Community Member

Six elements of effective co-design

October 7, 2025September 30, 2025

By Will Allen.

What does co-design for tackling complex challenges look like in practice?

Co-design is a collective way of navigating complexity, taking different forms depending on context. The following six elements are a reflection on patterns I’ve seen emerge through practice, especially in settings where multiple perspectives matter.

1. Starting with shared grounding: Creating early alignment through shared values, context, and purpose

In many collaborative projects, there’s a tendency to begin by defining tasks – what needs doing, by whom, and when. But in complex settings, where multiple perspectives and values come into play, it’s often more important to begin with relationships. It helps to understand where people are coming from, what matters to them, and how they see the purpose.

Lessons for transformative research from co-creating a conference without a fixed plan

November 27, 2025September 23, 2025

By Thomas Bruhn.

In developing a conference, what does it take for people to leave their comfort zones to co-design something new? What possibilities does this open up for more meaningful conference designs? What are the broader lessons for transformative research?

In 2023–2024, I worked with the German Federal Ministry of Education and Research to develop a conference format for the German sustainability research community – something to help re-establish connection after the isolating COVID pandemic years, and to strengthen interdisciplinary exchange. The Ministry wanted something new and innovative.

Early in the conversation, I sensed hesitation when unconventional, interactive conference formats were suggested.

Four core concepts for expanding a systems view to system dynamics

September 18, 2025September 16, 2025

By Andrei Savu.

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.

Five core concepts for understanding systems

October 7, 2025September 16, 2025

By Andrei Savu.

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.

Six tips for using research to influence policy

September 9, 2025

By David R. Garcia.

david-garcia — David R. Garcia (biography)

How can academics, researchers, and educators become skilled at the craft of engaging with policy makers? Who should they aim to engage with and what are some key factors in engaging effectively?

Based on my experiences as a US legislative staffer, state policy director, statewide political candidate and professor, here are my six best tips.

Tip #1: Be prepared to work with politicians. Yes, politicians

In academic contexts, “policymaker” is an ill-defined term that is often applied to all policy actors, and does not account for relevant distinctions between different policy actors.

Data variety and why it matters

October 8, 2025September 2, 2025

By Richard Berry.

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.

Considering context in transdisciplinary research: A framework and reflective questions

August 26, 2025August 26, 2025

By Nina Maria Frölich and Annika Weiser.

authors_nina-maria-frölich_annika-weiser — 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.

Transdisciplinary research with and for artificial intelligence

August 26, 2025August 19, 2025

By Florian Keil, Melina Stein and Flurina Schneider.

authors_florian-keil_melina-stein_flurina-schneider — 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

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

August 13, 2025August 12, 2025

By Lianghao Dai.

A Chinese version of this post is available

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:

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

December 8, 2025August 5, 2025

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

authors_ligtermoet_munera-roldan_robinson_sushil_leith — 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.

Seven quality choice points for contemporary action research

August 12, 2025July 29, 2025

By Hilary Bradbury.

How can action researchers empower system actors in impactfully responding to our deepening eco-social crisis? How can action research be a catalyst to successfully transmute the inexhaustible resource of human creativity in all spaces—self to society—toward addressing our global problems? How can we encourage deepening clarity of choices made to navigate a middle path between responding to problems within living communities and contributing to research-based theory?

Mitigating the worst of our global problems requires action research that draws on many kinds and sources of knowledge. In fact, it requires drawing much more from diverse people on the ground, who understand the problems at hand and can offer solutions anchored in their experience of what is meaningful for them.

The aim of the seven choice points described below is to support action researchers in:

deepening and speeding up the proliferation of good work,
connecting local niche experiments to global reach.

A tool for developing shared awareness of team member research interests and expertise

August 12, 2025July 22, 2025

By Melanie Bauer, Joshua Roney and Stephen M. Fiore.

authors_melanie-bauer_joshua-roney_stephen-fiore — 1. Melanie Bauer (biography)
2. Joshua Roney (biography)
3. Stephen M. Fiore (biography)

How can team members who have been working together for a while check assumptions, ensuring they are aware of each other’s breadth of expertise and research interests?

We have developed the “Linking-Relinking” tool to facilitate such a process. This tool supports science teams through development of a transactive memory system, which is a form of shared cognition having to do with “who knows what” on a team. Studies continually show that teams that develop an accurate transactive memory system are better able to coordinate their knowledge when working on challenging problems. The Linking-Relinking Tool can support transactive memory system development by helping members determine how accurate their knowledge is of their teammates and calibrate appropriately.