Conceptual modelling – Integration and Implementation Insights

Structured dialogical design

July 10, 2024July 9, 2024

By Yiannis Laouris

How can heterogeneous groups reach consensus on complex issues in a reasonably limited amount of time? What kind of process allows for meaningful community involvement that is genuinely participatory and democratic?

Structured Dialogical Design is a process that achieves both these aims. The key aspects of the process and steps are presented.

Triggering questions

Structured Dialogical Design processes are always structured around triggering questions, which frame the discussions and help define the stakeholders of the issues under consideration. The idea is that those primarily concerned with and/or affected by the issues under consideration should become the primary participants.

For Structured Dialogical Design all stakeholders (or their representatives) concerned with the issues at stake must be included, including those seemingly without a voice (which many of us may not be hearing and are not responsive to listening to, such as the voice of nature).

Collaboration: From groan zone to growth zone

September 11, 2024May 28, 2019

By Carrie Kappel

What is the groan zone in collaboration? What can you do when you reach that point?

As researchers and practitioners engaged in transdisciplinary problem-solving, we know the value of diverse perspectives. We also know how common it is for groups to run into challenges when trying to learn from diverse ideas and come to consensus on creative solutions.

This challenging, often uncomfortable space, is called the groan zone. The term comes from Sam Kaner’s diamond model of participation shown in the figure below. After an initial period of divergent thinking, where diverse ideas are introduced, groups have to organize that information, focus on what’s most important, and make decisions in order to move forward into the phase of convergent thinking.

Navigating that transition between divergent and convergent thinking is the realm in which creativity and innovation emerge, if we let them.

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

September 12, 2024December 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.

Problem framing and co-creation

August 24, 2022October 27, 2016

By Graeme Nicholas

How can people with quite different ways of ‘seeing’ and thinking about a problem discover and negotiate these differences?

A key element of co-creation is joint problem definition. However, problem definition is likely to be a matter of perspective, or a matter of how each person involved ‘frames’ the problem. Differing frames are inevitable when participants bring their differing expertise and experience to a problem. Methods and processes to support co-creation, then, need to manage the coming together of people with differing ways of framing the problem, so participants can contribute to joint problem definition.

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.