How – and why – do people decide to try new things?

Studies of diffusion have frequently demonstrated a mathematically consistent sigmoid pattern (the S-shaped curve, see figure below) of over-time adoption for innovations. Innovations include new beliefs, practices, programs, policies, and technologies.

The “S” shape is due to the positive engagement of informal opinion leaders in talking about and modeling an innovation for others to hear about and see. The initial slow rate of adoption gives way to a rapidly accelerating rate, which then slows as fewer non-adopters remain. Alternatively and more commonly, when informally influential people do not get positively engaged or when they ignore or actively reject an innovation, diffusion does not occur and the resulting slope of a cumulative curve stays flat or turns negative.

Key components of diffusion theory are:

1. The innovation, and especially potential adopter perceptions of its attributes of:
   • relative advantage (effectiveness and cost efficiency relative to alternatives),
   • complexity (how simple the innovation is to understand and use),
   • compatibility (the fit of the innovation to established ways of accomplishing the same goal),
   • observability (the extent to which outcomes can be seen), and
   • trialability (the extent to which the adopter must commit to full adoption);
2. The adopter, especially each adopter’s degree of innovativeness (earliness relative to others in trying and adopting the innovation) and their readiness to adopt the innovation in terms of personal motivation to try the innovation and their capacity to do a good job using it to accomplish a goal;
3. The social system, especially in terms of the structure of the system, its local informal opinion leaders, and potential adopter perception of social pressure to adopt;
4. The individual adoption-process, a mostly stage-ordered model of awareness, persuasion, decision, implementation, and sustained use;
5. The diffusion system, especially an external change agency and its paid change agents who, if well trained, correctly seek out and intervene with the client system’s opinion leaders, paraprofessional aides, and innovation champions, as well as the coordination among organizations that deliver and support the delivery of the dissemination of messages and the distribution of products that are necessary for effective implementation.

Awareness of an innovation can produce uncertainty about what an innovation is, how it functions, how well it works, and what the consequences of adoption and use will be. Uncertainty can propel the individual into a search for information to resolve the uncertainty they feel about how to respond to knowledge of the innovation. Either adoption or rejection can restore a sense of cognitive consistency (“I’m already doing the right thing and I don’t need this” or “This seems to have a lot going for it so I’ll give it a shot”).

Three sets of factors explain decisions about innovations:

1. what the person thinks about the innovation in terms of its pros and cons, ie., the attributes or characteristics of an innovation;
2. what the person thinks others think about the innovation’s pros and cons, ie., personal and social influence especially in the guise of informal opinion leaders;
3. when the innovation is introduced to potential adopters and how they understand it ie., timing and the meaning people make of an innovation.

Needs or motivations differ among people according to their degree of innovativeness (earliness in adoption):

• the first to adopt (innovators) tend to do so because of novelty and having little to lose due to their low degree of social integration in the social system in question;
• the next to adopt (early adopters, including the subset of opinion leaders) do so because of a cost-benefit appraisal of the innovation’s attributes and their overriding sense that the innovation is good for the social system of which they are key members;
• and the subsequent large majority adopts because others have done so and they come to believe that it is the right thing to do (an imitative effect).

These motivations and time of adoption are related to and can be predicted by each adopter’s structural position in the network of relations that tie a social system together.

There are multiple contexts in which diffusion can occur, ranging from external-to-the-community “broadcast” models of diffusion in which mass media and change agents from afar introduce ideas into communities, through to internal-to-the-community “contagion” models of diffusion in which strong friendship ties, weak acquaintance ties, structural equivalence (similarity in network position as a basis for expecting similar adoption behaviors and timing), or proximity account for diffusion outcomes.

The work of rural sociologist, Everett Rogers, was instrumental in popularizing diffusion of innovations. Rogers grew up on an Iowa farm watching his father not adopt innovations, so trying to explain this regressive behavior and in turn perhaps helping to improve farming conditions among poverty-stricken farmers came naturally.

Increasingly, government agencies, private foundations and research teams are applying diffusion concepts to affect the rate of adoption and the reach of social innovations and thus move diffusion scholarship more systematically into the realm of intervention research.

What has your experience been? Have you found an understanding of diffusion of innovations useful in your work? Have you used it to encourage uptake of new ideas and practices?

Recommended reading:

Dearing, J. W. and Cox, J. G. (2018). Diffusion of innovations theory, principles, and practice. Health Affairs, 37, 2: 183-190. (Online – open access): https://www.healthaffairs.org/doi/10.1377/hlthaff.2017.1104

Green, L. W., Gottlieb, N. H. and Parcel G. S. (1991). Diffusion theory extended and applied. In, W. B. Ward and F. M. Lewis (Eds.), Advances in health education and promotion. Jessica Kingsley Publishers, London, United Kingdom.

Kapoor, K. K., Dwivedi, Y. K. and Williams, M. D. (2014). Rogers’ innovation adoption attributes: A systematic review and synthesis of existing research. Information Systems Management, 31: 74-91.

Rogers, E. M. (2003). Diffusion of innovations. Fifth edition. Free Press: New York, United States of America.

Biography:

James W. Dearing Ph.D. is Brandt Endowed Professor in the Department of Communication at Michigan State University, East Lansing, USA. He studies the diffusion of innovations, including the adoption and implementation of new evidence-based practices, programs, technologies and policies. His research and teaching spans dissemination science, implementation science, program sustainability, and the psychological and sociological basis of the diffusion process. He works with research and practice improvement teams in environmental remediation, nursing care, water conservation, injury and fatality prevention, public health, and healthcare.

Article source: Diffusion of innovations. Republished by permission.

Header image source: Created by Bruce Boyes with Perchance AI Photo Generator.

By Diaa Ahmed Mohamed Ahmedien. Originally published on the Integration and Implementation Insights blog.

How can new-media art-science projects move beyond raising public awareness of science to achieve a high level of layperson involvement in a scientific process? How can such projects use two-path integration:

1. across multiple academic disciplines, and
2. including the participation of laypeople?

In 2017, I developed an interactive game, using a holographic scene, where participants had to interact physically with their neural activities to complete the required processes and tasks (see the figure immediately below). A participant was attached to EEG (electroencephalography) monitoring and then, when standing at a table that had a set of holographic plates laid out upon it, they had to puzzle-out a hologram of a toy. How the holographic plates were illuminated, and hence the possibility of seeing the holographic puzzle, depended on the participant’s brain responses.

