The essential conditions for, and characteristics of, complexity

By Jean Boulton.

jean-boulton
Jean Boulton (biography)

What are the underpinning necessities or conditions—the essential ingredients—that lead to and engender the qualities or characteristics of the complex world, especially its processual and emergent nature?

Three conditions for complexity: the essential ingredients

A watch or intricate machine is not complex. Nor is a saucer of water. So, when do we regard something as complex? What are the necessary conditions for complexity fully to be realised?

These are:

  • open boundaries
  • diversity
  • reflexive inter-relationships among constituents.

Let’s look at each of these in more detail.

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A communication framework for public engagement and impact

By Judith Friedlander and Tania Leimbach.

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1. Judith Friedlander (biography)
2. Tania Leimbach (biography)

How can researchers cut through ‘the infoglut’ to share their findings with communities? What communication strategies help raise the agenda of critical issues to drive impactful advocacy and action?

As researchers and practitioners, we want to better understand how to effectively frame critical issues in a hybrid media system, facilitate media uptake and engage the public in scalable change-making. To this end, we developed the MAVEN communication framework, which consists of:

  • Meta-frames (developing overarching concepts);
  • Actions and Applications (supporting local pilots and scalability);
  • Values (identifying shared community values and news values);
  • Evidence and Ethos (messaging from reputable stakeholders), and
  • News media (disseminating information within a hybrid media system).

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Five structural levers to reopen feedback loops that are resistant to external evidence

By Lachlan S. McGill.

lachlan-mcgill
Lachlan S. McGill (biography)

When feedback loops have become resistant to external evidence, what are some potential ways of intervening to reopen them?

This i2Insights contribution builds on my previous post which covers understanding why feedback loops can become resistant to external evidence and how to diagnose such a structural problem.

Here I introduce five structural ways to intervene in such a closed feedback loop. These are structural levers, each targeting a different aspect of how signals flow, how authority is allocated, and how evaluative standards are defined.

One practical note before beginning. Applying the interventions below often requires institutional authority, coalition building, or regulatory support, so that isolated actors may not be able to deploy them fully, leaving the problematic dominant structure intact.

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Understanding and diagnosing when feedback loops become resistant to external evidence

By Lachlan S. McGill.

lachlan-mcgill
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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Boundaries as opportunities for learning

By Roger Duck and Jane Searles.

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1. Roger Duck (biography)
2. Jane Searles (biography)

Think of a time when you noticed how different ‘they’ are from ‘us’. In that moment, did the relationship become more interesting and alive? Or did it flatten into what looked like a boundary – a barrier to be overcome or a connection to be engineered?

This i2Insights contribution is intended to stimulate your imagination by giving examples from practice of relationships between people and teams being treated as opportunities for learning, rather than boundaries.

Most readers of i2Insights work in research. We believe there is much of relevance here for any context in which people are working together, including research teams.

The context

We expand here on the idea that ‘a system boundary is simultaneously a process of drawing a distinction and identifying an active relationship of mutual learning’ (Duck and Searles 2021).

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Navigating inter- and transdisciplinary PhD supervision: Practical questions for students and supervisors

By Erika Angarita, Anna Hajdu, Yanyan Huang, BinBin Pearce, Guadalupe Peres-Cajías, Hussein Zeidan and Yuanyuan Zhu.

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1. Erika Angarita; 2. Anna Hajdu; 3. Yanyan Huang; 4. BinBin Pearce; 5. Guadalupe Peres-Cajías; 6. Hussein Zeidan; 7. Yuanyuan Zhu (biographies)

How can a student and their supervisors develop a shared map for a PhD project when they come from different disciplinary traditions, hold different assumptions about knowledge and quality, and operate within institutional systems that are still largely structured around single disciplines? How can they navigate what may feel obvious to one and may be invisible to another?

We developed a structured checklist of questions designed to support supervision conversations and reduce tensions resulting from unspoken assumptions.

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A framework for considering context in evaluation of transdisciplinary research projects

By Julia Schegg, Rea Pärli, Manuel Fischer and Eva Lieberherr.

