Two barriers to interdisciplinary thinking in the public sector and how time graphs can help

By Jane MacMaster

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Jane MacMaster (biography)

After one year or so delivering seminars that share practical techniques to help navigate complexity to public sector audiences, I’ve observed two simple and fundamental barriers to dealing more effectively with complex, interdisciplinary problems in the public sector.

First, is the lack of time to problem-solve – to pause and reflect on an issue, to build a deeper understanding of it, to think creatively about it from different angles, to think through some ideas, to test out some ideas. There is too much else going on.

Second, is that it’s often quite difficult to put one’s collective finger on what, exactly, the problem is.

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Learning through modeling

By Kirsten Kainz

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Kirsten Kainz (biography)

How can co-creation communities use models – simple visual representations and/or sophisticated computer simulations – in ways that promote learning and improvement? Modeling techniques can serve to generate insights and correct misunderstandings. Are they equally as useful for fostering new learning and adaptation? Sterman (2006) argues that if new learning is to occur in complex systems then models must be subjected to testing. Model testing must, in turn, yield evidence that not only guides decision-making within the current model, but also feeds back evidence to improve existing models so that subsequent decisions can be based on new learning.

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The ‘methods section’ in research publications on complex problems – Purpose

By Gabriele Bammer

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Gabriele Bammer (biography)

Do we need a protocol for documenting how research tackling complex social and environmental problems was undertaken?

Usually when I read descriptions of research addressing a problem such as poverty reduction or obesity prevention or mitigation of the environmental impact of a particular development, I find myself frustrated by the lack of information about what was actually done. Some processes may be dealt with in detail, but others are glossed over or ignored completely.

For example, often such research brings together insights from a range of disciplines, but details may be scant on why and how those disciplines were selected, whether and how they interacted and how their contributions to understanding the problem were combined. I am often left wondering about whose job it was to do the synthesis and how they did it: did they use specific methods and were these up to the task? And I am curious about how the researchers assessed their efforts at the end of the project: did they miss a key discipline? would a different perspective from one of the disciplines included have been more useful? did they know what to do with all the information generated?

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ICTAM: Bringing mental models to numerical models

By Sondoss Elsawah

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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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Integration – Part 2: The “how”

By Julie Thompson Klein

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Julie Thompson Klein’s biography

The “how” of integration focuses on pragmatics of process, with emphasis on methods. Toward that end, following the part 1 blog post on the “what” of integration, this blog post presents insights from major resources, with emphasis on collaborative research by teams.

Some widely used methods are well-known theories, for example general systems. Others are practiced in particular domains, such as integrated environmental assessment. Some utilize technologies, for example computer synthesis of data. And others, such as dialogue methods, target communication processes.

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Making predictions under uncertainty

By Joseph Guillaume

Joseph Guillaume (biography)

Prediction under uncertainty is typically seen as a daunting task. It conjures up images of clouded crystal balls and mysterious oracles in shadowy temples. In a modelling context, it might raise concerns about conclusions built on doubtful assumptions about the future, or about the difficulty in making sense of the many sources of uncertainty affecting highly complex models.

However, prediction under uncertainty can be made tractable depending on the type of prediction. Here I describe ways of making predictions under uncertainty for testing which conclusion is correct. Suppose, for example, that you want to predict whether objectives will be met. There are two possible conclusions – Yes and No, so prediction in this case involves testing which of these competing conclusions is plausible.

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Tool users old and new: Why we need models

By Suzanne A. Pierce

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Suzanne A. Pierce (biography)

Ask most 21st century citizens whether they like technology and they will respond with a resounding, “Yes!” Ask them why and you’ll get answers like, “Because it’s cool and technology is fun!” or “Technology systems help us learn and understand things.” Or “Technology helps us communicate with one another, keep up with current events, or share what we are doing.” Look at the day-to-day activities of most people on the planet and you’ll find that they use some form of technology to complete almost every activity that they undertake.

When you think about it, technologies are really just tools. And we humans are tool users of old.

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Serious gaming: Helping stakeholders address community problems

By Nagesh Kolagani

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Nagesh Kolagani (biography)

Citizens are increasingly coming together to solve problems that affect their communities. Participatory modeling is a method that helps them to share their implicit and explicit knowledge of these problems with each other and to plan and implement mutually acceptable and sustainable solutions.

While using this method, stakeholders need to understand large amounts of information relating to these problems. Various interactive visualization tools are being developed for this purpose. One such tool is ‘serious gaming’ which combines technologies from the video game industry – mystery, appealing graphics, etc., – with a purpose other than pure entertainment, a serious, problem driven, educational purpose.

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Playing Around with PARTICIPOLOGY

By Alister Scott

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Alister Scott (biography)

Have you ever wanted a new way to engage with stakeholders that is more engaging, fun and effective? PARTICIPOLOGY is a set of open-access web resources and associated guidance that sets out to achieve these aims. It uses a board game format where players encounter questions and challenges as a dice throw dictates. The board, questions and rules of the game can be designed from scratch or existing templates can be adapted to the specific goals you have in mind. The game was designed to be used in participatory forums about land use options, but the principles can be more widely applied to all kinds of participatory processes.

There are five key findings from developing and using these resources.

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Modeling as empowerment

By Laura Schmitt Olabisi

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Laura Schmitt Olabisi (biography)

Who can make systems change? The challenges of complexity are intensely felt by those who are trying to make strategic interventions in coupled human-environmental systems in order to fulfill personal, societal, or institutional goals. The activists, leaders, and decision-makers I work with often feel overwhelmed by trying to deal with multiple problems at once, with limited time, resources, and attention. We need tools to help leaders cut through the complexity so that they can identify the most effective strategies to make change.

This is where participatory system dynamics modelers like myself come in.

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La modélisation participative, un lieu privilégié pour l’interdisciplinarité? / Participatory modeling: An ideal place for interdisciplinarity?

By Pierre Bommel

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Pierre Bommel (biography)

An English version of this post is available

La modélisation participative cherche à impliquer un groupe de personnes dans la conception et la révision d’un modèle. L’objectif à terme consiste à mieux caractériser les problèmes actuels et imaginer collectivement comment tenter de les résoudre. Dans le domaine de l’environnement en particulier, il apparaît nécessaire que les acteurs concernés se sentent impliqués dans la démarche de modélisation, afin qu’ils puissent exprimer leurs propres points de vue, mais aussi pour mieux s’engager dans des décisions collectives. De ce fait, pour aborder la gestion intégrée des ressources, il est nécessaire de mettre les acteurs au centre des préoccupations de recherche, à la fois lors de la phase la conception du modèle mais aussi pour l’exploration de ces scénarios.

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Can mapping mental models improve research implementation?

By Katrin Prager

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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.

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