give appropriate credit and attribution to the original material and indicate if any changes are made.

Download a copy of this guidance

Submit your guidance article here.

Submit a teaching activity

 

Submit a teaching activity/resource

As well as choosing a topic, you will need to choose an angle for your resource.

For activities, contributors might consider one of the following:

  • Case studies that, through a real-world situation, illustrate the topic and its relation to complex systems, use cases for the tools that can be used to model / simulate this, techniques that promote development and use of systems architecture, and effects such as trade-offs, emergent properties, impacts, or unintended consequences. Case studies could also reference the implications for risk, security, ethics, sustainability, teamwork, and communication.
  • Demonstrator simulations that provide examples of how systems can be modelled.
    • This could include:
      • Examples of how the topic relates complex systems
      • Interactive examples showing how well-intentioned action can lead to failure
      • Interactive examples showing the best approaches to handling complexity
  • Teaching/learning activities, coursework, project briefs, lesson plans, modelling or simulation exercise/activities, technical content related to complex systems, worksheets, slides, robotics labs, swarm behaviour activities, system mapping exercises, hardware-in-the-loop demonstrations, digital twin exercises, or other teaching materials.
  • An angle of your own choosing.

These resources should promote active learning pedagogies and real-world teaching methods by showing how complex systems teaching can be embedded within technical problems and engineering practice. Through these resources, we aim to help upskill UK engineering educators so that they feel capable of and confident in integrating complex systems into their engineering teaching.

 

Step 1: Read the guidance for submitting a teaching activity/resource

Research:

Teaching activities are resources that users can access to help them know what to integrate and implement. These include use cases/case studies which provide examples of complex systems topics which can be directly utilised in teaching with the suggested tools, as well as other classroom activities such as coursework, project briefs, lesson plans, simulation exercises, robotics labs, swarm behaviour activities, system mapping exercises, hardware-in-the-loop demonstrations, digital twin exercises, or other exercises.

Before you begin, you should review existing Complex Systems Toolkit teaching resources, since we hope that contributions will be fairly consistent in length, style, tone, format and approach. Remember that the audience for these resources is educators seeking to embed complex systems topics within their engineering teaching.

 

Step 1a: Guidance for submitting a case study

Case studies present real-world scenarios that can be used in teaching about complex systems topics in engineering. They provide students with opportunities to explore complex systems tools, and trade-offs, in authentic contexts, and reflect on decisions made about them.

They are usually based on a real example, although fictionalised cases are acceptable when they are grounded in realistic detail. Case studies should enable students to identify or interpret key features of complex systems topics (feedback loops, interdependence or emergent behaviour) and apply relevant tools or frameworks to make sense of the situation.

Case studies will vary in length depending on scope and resource, but many are around 1500-2000 words. They should reference relevant online open-source resources.

Please see the current research on good practice in writing case studies, which you may find helpful as you write, as well as our article about a recipe for writing a case study. This ‘recipe’ can guide you as you write to include or develop other aspects of the case. Both articles are from our Engineering Ethics Toolkit, but the guidance given can be adapted for complex systems cases.

Overview:

The case study should be presented as a narrative about a complex systems issue in engineering.

Narrative strength: the case should be clearly structured with a compelling and coherent story.

System complexity: it should explore interdependencies, multiple stakeholders and/or competing goals.

Tool integration: systems tools should be mentioned or incorporated (e.g. soft systems methodology, SysML, Agent-based modelling etc).

Activities and Resources: there should be questions, prompts or teaching activities to guide discussion or classroom use.

Authenticity:

Case studies are most effective when they feel like they are realistic, with characters that you can identify or empathise with, and with situations that do not feel fake or staged. Giving characters names and backgrounds, including emotional responses, and referencing real-life experiences help to increase authenticity.

Complexity of issue:

Many cases are either overly complicated so that they become overwhelming, or so straightforward that they can be “solved” quickly. A good strategy is to try to develop multiple dimensions of a case, but not too many that it becomes unwieldy. Additionally, complexity can be added through different parts of the case so that instructors can choose a simpler or more complicated version depending on what they need in their educational context.

Activities and resources:

You should provide a variety of suggestions for discussion points and activities to engage learners, as well as a list of reliable, authoritative open-source online resources, to both help educators prepare and to enhance students’ learning. Where information is presented from another source, it needs to be properly referenced using Harvard referencing.

