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.
By Katrine Lindvig, Line Hillersdal and David Earle
How can we resolve the stark disparity between theoretical knowledge about interdisciplinary approaches and practical applications? How can we get from written guidelines to actual practices, especially taking into account the contextual nature of knowledge production; not least when the collaborating partners come from different disciplinary fields with diverse expectations and concerns?
For the past few years, we have been developing ways in which academic theory and physical interactions can be combined. The result is CoNavigator – a hands-on, 3-dimensional and gamified tool which can be used:
for learning purposes in educational settings
as a fast-tracking tool for interdisciplinary problem solving.
CoNavigator is a tool which allows groups to collaborate on a 3-dimensional visualisation of the interdisciplinary topography of a given field or theme. It addresses the contextual and local circumstances and the unique combinations of members in collaborative teams. CoNavigator is therefore short for both Context Navigation and Collaboration Navigation. The process of applying the tool takes around 3 hours.
CoNavigator is composed of writable tiles and cubes to enable rapid, collaborative visualisation, as shown in the first figure below. The tactile nature of the tool is designed to encourage collaboration and negotiation over a series of defined steps.
Making the Tacit Visible and Tangible
Each participant makes a personal tool swatch. By explaining their skills to a person with a completely different background, the participant is forced to re-evaluate, re-formulate, and translate skills in a way that increases their own disciplinary awareness. Each competency that is identified is written onto a separate tool swatch.
How can you improve your thinking – alone or in a group? How can mapping ideas help you understand the relationships among them? How can mapping a conversation create a new reality for those involved?
In what follows, I draw on the work of Daniel Kahneman’s (2011) best-selling book Thinking, Fast and Slow, which explains how human thinking occurs at different speeds, from the very fast thinking associated with face-to-face conversation to the very slow thinking associated with assembling information resources into encyclopedias. I use those ideas in my descriptions of knowledge maps.
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!
How can co-creation communities use models – simple visual representations and/or sophisticated computer simulations – in ways that promote learning and improvement? Modeling techniques can serve to generate insights and correct misunderstandings. Are they equally as useful for fostering new learning and adaptation? Sterman (2006) argues that if new learning is to occur in complex systems then models must be subjected to testing. Model testing must, in turn, yield evidence that not only guides decision-making within the current model, but also feeds back evidence to improve existing models so that subsequent decisions can be based on new learning.
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.