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1. Julia Schegg (biography)
2. Rea Pärli (biography)
3. Manuel Fischer (biography)
4. Eva Lieberherr (biography)

How do contextual factors influence the effects of transdisciplinary research projects? How can assessment of transdisciplinary research move from only considering outcome effectiveness to understanding the reasons behind how and why something works?

Our framework considers both context and the effects of transdisciplinary research projects, as summarised in the figure below. Each aspect of the framework is described in more detail.

Context

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Developing a conceptual framework to support communication, collaboration and integration

By Hanna Salomon, Jialin Zhang and Sabine Hoffmann.

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1. Hanna Salomon (biography)
2. Jialin Zhang (biography)
3. Sabine Hoffmann (biography)

How can the process of developing a conceptual framework in an inter- and transdisciplinary research project itself create valuable space for reflection, alignment, and learning?

What we have found when developing a project-specific conceptual framework is that the process is as important, if not more important, for the research team than the emerging conceptual framework itself. The process provides space and time to discuss and deep-dive into concepts and terms used within the research team leading to much needed discussions and insights for the individual researchers.

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The Cultiv8 tool Part 2: Actionable insights for navigating power

By Sobia Khan and Julia E. Moore.

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1. Sobia Khan (biography)
2. Julia E Moore (biography)

How can we move beyond considering power as the source of implementation challenges and bottlenecks, and instead focus on how we can change or shift the nature of power? How might you experience implementation differently if you knew how to unpack power dynamics and had strategies to navigate power in your implementation practice or research?

This i2Insights contribution is a companion to our previous post on cultivating trust. Trust and power go hand in hand and can’t be dealt with in silos – when considering trust, you also need to consider power and vice versa. The framework presented here helps to understand the dimensions of power and actionable steps for navigating each of these dimensions. Here we describe a second aspect of the Cultiv8 tool to unpack power dynamics.

Before you act: Reflect on the context

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The Cultiv8 tool Part 1: Actionable insights for cultivating trust

By Julia E. Moore and Sobia Khan.

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1. Julia E Moore (biography)
2. Sobia Khan (biography)

What are some useful ways of thinking about trust when developing plans to implement your research or strengthen your team? More importantly, what are some practical ways to build trust both as an individual and as an organisation?

Indeed, when asked about some of the most challenging parts of implementing changes and taking part in research collaborations, people often talk about trust. Trust is essential for equity and for working with people in effective ways, but so few of us are trained in how to build trust.

This i2Insights contribution provides a practical approach to thinking about trust, along with actionable steps to cultivate trust to help you achieve your goals, whether you are working with others to implement evidence or looking to strengthen your team dynamics.

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From slogan to practice: Restoring transdisciplinarity as a serious way of working

By Hussein Zeidan.

hussein-zeidan
Hussein Zeidan (biography)

Do you sense a growing gap between the promise of transdisciplinarity and the way it is often practised? Have you recognised instances where a paper praises integration, yet treats it as little more than a symbolic gesture, instead of a serious intellectual and ethical commitment?

How did we get here, and how can we reclaim transdiscipinarity from superficial habits that weaken its potential?

How did we get here?

The rise of transdisciplinarity has been remarkable. Funding agencies promote it. Universities showcase it. New centres and programmes are built around it. This visibility has helped many people experiment with new forms of collaboration.

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A framework for navigating the impact of using artificial intelligence on collaborative research communication

By Faye Miller.

faye-miller_2025
Faye Miller (biography)

How can research teams recognise when their use of artificial intelligence is affecting their ability to integrate different knowledge and perspectives? How can they navigate the impact of artificial intelligence on their collaborative processes?

When research teams use artificial intelligence in collaborative work, new complexities emerge, especially subtle shifts in communication patterns that can fundamentally alter how teams integrate different perspectives and knowledge forms. Consider an environmental team relying on artificial intelligence summaries across hydrology, ecology, and policy. They might miss crucial disciplinary nuances, or follow its “evidence-based” recommendations that may clash with community priorities.

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