Educational level and assessment:

Educational level: When writing your case study, you should consider which level it is aimed at. A Beginner level case is aimed at learners who have not had much experience in engaging with this complex systems topic or problem, and usually focuses on only one or two dimensions of a challenge. An Advanced-level case is aimed at learners who have had previous practice in engaging with this complex systems topic or problem, and often addresses multiple challenges. An Intermediate case is somewhere in between.

Assessment: If possible, suggest assessment opportunities for activities within the case, such as marking rubrics or example answers.

Format:

The case study should follow the following format:

  • Teaching notes (with learning objectives, time needed, materials): This is an overview of the case and its dilemma, and how it relates to AHEP4 and INCOSE competencies.
  • Learning and teaching resources: A list of reliable, authoritative, open-source online resources that relate to the case and its dilemma. These can be from a variety of sources, such as academic institutions, journals, news websites, business, and so on. We suggest a minimum of five sources that help to provide context to the case and its dilemmas.
  • Summary of system or context.
  • Narrative of the case (presenting the complexity).
  • Questions and activities. This is where you provide suggestions for discussions and activities related to the case and the dilemma.
  • Further discussion or challenge (optional). Some case studies are sufficiently complex at one dilemma, but if the case requires it you can provide further parts (up to a maximum of three).
  • References: use Harvard referencing.
  • If possible, suggest assessment opportunities for activities within the case, such as marking rubrics or example answers.
  • Keywords: On the submission form you will be prompted to provide keywords, including educational aims, issues and situations highlighted in the case.

 

Step 2a: Before you submit, review this checklist:

  • Does it follow the correct format?
  • Narrative strength: is the case clearly structured with a compelling and coherent story?
  • System complexity: does it explore interdependencies, multiple stakeholders and/or competing goals?
  • Tool integration: are systems tools mentioned or incorporated (e.g. soft systems methodology, SysML, Agent-based modelling etc)?
  • Activities and resources: are there questions, prompts or teaching activities to guide discussion or classroom use?
  • Are open resources or links to other toolkit materials included?
  • Are sources cited using Harvard referencing?
  • What additional references have you included?

 

Step 1b: Read the guidance for submitting a different teaching activity

 Purpose & outcomes:

Teaching activities/tools are intended to support educators’ ability to apply and embed complex systems concepts and topics within their engineering teaching.

Educators need to quickly and easily find help with:

  • Adapting and integrating existing complex systems resources to their disciplinary context.
  • Implementing new and different pedagogies that support complex systems learning.
  • Structuring lessons, modules, and programmes so that complex systems skills and outcomes are central themes.

Thus, these teaching activities/tools will provide crucial guidance for those who may be teaching complex systems related material for the first time, or who are looking for new and different ways to integrate complex systems concepts or topics into their teaching.

Teaching activities/tools may take the form of learning activities, coursework, project briefs, lesson plans, modelling or simulation exercise/activities, technical content related to complex systems, worksheets, slides, robotics labs, swarm behaviour activities, system mapping exercises, hardware-in-the-loop demonstrations, digital twin exercises, or other similar teaching materials.

Research:

Before you begin to write, you should familiarise yourself with existing Complex Systems Toolkit teaching resources, as well as content that has been created to complement case studies in our Ethics Toolkit and teaching tools in our Sustainability Toolkit, since we want these resources to be produced in a similar style and format.

Purpose:

Imagine that you are an engineering educator who is new to teaching complex systems concepts or topics. You turn to this teaching tool to help you apply and embed these in your module.

  • Does this resource help introduce or develop concepts related to complex systems or systems thinking so that learners can engage with these topics in the context of engineering?
  • If not, what is needed to make this possible?

Presentation and clarity:

Depending on the resource, you may choose to provide worksheets, slides, problem sets, narrative prompts, etc.

  • Is the resource explained in such a way that someone new to teaching complex systems could understand how to use it?
  • Is the material clearly introduced and described?

Resources and guidance:

Depending on the topic, educators may need additional resources or guidance to support their use of the material. For instance, background information may be required or a technical topic explained.

  • Have you provided sufficient material so that educators can easily employ the resource?
  • Do references use Harvard referencing?