As illustrated in the figure below, the artwork integrated three academic disciplines to allow the layperson to play with their own brain using the holographic puzzle as a connecting bridge:

• Applied physics – expertise in the holographic recording set-up
• Neurology – expertise in transferring players’ neural signals from frequency scale into wavelength scale that could be used again to illuminate pieces of the puzzle
• Interactive art – expertise in conceiving and designing the interactive holographic pieces of the puzzle that were displayed and combined using the neural signals emitted from the players’ brains.

The artwork also engaged with laypeople at three levels, leading to the provision of three types of data:

• Behavioral data derived from their responses to the interactive system
• Cognitive data derived from the skills they used to solve the problem of the game itself using their individual knowledge
• Neural data derived from their brains’ neural responses to both previous types of data.

In this way, the layperson provided a comprehensive feed to the system so that it processed their real data and reused those data to update the system, in turn leading the layperson to update their data to resend it to the system and so on in an infinite loop.

There were also three interacting outputs, also illustrated in the figure below:

1. For new-media art: Together, the physical actions of the laypeople which constituted the “performance” aspects of the artwork and the analysis of the interactive processes formed the basis of new empirical principles for contemporary interactive arts based on neuroaesthetics.
2. For neurology: Quantitative data based on the physical actions were combined with neural analysis to provide new empirical data about the relationship between behavioral, cognitive, and social activities and how these changed depending on the situation.
3. For applied holography: The visual features of the holographic images (decoded by the participants’ neural signals) provided a new empirical approach to study the holographic reconstruction system based on biological information.

Each discipline, therefore, benefitted from the interaction. The laypeople were an integral part of the artwork itself and, in essence, they co-governed the system.

Next steps

This practical model presents a case of integration among three disciplines and laypeople. It raises an interesting question of whether and how laypeople could be included in scientific research experiments and processes using artwork as the intermediary.

Do you have any experiences to share of using interactive art to integrate across disciplines and between science and society? Do you have any ideas about how interactive art could be used to include laypeople in scientific research? Can you imagine interactive art as a means of integration or a means of science outreach instead of being a means of entertainment? Can you foresee any ethical challenges?

To find out more:

Ahmedien, D. A. M. (2017). Reactivating the neural dimension role in interactive arts. Leonardo, 50, 2: 182-183. (Online) (DOI): https://doi.org/10.1162/leon_a_01376

Biography:

Diaa Ahmed Mohamed Ahmedien PhD is a lecturer of sciences of new-media art and technology in the Drawing and Painting Department, Helwan University in Cairo, Egypt. He is an artist, educator, researcher, and creator of interactive artworks. He specializes in the area of the sciences of new-media arts, digital humanities, visual communications, and medical humanities, all of which emphasize the crucial role of the intersections among the arts, sciences and technology in shaping knowledge-based societies.

Article source: A successful model of integration in an art-science project. Republished by permission.

Header image: An illustration how a participant’s neural brain signals can be converted into a particular light color to be used for reconstructing a holographic image. Source: © Ahmedien 2016.

What is the relationship between coloniality, forced displacement and knowledge production? How is this relevant to decolonization efforts?

The history of forced displacement can be traced back to the colonial era, during which European powers established colonies in various parts of the world, displacing and often subjugating indigenous populations. The displacement of indigenous peoples often involved the forced removal from their ancestral lands and the disruption of their social and cultural systems.

In this context, knowledge production was used to justify and legitimize the displacement of indigenous populations. European colonizers created and disseminated knowledge that portrayed indigenous peoples as “primitive” or “uncivilized,” and therefore in need of “civilizing” through the imposition of European values and systems. This knowledge served to legitimize colonial policies of forced displacement and cultural assimilation.

As colonialism gave way to the postcolonial era, forced displacement continued to be a significant issue, often taking the form of forced migration due to conflict, environmental degradation, or economic factors. In these contexts, knowledge production has continued to play a role, with dominant narratives often portraying displaced people as passive victims in need of assistance rather than as active agents with their own knowledge and perspectives.

The relationship between coloniality, forced displacement and knowledge production is therefore substantial, especially in creating ongoing power imbalances and epistemic violence.

In summary, key impacts include:

1. Marginalization of indigenous knowledge: As discussed earlier, colonialism often suppressed or devalued the knowledge systems of indigenous peoples, leading to the loss of valuable knowledge about local environments, cultures, and social systems. This has had a lasting impact on the ability of displaced communities to draw on their own knowledge and experiences.
2. Imposition of Western knowledge systems: European knowledge systems were often imposed on colonized peoples, often at the expense of local knowledge. This homogenized knowledge systems and marginalized local knowledge, which has continued to have an impact on the way knowledge is produced and disseminated.
3. Creation of knowledge hierarchies: The imposition of Western knowledge systems created a hierarchy of knowledge in which Western knowledge was often seen as superior to local knowledge. This has had long-lasting effects on the way knowledge is produced and disseminated, with Western knowledge often given greater legitimacy and authority than local knowledge.
4. Production of knowledge for colonial purposes: Knowledge production has often served colonial purposes, such as the exploitation of natural resources or the control of populations. This has led to biased and selective knowledge production that serves the interests of the powerful rather than the needs and perspectives of the displaced communities.
5. Intellectual dependency: Colonialism created intellectual dependency among colonized peoples, which perpetuated a cycle of subjugation and limited the ability of displaced communities to produce and disseminate their own knowledge.

There is growing recognition of the need to decolonize knowledge production related to forced displacement, by centering the perspectives and knowledge of displaced communities and challenging dominant narratives that perpetuate colonial attitudes and power dynamics. This includes efforts to amplify the voices and knowledge of displaced people, support participatory research and knowledge co-production, and challenge dominant narratives through critical analysis and activism.

What has your experience been? Are there other aspects of forced displacement resulting from coloniality that need to be considered? Are there other consequences that should not be ignored? What examples are there of how decolonization has taken these impacts of forced displacement into account?

