Mapping – Integration and Implementation Insights

The ontologies of restricted and general complexity

July 7, 2026

By Jean Boulton.

What is a useful way to clarify the underpinning ontological ground of complexity? What can we learn from the work of Edgar Morin (2006), who distinguishes between those working within the frames of restricted and general complexity? And how are these frames relevant to practice?

Morin makes a distinction between:

a framing of complexity that sits within the ontology of classical science, which he calls ‘restricted complexity’
the ‘general complexity’ of the ‘real world,’ where general complexity is more paradoxical, more integrating, more challenging, ambiguous and uncertain – but also ripe with potential.

Restricted complexity emanates from the world of models, maps and mathematics. The aim is to find ways to represent the complexity of the real world, by finding a good map.

General complexity, by contrast, starts further back into the primordial mud, and champions the attainment of knowledge through wandering the ‘territory’.

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).

Decision support interventions

May 7, 2024April 30, 2024

By Etiënne Rouwette and Alberto Franco

authors_etienne-rouwette_alberto-franco — 1. Etiënne Rouwette (biography)
2. Alberto Franco (biography)

What are interventions to support team decision making? And how can interventions enable team decision making to become a rigorous, transparent and defensible process?

Interventions are procedures designed to improve a decision making process. Within the content of team decision making, an intervention is comprised of designed facilitated activities carried out in order to help a team achieve its goals. Team goals include generating a better and shared understanding of a situation of interest or concern, producing a recommendation on how to respond to the situation, or simply deciding what to do next regarding the situation.

Because team members are likely to have different views and goals regarding the situation, facilitation is central to an intervention. Specifically, facilitated activities are designed to encourage the active participation of team members in discussions, so that a mutual understanding within the team can be achieved. In addition, these activities also play a critical role in fostering the development of integrative solutions that create a sense of shared responsibility for their implementation.

Living labs are learning labs: Creating and mapping conditions for social learning in transdisciplinary research

February 27, 2024February 27, 2024

By Marina Knickel and Guido Caniglia

authors_marina-knickel_guido-caniglia — 1. Marina Knickel (biography)
2. Guido Caniglia (biography)

What is required for social learning in living labs? How can social learning be mapped in living labs?

Living labs are conceived as spaces for social learning across difference in real-world situations through transdisciplinary research with diverse actors. We argue that the following conditions, often intertwined and building on each other, are required to set up living labs as learning spaces:

1. Epistemic: Learning to foster knowledge pluralism

We suggest supporting research and practice partners in developing a capacity for knowledge pluralism as the ability to appreciate and work with multiple kinds of knowledge and ways of knowing. Knowledge pluralism could be fostered by learning to recognise differences in knowledge, perspectives and socio-cultural identities as strengths and by strategically valorising them.

Using archetypes as a systemic lens to understand the complexity of sustainable development

August 3, 2023June 13, 2023

By Hossein Hosseini, Enayat A. Moallemi, Sibel Eker, Edoardo Bertone and Katrina Szetey

authors_hossein-hosseini_enayat-moallemi_sibel-eker_edoardo-bertone_katrina-szetey — 1. Hossein Hosseini; 2. Enayat A. Moallemi; 3. Sibel Ekern; 4. Edoardo Bertone; 5. Katrina Szetey (biographies)

What are systems archetypes and how can they be used to bring a deeper understanding of causal drivers, potential dynamic behaviour in the future, and policy resistance when tackling complex problems, including those in sustainable development?

Systems archetypes are recurring generic systems structures found in many kinds of organisations, under many circumstances, and at many levels and scales. They are distinctive combinations of reinforcing and balancing processes theoretically rooted in systems thinking and modelling.

There are eight common archetypes, each with specific underlying causal drivers (eg., feedback loops, delay), expected dynamic behaviour (eg., acceleration, disruption, tipping point), and policy implications (eg., how to respond, where to intervene). Archetypes can help shift an analytical focus from simple behavioural correlations or a limited understanding of interactions between certain goals to a generalised knowledge of recurring patterns, causes, and consequences.

Seven methods for mapping systems

March 27, 2026February 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

March 10, 2026January 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

May 22, 2024August 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).

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

July 1, 2025February 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

February 11, 2025March 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’

September 5, 2024January 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.