Learning through modeling

By Kirsten Kainz

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

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

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

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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Tool users old and new: Why we need models

By Suzanne A. Pierce

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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Modeling as empowerment

By Laura Schmitt Olabisi

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

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

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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The promise of using similar methods across disciplines

By Allison Metz

Alison Metz
Allison Metz (biography)

Interdisciplinarity has the potential to broaden and deepen our understanding and application of methods and tools to address complex challenges. When we embrace interdisciplinarity we broaden what we know about the potential methods for assessing and tackling problems, and we deepen our understanding of specific methods by applying these methods across different contexts. In my pursuit to understand co-creative processes by interconnected stakeholders – i.e., the deep and authentic engagement of stakeholders across governance, science, and community boundaries to identify and optimize the use of evidence for positive outcomes – I have been influenced by methods used outside of my discipline of implementation science and current context of child welfare services. For example, I recently read an article that studied the co-production of knowledge in soils governance (Prager & McKee, 2015) in the United Kingdom and was struck by the usefulness of these ideas for child welfare services in the United States.

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Modelling is the language of scientific discovery

By Steven Gray

Steven Gray (biography)

Modeling is the language of scientific discovery and has significant implications for how scientists communicate within and across disciplines. Whether modeling the social interactions of individuals within a community in anthropology, the trade-offs of foraging behaviors in ecology, or the influence of warming ocean temperatures on circulation patterns in oceanography, the ability to represent empirical or theoretical understanding through modeling provides scientists with a semi-standardized language to explain how we think the world works. In fact, modeling is such a basic part of human reasoning and communication that the formal practice of scientific modeling has been recently extended to include non-scientists, especially as a way to understand complex and poorly understood socio-environmental dynamics and to improve collaborative research.

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