Latest contributions
Five structural levers to reopen feedback loops that are resistant to external evidence
By Lachlan S. McGill.
When feedback loops have become resistant to external evidence, what are some potential ways of intervening to reopen them?
This i2Insights contribution builds on my previous post which covers understanding why feedback loops can become resistant to external evidence and how to diagnose such a structural problem.
Here I introduce five structural ways to intervene in such a closed feedback loop. These are structural levers, each targeting a different aspect of how signals flow, how authority is allocated, and how evaluative standards are defined.
One practical note before beginning. Applying the interventions below often requires institutional authority, coalition building, or regulatory support, so that isolated actors may not be able to deploy them fully, leaving the problematic dominant structure intact.
Understanding and diagnosing when feedback loops become resistant to external evidence
By Lachlan S. McGill.
Why does better evidence sometimes fail to improve decision making? How can we tell if this is caused by feedback loops becoming resistant to external evidence?
Understanding how structural patterns become problematic
In most organisations, decisions are embedded in feedback loops that connect indicators, incentives, and authority structures. These loops determine what counts as success, which signals influence decisions, and how performance is evaluated over time.
When feedback loops are well aligned with system goals, they support learning. However, feedback loops can also evolve in ways that reinforce a narrow definition of success. This is generally associated with a system relying on a small number of indicators to guide decisions.
Recent contributions
Boundaries as opportunities for learning
By Roger Duck and Jane Searles.
2. Jane Searles (biography)
Think of a time when you noticed how different ‘they’ are from ‘us’. In that moment, did the relationship become more interesting and alive? Or did it flatten into what looked like a boundary – a barrier to be overcome or a connection to be engineered?
This i2Insights contribution is intended to stimulate your imagination by giving examples from practice of relationships between people and teams being treated as opportunities for learning, rather than boundaries.
Most readers of i2Insights work in research. We believe there is much of relevance here for any context in which people are working together, including research teams.
The context
Navigating inter- and transdisciplinary PhD supervision: Practical questions for students and supervisors
By Erika Angarita, Anna Hajdu, Yanyan Huang, BinBin Pearce, Guadalupe Peres-Cajías, Hussein Zeidan and Yuanyuan Zhu.
How can a student and their supervisors develop a shared map for a PhD project when they come from different disciplinary traditions, hold different assumptions about knowledge and quality, and operate within institutional systems that are still largely structured around single disciplines? How can they navigate what may feel obvious to one and may be invisible to another?
A framework for considering context in evaluation of transdisciplinary research projects
By Julia Schegg, Rea Pärli, Manuel Fischer and Eva Lieberherr.
2. Rea Pärli (biography)
3. Manuel Fischer (biography)
4. Eva Lieberherr (biography)
How do contextual factors influence the effects of transdisciplinary research projects? How can assessment of transdisciplinary research move from only considering outcome effectiveness to understanding the reasons behind how and why something works?
Our framework considers both context and the effects of transdisciplinary research projects, as summarised in the figure below. Each aspect of the framework is described in more detail.
Context
Developing a conceptual framework to support communication, collaboration and integration
By Hanna Salomon, Jialin Zhang and Sabine Hoffmann.
2. Jialin Zhang (biography)
3. Sabine Hoffmann (biography)
How can the process of developing a conceptual framework in an inter- and transdisciplinary research project itself create valuable space for reflection, alignment, and learning?
What we have found when developing a project-specific conceptual framework is that the process is as important, if not more important, for the research team than the emerging conceptual framework itself. The process provides space and time to discuss and deep-dive into concepts and terms used within the research team leading to much needed discussions and insights for the individual researchers.
Highlighted contributions
A pattern language for knowledge co-creation
By Yuko Onishi
How can pattern language be used to share tips for knowledge co-creation in transdisciplinary research? What is pattern language?
Pattern language
Pattern language is an idea that originated in the field of architecture and city planning in the 1970s. The American architect Christopher Alexander and his colleagues created a common language, referred to as pattern language, that can be used by non-experts to participate in the process of city planning and building design.
In this pattern language, the rules of thumb for solving common and timeless problems in design are summarised in units called ‘patterns.’ Each pattern describes a specific problem, the situation or context in which it likely occurs, and the core of the solution to that problem.
The solutions are not written as specific procedures or manuals, but rather as ‘hints’ for solving the problem. Therefore, the solution can be used in many ways based on one’s own needs and situation.
An effective way to organize research coordination meetings
By Gemma Jiang, Diane Boghrat and Jenny Grabmeier
2. Diane Boghrat (biography)
3. Jenny Grabmeier (biography)
How can large cross-disciplinary science institutes consisting of multiple teams working on multiple research projects overcome significant challenges to research coordination? Key aspects are:
- Visibility: how to keep different project teams informed of each other’s progress?
- Learning: how to support cross-project learning?
- Accountability: how to keep project teams accountable for their goals and deliverables?
Tackling these challenges requires a combination of asynchronous communications such as Slack, newsletters and emails, as well as synchronous communications such as research coordination meetings.
Keyword quiz: an icebreaker method for interdisciplinary teams
By Sebastian Rogga and Anton Parisi
2. Anton Parisi (biography)
How can members of interdisciplinary teams quickly gain a better understanding of each other’s thematic preferences and skills in a way that is also engaging and fun?
We have developed a “keyword quiz” icebreaker method to facilitate exchange between members of interdisciplinary teams, especially between people who are not complete strangers to each other but are collaborating in a project context for the first time.
In brief, the idea is to communicate each member’s scientific profile based on keywords from publications that the team members have published and that they have selected based on specific categories.
The keywords of a publication are presented visually to the whole group and the team members then guess, in the form of a quiz, which team member published the associated publication.
Seven methods for mapping systems
By Pete Barbrook-Johnson and Alexandra S. Penn
2. Alexandra S. Penn (biography)
What are some effective approaches for developing causal maps of systems in participatory ways? How do different approaches relate to each other and what are the ways in which systems maps can be useful?
Here we focus on seven system mapping methods, described briefly in alphabetical order.
1. Bayesian Belief Networks: a network of variables representing their conditional dependencies (ie., the likelihood of the variable taking different states depending on the states of the variables that influence them). The networks follow a strict acyclic structure (ie., no feedbacks), and nodes tend to be restricted to maximum two incoming arrows. These maps are analysed using the conditional probabilities to compute the potential impact of changes to certain variables, or the influence of certain variables given an observed outcome.