Uncertainty in participatory modeling – What can we learn from management research?

By Antonie Jetter

antonie-jetter
Antonie Jetter (biography)

I frequently struggle to explain how participatory modeling deals with uncertainty. I found useful guidance in the management literature.

After all, participatory modeling projects and strategic business planning have one commonality – a group of stakeholders and decision-makers aims to understand and ultimately influence a complex system. They do so in the face of great uncertainty that frequently cannot be resolved – at least not within the required time frame. Businesses, for example, have precise data on customer behavior when their accountants report on annual sales. However, by this time, the very precise data is irrelevant because the opportunity to influence the system has passed.

Two key lessons from the management literature deal with the nature of uncertainty and responding to four major types of uncertainty.

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Model complexity – What is the right amount?

By Pete Loucks

p-loucks
Pete Loucks (biography)

How does a modeler know the ’optimal’ level of complexity needed in a model when those desiring to gain insights from the use of such a model aren’t sure what information they will eventually need? In other words, what level of model complexity is needed to do a job when the information needs of that job are uncertain and changing?

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Should I trust that model?

By Val Snow

val snow
Val Snow (biography)

How do those building and using models decide whether a model should be trusted? While my thinking has evolved through modelling to predict the impacts of land use on losses of nutrients to the environment – such models are central to land use policy development – this under-discussed question applies to any model.

In principle, model development is a straightforward series of steps:

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Creating a pragmatic complexity culture / La creación de una cultura pragmática de la complejidad

By Cristina Zurbriggen

cristina-zurbriggen
Cristina Zurbriggen (biography)

An English version of this post is available

¿Cómo pueden los gobiernos, las comunidades y el sector privado efectivamente trabajar juntos para lograr un cambio social hacia el desarrollo sostenible?

En este blog describo los procesos claves que permitieron a Uruguay lograr uno de los regímenes más avanzados de protección del suelo de tierras de cultivo de secano en el mundo. Una explicación del proceso es la creación de una cultura pragmática de la complejidad, una cultura inclusiva, deliberativa que reconoce la naturaleza compleja del problema y abraza el potencial de lo posible.

Read moreCreating a pragmatic complexity culture / La creación de una cultura pragmática de la complejidad

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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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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Art and participatory modelling

By Hara W. Woltz and Eleanor J. Sterling

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Hara W. Woltz (biography)

What can art contribute to participatory modelling? Over the past decade, participatory visual and narrative arts have been more frequently and effectively incorporated into scenario planning and visioning workshops.

We use arts-based techniques in three ways:

  1. incorporating arts language into the process of visioning
  2. delineating eco-aesthetic values of the visual and aural landscape in communities
  3. engaging art to articulate challenges and solutions within local communities.

The arts based approaches we use include collage, drawing, visual note taking, map making, storyboarding, photo documentation through shared cameras, mobile story telling, performance in the landscape, drawing as a recording device, and collective mural creation.

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Eleanor J. Sterling (biography)

They allow us to expand and deepen engagement strategies beyond the scope of traditional dialog tools such as opinion surveys, workshops, and meetings. And, they allow for both individual and collective work, from spending reflective time independently, to rejoining as a group to discuss process and products. They are also particularly effective in bicultural and multicultural settings.

Visual techniques can help foster a different type of discussion than one that is primarily verbal or quantitative because they involve participants in different patterns of thinking, questioning, and interacting.

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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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Models as narratives

By Alison Singer

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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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Serious gaming: Helping stakeholders address community problems

By Nagesh Kolagani

kolagani
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.

Such gamification is an opportunity for participatory modeling approaches to embed models and simulations of complex processes into games and to attract the stakeholders.

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Choosing a model: If all you have is a hammer…

By Jen Badham

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Jen Badham (biography)

As a modeller, I often get requests from research or policy colleagues along the lines of ‘we want a model of the health system’. It’s relatively easy to recognise that ‘health system’ is too vague and needs explicit discussion about the specific issue to be modelled. It is much less obvious that the term ‘model’ also needs to be refined. In practice, different modelling methods are more or less appropriate for different questions. So how is the modelling method chosen?

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Knowledge synthesis and external representations

By Deana Pennington

Deana Pennington (biography)

Over a decade ago I became interested in the role of external artifacts in enabling knowledge synthesis across disciplinary perspectives, where external artifacts are any simplified physical representation of real phenomena that enable human manipulation of complex concepts. A simulation model is one example of an external artifact. In general every simplified representation of reality is a model, whether that representation occurs in our heads (mental models), on paper (conceptual models) or in a sophisticated computer-based simulation model. And so I embarked on a research agenda to understand the role of data, models, and other forms of external representations in enabling integration and synthesis across perspectives.

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