By Graham Hubbs, Michael O’Rourke, Steven Hecht Orzack
Have you collaborated with people on a complex project and wondered why it is so difficult? Perhaps you’ve asked yourself, “Do my collaborators even conceive of the project and its goals in the way I do?” Projects involving collaborators from different disciplines or professions seem almost ready made to generate this kind of bewilderment. Collaborators on cross-disciplinary projects like these often ask different kinds of questions and pursue different kinds of answers.
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.
How can we affirm, value and capitalise on the unique strengths that each individual brings to interdisciplinary and transdisciplinary research? In particular, how can we capture diversity across individuals, as well as the richness and distinctness of each individual’s influence and impact?
In the course of writing ten reflective narratives (nine single-authored and one co-authored), eleven of us stumbled on a technique that we think could have broader utility in assessing influence and impact, especially in research but also in education (Bammer et al., 2019).
How can we best live in a VUCA (volatile, uncertain, complex and ambiguous) world? How can we shift from a worldview that looks to predict and control what is to be done through plans and strategies to being present and flexible in order to respond effectively as unexpected changes take place? How can we be open to not knowing what will emerge and embrace uncertainty as the opportunity to co-create and learn?
One powerful and promising way forward is Theory U, a change methodology developed by Otto Scharmer and illustrated below. Scharmer introduced the concept of “presencing”—learning from the emerging future. The concept of “presencing” blends “sensing” (feeling the future possibility) and “presence” (the state of being in the present moment). It acknowledges that we don’t know the answers. Staying at the bottom of the U until the best potential future starts emerging requires embracing uncertainty as fertile soil.
What’s a productive way to think about undesirable outcomes and how to avoid them, especially in an unpredictable future full of unknown unknowns? Here I describe the technique of vulnerability analysis, which essentially has three steps:
Step 1: Identify undesirable outcomes, to be avoided
Step 2: Look for conditions that can lead to such outcomes, ie. vulnerabilities
Step 3: Manage the system to mitigate or adapt to vulnerable conditions.
The power of vulnerability analysis is that, by starting from outcomes, it avoids making assumptions about what led to the vulnerabilities.
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.
How can researchers interested in complex societal and environmental problems best understand and deal with uncertainty, which is an inherent part of the world in which we live? Accidents happen, governments change, technological innovation occurs making some products and services obsolete, markets boom and inevitably go bust. How can uncertainty be managed when all possible outcomes of an action or decision cannot be known? In particular, are there lessons from the discipline of economics which have broader applicability?
By Dan Stokols, Maritza Salazar, Gary M. Olson, and Judith S. Olson
How can cross-disciplinary research teams increase their capacity for generating and integrating novel research ideas and conceptual frameworks?
A key challenge faced by research teams is harnessing the intellectual synergy that can occur when individuals from different disciplines join together to create novel ideas and conceptual frameworks. Studies of creativity suggest that atypical (and often serendipitous) combinations of dissimilar perspectives can spur novel insights and advances in knowledge. Yet, many cross-disciplinary teams fail to achieve intellectual synergy because they allot insufficient effort to generating new ideas. Here we describe a brainstorming tool that can be used to generate new ideas in cross-disciplinary teams.
How can toolboxes more effectively support those learning to deal with complex societal and environmental problems, especially novices such as PhD students and early career researchers?
In this blog post, I briefly describe four toolboxes and assess them for their potential to assist learning processes. My main aim is to open a discussion about the value of the four toolboxes and how they could better help novices.
Before describing the toolboxes, I outline the learning processes I have in mind, especially the perspective of legitimate peripheral participation.
What are conceptual models? How can conceptual modelling effectively represent complex topics and assist communication among people from different backgrounds and disciplines?
This blog post describes ConML, which stands for “Conceptual Modelling Language”. ConML is a specific modelling language that was designed to allow researchers who are not expert in information technologies to create and develop their own conceptual models. It is useful for the humanities, social sciences and experimental sciences.
Scatterplots are used in many disciplines, which makes them useful for communicating across disciplines. They are also common in newspapers, online media and elsewhere as a tool to communicate research results to stakeholders, ranging from policy makers to the general public. What makes a good scatterplot? Why do scatterplots work? What do you need to watch out for in using scatterplots to communicate across disciplines and to stakeholders?