To find out more:

This i2Insights contribution is a lightly modified extract from Tesfaye, A. and Mukuna, T. (no date). Decolonizing Knowledge Production in Forced Displacement: Challenging Colonial Narratives and Amplifying Displaced Voices. Organization for Social Science Research in Eastern and Southern Africa (OSSREA). (Online): http://ossrea.net/images/DKS/PDFs/Impact_of_Coloniality_in_Knowledge_Production_in_Forced_Displacement_Context.pdf (PDF 806KB).

Biographies:

Alemu Tesfaye MBA is Regional Programs Manager (Research, Communication, Knowledge Management and ICT (Information and Communications Technology)) at the Organization for Social Science Research in Eastern and Southern Africa (OSSREA), in Addis Ababa, Ethiopia. His interests include communication, knowledge management and translation, and community engagement.

Truphena Mukuna PhD is Executive Director at the Organization for Social Science Research in Eastern and Southern Africa (OSSREA), in Addis Ababa, Ethiopia. She conducts transdisciplinary transformative research and feminist participatory action research on vulnerable populations to offer life-changing, cost-effective solutions and see improvement in people’s lives.

Article source: Understanding the links between coloniality, forced displacement and knowledge production. Republished by permission.

Header image source: Tesfaye, A. and Mukuna, T. (n.d.).

In the context of knowledge for development, what does it require to deconstruct the dominant narratives and personal privileges embodied in our race, class, gender, etc.? And, in a knowledge landscape littered with potential minefields, how do we go about shifting the mindsets that shape the ways in which ‘we’ understand the world and our subsequent values, behaviours, and attitudes?

Drawing on our own experiences, and learning that has emerged through many valued interactions with others, we have identified four approaches which we believe may help to make a difference.

1. Identifying what, and whose, knowledge is valued, counted, and integrated into development processes

Researchers often fail to recognise or value the different knowledges needed to address some of the world’s greatest challenges, because of where knowledge resides and who has generated it. To decolonise knowledge, we need to recognise people as knowers of their experience and weave together knowledges from various sources, including from Indigenous and local knowledge systems. The most compelling narratives in an era of increasing uncertainty are shaped by multiple perspectives and different forms and expressions of knowledge, and by working in a spirit of inclusion and in participatory ways.

The perception often persists that ‘expert knowledge’ is of a higher order to a wide range of other knowledges simply because of the power structures and hierarchies that give it authority. Since power is such a critical element in the struggle for social justice, we have found the concept of ‘cognitive justice’ – or whose knowledge counts – helpful in understanding how and in which ways attention is paid to epistemologies.

Knowledge systems are diverse, multi-faceted and bring locally contextualized ways of thinking and being in the world.

2. Decolonising knowledge asymmetries – learning through research, and as researchers

Doing research provides researchers with a wealth of opportunity to learn about, and address, decolonisation of knowledge. ‘Inclusive’ research methods may not provide an opportunity to decolonise knowledge, however, because this may not be part of the intent, and is often avoided because it is uncomfortable or difficult. The work requires recognizing and challenging the historical and ongoing impacts of colonization and the ways in which colonial structures have prioritized certain knowledges and marginalized others.

The Covid-19 pandemic has revealed multiple benefits of local participation, including contributing to more effective information sharing, mobilising local life and livelihoods saving networks in the area, and promoting community empowerment, resilience, and trust. Yet, agencies supporting communities still often struggle to integrate participation in their operations, and many are now asking whether inadequacies in international cooperation may open the space for a reimagination of agency and power in the conceptualisation and realisation of development and research.

3. Investing resources to transform existing colonialities

To learn and change, it is necessary to invest. Multiple, diverse knowledge systems need a strong financial and economic base which allows them to grow. Researchers will need to unpack what they have learned about who they are, the powers and privileges they hold, and their ideas and practices of ‘leadership’. They will need to recognise and break down barriers and walls between them and a wide array of other societal members if they are serious about change. They will need to invoke and experience connection and belonging on this shared journey. This takes time and requires thoughtfulness, although this is not to suggest a disregard for the urgent need for transformation of knowledge systems.

The processes required to achieve this kind of change take several resources:

• Patience, humility, time – to allow for the discomfort of “unlearning” and the wonders of continually “relearning” with others;
• Transparency about how researchers live and model diversity and inclusion in their activities, organisations and communities;
• Courage to interrogate history and privilege and to work toward change;
• Power sharing – recognize inherent power imbalances and make bold moves to cultivate shared decision making in all aspects of collaboration – the outcomes will ultimately be positive for all;
• Recognition of people as knowers of their own experience; and
• Financial resources, since decolonising knowledge also requires decolonising wealth.

4. Identifying ‘our’ role as individuals, as organisations, as institutions

What do ‘we’ need to do if we are serious about taking on the challenge of decolonising knowledge for development? We recognise that as researchers, given our identity, positionality and privilege, we need to work on ourselves, which can be very uncomfortable.

We suggest several actions which have implications for our roles as researchers wherever we may be located, and for the research in which we engage with others in knowledge co-construction processes:

• Ensure solutions are shaped/created by those who experience the challenges being addressed if they are to succeed and be sustained.
• Establish reflective spaces for inclusive processes, in which participants are aware of and interrogate their privilege and how they can use it to make change that disrupts inequalities.
• Check and challenge policies and practices that discriminate and continue to uphold oppressive systems.
• Find connections and ways in which we belong with each other, as communities, on a shared journey.
• Appreciate that the benefits of decolonising knowledge are not obvious to everyone, nor are they desired by those who believe they may ‘lose’ status or privilege.
• Ground our efforts in trust, and consciousness of who is setting and controlling the research agenda, and what kinds of power dynamics are at play.
• Ensure that the expectations of participants, and the gifts they make of time, energy, belief, and sometimes personal risk, are not taken lightly or squandered needlessly.

We would be happy to hear what others feel about these ideas and potential actions we could take as researchers. What suggestions would others like to bring to this conversation?

To find out more:

Taylor, P. and Tremblay, C. (2022). Decolonising Knowledge for Development in the Covid-19 Era. IDS Working Paper 566, Institute of Development Studies: Brighton, United Kingdom. (Online – open access) (DOI): https://doi.org/10.19088/IDS.2022.018

Biographies:

Peter Taylor PhD is Director of Research at the Institute of Development Studies (IDS), University of Sussex, Brighton, UK. With over 30 years of experience in international development, he has research, teaching and writing interests in the theory and practice of organisational development and capacity strengthening, evaluation and learning, and facilitation of participatory and social change processes in a diverse range of international contexts.

