Integration and Implementation Insights
A community blog and repository of resources for improving research impact on complex real-world problems
By Sondoss Elsawah and Anthony J. Jakeman
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:
By Sondoss Elsawah and Serena Hamilton
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.
By Sondoss Elsawah and Joseph Guillaume
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.
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.
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.