Mapping – Integration and Implementation Insights

Seven methods for mapping systems

February 28, 2023

By Pete Barbrook-Johnson and Alexandra S. Penn

authors_pete-barbrook-johnson_alexandra-penn — 1. Pete Barbrook-Johnson (biography)
2. Alexandra S. Penn (biography)

What are some effective approaches for developing causal maps of systems in participatory ways? How do different approaches relate to each other and what are the ways in which systems maps can be useful?

Here we focus on seven system mapping methods, described briefly in alphabetical order.

1. Bayesian Belief Networks: a network of variables representing their conditional dependencies (ie., the likelihood of the variable taking different states depending on the states of the variables that influence them). The networks follow a strict acyclic structure (ie., no feedbacks), and nodes tend to be restricted to maximum two incoming arrows. These maps are analysed using the conditional probabilities to compute the potential impact of changes to certain variables, or the influence of certain variables given an observed outcome.

Public participation geographical information systems

February 7, 2023January 31, 2023

By Nora Fagerholm, María García-Martín, Mario Torralba, Claudia Bieling and Tobias Plieninger

authors_nora-fagerholm_maría-garcía-martín_mario-torralba_claudia-bieling_tobias-plieni — 1. Nora Fagerholm; 2. María García-Martín; 3. Mario Torralba; 4. Claudia Bieling; 5. Tobias Plieninger (biographies)

What is encompassed by public participation geographical information systems? What resources are required? What are the strengths and weaknesses of involving stakeholders?

Participatory mapping combines cartography with participatory approaches to put the knowledge, experiences, and aspirations of people on a map. Under this umbrella term, public participation geographical information systems refers to the use of geographical information systems (GIS) and modern communication technologies to engage the general public and stakeholders in participatory planning and decision-making.

Theory of Change in a nutshell

September 23, 2021August 24, 2021

By Heléne Clark

author_helene-clark — Heléne Clark (biography)

How can you plan to make change happen or evaluate the effectiveness of actions you took? How can you link desired long-term goals with all the conditions that must be in place? How can you map out a step-by-step pathway that highlights your assumptions and expectations?

Theory of Change (ToC) is a graphic and narrative explanation of how and why a change process is expected to happen in a particular context.

At its heart, Theory of Change spells out initiative or program logic. It defines long-term goals and then maps backward to identify changes thought to be necessary to the goal that need to happen earlier (preconditions).

Theory of Change purports to explain change process in diagrammatically modeling all the causal linkages in an initiative, ie., its shorter-term, intermediate, and longer-term outcomes.

Breaking through disciplinary barriers with practical mapping

January 10, 2023June 9, 2020

By Steven E. Wallis and Bernadette Wright

author_steven-wallis_and_bernadette-wright — 1. Steven E. Wallis (biography)
2. Bernadette Wright (biography)

How can practical mapping help develop interdisciplinary knowledge for tackling real-world problems — such as poverty, justice and health — that have many causes? How can it help take into account political, economic, technological and other factors that can worsen or improve the issues?

Maps are useful because they show your surroundings – where things are in relation to each other (and to you). They show the goals we want to achieve and what it takes to get there.

‘Practical mapping’ is a straight-forward approach for using concepts and connections to integrate knowledge across and between disciplines, to support effective action.

CoNavigator: Hands-on interdisciplinary problem solving

November 9, 2021February 13, 2018

By Katrine Lindvig, Line Hillersdal and David Earle

authors_mosaic_katrine-lindvig-lin-hillersdal_david-earle — 1. Katrine Lindvig (biography)
2. Line Hillersdal (biography)
3. David Earle (biography)

How can we resolve the stark disparity between theoretical knowledge about interdisciplinary approaches and practical applications? How can we get from written guidelines to actual practices, especially taking into account the contextual nature of knowledge production; not least when the collaborating partners come from different disciplinary fields with diverse expectations and concerns?

For the past few years, we have been developing ways in which academic theory and physical interactions can be combined. The result is CoNavigator – a hands-on, 3-dimensional and gamified tool which can be used:

for learning purposes in educational settings
as a fast-tracking tool for interdisciplinary problem solving.

CoNavigator is a tool which allows groups to collaborate on a 3-dimensional visualisation of the interdisciplinary topography of a given field or theme.

Knowledge mapping technologies

November 30, 2022March 30, 2017

By Jack Park

How can you improve your thinking – alone or in a group? How can mapping ideas help you understand the relationships among them? How can mapping a conversation create a new reality for those involved?

In what follows, I draw on the work of Daniel Kahneman’s (2011) best-selling book Thinking, Fast and Slow, which explains how human thinking occurs at different speeds, from the very fast thinking associated with face-to-face conversation to the very slow thinking associated with assembling information resources into encyclopedias. I use those ideas in my descriptions of knowledge maps.

Three kinds of knowledge maps

Models as ‘interested amateurs’

April 15, 2022January 24, 2017

By Pete Barbrook-Johnson

How can we improve the often poor interaction and lack of genuine discussions between policy makers, experts, and those affected by policy?

As a social scientist who makes and uses models, an idea from Daniel Dennett’s (2013) book ‘Intuition Pumps and Other Tools for Thinking’ struck a chord with me. Dennett introduces the idea of using lay audiences to aid and improve understanding between experts. Dennett suggests that including lay audiences (which he calls ‘curious nonexperts’) in discussions can entice experts to err on the side of over-explaining their thoughts and positions. When experts are talking only to other experts, Dennett suggests they under-explain, not wanting to insult others or look stupid by going over basic assumptions. This means they can fail to identify areas of disagreement, or to reach consensus, understanding, or conclusions that may be constructive.

For Dennett, the ‘curious nonexperts’ are undergraduate philosophy students, to be included in debates between professors. For me, the book sparked the idea that models could be ‘curious nonexperts’ in policy debates and processes. I prefer and use the term ‘interested amateurs’ over ‘curious nonexperts’, simply because the word ‘amateur’ seems slightly more insulting towards models!

Learning through modeling

August 24, 2022November 17, 2016

By Kirsten Kainz

How can co-creation communities use models – simple visual representations and/or sophisticated computer simulations – in ways that promote learning and improvement? Modeling techniques can serve to generate insights and correct misunderstandings. Are they equally as useful for fostering new learning and adaptation? Sterman (2006) argues that if new learning is to occur in complex systems then models must be subjected to testing. Model testing must, in turn, yield evidence that not only guides decision-making within the current model, but also feeds back evidence to improve existing models so that subsequent decisions can be based on new learning.

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.

Can mapping mental models improve research implementation?

August 17, 2020January 27, 2016

By Katrin Prager

We all have different mental models of the environment and the people around us. They help us make sense of what we experience. In a recent project exploring how to improve soil management (PDF 250KB), Michiel Curfs and I used data collected from Spanish farmers and our own experience to develop and compare the mental model of a typical Spanish farmer growing olives with that of a hypothetical scientist. How did their mental models of soil degradation differ? Mainly in terms of understanding the role of ploughing, and the importance of drivers for certain soil management activities. There were only a few areas of overlap: both scientist and farmer were concerned about fire risk and acknowledged weeds. We emphasise the importance of two-way communication, and recommend starting by focusing on areas of overlap and then moving to areas that are different. Without integrating understandings from both mental models, the scientist will carry on making recommendations for reducing soil degradation that the farmer cannot implement or does not find relevant.