How can we ignite discovery conversations and foster open, psychologically safe conversations among researchers from different disciplines who have not met previously?
This blog post is based on the findings of a workshop with pre-doctoral trainees (Strekalova and McCormack 2020), but is likely to have broader relevance. The workshop was structured around the initial steps of Strategic DoingTM (Morrison et al., 2019), a disciplined approach to facilitating complex collaborative projects. The conversations in the room progressed by addressing five key PROBE-Action questions.
We know that reflecting can make a marked difference to the quality of our collective endeavour. However, in the daily busyness of inter- and trans- disciplinary research collaborations, time for reflection slides away from us as more immediate tasks jostle for attention. What would help us put into regular practice what we know in theory about prioritising time to reflect and learn?
Discomfort sometimes provides the necessary nudge in the ribs that reminds us to keep reflecting and learning. The discomfort of listening to the presentation of a colleague you like and respect, but having very little idea what they’re talking about. Or, worse, failing to see how their research will make a worthy contribution to the collective project. The discomfort when an intellectual debate with a colleague turns personal. The discomfort of watching project milestones loom, knowing you’re seriously behind schedule because others haven’t done what they said.
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. These tensions can foster interpersonal conflict, particularly when team members treat the apparent contradictions as if only one was ‘right’.
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:
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:
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.
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?
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. I prefer and use the term ‘interested amateurs’ over ‘curious nonexperts’, simply because the word ‘amateur’ seems slightly more insulting towards models!
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?