Interdisciplinarity and synergy in collaborations

By Loet Leydesdorff

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Loet Leydesdorff (biography)

What is the difference between “interdisciplinarity” and “synergy?” Why does it matter? How can indicators of interdisciplinarity and synergy be conceptualized and defined mathematically? Can one measure interdisciplinarity and synergy?

Problem-solving often requires crossing boundaries, such as those between disciplines. However, interdisciplinarity is not an objective in itself, but a means for creating synergy. When policy-makers call for interdisciplinarity, they may mean synergy. Synergy means that the whole offers more possibilities than the sum of its parts. The measurement of synergy, however, requires a methodology very different from interdisciplinarity. In this blog post, I consider each of these measures in turn, the logic underpinning each of them, and I specify the definitions in mathematical terms.

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Integration: The IPO model

By Stephen Crowley and Graham Hubbs

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1. Stephen Crowley (biography)
2. Graham Hubbs (biography)

How can we improve our understanding of knowledge integration? What are the elements of integration?

Sometimes what gets integrated are products of science, such as data sets or scientific models. Sometimes it is not the products that are integrated but instead the methods, as can happen on interdisciplinary teams. On these teams, scientists work together, so sometimes it is the people themselves (scientists are people!) or their disciplinary cultures that get integrated.

These are only some of the possible elements of integration. There is just as wide a variety of processes and products of integration as there are elements. The process of integrating data sets might be a sort of analysis, and the result might be a table or graph that displays the results of research in a conspicuous manner. Integrating diverse scientists into an interdisciplinary team, by contrast, is a matter of people working together, and the result of the integration is not a table or a graph but the team itself.

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HIBAR research: What is it and how can it be reinvigorated?

By Lorne A. Whitehead, Scott H. Slovic and Janet E. Nelson

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1. Lorne A. Whitehead (biography)
2. Scott H. Slovic (biography)
3. Janet E. Nelson (biography)

How can we recognize and encourage investigations that holistically fuse fundamental and applied research on a problem of interest in a manner that is both (a) integrative and recursive and (b) highly collaborative with non-university experts?

Recognition

We refer to this form of research as “Highly Integrative Basic And Responsive” (HIBAR). It adds deep university-society engagement to the work that Donald Stokes named “Pasteur’s quadrant” (Stokes 1997) and others have called “use-inspired basic research”.

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Breaking through disciplinary barriers with practical mapping

By Steven E. Wallis and Bernadette Wright

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1. Steven E. Wallis (biography)
2. Bernadette Wright (biography)

How can practical mapping help develop interdisciplinary knowledge for tackling real-world problems — such as poverty, justice and health — that have many causes? How can it help take into account political, economic, technological and other factors that can worsen or improve the issues?

Maps are useful because they show your surroundings – where things are in relation to each other (and to you). They show the goals we want to achieve and what it takes to get there.

‘Practical mapping’ is a straight-forward approach for using concepts and connections to integrate knowledge across and between disciplines, to support effective action.

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Choosing a suitable transdisciplinary research framework

By Gabriele Bammer

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Gabriele Bammer (biography)

What are some of the key frameworks that can be used for transdisciplinary research? What are their particular strengths? How can you choose one that’s most suitable for your transdisciplinary project?

The nine frameworks described here were highlighted in a series for which I was the commissioning editor. The series was published in the scientific journal GAIA: Ecological Perspectives in Science and Society between mid-2017 and end-2019.

Choosing among them is not a matter of right or wrong, but of each being more or less helpful for a particular problem in a particular context.

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How can expertise in research integration and implementation help tackle complex problems?

By Gabriele Bammer

author - gabriele bammer
Gabriele Bammer (biography)

What is expertise in research integration and implementation? What is its role in helping tackle complex societal and environmental problems, especially those dimensions that define complexity?

Expertise in research integration and implementation

Addressing complex societal and environmental problems requires specific expertise over and above that contributed by existing disciplines, but there is little formal recognition of what that expertise is or reward for contributing it to a research team’s efforts. In brief, such expertise includes the ability to:

  • identify relevant disciplinary and stakeholder inputs
  • effectively integrate them for a more comprehensive understanding of the problem
  • support more effective actions to ameliorate the problem.

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Transdisciplinary action research: A guiding framework for collaboration

By Steven Lam, Michelle Thompson, Kathleen Johnson, Cameron Fioret and Sarah Hargreaves

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Steven Lam (biography)

How can graduate students work productively with each other and community partners? Many researchers and practitioners are engaging in transdisciplinarity, yet there is surprisingly little critical reflection about the processes and outcomes of transdisciplinarity, particularly from the perspectives of graduate students and community partners who are increasingly involved.

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Michelle Thompson (biography)

Our group of four graduate students from the University of Guelph and one community partner from the Ecological Farmers Association of Ontario, reflect on our experiences of working together toward community food security in Canada, especially producing a guidebook for farmer-led research (Fioret et al. 2018). As none of us had previously worked together, nor shared any disciplines in common, we found it essential to first develop a guiding framework for collaboration. Our thinking combined the following key principles from action research and transdisciplinarity:

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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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Agent-based modelling for knowledge synthesis and decision support

By Jen Badham

Jen Badham (biography)

The most familiar models are predictive, such as those used to forecast the weather or plan the economy. However, models have many different uses and different modelling techniques are more or less suitable for specific purposes.

Here I present an example of how a game and a computerised agent-based model have been used for knowledge synthesis and decision support.

The game and model were developed by a team from the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), a French agricultural research organisation with an international development focus. The issue of interest was land use conflict between crop and cattle farming in the Gnith community in Senegal (D’Aquino et al. 2003).

Agent-based modelling is particularly effective where understanding is more important than prediction. This is because agent-based models can represent the real world in a very natural way, making them more accessible than some other types of models.

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Collaboration: From groan zone to growth zone

By Carrie Kappel

Carrie Kappel (biography)

What is the groan zone in collaboration? What can you do when you reach that point?

As researchers and practitioners engaged in transdisciplinary problem-solving, we know the value of diverse perspectives. We also know how common it is for groups to run into challenges when trying to learn from diverse ideas and come to consensus on creative solutions.

This challenging, often uncomfortable space, is called the groan zone. The term comes from Sam Kaner’s diamond model of participation shown in the figure below. After an initial period of divergent thinking, where diverse ideas are introduced, groups have to organize that information, focus on what’s most important, and make decisions in order to move forward into the phase of convergent thinking.

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Incommensurability, plain difference and communication in interdisciplinary research

By Vincenzo Politi

Vincenzo Politi (biography)

Where does the term incommensurability come from? What is its relevance to interdisciplinarity? Is it more than plain difference? Does incommensurability need to be reconceptualized for interdisciplinarity?

Incommensurability: its origins and relevance to interdisciplinarity

‘Incommensurability’ is a term that philosophers of science have borrowed from mathematics. Two mathematical magnitudes are said to be incommensurable if their ratio cannot be expressed by a number which is an integer. For example, the radius and the circumference of a circle are incommensurable because their ratio is expressed by the irrational number π.

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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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