Using archetypes as a systemic lens to understand the complexity of sustainable development

By Hossein Hosseini, Enayat A. Moallemi, Sibel Eker, Edoardo Bertone and Katrina Szetey

What are systems archetypes and how can they be used to bring a deeper understanding of causal drivers, potential dynamic behaviour in the future, and policy resistance when tackling complex problems, including those in sustainable development?

Systems archetypes are recurring generic systems structures found in many kinds of organisations, under many circumstances, and at many levels and scales. They are distinctive combinations of reinforcing and balancing processes theoretically rooted in systems thinking and modelling.

There are eight common archetypes, each with specific underlying causal drivers (eg., feedback loops, delay), expected dynamic behaviour (eg., acceleration, disruption, tipping point), and policy implications (eg., how to respond, where to intervene). Archetypes can help shift an analytical focus from simple behavioural correlations or a limited understanding of interactions between certain goals to a generalised knowledge of recurring patterns, causes, and consequences.

The eight archetypes are commonly known as:

1. Fixes that Fail
2. Band-Aid Solutions, also known as shifting the burden or addiction
3. Eroding Ambitions, also known as eroding or drifting goals
4. Downplayed Problems, also known as growth and underinvestment
5. Escalating Tensions, also known as escalation
6. Success to the Successful
7. Limits to Progress, also known as limits to success or growth
8. The Tragedy of the Commons.

We used the complexity of managing interactions between different sustainable development goals (SDGs) that create synergies and trade-offs (eg., poverty, food, well-being, water, energy, housing, climate, and land use) to explore how systems archetypes could provide a deeper understanding, with the results summarised in the figure below.

Causal loop diagrams are used to depict archetypes

Archetypes are usually depicted using causal loop diagrams, which represent feedback relationships among various system elements (eg., different SDGs) and which drive the system’s behaviour over time. Different elements are connected via causal links, shown by arrows, which represent causal relationships.

The causal links are assigned positive or negative polarity to indicate the direction of the relationship between two system elements. A positive relationship implies that a change in the cause variable changes the effect variable in the same direction. A negative relationship implies that a change in the cause variable results in a change in the effect variable in the opposite direction.

A closed chain of causal relationships creates a feedback loop. Feedback loops can be reinforcing (eg., a positive change in one system element leads to a positive change in another, potentially with exponential behaviour) or balancing (eg., a positive change in one system element leads to a negative change in another) over time.

We describe the causal loop diagram for the Fixes that Fail systems archetype using a hypothetical example.

Describing the Fixes that Fail archetype

To reiterate, the Fixes that Fail archetype represents interactions that are driven by the interplay and conflict between (short-term) planned and (long-term) unexpected outcomes of interventions, resulting in unanticipated side effects. The archetype implies that interventions which can positively impact a goal in the short term can sometimes result in unintended consequences and trade-offs with other goals, stopping or even reversing the progress made.

The causal drivers behind this type of interaction involve balancing and reinforcing feedback loops as shown in the figure below (left hand side). Let’s explore this with a hypothetical example in the context of food and agriculture (linked to SDGs 2 (Food Security) and 13 (Climate Action)). Imagine a situation of food insecurity, which is addressed by boosting food production via unsustainable practices, such as agricultural land expansion or excessive fertilizer use. In the causal loop diagram, food insecurity is linked to unsustainable food production practices with a positive polarity (more food security, more unsustainable food production practices). The food production practices do however lead to less food insecurity (negative polarity) leading to a balancing feedback loop, ie., food security is achieved.

However, unsustainable food production practices also have adverse environmental impacts, such as deforestation from agricultural expansion and increasing greenhouse gas emissions from agricultural production. This in turn leads to the adverse impacts of environmental externalities of food production shown in the figure below (left hand side), leading to a reinforcing feedback loop where more adverse impacts lead to more food insecurity which leads to more unsustainable practices and then more adverse impacts and so on.

The potential dynamic behaviour is shown in the figure below (right hand side) which is color-coded to match what is happening in the causal loop diagram on the left. It can be seen that the unintended consequences of unsustainable agriculture increase exponentially over time. For long-term food security, the trend is short episodes of progress improvement due to short-term actions but with a steadily worsening long-term trend due to delayed unintended consequences of those temporary actions. As a result, the original sustainability problems persist and progress is slowed (or reversed) despite increasing efforts.

A policy implication of this interaction archetype is the importance of understanding and preparing for policy side-effects of short-term fixes. In practice, this means whenever temporary, short-term fixes are necessary to address immediate problems, corrective actions should also be taken to mitigate unexpected negative consequences. At the same time, preparing and planning for long-term, high-leverage interventions can also become important to address the main cause of the problems and ensure long-term progress.

Concluding questions

Could systems archetypes be helpful for identifying potential unintended consequences in your work? Do you have experiences to share of using systems archetypes?

To find out more:

Moallemi, E. A., Hosseini, S. H., Eker, S., Gao, L., Bertone, E., Szetey, K. and Bryan, B. A. (2022). Eight archetypes of Sustainable Development Goal (SDG) synergies and trade-offs. Earth’s Future, 10, e2022EF002873. (Online – open access) (DOI): https://doi.org/10.1029/2022EF002873
This paper describes the causal loop diagrams for each of the systems archetypes and presents hypothetical and real-world examples showing synergies and trade-offs between different Sustainable Development Goals.

Biographies:

Hossein Hosseini PhD is an early career researcher for the Brain and Mind Centre at the University of Sydney in Australia. His research is focused on systems modelling approaches to analyse complex socio-technical problems. He uses interdisciplinary and participatory approaches to inform policy interventions that can help achieve targets under future scenarios.

Enayat Moallemi PhD is a principal research scientist in sustainability transitions at CSIRO, in Canberra, Australia. He uses modelling tools and knowledge co-production methods to design and evaluate decisions for sustainability transitions in an uncertain future. He designs his research methodology using a range of knowledge sources and methods often from Decision-Making Under Deep Uncertainty, Multi-Sector Dynamics, System Dynamics, and Integrated Assessment Modelling.

Sibel Eker PhD is an assistant professor at Radboud University, Nijmegen School of Management in the Netherlands and a Research Scholar at the International Institute for Applied Systems Analysis (IIASA) in Vienna, Austria. Her interdisciplinary research profile combines systems analysis and engineering, decision sciences and social sciences, and her work brings systems thinking and uncertainty focus to climate change and sustainability problems with model-based approaches.

Edoardo Bertone PhD is a senior lecturer with the School of Engineering & Built Environment, and a member of Cities Research Institute and Australian Rivers Institute, Griffith University, Brisbane, Australia. He has a research focus on data-driven modelling, Bayesian Network and System Dynamics modelling applied to the water resources management, water-energy nexus, environmental health, sustainability and climate change adaptation fields.

Katrina Szetey PhD is a postdoctoral fellow with CSIRO in Canberra, Australia, in the Valuing Sustainability Future Science Platform and part of the Future States project which seeks to predict future change in Australian socioecological landscapes with a state-and-transition model framework.