Crystal Tremblay PhD is an Assistant Professor and co-chair of the Map Shop in the Department of Geography and Director of CIFAL Victoria (Centre International de Formation Autorités et Leaders also known as International Training Centre for Authorities and Leaders) a United Nations training centre, at the University of Victoria, Canada. She is a social geographer and community-based research scholar with interests in environmental stewardship, participatory governance, decolonisation, and spatial justice.

Article source: Four approaches to shifting mindsets for decolonising knowledge. Republished by permission.

Header image source: rawpixel.com on PxHere, Public Domain.

How can I ensure that marginalized voices are heard in this project? Whom do I call on to offer the next perspective in this workshop and why? How can I intervene in this particular disagreement in a productive way? These are typical questions that researchers and practitioners involved in knowledge co-production processes ask themselves. They express deep ethical concerns, which also have epistemological and political implications, as they address the question: What should I do in this situation? What is right and wrong for me to do here?

We suggest that a perspective based on the ancient virtue of practical wisdom may help researchers and practitioners alike working in knowledge co-production to navigate the complexities of these questions.

Practical wisdom: An ancient virtue for wise navigation

Our answers to the deep ethical questions that emerge in collaborative and participatory research will vary depending on the specifics of the situation we are in, who is involved, as well as our own positionality and role in research projects or academic institutions. There is no formula to follow.

Instead, we are required to learn from experience and from one another how to navigate the many complexities embedded in the flux of relationships, commitments and conversations in the daily operations of collaborative work. The ancient virtue of practical wisdom can help us to cultivate both the will and skill for such navigation.

In Aristotle’s terms, practical wisdom (phrónēsis) is the capacity for making decisions and taking action on issues that matter to people’s lives, that is practical issues. In his best known work, Nichomachean Ethics, Aristotle distinguishes this kind of wisdom from the capacity to produce things (téchnē) or to generate and work with abstract thoughts (epistème).

Practical wisdom is the central virtue, that is a learning-by-doing and cultivated capacity of citizens involved in public and social life. It is the virtue, Aristotle would say, of things that are relevant for managing human affairs towards good ends, that includes redefining what the good ends may be, dealing with other people and engaging with challenging questions, dilemmas, and situations.

Practical wisdom is the capacity to wisely navigate complex and contentious situations towards good ends. We illustrate practical wisdom through a visual metaphor in the figure below that depicts researchers as sailboats navigating difficult situations. Learning how to navigate wisely difficult situations requires both will and skill.

Will for establishing direction and sustaining determination

Practical wisdom supports developing the necessary will to get oriented when making decisions and taking action, while also adapting to ever-changing situations. Practical wisdom supports will also in the sense of sustaining determination and engagement through the many drawbacks one is likely to encounter along the way.

The circle in the middle of the figure represents a typical situation of difficulty (the big wave) and points out some important capacities that support the will to navigate such difficulties wisely: A) committing to justice (the steering wheel, which gives direction); B) cultivating care (a life buoy used for keeping people afloat); C) embracing humility (the keel that provides stability to the sailboat); and D) developing courage (the sails filled by wind pushing the boat in a particular direction). All together, these capacities may support researchers’ willful engagement and action.

Skill for deliberation and action

Practical wisdom also supports skill when making decisions in complex situations towards good ends. Thoughtful deliberation relies on the capacity to reason through the many complexities of decision-making and action, so-called practical reason, by giving arguments while also mobilizing intuitions and emotions.

Going beyond the decision-making process, practical wisdom is also a capacity to act skillfully. Skillful action refers to the capacity to put such considerations to work in specific contexts, whether individually or collectively.

The other situations in the journey illustrated in the figure show how practical wisdom includes skills such as: E) the agility to deal with many values, which may also be incommensurable (the many kinds of boats); F) the intelligence to work through power (going through a vortex); G) the discernment to apply general principles and concrete situations (circumventing sea stacks or icebergs to move in the desired direction); and H) the capacity to adaptively develop means and goals with strategy (different constellations of boats navigating waterscapes towards different destinations). These multiple skills are learned through experience when researchers navigate the obstacles and opportunities of co-produced research.

Wise navigation: from individual to collective

We may learn to recognize practical wisdom by looking at how (practically) wise people operate from a cultivated understanding of the world, when they endure the difficulties they encounter, or when they are able to learn reflexively from the consequences of their actions.

Practical wisdom may be seen at work when individual researchers or groups work through the complexities of collaboration. This is the case, for example, when researchers attempt to give space in a project to narratives and voices that had not been taken into consideration in its original design. And this despite the fact that attempting to create such space implies questioning their own assumptions of what is important in the project or reconsidering certain deliverables.

Moreover, practical wisdom can also be collective, such as when a research team engages constructively and courageously through dialogue and reflexive conversations to overcome tough moments. For example, a research team may realize that certain choices they have made reinforce power asymmetries or dynamics of marginalization. Rather than deciding to not see these problems, they decide to face the challenges that might emerge to make sure that marginalized voices are included and heard.

Towards an ethics of practical wisdom for knowledge co-production?

Practical wisdom gives an integrated and learning-oriented approach to the many capacities needed to address difficult situations that emerge in knowledge co-production. Yet, practical wisdom does not provide either easy recipes or certainty of success. It rather cautions against approaches that consider only one way to assess what is good or bad. It invites us to recognize the complexities of collaborative research and to develop the capacities, individual or collective, to deal with them.

Do these considerations matter in the work you do as a researcher or as a practitioner? How can the educational system foster the capacities required for practical wisdom? Do you know of examples of institutions that actually foster an ethics of practical wisdom? What can we learn from them?

To find out more:

Caniglia, G., Freeth, R., Luederitz, C., Leventon, J., West, S. P., John, B., Peukert, D., Lang, D. J., von Wehrden, H., Martín-López, B., Fazey, I., Russo, F., von Wirth, T., Schlüter, M. and Vogel, C. (2023). Practical wisdom and virtue ethics for knowledge co-production in sustainability science. Nature Sustainability. (Online) (DOI): https://doi.org/10.1038/s41893-022-01040-1. The reflections in this i2Insights contribution are the results of exchanges and conversations among the co-authors of this article.

