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

Five lessons to improve how models serve society

May 17, 2022May 3, 2022

By Andrea Saltelli

Models are mathematical constructs better understood by their developers than by users. So should the public trust models? What insights can help society demand the quality it needs from modeling?

Mathematical modelling is a multiverse, where each scientific discipline adopts its own styles of modeling and quality control. Very little in the way of ‘user instructions’ is available to those affected by modeling practices.

This blog post presents five lessons to improve modelling that were developed as a manifesto by a cross-disciplinary group of natural and social scientists (Saltelli et al., 2020).

Lesson 1: Mind the assumptions

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?

Eight grand challenges in socio-environmental systems modeling

August 11, 2023March 31, 2020

By Sondoss Elsawah and Anthony J. Jakeman

authors_sondoss-elsawah_anthony-jakeman — 1. Sondoss Elsawah (biography)
2. Anthony Jakeman (biography)

As we enter a new decade with numerous looming social and environmental issues, what are the challenges and opportunities facing the scientific community to unlock the potential of socio-environmental systems modeling?

What is socio-environmental systems modelling?

Socio-environmental systems modelling:

involves developing and/or applying models to investigate complex problems arising from interactions among human (ie. social, economic) and natural (ie. biophysical, ecological, environmental) systems.
can be used to support multiple goals, such as informing decision making and actionable science, promoting learning, education and communication.
is based on a diverse set of computational modeling approaches, including system dynamics, Bayesian networks, agent-based models, dynamic stochastic equilibrium models, statistical microsimulation models and hybrid approaches.

Why model?

August 11, 2023August 20, 2019

By Steven Lade

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):

Prediction
Understanding
Exploration
Communication.

I start with mental models – the informal representations of the world that we all use as we go about both our personal and professional lives – and then move on to formal models.

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:

Designing scenarios to guide robust decisions

April 4, 2023July 2, 2019

By Bonnie McBain

What makes scenarios useful to decision makers in effectively planning for the future? Here I discuss three aspects of scenarios:

goals;
design; and,
use and defensibility.

Goals of scenarios

Since predicting the future is not possible, it’s important to know that scenarios are not predictions. Instead, scenarios stimulate thinking and conversations about possible futures.

Key goals and purposes of scenarios can be any of the following:

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).

Managing uncertainty in decision making: What can we learn from economics?

August 11, 2023June 4, 2019

By Siobhan Bourke and Emily Lancsar

authors_siobhan-bourke_emily-lancsar — 1. Siobhan Bourke (biography)
2. Emily Lancsar (biography)

How can researchers interested in complex societal and environmental problems best understand and deal with uncertainty, which is an inherent part of the world in which we live? Accidents happen, governments change, technological innovation occurs making some products and services obsolete, markets boom and inevitably go bust. How can uncertainty be managed when all possible outcomes of an action or decision cannot be known? In particular, are there lessons from the discipline of economics which have broader applicability?

While uncertainty is often discussed alongside risk, a fundamental difference between uncertainty and risk is that risk involves events with known probabilities (or probabilities based on reliable empirical evidence), whereas under uncertainty probabilities are unknown and reflect an individual’s subjective belief concerning the likelihood of a given outcome. Given the subjectivity, that likelihood can differ from person to person. It can also involve a perceived zero probability in the case of unforeseen events (or ‘unknown unknowns’).

Conceptual modelling of complex topics: ConML as an example / Modelado conceptual de temas complejos: ConML como ejemplo

August 11, 2021December 11, 2018

By Cesar Gonzalez-Perez

A Spanish version of this post is available

What are conceptual models? How can conceptual modelling effectively represent complex topics and assist communication among people from different backgrounds and disciplines?

This blog post describes ConML, which stands for “Conceptual Modelling Language”. ConML is a specific modelling language that was designed to allow researchers who are not expert in information technologies to create and develop their own conceptual models. It is useful for the humanities, social sciences and experimental sciences.