System dynamics approach

Understanding and diagnosing when feedback loops become resistant to external evidence

By Lachlan S. McGill.

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Lachlan S. McGill (biography)

Why does better evidence sometimes fail to improve decision making? How can we tell if this is caused by feedback loops becoming resistant to external evidence?

Understanding how structural patterns become problematic

In most organisations, decisions are embedded in feedback loops that connect indicators, incentives, and authority structures. These loops determine what counts as success, which signals influence decisions, and how performance is evaluated over time.

When feedback loops are well aligned with system goals, they support learning. However, feedback loops can also evolve in ways that reinforce a narrow definition of success. This is generally associated with a system relying on a small number of indicators to guide decisions. Common examples include financial return on investment, productivity or output measures, growth targets, publication counts or grant income, and compliance indicators.

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Four core concepts for expanding a systems view to system dynamics

By Andrei Savu.

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Andrei Savu (biography)

Once you understand the basic concepts underpinning systems, what other concepts are key to understanding system dynamics?

While systems thinking teaches you to see and shape system structure, system dynamics focuses on understanding nonlinear behavior over time. An additional four key concepts are added to five core concepts in systems thinking described in a companion post.

The four additional key concepts for understanding system dynamics are: stocks, flows, delays and dynamic behavior patterns.

Stocks and flows

Stocks and flows are foundational concepts, essential for analyzing and designing effective systems.

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Why interdisciplinarity and transdisciplinarity are not enough for addressing complex problems

By Gabriele Bammer.

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Gabriele Bammer (biography)

As the importance of interdisciplinary and transdisciplinary research approaches becomes more widely recognised, how can we overcome the danger that they are seen to be all that is needed for tackling complex problems? What are the limitations of these approaches? What else might be required?

My starting point is that improved understanding of, and action on, a complex societal or environmental problem usually requires a number of research questions to be addressed. Different questions require different kinds of research approaches. Let’s illustrate this by considering the following complex problem:

As effort goes into making cities more sustainable, how can we incorporate illicit drug users into a more sustainable city X?

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Competencies for systems thinking practitioners. Part 1: Overall expectations and knowledge

Edited by Gabriele Bammer

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What knowledge, skills and behaviours are required by those seeking to provide expert systemic analysis, advice and facilitation to support decision-makers in understanding and addressing complex problems, ie., problems that have no single ‘owner’ or cause, and no simple solution? What should decision makers be able to expect from the systems thinking practitioners they engage with?

The UK Institute for Apprenticeships and Technical Education (no date) provides competencies in knowledge, skills and behaviours and these are reproduced here (knowledge competencies), and in a companion blog post (skills and behaviour competencies). This list of competencies provides a very useful way of getting an overview of systems thinking and the skills required.

What should systems thinking practitioners be able to do?

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Key systems thinking lessons from Donella Meadows

By Geoff Marlow

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Geoff Marlow (biography)

The book “Thinking In Systems: A Primer” by Donella (Dana) Meadows (2008) offers a useful entry point into systems thinking via seven lessons.

Lesson 1: Systems are always more than the sum of their parts

Feedback loops are pivotal, as is looking beyond the players to the underlying rules of the game.

Meadows (p. 13) offers guidance as to “whether you are looking at a system or just a bunch of stuff:

  • Can you identify parts? . . . and
  • Do the parts affect each other? . . . and
  • Do the parts together produce an effect that is different from the effect of each part on its own? . . . and perhaps
  • Does the effect, the behavior over time, persist in a variety of circumstances?” 

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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

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1. Hossein Hosseini; 2. Enayat A. Moallemi; 3. Sibel Ekern; 4. Edoardo Bertone; 5. Katrina Szetey (biographies)

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.

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Seven methods for mapping systems

By Pete Barbrook-Johnson and Alexandra S. Penn

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1. Pete Barbrook-Johnson (biography)
2. Alexandra S. Penn (biography)

What are some effective approaches for developing causal maps of systems in participatory ways? How do different approaches relate to each other and what are the ways in which systems maps can be useful?