Biographies:

Guido Caniglia PhD is a philosopher and historian of science working in sustainability science. His research interests revolve around the intersecting epistemological, ethical, and political dimensions of knowledge co-production in this field, including in the development of new educational formats. He is the Scientific Director of the Konrad Lorenz Institute for Evolution and Cognition Research in Klosterneuburg, Austria.

Rebecca Freeth PhD is a senior consultant with Reos Partners and an affiliate scholar with the Research Institute for Sustainability (RIFS) in Potsdam, Germany (formerly the Institute for Advanced Sustainability Studies (IASS)). She is based in South Africa. As a practitioner, teacher and researcher, she has an abiding interest in how to strengthen collaboration, especially in situations where competition, conflict or controversy are the more familiar ways of engaging.

Article source: Wisely navigating knowledge co-production: Towards an ethics that builds capacities. Republished by permission.

Header image source: Authors, based on Caniglia et al., 2023.

Would systems thinking realize its potential as a force for good in the world if it rediscovered and developed its pragmatist roots? Does the link between the past and future of systems thinking lie through critical systems thinking and practice?

In brief, I suggest that:

• Pragmatism provides an appropriate philosophy for systems thinking.
• Systems thinking has pragmatist roots.
• Critical systems thinking and practice shows how to develop those roots.
• Pragmatism can help systems thinking realize its potential and systems thinking can help pragmatism achieve what it set out to do.

What is pragmatism?

Kant was in awe of Newton’s science but believed it could supply certainty only about the physical world. In most areas of human endeavor, he argued, we have to use ‘pragmatic belief’ to guide our actions.

Charles Sanders Pierce, William James, and John Dewey borrowed the term when founding the philosophy of pragmatism. They viewed the realm in which we are forced to act on the basis of pragmatic belief as vast and hoped to make philosophy relevant again by offering guidance to help navigate it. In particular, they argued that:

• There are no universal truths. Cognition is an adaptation intimately related to our biological and historical evolution, and has developed to help us cope with the world.
• Truth should, therefore, be judged in terms of its consequences. James said – “The whole function of philosophy ought to be to find out what definite difference it will make to you and me, at definite instants of our life, if this world-formula or that world-formula be the true one”.
• The ‘multiverse’ which we inhabit allows multiple truths. James calls this ‘pluralism’, introducing the term for the first time into English-language philosophy. A multiplicity of theories should be encouraged and tested according to their consequences.
• Theories should not be seen as attempts to mirror the world but as instruments of purposeful action that can be used to change existing realities and make the world better. James said – “The truth of an idea is not a stagnant property inherent in it. Truth happens to an idea. It becomes true, is made true by events”. (The sources for this and other quotations are referenced in Jackson, 2022a.)

Systems thinking’s pragmatist roots

Warren Weaver follows a Kantian rationale in setting out the challenge posed by complexity, stating in 1948 that:
“… science has, to date, succeeded in solving a bewildering number of relatively easy problems, whereas the hard problems, and the ones which perhaps promise most for man’s (sic) future, lie ahead”.

These ‘hard’ problems – human, political, economic, social, and environmental – cause difficulties for classical scientific tools. They are, he argued, made up of too many variables to yield to simple mathematical formulae and the variables are too interrelated to yield to probability statistics. They constitute ‘a great middle region’ of ‘organized complexity’. ‘Something more is needed’, Weaver wrote, to help decision-makers tackle problems of this type. It is systems thinking that set out to provide that ‘something more’.

The three pioneers of the systems approach – Alexander Bogdanov, Ludwig von Bertalanffy, and Norbert Wiener – all adopted a pragmatist orientation in seeking to get to grips with ‘organized complexity’. For example:

• Bogdanov saw truth as “a tool for living … for the general guidance of human practice”
• von Bertalanffy championed ‘perspectivism’, arguing that all forms of knowledge can only capture certain aspects of the truth because any perspective is dependent on a “…multiplicity of factors of a biological, psychological, cultural, linguistic, etc., nature”
• Wiener gloried in taking ‘epistemological short-cuts’ on the basis of what worked.

All wanted their endeavours to secure improvement in the world:

• Bogdanov hoped his ‘tektology’ would enable people to become competent ‘world-builders’
• von Bertalanffy regarded ‘general system theory’ as entailing a rejection of the ‘robot model’ of people and as demanding “a basic reevaluation of problems of education, training, psychotherapy, and human attitudes in general”
• Wiener saw cybernetics as having major implications for the organization of society and the ‘human use of human beings’.

Critical systems thinking and practice as a development of systems thinking’s pragmatist roots

Many later systems thinkers see themselves as indebted to von Bertalanffy and/or Wiener and some acknowledge pragmatist roots (eg., C. West Churchman and Russell Ackoff). However, this is far from universal. Recently, I have been explicitly developing critical systems thinking and practice on the basis of pragmatism and seeking to show that this can enable systems thinking to realize its potential. In particular, critical systems thinking and practice argues that:

• General complexity (with interacting ontological and cognitive elements) resists universal truth. All attempts to model it are partial and, therefore, the fundamental problem posed is “epistemological, cognitive, paradigmatic” (Edgar Morin) – concerned with the ways we seek to understand and manage complexity.
• In engaging with general complexity, systems thinking should make use of ‘systemic perspectives’ which have enabled the human species to secure coherent encounters with ‘reality’. In other words, those systemic perspectives have provided for our successful functioning in the physical and cultural worlds – specifically, the machine, organism, cultural/political, societal/environmental, and interrelationships perspectives.
• Systems thinking should embrace ‘pluralism’. It must make use of the variety of insightful systemic perspectives to view the world in different ways, and employ their associated systems methodologies to learn which of these can bring about beneficial change in a particular context.
• The purpose of critical systems thinking and practice is to bring about improvement in the world. This is not just in terms of increased efficiency and efficacy but also effectiveness, mutual understanding, resilience, antifragility, empowerment, emancipation, and sustainability.

A brighter future for both systems thinking and pragmatism

By explicitly embracing pragmatism, and taking it forward through critical systems thinking and practice, systems thinking can realize the hopes of the original pioneers and chart a bright future for itself. A shared philosophical orientation will bring greater mutual understanding between the currently disparate strands of the systems movement and more unity of purpose.

