Participatory modelling – Integration and Implementation Insights

Improving facilitated modelling

May 23, 2023

By Vincent de Gooyert

Here I explore two outcomes of facilitated modelling – cognitive change and consensus forming – and ask: how can achieving those outcomes be improved?

But first, what is facilitated modelling?

Facilitated modelling is an approach where operational researchers act as facilitators to model an issue collaboratively with stakeholders, usually in a workshop. Operational research, also known as operations research, seeks to improve decision-making by developing and applying analytical methods.

Two central aims of facilitated modelling are to achieve cognitive change and to form consensus.

Cognitive change is the idea that participants of facilitated modelling workshops come in with a certain worldview, and that the intervention leads them to learn about the issue and accordingly change their minds.

Five core competency areas for participatory modeling

April 25, 2023April 18, 2023

By Sondoss Elsawah, Elena Bakhanova, Raimo P. Hämäläinen and Alexey Voinov

mosaic_authors_sondoss-elsawah_elena-bakhanova_raimo-hamalainen_alexey-voinov — 1. Sondoss Elsawah (biography)
2. Elena Bakhanova (biography)
3. Raimo P. Hämäläinen (biography)
4. Alexey Voinov (biography)

What knowledge and skills do individuals and teams need to be effective at participatory modeling?

We suggest that five core competency areas are essential for participatory modeling:

systems thinking
modeling
group facilitation
project management and leadership
operating in the virtual space.

These are illustrated in the figure below.

These competency areas have naturally overlapping elements and should therefore be seen as a holistic and interdependent set. Further, while certain competencies such as modeling skills can be addressed by individual members of a participatory modeling team, the entire process is a team effort and it is necessary to also consider the competencies as a group skill.

Participatory scenario planning

August 3, 2023December 6, 2022

authors_maike-hamann_tanja-hichert_nadia-sitas — 1. Maike Hamann (biography)
2. Tanja Hichert (biography)
3. Nadia Sitas (biography)

By Maike Hamann, Tanja Hichert and Nadia Sitas

Within the many different ways of developing scenarios, what are useful general procedures for participatory processes? What resources are required? What are the strengths and weaknesses of involving stakeholders?

Scenarios are vignettes or narratives of possible futures, and when used in a set, usually depict purposefully divergent visions of what the future may hold. The point of scenario planning is not to predict the future, but to explore its uncertainties. Scenario development has a long history in corporate and military strategic planning, and is also commonly used in global environmental assessments to link current decision-making to future impacts. Participatory scenario planning extends scenario development into the realm of stakeholder-engaged research.

In general, the process for participatory scenario planning broadly follows three phases.

1. Identifying stakeholders and setting the scene

Leadership in participatory modelling

September 23, 2021February 2, 2021

By Raimo P. Hämäläinen, Iwona Miliszewska and Alexey Voinov

moasaic_authors_raimo-hamalainen_iwona-miliszewska_alexey-voinov — 1. Raimo P. Hämäläinen (biography)
2. Iwona Miliszewska (biography)
3. Alexey Voinov (biography)

What can leadership discourse in the business literature tell us for leadership in participatory modelling?

Here we explore:

the difference between leadership and management in participatory modelling
different leadership styles and participatory modelling
three key leadership issues in participatory modelling: responsibility for best practices and ethics, competences, and who in the participatory modelling team should lead.

How does leadership differ from management in participatory modelling?

Four patterns of thought for effective group decisions

April 15, 2022July 30, 2019

By George P. Richardson and David F. Andersen

authors_george-richardson_david-andersen — 1. George P. Richardson (biography)
2. David F. Andersen (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?

The fields of systems thinking and system dynamics modelling bring four important patterns of thought to such a group decision and negotiation:

Agent-based modelling for knowledge synthesis and decision support

April 15, 2022June 25, 2019

By Jen Badham

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).

Synthesis of knowledge about participatory modeling: How a group’s perceptions changed over time

August 24, 2022July 3, 2018

By Rebecca Jordan

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.

What’s in a name? The role of storytelling in participatory modeling

August 24, 2022June 13, 2017

By Alison Singer

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.

Citizen science and participatory modeling

August 24, 2022February 2, 2017

By Rebecca Jordan and Steven Gray

authors_rebecca-jordan_steven-gray — 1. Rebecca Jordan (biography)
2. Steven Gray (biography)

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.

Dealing with deep uncertainty: Scenarios

October 25, 2022January 5, 2017

By Laura Schmitt Olabisi

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.

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

August 24, 2022November 10, 2016

By Antonie Jetter

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.

Model complexity – What is the right amount?

August 24, 2022November 3, 2016

By Pete Loucks

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?

Simplification is why we model. We wish to abstract the essence of a system we are studying, and estimate its likely performance, without having to deal with all its detail. We know that our simplified models will be wrong. But, we develop them because they can be useful. The simpler and hence the more understandable models are the more likely they will be useful, and used, ‘as long as they do the job.’