Format:

The teaching activity/tool should follow this format:

  • Overview:
    • Short description of what the resource is and what it aims to do.
    • States how it is related to complex systems or systems thinking topic(s), referring to external content such as INCOSE Competencies and AHEP 4.
    • Provides an overview of the activity, suggesting how it might be implemented and in what contexts, how long it might take, and any other relevant delivery information.
  • Details any specific materials or software required for the activity, as well as any modelling or simulation tools to be used.
  • Lists any learning and teaching resources recommended in order to undertake the activity, including suggested pre-reading or other references.
  • Explains the activity in as much detail as is required (this will vary depending on the type of material the resource addresses.)
  • If relevant, provides assessment guidance–marking rubrics, sample answers, etc.

 

Step 2b: Before you submit, review this checklist:

  • Does this resource help introduce or develop concepts/topics related to complex systems or systems thinking so that learners can engage with these topics in the context of engineering?
  • Is the resource explained in such a way that someone new to teaching complex systems could understand how to use it?
  • Is the material clearly introduced and described?
  • Have you provided sufficient material so that educators can easily employ the resource?
  • Do references use Harvard referencing?
  • Does it follow the correct format?

 

Step 3: Submitting your teaching activity/resource

 Teaching resources should be submitted in Word file format (.doc or .docx).

Also submit any additional resources such as spreadsheets, handouts etc., and ensure that they are in an editable format. Please clarify where in the resource these should be embedded or linked.

Any corresponding images should be submitted in either .jpeg, .jpg or .png format. We need these to be uploaded separately from the Word file, as we will be embedding them in a web page. Please ensure that they are of high resolution and adequate size (we suggest a minimum of 800 pixels wide); that you have the right or permission to use them (bearing in mind they will be published under a Creative Commons license); and that you have added any permissions, sources, credits or other details for them in the body of the document that you are submitting.

To ensure that everyone can use and adapt the Toolkit resources in a way that best fits their teaching or purpose, this work will be licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Under this licence users are free to share and adapt this material, under terms that they must give appropriate credit and attribution to the original material and indicate if any changes are made.

Download a copy of this guidance

Submit your teaching resource here

 

 

Register your interest

 

Additional information

In undertaking this work, contributors will become part of the growing community of educators who are helping to ensure that tomorrow’s engineering professionals have the complex systems skills, knowledge, and attributes that they need to provide a better future for us all. Contributors will be fully credited for their work on any relevant Toolkit materials and will be acknowledged as authors should the resources be published in any form. Developing these resources will provide the chance to work with a dynamic, diverse and passionate group of people leading the way in expanding engineering teaching resources, and may help in professional development, such as preparing for promotion or fellowship.

 

This post is also available here.

 

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.  

Peter Martin, Director of Research & Development at Quanser, and co-chair of the Complex Systems Toolkit Working Group, reflects on the importance of engineers understanding complex systems when working in the field of intelligent robotics.

 

“In late 2024 I had the opportunity to join the EPC Complex Systems Toolkit team as co-chair of the working group. At the time I felt a little fraudulent, as the intricacies of complex systems thinking was new to me. I had brushed up against complex systems numerous times over the years as I had studied and worked in the world of robotics for over 20 years. However, I had never discovered the world of formal complex systems analysis. Looking back, this is a perfect validation for the need to create a toolkit to better prepare students for careers like mine. As I have learned more over the last 18 months about the tools and techniques that systems engineers employ to model and manage complexity, the critical value that these techniques offer engineers in the world of intelligent robotics has become obvious. As we hear often in the field of engineering lab equipment for the academic space, “I wish I’d had this when I was at university”.

The other reassuring aspect of my experience, for me, is that I’m not alone. A growing need for better approaches to managing complexity has emerged in industry over the last couple of decades as robotics and their governing systems have become increasingly integrated into society. This transition of robotics out of the structured environment of the factory floor and into direct contact with both the dynamic and unstructured world and the public, has introduced a high degree of non-linear predictability, complex interactions with multiple robotic agents, and emergent behaviours as the decision-making algorithms that dictate robotic behaviour adapt. All of these elements are central to the world of complex systems analysis.

