How does the way we approach complex problems differ from how we approach problems that are familiar or obvious?
In this i2Insights contribution, I explore four kinds of reasoning:
Design abduction is the brain-child of Professor Kees Dorst (2015). In simplified terms, these different kinds of reasoning can be compared as follows (Watson and Dorst, 2022, p. 3; taken from Dorst, 2015, pp. 46-49):
Post-normal science comes into play for decision-making on policy issues where facts are uncertain, values in dispute, stakes high and decisions urgent.
A good example of a problem requiring post-normal science is the actions that need to be taken to mitigate the effects of sea level rise consequent on global climate change. All the causal elements are uncertain in the extreme, at stake is much of the built environment and the settlement patterns of people, what to save and what to sacrifice is in dispute, and the window for decision-making is shrinking. The COVID-19 pandemic is another instance of a post-normal science problem. The behaviour of the current and emerging variants of the virus is uncertain, the values of socially intrusive remedies are in dispute, and obviously stakes are high and decisions urgent.
In such contexts of policy making, normal science (in the Kuhnian sense, see Kuhn 1962) is still necessary, but no longer sufficient.
How can knowledge integration for addressing societal challenges be mapped, ‘measured’ and assessed?
In this blog post I argue that measuring averages or aggregates of ‘interdisciplinarity’ is not sufficiently focused for evaluating research aimed at societal contributions. Instead, one should take a portfolio approach to analyze knowledge integration as a systemic process over research landscapes; in particular, focusing on the directions, diversity and synergies of research trajectories.
What is expertise in research integration and implementation? What is its role in helping tackle complex societal and environmental problems, especially those dimensions that define complexity?
Expertise in research integration and implementation
Addressing complex societal and environmental problems requires specific expertise over and above that contributed by existing disciplines, but there is little formal recognition of what that expertise is or reward for contributing it to a research team’s efforts. In brief, such expertise includes the ability to:
identify relevant disciplinary and stakeholder inputs
effectively integrate them for a more comprehensive understanding of the problem
support more effective actions to ameliorate the problem.
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.
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?
¿Cómo pueden los gobiernos, las comunidades y el sector privado efectivamente trabajar juntos para lograr un cambio social hacia el desarrollo sostenible?
En este blog describo los procesos claves que permitieron a Uruguay lograr uno de los regímenes más avanzados de protección del suelo de tierras de cultivo de secano en el mundo. Una explicación del proceso es la creación de una cultura pragmática de la complejidad, una cultura inclusiva, deliberativa que reconoce la naturaleza compleja del problema y abraza el potencial de lo posible.
1. The idea of “catching the rhythm” of the “patterns of movement” in our constantly changing world.
2. More effectively taking context into account.
3. “We cannot know the systems, but we can know more. We cannot perfect the systems, but we can do better.”
The challenge is to develop methods and processes to better achieve these goals. (Reblogged by Gabriele Bammer)