The Engineering Mindset Part 1: Complex or Complicated

Are the individual, societal, and global challenges we face complex or complicated, and do you understand the distinction? Chris and Penny Hamlin explain how a complexity-based approach creates more meaningful and relevant insights

IMAGINE if we could channel the aptitude and innate problem-solving capabilities of engineers to tackle challenges such as climate change. Applying an engineering mindset to non-technical problems presents a great opportunity to look at things differently, and for each of us to have much greater impact.

To do this effectively it is essential to understand the differences between complicated and complex systems and know how to engage with each. The distinction can appear ambiguous but is very relevant to engineering and has the potential to make our contributions significantly more impactful.

What is a complex system and how do you recognise one?

Complex systems such as living organisms (including human beings) are inherently uncertain. Meaningful accurate prediction is not possible and very different trajectories and outcomes can arise from apparently similar starting points. But they are not chaotic or random. One of the most surprising and counterintuitive observations is that, due to the dynamic interplay of positive and negative feedback loops, complex systems are self-organising without any overarching coordination or intervention. Structures and characteristics emerge that are stable and robust over time. These can be influenced by actors within the system but cannot be overridden without destroying the system itself. Broadly speaking, we all share the same body shape without there being a blueprint and to some extent we can exert influence over how we look but we can’t choose to grow three arms or four legs.

Truly complex systems are inherently ambiguous – it’s not just that there is too much data, or that modelling would take too much effort. Accurate prediction is impossible because boundary conditions and feedback dynamics cannot be described or anticipated with sufficient precision. Complex systems can be modelled, but the results are only valid for understanding directionality and indicative behaviours. Psychology is useful in helping us to understand how people might behave or react but doesn’t enable clairvoyance.

By contrast, ordered systems, like trains and planes, always follow the same set of cause-and-effect relationships with the same end results. Subject to sufficient data and modelling effort, they are fully predictable. To a reasonably well-informed observer many systems are obviously ordered. However, there is a specific class – complicated systems – in which the order is only understood and comprehended by experts. Most of what we traditionally consider to be engineering falls into this category.

To a non-expert it is hard, if not impossible, to distinguish between complex and complicated systems. How many people on a plane to Ibiza understand why it stays in the sky or can predict how much fuel it’s going to need to get there, for instance? They are perceiving a complicated system as being complex even when it is not. Experts and non-experts will understand and interact with complicated systems in fundamentally different ways, but only the experts can adjust to the perspective of the others.

Truly complex systems are inherently ambiguous – it’s not just that there is too much data, or that modelling would take too much effort

A complexity-based approach in action

The IChemE-supported C-THRU project was set up to investigate how interventions in the petrochemical sector could be used to mitigate its impact on greenhouse gas emissions and climate change. To make investigations in this area manageable, researchers typically limit scope or apply restrictive boundaries to enable an ordered systems approach. Because each piece of work chooses a different subset of the overall system, the conclusions and recommendations often appear inconsistent or contradictory.

The petrochemical sector itself is a complex system. Its boundaries are both porous and unclear with material, money, and knowledge flowing between it and the energy and consumer manufacturing sectors. The value chain is highly interactive and interdependent with disturbances spreading widely through the system in complex ways. Its current state and configuration only make sense through the lens of history, and its future development is highly uncertain and significantly influenced by much wider geopolitical issues. No two companies, manufacturing clusters or jurisdictions behave in the same way, making generalisations insufficient for driving change.

The system of causes, mitigations, and actors associated with climate change is even more clearly complex. We recognised that the C-THRU project straddled the intersection between these two complex systems. The solution space is large with similar outcomes potentially achievable using a wide variety of different approaches. Companies, governments, NGOs, and regulators all have differing and unique contributions to make. Our aspiration was to provide a framework that enabled each of these to understand and select the most impactful combination for their individual context.

It was clear that a complexity-based approach would be necessary.

C-THRU – how the project adopted a complexity-based approach

We have already established that the petrochemical industry itself is a complex system, as is the generation, mitigation, and reduction of greenhouse gas emissions and the implications for climate change. It was clear from the outset that a complexity-based approach should be considered.

The C-THRU project assembled a multidisciplinary team, organised into seven distinct workstreams, reflecting the diverse perspectives needed to fully explore and understand the interaction of the two complex systems at the heart of the project.

One group reviewed existing emissions data to understand the current global picture and identify gaps and uncertainty in the data. Another applied their industrial ecology and process technology knowledge to explore the technological levers that reduce emissions in different parts of the chemicals chain. Resource efficiency specialists researched demand for chemical products and the flow of chemicals from production to end use application. Another group researched the impact of recycling and substitutes on the petrochemical supply chain. Other groups worked on the macro-economic context and the business landscape and power and influence network structures in the industry on a global scale.

Applying different lenses to the issue of greenhouse gas emissions, C-THRU developed a broader perspective of the issue. Each workstream developed its own scope of work, data sources, and models and frameworks. Periodically, the full team came together to share progress and findings; a fundamental part of this process was a critical review of each other’s work with a view to reframing and refining each team’s activities. Through this process we were able to converge on a set of underlying principles and common insights that were consistent with and supported by the multiplicity of methods and approaches adopted.

The top-level C-THRU key messages are presented as principles and insights that guide decision-makers in taking their first and subsequent steps in a positive direction. They do not try to prescribe specific actions for individual actors in the system or second guess definitive predictions of final-end states. The sophisticated models and detailed insight developed by each of the workstreams is available to assist and inform the process of converting the general principles to specific actions for any particular context.

Conclusions

Complex systems are fundamentally different from complicated, ordered systems and need to be explored and approached in inherently different ways for maximum impact.

C-THRU recognised that mitigating emissions in the petrochemical sector is a complex problem that requires the input of many different people and perspectives. The petrochemical industry, governments, and society must tailor their approaches to emissions reductions to their own contexts. Blanket global solutions with idealised future goals may grab headlines but are less effective than approaches which are adapted to local dynamics and needs.

The top-level guidelines were developed using, and are underpinned by, a rigorous set of tools and highly detailed granular data. The C-THRU team have developed a methodology for guiding interested parties through the process of developing strategy and implementation plans specific to their context – this will be described in more detail in a later article in the series.

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We’ll be hosting an IChemE webinar discussing how this complex vs complicated approach relates to digitalisation and digital transformation on 9 October. Register to attend here: https://bit.ly/4dP83ZN