It will enable systems thinking to engage more fully with, and have greater influence on, contemporary debates in the specialist disciplines. Much of that debate, in philosophy and the social sciences, centres on pragmatist themes. It is not just systems thinking that stands to benefit from an alliance with pragmatism. As an applied transdiscipline, systems thinking can assist pragmatism in achieving what it set out to do – make philosophy relevant to everyday affairs.

What do you think? If you are a systems thinker, does this argument look like it provides a way forward? If you are not a systems thinker, what would help you better connect with our field?

To find out more:

Jackson, M. C., (2022a). Rebooting the systems approach by applying the thinking of Bogdanov and the pragmatists. Systems Research and Behavioral Science. 1-17 (Online) (DOI): https://doi.org/10.1002/sres.2908

Key references to critical systems thinking and practice:

Jackson, M. C. (2019). Critical systems thinking and the management of complexity. John Wiley & Sons: New Jersey, United States of America.

Jackson, M. C. (2020). Critical systems practice 1: Explore—Starting a multimethodological intervention. Systems Research and Behavioral Science, 37, 5: 839– 858. (Online) (DOI): https://onlinelibrary.wiley.com/doi/full/10.1002/sres.2746

Jackson, M. C. (2021). Critical systems practice 2: Produce—Constructing a multimethodological intervention strategy. Systems Research and Behavioral Science, 38, 5: 594– 609. (Online) (DOI): https://doi.org/10.1002/sres.2809

Jackson, M. C. (2022b). Critical systems practice 3: Intervene—Flexibly executing a multimethodological intervention. Systems Research and Behavioral Science, 39, 6: 1014–1023. (Online) (DOI): https://doi.org/10.1002/sres.2909

Jackson, M. C. (2022c). Critical systems practice 4: Check—Evaluating and reflecting on a multimethodological intervention. Systems Research and Behavioral Science. 1–16. (Online) (DOI): https://doi.org/10.1002/sres.2912

Biography:

Michael C. Jackson PhD OBE is an emeritus professor at the Centre for Systems Studies, University of Hull, UK. His teaching and research interests are systems thinking, organizational cybernetics, creative problem solving, critical systems thinking, management science and systems science.

Article source: Pragmatism and critical systems thinking: Back to the future of systems thinking. Republished by permission.

Header image source: Diego Dotta on Open Clipart, Public Domain.

How can pattern language be used to share tips for knowledge co-creation in transdisciplinary research? What is pattern language?

Pattern language

Pattern language is an idea that originated in the field of architecture and city planning in the 1970s. The American architect Christopher Alexander and his colleagues created a common language, referred to as pattern language, that can be used by non-experts to participate in the process of city planning and building design.

In this pattern language, the rules of thumb for solving common and timeless problems in design are summarised in units called ‘patterns.’ Each pattern describes a specific problem, the situation or context in which it likely occurs, and the core of the solution to that problem.

The solutions are not written as specific procedures or manuals, but rather as ‘hints’ for solving the problem. Therefore, the solution can be used in many ways based on one’s own needs and situation. 

Each pattern is assigned a name, so it can be used to refer to a particular combination of ‘problem statement,’ ‘context description,’ and ‘proposed solution’ when talking with others. The fact that patterns can be used as ‘words’ and can be linked to each other is the reason why a set of patterns is called a ‘language.’

Pattern languages have subsequently been created in other fields such as education and healthcare. For their use in modelling, see the i2Insights contributions by Scott Peckham on Looking for patterns: An approach for tackling tough problems and Sondoss Elsawah and Joseph Guillaume on Sharing integrated modelling practices, both Part 1: Why use “patterns”? and Part 2: How to use “patterns”?

Pattern language for knowledge co-creation

Colleagues and I are developing a collection of patterns to guide knowledge co-creation in transdisciplinary research and to overcome common challenges, as described in the four patterns to follow and as replicated in the image below.

Expressing lessons learned from dealing with such re-occurring problems in the field as patterns can create a pattern language for transdisciplinary research.

Pattern 1: Communicative language.

The problem: Scientists often unconsciously use jargon or terms with a specific meaning. Communication troubles may arise because non-scientists cannot understand the terms in the same way as the scientists.
The solution: Use plain language when communicating with stakeholders and avoid scientific jargon. When this is not possible, explain the meaning of the jargon or term and why it is necessary to use it.

Pattern 2: Mind the difference.

The problem: Even within the same country, differences in culture, lifestyle, thought styles, and values exist.
The solution: Researchers entering a field site, for example a community, should recognise these differences and adapt to the situation.

Pattern 3: Make use of skills.

The problem: Without trust, stakeholders are unlikely to share honest opinions or provide key information.
The solution: Do what you can do for the community, rather than focusing on your research topic. Sincere attitudes and actions will help build trust with the community.

Pattern 4: Enjoy exploring new ideas together.

The problem: Researchers think that they are unable to find solutions to the problem and frustrated that progress is slow.
The solution: It takes time to find innovative ideas and solutions. Be aware that this is a time-consuming process and try to enjoy the exploration.

Conclusion

Are any of these patterns useful or applicable in your research context or regions? Does this i2Insights contribution highlight any ideas about patterns in transdisciplinary research for you?

To find out more:

Co-creation Project. (2021). Tips to foster knowledge co-creation and guide transdisciplinary research (TDR). (Online – webpage): https://cocreationproject.jp/en/learn/tips/

Biography:

Yuko Onishi PhD is an assistant researcher at the Research Institute for Humanity and Nature, Kyoto, Japan. Her interests are in transdisciplinarity, co-creation concepts and societal outcomes.

Article source: A pattern language for knowledge co-creation. Republished by permission.

Header image source: © Co-creation Project, Research Institute for Humanity and Nature.

Biography: Yuko Onishi PhD is an assistant researcher at the Research Institute for Humanity and Nature, Kyoto, Japan. Her interests are in transdisciplinarity, co-creation concepts and societal outcomes.

What is Inclusive Systemic Thinking and how can it be effective in achieving transformational change? How can it contribute to a more inclusive and equitable world?

