Concepts

Five core concepts for understanding systems

By Andrei Savu.

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Andrei Savu (biography)

What concepts are key to understanding systems?

A system is a set of interdependent elements whose coordinated interactions give rise to an outcome none of the pieces can deliver alone. The key word is relationship: change the relationships and the behavior of the whole shifts, even if every component remains identical.

Five core concepts for systems thinking are: purpose, boundary, feedback, leverage and emergence.

Purpose and boundary

Every system exists to fulfill a purpose, defined by boundaries that separate internal elements from external factors. These two fundamental concepts—purpose and boundary—determine how we understand, analyze, and influence systems of all types.

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Data variety and why it matters

By Richard Berry.

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Richard Berry (biography)

What are the differing characteristics of data? Why are they important for systems to function effectively? What is requisite variety of data?

There are nine characteristics of data variety which agitate systems. These are volume, velocity, variety, veracity, validity, vulnerability, viscosity, vectors and virtualisation. Together, the ‘9Vs’ constitute a data requisite variety framework and are described below. 

1. Volume

Description: The amounts of available data.

Example: Volume can vary widely from the results of small-scale research to the tsunami of digital material accessible through the internet. The latter can overwhelm both people and organisations.

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Considering context in transdisciplinary research: A framework and reflective questions

By Nina Maria Frölich and Annika Weiser.

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1. Nina Maria Frölich (biography)
2. Annika Weiser (biography)

Which contextual factors affect the design, processes, methods and outcomes of transdisciplinary research projects? How can they best be considered by teams designing transdisciplinary research?

Most would agree that context matters, especially in transdisciplinary approaches. But how can we make it work for us in designing impactful context-sensitive transdisciplinary research? Here we provide a useful framework that structures the various aspects of “context,” here understood as a combination of circumstances that interact with and influence a transdisciplinary research project. Based on theoretical literature, as well as an analysis of 17 semi-structured interviews about international transdisciplinary research projects (Tolksdorf et al., 2025), we identified three dimensions, with a total of nine key context factors, illustrated in the figure below.

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Transdisciplinary research with and for artificial intelligence

By Florian Keil, Melina Stein and Flurina Schneider.

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1. Florian Keil’s biography
2. Melina Stein (biography)
3. Flurina Schneider (biography)

Is artificial intelligence, a technology aggressively advertised as the ultimate cure-all, fundamentally incompatible with transdisciplinarity and its decades-old insight that the “wicked” problems of the real world do not lend themselves to one-dimensional solutions? Should transdisciplinary research outright reject a technology that is already undermining efforts to achieve social and environmental justice? Or can artificial intelligence actually support transdisciplinary research when used responsibly?

Using artificial intelligence in transdisciplinary research requires a critical mindset

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Three social mechanisms leading to fake interdisciplinary collaborations / 形成伪跨学科合作的三种社会形成机制

By Lianghao Dai.

A Chinese version of this post is available

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Lianghao Dai (biography)

What are fake interdisciplinary collaborations and how do they arise?

Fake interdisciplinary collaborations are a form of performative scientific behaviour that claims to be interdisciplinary but lacks knowledge integration across disciplines. There are three social mechanisms that can result in such fake collaborations.

1. Irresponsible project management

Irresponsible project management has two manifestations:

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Building co-production capabilities in researchers: Strengthening reflexivity via learning opportunities

By Emma Ligtermoet, Claudia Munera-Roldan, Cathy Robinson, Zaynel Sushil and Peat Leith.

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1. Emma Ligtermoet; 2. Claudia Munera-Roldan; 3. Cathy Robinson; 4. Zaynel Sushil; 5. Peat Leith (biographies)

What forms of learning can support interdisciplinary teams to rapidly build reflexivity capabilities, especially in preparation for doing transdisciplinary (engaged) science with non-researcher societal actors?

Transdisciplinary co-production requires deep and reflexive learning. Reflexivity is a key capability for researchers doing inter- and transdisciplinary science, involving the critical enquiry of existing assumptions, values and norms underlying our decisions and actions, with the aim to adapt or change current practices or discourses.