At a high level, modern robotics systems no longer represent technical engineering challenges in the narrow, discipline-specific sense that engineers would traditionally have seen in higher education. They are complex adaptive systems that routinely demonstrate behaviours that emerge from interactions with their environment rather than being fully specified in advance. A robot navigating a hospital corridor, a swarm coordinating warehouse logistics, or a surgical assistant adjusting in real time to tissue variability represent challenges in undefined, non-linear, and largely unpredictable spaces. Students, and later robotics engineers who lack a complex systems vocabulary are essentially tasked with trying to understand emergence without the tools to describe it.

An example that I like to use is one that we encountered a couple of years ago: a team of mobile robots transporting parts around a manufacturing space. In many cases, the agents (ground robots, arms, etc.) in this scenario are programmed with independent control and decision-making code to govern their behaviour, with some overarching supervisory code to manage tasks and assignments. The ground robots would have algorithms to localise, path plan, navigate, and avoid obstacles while communicating with other complementary agents and central task management. However, as I have learned, complexity lies in the emergence of unexpected interactions between the agents and their environment. How they avoid each other and the environment while achieving their tasks is largely a complex non-linear system where conflicts can routinely delay or disrupt their operation. Introducing more sources of disruption such as humans, unstructured environments, weather conditions etc. only makes dealing with unpredictable scenarios more and more complicated using traditional techniques.

Luckily, many of the tools and techniques that are highlighted in the toolkit have direct applications to the challenges faced by engineers in the world of robotics. Causal Loop Diagrams (CLDs) are an excellent way to model the feedback dynamics that are at play in adaptive control systems. When a robot’s perception system updates its world model based on changes in what the sensors can perceive, that leads to changes in its action policy that when executed create a feedback loop. These diagrams are a great way to visualise and analyse these loops. Agent-Based Modelling (ABM) is directly relevant to the scenario I described above where swarms of robot must be coordinated or manage human-robot interaction scenarios. Using these simulation tools, engineers can test and manage emergent fleet behaviour without hardware. If things do go sideways, Fault Tree Analysis is a common approach to mapping causes and evaluating data to help develop robots that work in safety-critical applications. Finally, for long-term operations such as field robotics missions, Systems Dynamics Modelling can be a useful tool for predicting and managing a robot’s resource consumption (battery, compute, bandwidth) depending on the required task performance over time.

In addition to these considerations, there is a whole world of network modelling and the management of behaviour stemming from machine learning and applied AI algorithms that also overlaps quite closely with complex systems. Engineers that understand emergence, feedback loops, and attractors are far better equipped to reason about why a robot does something unexpected, than students who only have a component-driven technical understanding of the behaviour of an intelligent robot. Beyond the decisions, at an actual component level there are critical decisions that need to be made for efficient deployment of physical and edge AI algorithms.  What data is processed locally and what goes to the cloud, when models are updated and how decision making is distributed across a robot swarm are exactly the kind of questions that systems thinking trains engineers to answer. Systems tools are ready to help, including influence diagrams to manage information exchange and action planning.

Overall, the field of complex systems introduces a set of tools, techniques, and mental models that are increasingly essential to robotics engineers that seek to prepare their agents to be effective in performing complicated tasks in increasingly complex systems.”

 

Click here to see details of our newest call for content, on the subject of intelligent robotics and its relationship to complex systems. Register your interest by 12th July 2026

Intelligent robotics, and the more recent applications to physical AI, generally refers to artificial intelligence systems that are embedded in and interact directly with the physical world, rather than operating purely in digital environments. For engineering students, learning about intelligent robotics and physical AI workflows matters because it sits at the intersection of software, hardware, and real-world problem solving. It helps engineers think holistically: not just “does the code work?” but “does the system behave safely and effectively in the real world?”. 

We are seeking experts in intelligent robotics, from academia, industry, and engineering organisations, to develop resources on this topic for publication in the Toolkit. These resources will inform, guide and aid educators to embed teaching on intelligent robotics into their engineering lessons, modules or courses.

 

This post is also available here.

 

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.  

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: Emergence; Boundaries; Unpredictability; Instability; System dynamics model; Digital engineering tools.

Licensing: This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. 

Downloads: A PDF of this resource will be 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.

 

2. Understanding emergence:

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: 

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: 

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:

Engineers can identify emergence by looking for: 

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: 

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:

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. 

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. 

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. 

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: 

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:

 

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.  

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