Introducing Inclusive Systemic Thinking

We have coined the term Inclusive Systemic Thinking to describe an approach that is influenced by a field of systems thinking called ‘Critical Systems Thinking,’ as well as by the social and behavioural sciences, fourth-wave feminism, and more recently, our work in the global development sector. Inclusive Systemic Thinking uses the ‘GEMs’ framework for complex systemic intersectional analysis based on: Gender equality/equity (non-binary), Environments (natural and/or contextual) and Marginalised voices (human and non-human). We described the GEMS framework in a recent i2Insights contribution, A responsible approach to intersectionality.

In our work, Inclusive Systemic Thinking is inclusive because we actively reflect on, advocate, mentor, and adapt our practices through an ethos of engagement that is widespread and that uses non-conventional approaches. We engage with local voices and collectively identify other relevant stakeholders, including non-human voices, as well as people pushed to the margins. In our global development practitioner and academic work, we aim to contribute to the present-day decolonization of knowledge, access, and power. What that means is that we practice two crucial activities:

1. critical reflexivity on all our ideas and decisions; and
2. in partnership with our country level colleagues, creating a mutual learning environment to design and implement our projects.

These practices get to the heart of a systems thinking truism, ‘we don’t know, what we don’t know’ and to find out at least some of what we don’t know.

Why is Inclusive Systemic Thinking needed for transformative change?

Transformation ideally means the ability to sustain wanted changes in attitudes, behaviours and practices, that can result in lasting societal change to create a world that works for everyone, now and in the future, for human and non-human alike.

This then requires us, in all that we do, to explore the root causes of inequality and ensure that discussions of the conditions needed for lasting social change, at the household, community and institutional levels, are grounded in participant perspectives. This, in turn, requires practices grounded in Inclusive Systemic Thinking.

Diversity here is key: diversity of participants and priorities, diverse settings and contexts, and multiple tools and methods. Research processes must unpack intersecting inequalities, discriminations and vulnerabilities that push some further to the margins than others. This is where the GEMs Framework comes into effect because it does not predetermine what these intersecting factors will be but gives space for them to emerge and arise from dialogue, story- and truth- telling with the people living with the experience.

Genuine decolonising practice

The most prevailing driver of our work is the acknowledgement that the work we do, using Inclusive Systemic Thinking, is an urgent, yet daunting reality, responding to the global polycrises. Polycrises occur when “multiple global systems become entangled in a way that significantly degrades humanity’s prospects” (Lawrence et al., 2022). Put simply, we are motivated to respond to global injustice and wish to do this without reinforcing or creating more of it. As well as prioritising engagement with stakeholders in a way that does not reinforce or risk further discrimination, we reflect and ask whether our work contributes to a desirable, transformational change.

Decolonizing practice is an attempt to challenge Eurocentric practices by prioritising local knowledge and experiences of marginalised population groups.

Inclusive Systemic Thinking provides the mindset needed for a decolonising practice and using the GEMs framework can enable practitioners to seek out and drive transformational change and contribute to an intersectional analysis that is determined by the people and environmental systems, central to the setting in focus.

Mutual capacity development

A key value inherent in Inclusive Systemic Thinking is mutual learning, also known as two-way learning. This is about balancing the power and processes in our relationships. Capacity is not developed by one party or given by another. It is recognition of the systemic nature of learning, of reciprocity, feedback, curiosity, transparency, and enquiry. It is the commitment to reflective behaviour that looks to understand one’s true impact. At the end of the process, it is finding ways to exit that don’t leave others with a bigger mess to clean up than when you entered the scene, or with a financial, strategic, and logistical burden. It is also about a mutual transfer of knowledge.

An emerging practice in our work in Colombia, East Timor and Kenya is to collaboratively develop consulting contracts with local teams which we deliver together. This includes writing in our own redundancy within ten years giving local teams full ownership to lead change in their own country and regions. Simultaneously, those local teams are building country teams that they can mentor and support.

Conclusion

Addressing the polycrises starts by being present, supporting others to participate meaningfully and equitably to bring about the transformative change they wish to see happen, while supporting learning so that our own place in the process is temporary.

Are there ways that Inclusive Systemic Thinking might be used in your work or life? What are the key lessons you take from our example? Do you have lessons to share that could improve our practice?

Reference:

Lawrence, M., Janzwood, S. and Homer-Dixon, T. (2022). What Is a Global Polycrisis? And how is it different from a systemic risk? Version 2.0, Discussion Paper 2022-4, Cascade Institute: Victoria, British Columbia, Canada. (Online – open access). https://cascadeinstitute.org/technical-paper/what-is-a-global-polycrisis/

Biographies:

Ellen Lewis PhD is an organization development consultant and systems thinker, as well as co-founder and co-director of Ethos of Engagement Consulting (EoE). She is based in Portugal. She advises and designs with systems thinking for organisational change, leads EoE’s training and professional development as well as teaching, and conducting research and evaluation projects.

Anne Stephens PhD is a sociologist, as well as co-founder and co-director of Ethos of Engagement Consulting (EoE). She is based in Australia. She is the Vice President of the Australian Evaluation Society and leads human-rights and gender responsive evaluations, as well as supporting in-country teams. She is a lecturer, writer and researcher and Adjunct of the Cairns Institute at James Cook University in Cairns, Australia.

Article source: Inclusive Systemic Thinking for transformative change. Republished by permission.

Header image source: © Ethos of Engagement Consulting.

How can communities, businesses, regions, and nations – which can all be thought of as organisations – be designed to be capable of dealing quickly and effectively with environmental fluidity and complexity?

The Viable System Model, often referred to as VSM, is a theory that posits that a complex organisation is more capable of responding to a changing and unpredictable environment, if it is:

• composed of autonomous, effective, and agile subsidiary organisations,
• highly connected to each other, and
• cohesively operating with shared ethos, purpose, processes, and technologies.

A complex organisation therefore has multiple levels of nested organisations, each adhering to these principles.

The building blocks of the Viable System Model are five interconnected systems. These are illustrated in the figure below which depicts a simple Viable System Model, and which also shows the interactions between the organisation and its environment (E).

The two main components of the viable system model are the operation (O) and the management (M) and these apply to each nested organisation in the complex organisation. The operation is System 1 and is composed of three operational elements (labelled 1a, 1b, and 1c in the graphical model; there can be more in an actual organisation). The management is composed of Systems 2, 3, 4 and 5. The various arrows represent the many and often highly complex interactions among the five systems and between the systems and the environment.

