Latest contribution
A framework for navigating the impact of using artificial intelligence on collaborative research communication
By Faye Miller.
How can research teams recognise when their use of artificial intelligence is affecting their ability to integrate different knowledge and perspectives? How can they navigate the impact of artificial intelligence on their collaborative processes?
When research teams use artificial intelligence in collaborative work, new complexities emerge, especially subtle shifts in communication patterns that can fundamentally alter how teams integrate different perspectives and knowledge forms. Consider an environmental team relying on artificial intelligence summaries across hydrology, ecology, and policy. They might miss crucial disciplinary nuances, or follow its “evidence-based” recommendations that may clash with community priorities. Such changes in communication rhythms can compromise decision-making quality and the integration of different viewpoints.
Recent contributions
Three lessons for designing serious games for educational settings
By Alice H. Aubert.
What is Triadic Game Design and what lessons does it provide for designing and analysing serious games in an educational setting?
Triadic Game Design
The Triadic Game Design is a design framework for serious games that defines three essential, interrelated elements—Reality, Meaning, and Play—that need to be integrated and balanced (Harteveld 2011).
Reality ensures the game represents the real world sufficiently (ie., in a valid and reliable way that can be understood by the target players). Subject-matter experts model the Reality in the game focusing on the problem, its influencing factors, and relationships.
Meaning pertains to the game’s purpose and its transference to the real world, to create added value through playing the game.
Moving from epistemic paternalism to transformative transdisciplinarity
By David Ludwig and Charbel N. El-Hani.
2. Charbel N. El-Hani (biography)
How can we overcome the epistemic paternalism that has long shaped relations between science and society? How can a transformative vision of transdisciplinarity emerge from the interplay between epistemic diversity and epistemic decolonization?
Demands for transdisciplinary research reflect an intricate politics of knowledge that can be described through a triad of paternalism, diversity, and decolonization. Epistemic paternalism has become widely criticized in many debates about development and modernization. For example, international development projects are often deeply paternalistic by assuming that science and technology of the “developed world” should be simply exported into the “underdeveloped world,” where they are imagined as generating economic growth and societal progress.
Recognize and value linguistic and conceptual pluralism!
By Ulli Vilsmaier.
How can we best recognise and value linguistic and conceptual pluralism in naming what we do when we work in international environments? What are the limitations of descriptors such as transdisicplinarity, participatory action research and co-creation?
Terminology is really an issue when working across linguistic, disciplinary and professional boundaries. Working internationally we are now accustomed to using the hyper-centralized language, English; we tend to delegate translation more and more to machine-based algorithms; and we easily forget the consequences of working in a language that is not our mother tongue nor anchored in our cultural and social environment.
A hyper-centralized language has great benefits, but also major weaknesses.
Training specialists to solve wicked problems
By Vladimir Mokiy.
How can a modern university train highly qualified specialists who are able to rethink and unambiguously solve wicked problems?
Here I build on my previous i2Insights contribution Systems transdisciplinarity as a metadiscipline, the methodology of which aims to unify and generalize complementary and non-complementary disciplinary knowledge and methodologies. This metadiscipline provides the basis of a proposed curriculum for a two-year training program at the masters level. The intention is that specialists would be trained in systems transdisciplinarity using a single curriculum to ensure a uniform level of professional capabilities and competencies.
The curriculum
The curriculum involves the organization of training in four sections.
Highlighted contributions
Viable System Model: A theory for designing more responsive organisations
By Angela Espinosa
How can communities, businesses, regions, and nations – which can all be thought of as organisations – be designed to be capable of dealing quickly and effectively with environmental fluidity and complexity?
The Viable System Model, often referred to as VSM, is a theory that posits that a complex organisation is more capable of responding to a changing and unpredictable environment, if it is:
- composed of autonomous, effective, and agile subsidiary organisations,
- highly connected to each other, and
- cohesively operating with shared ethos, purpose, processes, and technologies.
A complex organisation therefore has multiple levels of nested organisations, each adhering to these principles.
The building blocks of the Viable System Model are five interconnected systems.
Externalizing implicit expectations and assumptions in transdisciplinary research
By Verena Radinger-Peer, Katharina Gugerell and Marianne Penker
2. Katharina Gugerell (biography)
3. Marianne Penker (biography
How can implicit expectations and assumptions of team members in transdisciplinary research collaborations be identified?
We used Q-methodology as a tool to make diverse expectations and perceptions of transdisciplinary research collaborations tangible and thus negotiable.
Q-methodology is an established explorative, semi-quantitative method for investigating distinctive viewpoints of a given population based on inverted factor analysis. While we do not explain Q methodology here, it is increasingly used and we refer those who want to find out more to Watts and Stenner (2012).
One disadvantage of the Q-method is the amount of time and effort that has to be invested in developing the Q-statements.
Managing complexity with human learning systems
By Toby Lowe
How can those in public service – be they researchers, policy makers or workers in government agencies, private businesses managers, or voluntary and community organisation leaders – think more effectively about improving people’s lives, when they understand that each person’s life is a unique complex system?
A good starting point is understanding that real outcomes in people’s lives aren’t “delivered” by organisations (or by projects, partnerships or programmes, etc). Outcomes are created by the hundreds of different factors in the unique complex system that is each person’s life.
In other words, an outcome is the product of hundreds of different people, organisations, and factors in the world all coming together in a unique and ever-changing combination in a particular person’s life. Very little of what influences the outcome is under the control or influence of those who undertake public service.
Dealing with imperfection in tackling complex problems
By Gabriele Bammer.
Why is an appreciation of imperfection and its inevitability important for those seeking to understand and act on complex societal and environmental problems? Which traps can imperfection lead to and what are the most effective ways of dealing with it?
The inevitability of imperfection
Imperfection is inevitable both in attempting to develop a comprehensive understanding of complex societal and environmental problems and in acting on them. The multiple underpinning reasons include:
● Complex problems are systems problems, and all systems views are partial, so that the whole system cannot be taken into account. Even then, boundaries need to be set to effectively deploy available resources and these artificial boundaries further constrain understanding of the whole system.