Integration and Implementation Insights
A global, comprehensive, living toolkit improving research, education and impact on complex real-world problems
By Bonnie McBain
What makes scenarios useful to decision makers in effectively planning for the future? Here I discuss three aspects of scenarios:
Goals of scenarios
Since predicting the future is not possible, it’s important to know that scenarios are not predictions. Instead, scenarios stimulate thinking and conversations about possible futures.
Key goals and purposes of scenarios can be any of the following:
By Jen Badham
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).
By Siobhan Bourke and Emily Lancsar
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?
While uncertainty is often discussed alongside risk, a fundamental difference between uncertainty and risk is that risk involves events with known probabilities (or probabilities based on reliable empirical evidence), whereas under uncertainty probabilities are unknown and reflect an individual’s subjective belief concerning the likelihood of a given outcome. Given the subjectivity, that likelihood can differ from person to person. It can also involve a perceived zero probability in the case of unforeseen events (or ‘unknown unknowns’).
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.
The idea tree exercise
A Spanish version of this post is available
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.
By Erin Walsh
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?
What makes a good scatterplot?
A Spanish version of this post is available
Can boundary objects be designed to help researchers and decision makers to interact more effectively? How can the socio-political setting – which will affect decisions made – be reflected in the boundary objects?
Here I describe a new context-specific boundary object to promote decision making based on scientific evidence. But first I provide a brief introduction to boundary objects.
What is a ‘boundary object’?
By Jan Kwakkel
How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:
Deep uncertainty means that the various parties to a decision do not know or cannot agree on how the system works, how likely various possible future states of the world are, and how important the various outcomes of interest are.
By Gabriele Bammer
If you want to undertake transdisciplinary research, where can you find relevant concepts and methods? Are there compilations or toolkits that are helpful?
I’ve identified eight relevant toolkits, which are described briefly below and in more detail in the journal GAIA’s Toolkits for Transdisciplinarity series.
One toolkit provides concepts and methods relevant to the full range of transdisciplinary research, while the others cover four key aspects: (i) collaboration, (ii) synthesis of knowledge from relevant disciplines and stakeholders, (iii) thinking systemically, and (iv) making change happen.
By Erica Lawlor and Cheryl Vaughan
How can scientists whose careers were formed in an incentive system that cultivates competitive and territorial behaviors be helped to meet the expectations of collaborative research frameworks? A team-based approach that transcends disciplinary boundaries may be a tall order for scientists who “grew up” in a system where funding and promotion are based upon a proven record of individual contributions to a field of research. But that is the direction in which much of science is heading.
By Matthias Bergmann
To make progress in contributing to the solution of complex real-world problems, transdisciplinary research has come to the forefront. By integrating multiple disciplines as well as the expertise of partners from societal practice, transdisciplinary researchers are able to look at a problem from many angles, with the goal of making both societal and scientific advances.
But how can these different types of expertise be integrated into both a better understanding of the problem and more effective ways of addressing it?
Colleagues and I have collected 43 methods from a number of transdisciplinary research projects dealing with a variety of research topics. We have grouped them into seven classes following an epistemological hierarchy. We start with methods in the narrower sense, progressing to integration instruments.
By Claude Garcia, Anne Dray and Patrick Waeber
A Spanish version and a French version of this post are available
Can we help the next generation of policy makers, business leaders and citizens to become creative, critical and independent thinkers? Can we make them aware of the nature of the problems they will be confronted with? Can we strengthen their capacity to foster and lead stakeholder processes to address these problems?
Yes.
We build on our experience as field researchers on environmental issues. We develop models that link ecological, social and economic processes, based on real study cases. We transform these models into role-playing games, both cooperative and competitive. And we use them to let students – the next generation of policy makers, business leaders and citizens – explore the complexities of natural resources management. They discover the roles of trust, knowledge, communication and conflict in a friendly environment. As games unfold, players observe, experience, experiment, and devise rules to resolve the tensions between competing demands.