Here we focus on seven system mapping methods, described briefly in alphabetical order.

1. Bayesian Belief Networks: a network of variables representing their conditional dependencies (ie., the likelihood of the variable taking different states depending on the states of the variables that influence them). The networks follow a strict acyclic structure (ie., no feedbacks), and nodes tend to be restricted to maximum two incoming arrows. These maps are analysed using the conditional probabilities to compute the potential impact of changes to certain variables, or the influence of certain variables given an observed outcome.

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Why awareness raising campaigns cannot fix structural problems

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Pei Shan Loo (biography)

By Pei Shan Loo

Why are awareness raising campaigns popular? Why can’t they fix structural problems? And how can system dynamics help?

Large amounts of funding for health, societal, environmental and other complex problems are channelled into “awareness raising” to build public recognition of the problem in the hope that understanding will lead to change and a lasting solution.

Why awareness raising campaigns are popular

There are at least four reasons why funding is spent on awareness raising campaigns:

  • Such campaigns can readily be conducted in short timeframes. For example, if funding is available for a one-year project, this is enough to successfully complete an awareness raising campaign.
  • Similarly, awareness raising campaigns can usually be tailored to fit limited budgets.
  • Training for local project staff to undertake such campaigns is relatively straight-forward.
  • Pre-post campaign evaluations are straight-forward and usually demonstrate increased awareness and interest in the topic of the campaign.

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Fifteen characteristics of complex social systems

By Hamilton Carvalho

Author - Hamilton Carvalho
Hamilton Carvalho (biography)

What is it about complex social systems that keeps reproducing old problems, as well as adding new ones? How can public policy move away from what I call the Mencken Syndrome (in reference to a quotation from American journalist Henry Mencken) – that is, continually proposing clear and simple solutions to complex social problems – that are also wrong!

With this goal in mind, I have compiled a list of fifteen major characteristics of complex social systems based on the system dynamics and complexity sciences literatures, as well as my own research.

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Four patterns of thought for effective group decisions

By George P. Richardson and David F. Andersen

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1. George P. Richardson (biography)
2. David F. Andersen (biography)

What can you do if you are in a group that is trying to deal with problems that are developing over time, where:

  • root causes of the dynamics aren’t clear;
  • different stakeholders have different perceptions;
  • past solutions haven’t worked;
  • solutions must take into account how the system will respond; and,
  • implementing change will require aligning powerful stakeholders around policies that they agree have the highest likelihood of long-term success?

The fields of systems thinking and system dynamics modelling bring four important patterns of thought to such a group decision and negotiation:

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Toolkits for transdisciplinary research

By Gabriele Bammer

gabriele-bammer
Gabriele Bammer (biography)

If you want to undertake transdisciplinary research, where can you find relevant concepts and methods? Are there compilations or toolkits that are helpful?

I’ve identified eight relevant toolkits, which are described briefly below and in more detail in the journal GAIA’s Toolkits for Transdisciplinarity series.

One toolkit provides concepts and methods relevant to the full range of transdisciplinary research, while the others cover four key aspects: (i) collaboration, (ii) synthesis of knowledge from relevant disciplines and stakeholders, (iii) thinking systemically, and (iv) making change happen.

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Good practices in system dynamics modelling

By Sondoss Elsawah and Serena Hamilton

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1. Sondoss Elsawah (biography)
2. Serena Hamilton (biography)

Too often, lessons about modelling practices are left out of papers, including the ad-hoc decisions, serendipities, and failures incurred through the modelling process. The lack of attention to these details can lead to misperceptions about how the modelling process unfolds.

We are part of a small team that examined five case studies where system dynamics was used to model socio-ecological systems. We had direct and intimate knowledge of the modelling process and outcomes in each case. Based on the lessons from the case studies as well as the collective experience of the team, we compiled the following set of good practices for systems dynamics modelling of complex systems.

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