Sondoss Elsawah – Integration and Implementation Insights

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.

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.

Good practices in system dynamics modelling

December 19, 2024July 18, 2017

By Sondoss Elsawah and Serena Hamilton

authors_sondoss-elsawah_serena-hamilton — 1. Sondoss Elsawah (biography)
2. Serena Hamilton (biography)

Too often, lessons about modelling practices are left out of papers, including the ad-hoc decisions, serendipities, and failures incurred through the modelling process. The lack of attention to these details can lead to misperceptions about how the modelling process unfolds.

We are part of a small team that examined five case studies where system dynamics was used to model socio-ecological systems. We had direct and intimate knowledge of the modelling process and outcomes in each case. Based on the lessons from the case studies as well as the collective experience of the team, we compiled the following set of good practices for systems dynamics modelling of complex systems.

Sharing integrated modelling practices – Part 2: How to use “patterns”?

October 9, 2023June 1, 2017

By Sondoss Elsawah and Joseph Guillaume

authors_sondoss-elsawah_joseph-guillaume — 1. Sondoss Elsawah (biography)
2. Joseph Guillaume (biography)

In part 1 of our blog posts on why use patterns, we argued for making unstated, tacit knowledge about integrated modelling practices explicit by identifying patterns, which link solutions to specific problems and their context. We emphasised the importance of differentiating the underlying concept of a pattern and a pattern artefact – the specific form in which the pattern is explicitly described.

In order to actually use patterns to communicate about practices, the artefact takes on greater importance: what form could artefacts describing the patterns take, and what mechanisms and platforms are needed to first create, and then share, maintain, and update these artefacts?

While the concepts of ‘problem, solution and context’ should be discussed in some way, there is no single best way of representing patterns as artefacts. The form of artefacts will differ depending on many factors, including how the users perceive the ease of:

Sharing integrated modelling practices – Part 1: Why use “patterns”?

September 12, 2024May 30, 2017

By Sondoss Elsawah and Joseph Guillaume

How can modellers share the tacit knowledge that accumulates over years of practice?

In this blog post we introduce the concept of patterns and make the case for why patterns are a good candidate for transmitting the ‘know-how’ knowledge about modelling practices. We address the question of how to use patterns in a second blog post.

In broad terms, a pattern links a solution to a problem and its context. As a means of externalizing understanding of practices, the concept has been used productively in various fields, including architecture, computer science, and design science. For a more general introduction to patterns, see Scott Peckham’s blog post. While a “pattern” is ultimately a simple idea, there tends to be disagreement about a precise definition. This poses a problem for this blog post.

ICTAM: Bringing mental models to numerical models

August 24, 2022September 22, 2016

By Sondoss Elsawah

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.