When we think of research infrastructure, it is easy to associate astronomers with telescopes, oceanographers with research vessels and physicists with particle accelerators.
But what sort of research infrastructure (if any) do we need in order to do more effective multidisciplinary, interdisciplinary and transdisciplinary research on big, complex, ‘wicked’ challenges like climate change or food security?
Some eminent colleagues and I argue in a new paper (Baron et al., 2017) that the answers include:
What is involved in effective scaling up of innovations in order to achieve social impact? Here are four best practices, drawn from our experience in scaling up human services innovations and programs for children and families. We also provide definitions of the key terms used.
1. Understand the target audiences
Effectively scaling innovations first requires attention to defining the denominator, or population of interest for the scale-up effort, as well as the numerator, or the number of children and families who are receiving the innovation with fidelity and good outcomes.
2. Purposeful design leads to high-fidelity use
Human service systems are legacy systems comprised of an accumulation of fragments of past mandates, good ideas, beliefs, and ways of work that evolved over many decades as legislators, leaders, and staff have come and gone. These legacy systems can be fragmented, siloed and inefficient.
To realize social impact, organizations and systems need to be designed, or re-designed, on purpose to produce and sustain high-fidelity use of effective innovations.
3. Focus on scaling proven programs
Attempts to scale ineffective or harmful programs are a waste of time, money and opportunity, so programs must reliably produce positive outcomes for the population of interest.
Given that we are focused on scaling interaction-based programs that require service providers to use the program within a larger systems context, there is a great deal of complexity involved in “scaling up.” It may be difficult to assess the quality of the program for the children and families who are receiving it, as good fidelity measures for programs are not common.
In a previous blog post I described multivocality – ie., harnessing multiple voices – in interdisciplinary research and how research I was involved in (Suthers et al., 2013) highlighted pitfalls to be avoided. This blog post examines four ways in which epistemological engagement can be achieved. Two of these are positive and two may have both positive and negative aspects, depending on how the collaboration plays out.
Once a team begins analyzing a shared corpus from different perspectives — in our case, it was a corpus of people solving problems together — it’s the comparison of researchers’ respective analyses that can be a motor for productive epistemological encounters between the researchers.
Many voices are expressed when researchers from different backgrounds come together to work on a new project and it may sound like cacophony. All those voices are competing to be heard. In addition, researchers make different assumptions about people and data and if these assumptions are not brought to light, the project can reach an impasse later on and much time can be wasted.
So how can such multivocality be positive and productive, while avoiding trouble? How can multiple voices be harnessed to not only achieve the project’s goals, but also to make scientific progress?
Too often, lessons about modelling practices are left out of papers, including the ad-hoc decisions, serendipities, and failures incurred through the modelling process. The lack of attention to these details can lead to misperceptions about how the modelling process unfolds.
We are part of a small team that examined five case studies where system dynamics was used to model socio-ecological systems. We had direct and intimate knowledge of the modelling process and outcomes in each case. Based on the lessons from the case studies as well as the collective experience of the team, we compiled the following set of good practices for systems dynamics modelling of complex systems.
Researchers are constantly being challenged to demonstrate that their research can make a difference and has impact. Practice and policy partners are similarly challenged to demonstrate that their decisions and activity are informed by the evidence base. It sounds like all we need to do is join the two groups together – simple, right?
In Fuse (the Centre for Translational Research in Public Health, www.fuse.ac.uk) we wanted to do exactly that. We wanted to supply the evidence that end-users said they wanted (supply and demand), and make it easy for them to access and use research evidence.
Yet, we knew that current approaches to supplying evidence (briefs, guidelines, publications) do not work as well as we once thought they did. It needed a re-think…
How can new or under-utilized healthcare practices be expanded and institutionalized to achieve audacious and diverse global health outcomes, ranging from eliminating polio to reversing the rise in non-communicable diseases? How can complex adaptive systems with diverse components and actors interacting in multiple ways with each other and the external environment best be dealt with? What makes for an effective scale-up effort?
Four in-depth case studies of scale-up efforts were used to explore if there were different pathways to positively change a complex adaptive system.
By Russell Gorddard, Matthew Colloff, Russell Wise and Michael Dunlop
Adapting to climate change can require profound alterations in environmental management and policy. However the social context of a decision process limits options and resists change, often dooming attempts to adapt to climate change even before they begin. How can decision makers in policy and management more effectively see the institutional and social water they swim in, in order to better drive change?
Values, rules and knowledge (vrk) provide a useful heuristic to help decision makers analyze how the social system shapes their decision context. Put simply, decisions require:
knowledge of options and their implications
values to assess the options
rules that enable implementation.
Figure adapted from original in Gordardd et al. (2016)
Viewing the decision context as an interconnected system of values, rules and knowledge can reveal limits to adaptation and suggest strategies for addressing them (Gorddard et al. 2016).
Values are the set of ethical precepts that determine the way people select actions and evaluate events.
Rules are both rules-in-use (norms, practices, habits, heuristics) and rules-in-form (regulations, laws, directives).
Knowledge is both evidence-based (scientific and technical) knowledge and experiential knowledge.
Decision context is the subset of interacting subsystems that are at play in a particular decision process. One core idea is that the decision context may exclude relevant values, knowledge or rules from being considered in decisions. Adaptation may therefore involve change in the decision context.
Processes to support the uptake of research evidence call for each of the key stakeholders to consider the challenges faced by other key stakeholders in making good use of research evidence. When stakeholders have the opportunity to consider perspectives other than their own, they will generally have a broader understanding of the problem space, and, in turn a greater commitment to co-creating prototypes for improving research translation.
Let’s consider a real world example in New York City’s public child welfare system.
That which we call a rose, by any other name would smell as sweet.
That Shakespeare guy really knew what he was talking about. A rose is what it is, no matter what we call it. A word is simply a cultural agreement about what we call something. And because language is a common thread that binds cultures together, participatory modeling – as a pursuit that strives to incorporate knowledge and perspectives from diverse stakeholders – is prime for integrating stories into its practice.
To an extent, that’s what every modeling activity does, whether it’s through translating an individual’s story into a fuzzy cognitive map, or into an agent-based model. But I would argue that the drive to quantify everything can sometimes make us lose the richness that a story can provide.
In part 1 of our blog posts on why use patterns, we argued for making unstated, tacit knowledge about integrated modelling practices explicit by identifying patterns, which link solutions to specific problems and their context. We emphasised the importance of differentiating the underlying concept of a pattern and a pattern artefact – the specific form in which the pattern is explicitly described.
In order to actually use patterns to communicate about practices, the artefact takes on greater importance: what form could artefacts describing the patterns take, and what mechanisms and platforms are needed to first create, and then share, maintain, and update these artefacts?
While the concepts of ‘problem, solution and context’ should be discussed in some way, there is no single best way of representing patterns as artefacts. The form of artefacts will differ depending on many factors, including how the users perceive the ease of:
How can science truly support sustainability transformations?
In our research projects we often find that the very process of co-producing knowledge with stakeholders has transformative impacts. This requires careful design and implementation. Knowledge co-production in transdisciplinary and other research leads to social learning and can make a difference in the lives of those involved.