Such learning is foundational for understanding and proactively engaging with knowledge-power dynamics, including potentially catalysing shifts in incumbent dynamics when preparing to engage with non-societal actors.

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Combining subjectivity and objectivity in systems thinking: The SOS sandwich

By James Stauch and Daniela Papi-Thornton.

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1. James Stauch (biography)
2. Daniela Papi-Thornton (biography)

In seeking to understand, map, and then act to intervene in a system, how can we make the best use of both subjectivity and objectivity? How can we effectively toggle between facts and norms, between what is true (or at least broadly verifiable) and what is valued (or valuable)?

In the book that this i2Insights contribution is based on (Stauch et al., 2025), the case is made for people to spend far more time understanding a problem, and proportionally less time acting to “solve” the problem. To help frame this approach, the SOS (subjective-objective-subjective) sandwich is used as a simple heuristic to show where subjectivity and objectivity can be taken into account when dealing with a system.

In this work, objectivity is considered as a vector, not a destination, with true objectivity always out of reach, as we can never be completely objective in our approach to research. That said, we can strive for it by recognizing our biases and seeking diverse viewpoints.

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Principles for place-based community participation

By James A. Turner.

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James A. Turner (biography)

How can a community’s sense of connection and responsibility to care for their place be strengthened? How can this lead to ground-up change, driven by communities, to tackle complex social, economic, and environmental issues? How can such change draw on the deep sense of care and belonging people feel for their communities and environments to tailor solutions to the unique needs and context of a place?

We identified eleven key principles associated with successful place-based community-led projects. These are the first principles to be developed in-country, rather than being imported from overseas and, because these are place-based, they are specific to Aotearoa New Zealand. We share them here to illustrate what specific place-based principles look like.

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Harnessing the collision of four ways of knowing

By Adrian Wolfberg.

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Adrian Wolfberg (biography)

How can solving today’s most complex challenges reckon with four fundamentally different ways of knowing? How can the collision of their distinct epistemic strengths and blind spots be harnessed for innovation in threat assessment and decision-making on complex problems?

Let me unpack these four ways of knowing and how they shape, support, and sometimes undermine each other. Here, I use the example of climate security intelligence, but the insights and lessons are likely to apply to a wider range of complex societal and environmental issues. The four ways of knowing are:

  1. Scientific knowledge from the physical sciences
  2. Scientific knowledge from the social sciences
  3. Judgment under uncertainty by knowledge-producing professionals
  4. Practical decision-making by practitioners who are senior executives.

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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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Navigating the spectrum of leadership styles

By Gemma Jiang, Jenny Grabmeier and Joan Lurie.

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1. Gemma Jiang (biography)
2. Jenny Grabmeier (biography)
3. Joan Lurie (biography)

When you are in a leadership role, are you able to shift your leadership style to accommodate the needs of your team and project? When consensus is hard to reach, are you able to step in with a directive approach? Are you able to hold back from being directive when creativity and participation are needed?

A Spectrum of Leadership Styles

Lewin, Lippitt and White in their foundational 1939 study on group dynamics suggested three leadership styles. In the context of cross-disciplinary science, we do not see these as separate styles or the only three styles, but as reference points along a continuum. At one end of their spectrum lies directive leadership, and at the other, delegative leadership, and somewhere in the middle, participative leadership.

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Using cross-cultural dialogue to break down inappropriate knowledge hierarchies

By Roxana Roos.

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Roxana Roos (biography)

How can indigenous, local, artisanal, craft, tacit, counter, gendered and experiential knowledge better inform solutions to complex problems, such as climate change? How—when faced with conditions of complexity, uncertainty and competing tenable knowledge claims—can the actionable knowledge base be pluralized and diversified to include the widest possible range of high-quality, potentially actionable knowledges and sources of relevant wisdom? What are the pitfalls and challenges ahead?

I start with some cautions for the usual practice of transdisciplinary research and then highlight key aspects of cross-cultural dialogue, alongside pitfalls and challenges.

Integration can reproduce undue asymmetries

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