A heuristic framework for reflecting on joint problem framing

By BinBin Pearce and Olivier Ejderyan

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1. BinBin Pearce (biography)
2. Olivier Ejderyan (biography)

What is joint problem framing? What are the key issues that joint problem framing has to address? How can joint problem framing be improved?

What is joint problem framing?

A key aspect of tackling complex problems is effectively bringing together differing points of view. These points of view are what Craik (1943) refers to as “small-scale models” of the problem situation. These are mental models formed from each individual’s experiences, interests, knowledge and environment. These mental models then set the boundaries for what problem definitions and solutions are possible and relevant to consider.

Joint problem framing then, involves a process of eliciting, clarifying, reconfiguring and reconciling (though not necessarily agreeing upon) different mental models in order to formulate the problem clearly

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Why model?

By Steven Lade

Steven Lade
Steven Lade (biography)

What do you think about mathematical modelling of ‘wicked’ or complex problems? Formal modelling, such as mathematical modelling or computational modelling, is sometimes seen as reductionist, prescriptive and misleading. Whether it actually is depends on why and how modelling is used.

Here I explore four main reasons for modelling, drawing on the work of Brugnach et al. (2008):

  • Prediction
  • Understanding
  • Exploration
  • Communication.

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Four patterns of thought for effective group decisions

By George P. Richardson and David F. Andersen

George Richardson
George P. Richardson (biography)

What can you do if you are in a group that is trying to deal with problems that are developing over time, where:

  • root causes of the dynamics aren’t clear;
  • different stakeholders have different perceptions;
  • past solutions haven’t worked;
  • solutions must take into account how the system will respond; and,
  • implementing change will require aligning powerful stakeholders around policies that they agree have the highest likelihood of long-term success?

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Metacognition as a prerequisite for interdisciplinary integration

By Machiel Keestra

Machiel Keestra (biography)

What’s needed to enable the integration of concepts, theories, methods, and results across disciplines? Why is communication among experts important, but not sufficient? Interdisciplinary experts must also meta-cognize: both individually and as a team they must monitor, evaluate and regulate their cognitive processes and mental representations. Without this, expertise will function suboptimally both for individuals and teams. Metacognition is not an easy task, though, and deserves more attention in both training and collaboration processes than it usually gets. Why is metacognition so challenging and how can it be facilitated?

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Five principles of holistic science communication

By Suzi Spitzer

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Suzi Spitzer (biography)

How can we effectively engage in the practice and art of science communication to increase both public understanding and public impact of our science? Here I present five principles based on what I learned at the Science of Science Communication III Sackler Colloquium at the National Academy of Sciences in Washington, DC in November 2017.

1. Assemble a diverse and interdisciplinary team

  1. Scientists should recognize that while they may be an expert on a particular facet of a complex problem, they may not be qualified to serve as an expert on all aspects of the problem. Therefore, scientists and communicators should collaborate to form interdisciplinary scientific teams to best address complex issues.
  2. Science is like any other good or service—it must be strategically communicated if we want members of the public to accept, use, or support it in their daily lives. Thus, research scientists need to partner with content creators and practitioners in order to effectively share and “sell” scientific results.
  3. Collaboration often improves decision making and problem solving processes. People have diverse cognitive models that affect the way each of us sees the world and how we understand or resolve problems. Adequate “thought world diversity” can help teams create and communicate science that is more creative, representative of a wider population, and more broadly applicable.

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Sharing mental models is critical for interdisciplinary collaboration

By Jen Badham and Gabriele Bammer

badham
Jen Badham (biography)

What is a mental model? How do mental models influence interdisciplinary collaboration? What processes can help tease out differences in mental models?

Mental models

Let’s start with mental models. What does the word ‘chair’ mean to you? Do you have an image of a chair, perhaps a wooden chair with four legs and a back, an office chair with wheels, or possibly a comfortable lounge chair from which you watch television?

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ICTAM: Bringing mental models to numerical models

By Sondoss Elsawah

sondoss-elsawah
Sondoss Elsawah (biography)

How can we capture the highly qualitative, subjective and rich nature of people’s thinking – their mental models – and translate it into formal quantitative data to be used in numerical models?

This cannot be addressed by a single method or software tool. We need multi-method approaches that have the capacity to take us through the learning journey of eliciting and representing people’s mental models, analysing them, and generating algorithms that can be incorporated into numerical models.

More importantly, this methodology should allow us to see in a transparent way the progression on this learning journey.

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Models as narratives

By Alison Singer

singer
Alison Singer (biography)

I don’t see the world in pictures. I mean, I see the world in all its beautiful shapes and colors and shadings, but I don’t interpret the world that way. I interpret the world through the stories I create. My interpretations of these stories are my own mental models of how I view the world. What I can do then, to share this mental model, is create a more formalized model, whether it is a simple picture (in my case a very badly drawn one), or a system dynamics model, or an agent-based model. People think of models as images, as representations, as visualizations, as simulations. As tools to represent, to simplify, to teach, and to share. And they are all these things, and we need them to function as these things, but they are also stories, and can be interpreted and shared as such.

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Can mapping mental models improve research implementation?

By Katrin Prager

katrin-prager
Katrin Prager (biography)

We all have different mental models of the environment and the people around us. They help us make sense of what we experience. In a recent project exploring how to improve soil management (PDF 250KB), Michiel Curfs and I used data collected from Spanish farmers and our own experience to develop and compare the mental model of a typical Spanish farmer growing olives with that of a hypothetical scientist. How did their mental models of soil degradation differ? Mainly in terms of understanding the role of ploughing, and the importance of drivers for certain soil management activities. There were only a few areas of overlap: both scientist and farmer were concerned about fire risk and acknowledged weeds. We emphasise the importance of two-way communication, and recommend starting by focusing on areas of overlap and then moving to areas that are different. Without integrating understandings from both mental models, the scientist will carry on making recommendations for reducing soil degradation that the farmer cannot implement or does not find relevant.

Read moreCan mapping mental models improve research implementation?