Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

Community member post by Jan Kwakkel

jan-kwakkel
Jan Kwakkel (biography)

How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:

  1. Intrinsic limits to predictability in complex systems.
  2. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
  3. Complex systems are generally subject to dynamic change, and can never be completely understood.

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. Continue reading

Four best practices for scaling up effective innovations

Community member post by Amanda Fixsen, Karen Blase and Dean Fixsen

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.

amanda-fixsen
Amanda Fixsen (biography)

karen-blase
Karen Blase (biography)

dean-fixsen
Dean Fixsen (biography)

Continue reading

Productive multivocal analysis – Part 2: Achieving epistemological engagement

Community member post by Kristine Lund

kristine-lund
Kristine Lund (biography)

In a previous blog post I described multivocalityie., 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. Continue reading

Productive multivocal analysis – Part 1: Avoiding the pitfalls of interdisciplinarity

Community member post by Kristine Lund

kristine-lund
Kristine Lund (biography)

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? Continue reading

Toolkits for transdisciplinary research

Community member post by Gabriele Bammer

gabriele-bammer
Gabriele Bammer (biography)

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. Continue reading

Good practices in system dynamics modelling

Community member post by Sondoss Elsawah and Serena Hamilton

sondoss-elsawah
Sondoss Elsawah (biography)

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.

serena-hamilton
Serena Hamilton (biography)

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. Continue reading