Tensions inevitably arise in inter- and transdisciplinary research. Dealing with these tensions and resulting conflicts is one of the hardest things to do. We are meant to avoid or get rid of conflict and tension, right? Wrong!
Tension and conflict are not only inevitable; they can be a source of positivity, emergence, creativity and deep learning. By tension we mean the pull between the seemingly contradictory parts of a paradox, such as parts and wholes, stability and chaos, and rationality and creativity.
Interdisciplinary collaboration to tackle complex problems is challenging! In particular, interdisciplinary communication can be very difficult – how do we bridge the gulf of mutual incomprehension when we are working with people who think and talk so very differently from us? What skills are required when mutual incomprehension escalates into conflict, or thwarts decision making on important issues?
It is often at this point that collaborations lose momentum. In the absence of constructive or productive exchange, working relationships stagnate and people retreat to the places where they feel safest: their own disciplines, their offices, or the colleagues who are on their ‘side’. As a consequence, prospects for meaningful collaboration and integration dwindle.
What are the key lessons for building a successful collaborative team? A new version of the Collaboration and Team Science Field Guide (Bennett et al., 2018) provides ten top take aways:
1. TRUST
It is almost impossible to imagine a successful collaboration without trust. Trust provides the foundation for a team. Trust is necessary for establishing other aspects of a successful collaboration such as psychological safety, candid conversation, a positive team dynamic, and successful conflict management.
How can we improve interdisciplinary collaborations? There are many lessons to be learned from the Science of Team Science. The following ten lessons summarize many of the ideas that were shared at the International Science of Team Science Conference in Galveston, Texas, in May 2018 [Moderator update – In April 2026, an embedded link to the Galveston 2018 conference in this sentence was no longer available and so the link structure has been left in place but the active link deleted: scienceofteamscience.org/2018-scits-conference/].
1. Team up with the right people
On the most basic level, scientists working on teams should be willing to integrate their thoughts with their teammates’ ideas. Participants should also possess a variety of social skills, such as negotiation and social perceptiveness. The most successful teams also encompass a moderate degree of deep-level diversity (values, perspectives, cognitive styles) and include women in leadership roles.
Incommensurability is a recognized problem in interdisciplinary research. What is it? How can we understand it? And what can we do about it?
What is it?
Incommensurability is best illustrated by a real example. I once co-taught a class with a colleague from another discipline. Her discipline depends on empirical analysis of data sets, literally on counting things. I, on the other hand, am a philosopher. We don’t count. One day she said to our students, “If you don’t have an empirical element in what you’re doing, it’s not research.” I watched the students start nodding, paused for half a beat, and volunteered, “So, I’ve never done any research in my entire career.” “That’s right!” she replied, immediately, yet hesitating somewhere between a discovery and a joke.
What causes interdisciplinary collaborations to default to the standard frameworks and methods of a single discipline, leaving collaborators feeling like they aren’t being taken seriously, or that what they’ve brought to the project has been left on the table, ignored and underappreciated?
Sometimes it is miscommunication, but sometimes it is that collaborators disagree. And sometimes disagreements are both fundamental and intractable.
Often, these disagreements can be traced back to different epistemological frameworks. Epistemological frameworks are beliefs about how particular disciplines conceive of what it is they investigate, how to investigate it, what counts as sufficient evidence, and why the knowledge they produce matters.
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?
In a previous blog post I described multivocality – ie., 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.
How can we improve the often poor interaction and lack of genuine discussions between policy makers, experts, and those affected by policy?
As a social scientist who makes and uses models, an idea from Daniel Dennett’s (2013) book ‘Intuition Pumps and Other Tools for Thinking’ struck a chord with me. Dennett introduces the idea of using lay audiences to aid and improve understanding between experts. Dennett suggests that including lay audiences (which he calls ‘curious nonexperts’) in discussions can entice experts to err on the side of over-explaining their thoughts and positions. When experts are talking only to other experts, Dennett suggests they under-explain, not wanting to insult others or look stupid by going over basic assumptions. This means they can fail to identify areas of disagreement, or to reach consensus, understanding, or conclusions that may be constructive.
For Dennett, the ‘curious nonexperts’ are undergraduate philosophy students, to be included in debates between professors. For me, the book sparked the idea that models could be ‘curious nonexperts’ in policy debates and processes.
What is an analogy? How can analogies be used to work productively across disciplines in teams?
We know from the pioneering work of Kevin Dunbar (1995), in studying molecular biology labs, that analogies were a key factor in why multidisciplinary labs were much more successful than labs composed of many researchers from the same backgrounds. What is it about analogies that assists multi- and interdisciplinary work?
The advice that follows comes from a decade of research involving intensive analyses of hundreds of hours of interdisciplinary science and engineering teams, following the minute-by-minute processes of the teams, and using advanced statistical techniques to look for robust patterns in behavior over time and across teams. In general, our research has shown that creative teams generate twice as many ideas per unit time when they use analogies than when they do not.
Why does the theory and practice of co-creation need to be informed by systems thinking? Co-creation without a thorough understanding of systems thinking can be deeply problematic. Essentially, we need a theory and practice of systemic co-creation.
Three key things happen in any co-creation:
It is necessary for a diversity of perspectives to engage.
There is the synergistic innovation that results from this engagement.
Imagine this scenario. You are confronted by a wicked problem, such as the obesity epidemic. You know it’s a wicked problem – many previous attempts to resolve it have failed.
Suppose that you wish to develop a plan to remedy obesity. You have identified as many relevant areas of expertise and experience as you can and approached appropriate people – researchers, health practitioners, people with political influence, and so on.
You bring them together to pool their expertise—only to find that you now have another problem. Encouraging them to work collaboratively is more difficult than you expected. They talk in jargon. Their understanding is narrow. Their commitment is to their own discipline. Some of their understanding is tacit. Some of them are argumentative. And more. What are you to do?
