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):
The most familiar models are predictive, such as those used to forecast the weather or plan the economy. However, models have many different uses and different modelling techniques are more or less suitable for specific purposes.
Here I present an example of how a game and a computerised agent-based model have been used for knowledge synthesis and decision support.
The game and model were developed by a team from the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), a French agricultural research organisation with an international development focus. The issue of interest was land use conflict between crop and cattle farming in the Gnith community in Senegal (D’Aquino et al. 2003).
Agent-based modelling is particularly effective where understanding is more important than prediction. This is because agent-based models can represent the real world in a very natural way, making them more accessible than some other types of models.
How do a group’s perceptions change over time, when members across a range of institutions are brought together at regular intervals to synthesize ideas? Synthesis centers have been established to catalyze more effective cross-disciplinary research on complex problems, as described in the blog post ‘Synthesis centers as critical research infrastructure‘, by Andrew Campbell.
I co-led a group synthesizing ideas about participatory modeling as one of the activities at the National Socio-Environmental Synthesis Center (SESYNC). We met in Annapolis, Maryland, USA, four times over three years for 3-4 days per meeting. Our task was to synthesize what is known about participatory modeling tools, processes, and outcomes, especially in environmental and natural resources management contexts.
That which we call a rose, by any other name would smell as sweet.
That Shakespeare guy really knew what he was talking about. A rose is what it is, no matter what we call it. A word is simply a cultural agreement about what we call something. And because language is a common thread that binds cultures together, participatory modeling – as a pursuit that strives to incorporate knowledge and perspectives from diverse stakeholders – is prime for integrating stories into its practice.
To an extent, that’s what every modeling activity does, whether it’s through translating an individual’s story into a fuzzy cognitive map, or into an agent-based model. But I would argue that the drive to quantify everything can sometimes make us lose the richness that a story can provide.
As investigators who engage the public in both modeling and research endeavors we address two major questions: Does citizen science have a place within the participatory modeling research community? And does participatory modeling have a place in the citizen science research community?
Let us start with definitions. Citizen science has been defined in many ways, but we will keep the definition simple. Citizen science refers to endeavors where persons who do not consider themselves scientific experts work with those who do consider themselves experts (around a specific issue) to address an authentic research question.
What is deep uncertainty? And how can scenarios help deal with it?
Deep uncertainty refers to ‘unknown unknowns’, which simulation models are fundamentally unsuited to address. Any model is a representation of a system, based on what we know about that system. We can’t model something that nobody knows about—so the capabilities of any model (even a participatory model) are bounded by our collective knowledge.
One of the ways we handle unknown unknowns is by using scenarios. Scenarios are stories about the future, meant to guide our decision-making in the present.
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.
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.
How can we resolve debates about participatory processes between proponents and skeptics? What role can participatory modelling play in improving participatory processes?
Proponents argue for the merits of participatory processes, which include learning; co-production of knowledge; development of shared understanding of a problem and shared goals; creation of trust; and local power and ownership of a problem.
Sceptics point to evidence of inefficient, time-consuming, participatory processes that escalate conflict and mistrust. They also highlight democratic problems; lack of transparency; and powerful actors that benefit in relation to weaker ones such as the unorganized, poor, and uneducated.
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:
incorporating arts language into the process of visioning
delineating eco-aesthetic values of the visual and aural landscape in communities
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.
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.
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.