Clarifying incentives and expectations in research collaborations

By Alisa Zomer and Varja Lipovsek

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1. Alisa Zomer (biography)
2. Varja Lipovsek (biography)

In which areas do research collaborations between academics and practitioners often run into trouble? What difficult questions can we ask ourselves and our partners at the outset of a research collaboration that can set us up for a successful partnership? How can we learn from past successful and failed aspects of research partnerships?

In our experience four areas where collaborations can have problems are:

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Understanding diversity primer: 2. Mental models

By Gabriele Bammer

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What are mental models and why are they important? How do they affect how problems are framed, understood and responded to? How do they affect how well those contributing to the research work together?

Mental models are a person’s understanding of the world and how it works, and are unique to each person. They exist in a person’s mind as a set of small-scale simplified models about different aspects of reality that are functional but necessarily incomplete.

Mental models apply to all aspects of reality ranging from concrete objects such as a ‘chair;’ to abstract concepts such as ‘trust;’ to geographical locations such as ‘Sydney;’ to connections, interconnections and causal relationships; and to simple and complex situations.

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Boosting the transformative power of transdisciplinarity with quantum theory

By Cyrille Rigolot

cyrille-rigolot
Cyrille Rigolot (biography)

How can transdisciplinarity improve its ability to foster very deep, very fast and very large transformations toward sustainability?

Quantum theory might be a major source of insights in that direction. Although quantum theory is not new to transdisciplinarity, lately it has become much more accessible, practical, and potentially transformative on the ground.

Quantum theory for transdisciplinarity research

In the debates last century about the emerging transdisciplinary research field, quantum theory inspired theorist Basarab Nicolescu to develop three basic ‘axioms’, which he argues should be recognized at the core of transdisciplinarity research, namely:

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A heuristic framework for reflecting on joint problem framing

By BinBin Pearce and Olivier Ejderyan

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1. BinBin Pearce (biography)
2. Olivier Ejderyan (biography)

What is joint problem framing? What are the key issues that joint problem framing has to address? How can joint problem framing be improved?

What is joint problem framing?

A key aspect of tackling complex problems is effectively bringing together differing points of view. These points of view are what Craik (1943) refers to as “small-scale models” of the problem situation. These are mental models formed from each individual’s experiences, interests, knowledge and environment. These mental models then set the boundaries for what problem definitions and solutions are possible and relevant to consider.

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Why model?

By Steven Lade

Steven Lade
Steven Lade (biography)

What do you think about mathematical modelling of ‘wicked’ or complex problems? Formal modelling, such as mathematical modelling or computational modelling, is sometimes seen as reductionist, prescriptive and misleading. Whether it actually is depends on why and how modelling is used.

Here I explore four main reasons for modelling, drawing on the work of Brugnach et al. (2008):

  • Prediction
  • Understanding
  • Exploration
  • Communication.

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Metacognition as a prerequisite for interdisciplinary integration

By Machiel Keestra

Machiel Keestra (biography)

What’s needed to enable the integration of concepts, theories, methods, and results across disciplines? Why is communication among experts important, but not sufficient? Interdisciplinary experts must also meta-cognize: both individually and as a team they must monitor, evaluate and regulate their cognitive processes and mental representations. Without this, expertise will function suboptimally both for individuals and teams. Metacognition is not an easy task, though, and deserves more attention in both training and collaboration processes than it usually gets. Why is metacognition so challenging and how can it be facilitated?

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Five principles of holistic science communication

By Suzi Spitzer

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Suzi Spitzer (biography)

How can we effectively engage in the practice and art of science communication to increase both public understanding and public impact of our science? Here I present five principles based on what I learned at the Science of Science Communication III Sackler Colloquium at the National Academy of Sciences in Washington, DC in November 2017.

1. Assemble a diverse and interdisciplinary team

  1. Scientists should recognize that while they may be an expert on a particular facet of a complex problem, they may not be qualified to serve as an expert on all aspects of the problem. Therefore, scientists and communicators should collaborate to form interdisciplinary scientific teams to best address complex issues.
  2. Science is like any other good or service—it must be strategically communicated if we want members of the public to accept, use, or support it in their daily lives. Thus, research scientists need to partner with content creators and practitioners in order to effectively share and “sell” scientific results.
  3. Collaboration often improves decision making and problem solving processes. People have diverse cognitive models that affect the way each of us sees the world and how we understand or resolve problems. Adequate “thought world diversity” can help teams create and communicate science that is more creative, representative of a wider population, and more broadly applicable.

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Sharing mental models is critical for interdisciplinary collaboration

By Jen Badham and Gabriele Bammer

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Jen Badham (biography)

What is a mental model? How do mental models influence interdisciplinary collaboration? What processes can help tease out differences in mental models?

Mental models

Let’s start with mental models. What does the word ‘chair’ mean to you? Do you have an image of a chair, perhaps a wooden chair with four legs and a back, an office chair with wheels, or possibly a comfortable lounge chair from which you watch television?

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Productive multivocal analysis – Part 2: Achieving epistemological engagement

By Kristine Lund

kristine-lund
Kristine Lund (biography)

In a previous blog post I described multivocalityie., harnessing multiple voices – in interdisciplinary research and how research I was involved in (Suthers et al., 2013) highlighted pitfalls to be avoided. This blog post examines four ways in which epistemological engagement can be achieved. Two of these are positive and two may have both positive and negative aspects, depending on how the collaboration plays out.

Once a team begins analyzing a shared corpus from different perspectives — in our case, it was a corpus of people solving problems together — it’s the comparison of researchers’ respective analyses that can be a motor for productive epistemological encounters between the researchers.

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Productive multivocal analysis – Part 1: Avoiding the pitfalls of interdisciplinarity

By Kristine Lund

kristine-lund
Kristine Lund (biography)

Many voices are expressed when researchers from different backgrounds come together to work on a new project and it may sound like cacophony. All those voices are competing to be heard. In addition, researchers make different assumptions about people and data and if these assumptions are not brought to light, the project can reach an impasse later on and much time can be wasted.

So how can such multivocality be positive and productive, while avoiding trouble? How can multiple voices be harnessed to not only achieve the project’s goals, but also to make scientific progress?

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ICTAM: Bringing mental models to numerical models

By Sondoss Elsawah

sondoss-elsawah
Sondoss Elsawah (biography)

How can we capture the highly qualitative, subjective and rich nature of people’s thinking – their mental models – and translate it into formal quantitative data to be used in numerical models?

This cannot be addressed by a single method or software tool. We need multi-method approaches that have the capacity to take us through the learning journey of eliciting and representing people’s mental models, analysing them, and generating algorithms that can be incorporated into numerical models.

More importantly, this methodology should allow us to see in a transparent way the progression on this learning journey.

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Models as narratives

By Alison Singer

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Alison Singer (biography)

I don’t see the world in pictures. I mean, I see the world in all its beautiful shapes and colors and shadings, but I don’t interpret the world that way. I interpret the world through the stories I create. My interpretations of these stories are my own mental models of how I view the world. What I can do then, to share this mental model, is create a more formalized model, whether it is a simple picture (in my case a very badly drawn one), or a system dynamics model, or an agent-based model. People think of models as images, as representations, as visualizations, as simulations. As tools to represent, to simplify, to teach, and to share. And they are all these things, and we need them to function as these things, but they are also stories, and can be interpreted and shared as such.

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