An understanding of the theory begins with the observation that operational units must be as autonomous as possible. The Viable System Model proposes that any organisation is a cluster of autonomous operational parts which bind together in mutually supportive interactions to create a new, larger whole system.

The job of management is to provide the “glue” which enables this to happen. The role of the systems in management is as follows:

• System 2 deals with the inevitable problems which emerge as a number of autonomous, self-organising operational parts interact. There will be conflicts of interest which must be resolved. System 2 is there to harmonise the interactions, to keep the peace, to deal with the problems.
• System 3 is concerned with synergy. It looks at the entire interacting cluster of operational units and considers ways to maximise effectiveness through collaboration. System 3 ensures that the whole system works better than the operational parts working in isolation.
• System 4 ensures that the whole system can adapt to a rapidly changing and sometimes hostile environment. It scans the outside world in which it operates, looks for threats and opportunities, undertakes research and simulations, and proposes plans to guide the system through the various possible pathways it could follow.
• System 5 provides closure to the whole system. It defines and develops the vision and values of the system through policies. System 5 creates the identity, the ethos, the ground rules under which everyone operates. It aligns the tasks of everyone in the organisation.

For an organisation to be a viable system these criteria for organisational robustness should be maintained recursively at each level of a nested organisation.

The overall Viable System Model is defined by the interactions among the 5 systems and the way they respond to and affect the external environment. The essence of these interactions is as follows:

• The operational units are given as much autonomy as possible so they can respond quickly and effectively. This is limited only by the requirements of system cohesion.
• Systems 1, 2 and 3 make up the internal environment of the viable system – the Inside and Now. The autonomous parts function in a harmonising internal environment which maximises its effectiveness through creating mutually supportive relationships.
• System 4 is concerned with the Outside and Then. It formulates plans in the context of both the outside world and its intense interaction with System 3 which ensures that all plans are grounded in the knowledge of the capabilities of the organisation.
• System 5 monitors the interaction between System 3 and System 4 to ensure all plans are within policy guidelines. If not, it steps in and applies its ultimate authority.
• All parts of the system work together holistically. Information is designed to flow throughout the structure in real-time, binding together Systems 1-5 within and across each level of a nested organisation.

The Viable System Model as originally designed by Stafford Beer works for organisations with a range of values. It has been tested in several types of businesses, public and not-for-profit organisations and communities in a variety of countries and contexts.

When the values are aligned with an ethos of sustainability and social responsibility, the organisations can become more resilient and capable of developing sustainable self-governance. Practitioners can use the Viable System Model to co-design such organisations. This involves critically reflecting on each of the five systems in the current organisation and how they can be improved to develop more adaptive and self-governance capabilities.

Do you have experience to share with applying the Viable System Model? If you are new to this theory, can you see ways in which it could be applied in your work?

To find out more:

Beer, S. (1979). The Heart of the Enterprise. John Wiley & Sons: London, United Kingdom.

Espinosa, A. (2023). Sustainable Self-governance in Business and Societies: The Viable System Model in Action. Routledge: London, United Kingdom. (Online – book details): https://www.routledge.com/Sustainable-Self-Governance-in-Businesses-and-Society-The-Viable-System/Espinosa/p/book/9781032354972

Website: Metaphorum: A Community of Practice Developing and Applying the Work of Stafford Beer. (Online): https://metaphorum.org/

Biography:

Angela Espinosa PhD develops Organisational Cybernetics, a theory for effective organisation pioneered by Professor Stafford Beer. She worked closely with Stafford Beer and when he passed away in 2002, she co-founded and still leads the Metaphorum, a non-governmental organisation to develop his legacy. She has advised governments, businesses, and communities in more effective ways of self-organising and being socially and environmentally responsible in several countries in Latin America, the United Kingdom and Europe. She is an Emeritus Fellow at the Centre for Systems Studies at the University of Hull, UK.

Editor’s note: See also Managing in the face of complexity (part 4.6): Appropriate approaches – 6. Viable System Model (VSM).

Article source: Viable System Model: A theory for designing more responsive organisations. Republished by permission.

Header image source: Metaphorum.

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.

2. Causal Loop Diagrams: networks of variables and causal influences, which normally focus on feedback loops of different lengths and are built around a ‘core system engine’. Maps vary in their complexity and size and are not typically exposed to any formal analysis, but are often the first stage in a system dynamics model.

3. Fuzzy Cognitive Mapping: networks of factors and their causal connections. They are especially suited to participatory contexts, and often multiple versions are created to capture diverse mental models of a system. Described as ‘semi-quantitative’, factors and connections are usually given values, and the impacts of changes in a factor value on the rest of the map are computed in different ways.

4. Participatory Systems Mapping: a network of factors and their causal connections, annotated with salient information from stakeholders (eg., what is important, what might change). Maps tend to be large and complex. They are analysed using network analysis and information from stakeholders to extract noteworthy submaps and narratives.

5. Rich Pictures: a free-form drawing approach in which participants are asked to draw the situation or system under consideration as they wish, with no or only a handful of gentle prompts. This method is part of the wider group of soft systems methodologies.

6. System Dynamics: a network of stocks (numeric values for key variables) and flows (changes in a stock usually represented by a differential equation), and the factors that influence these. Normally, these maps are fully specified quantitatively and used to simulate future dynamics.

7. Theory of Change Maps: networks of concepts usually following a flow from inputs, activities, outputs, and outcomes to final impacts. Maps vary in their complexity and how narrowly they focus on one intervention and its logic, but they are always built around some intervention or action. Maps are often annotated and focused on unearthing assumptions in the impact of interventions.

How do these methods relate to each other?

The following three figures show how these methods relate to each other. While individual projects could use any of these methods in a different way, these figures give a rough sense of where these methods sit in relation to one another, and what some of the most important axes on which to differentiate them are.

The first figure looks at the overall focus and nature of the different system mapping methods.

The second figure focuses on the mode and ease of use of the different system mapping methods.

The third figure presents the outputs and analysis the different system mapping methods produce.

How can systems mapping be useful?

We next suggest five broad types of use, which also apply to most types of modelling or analysis.

1. Helping us think: system maps of all types force us to be more specific about our assumptions, beliefs, and understanding of a system. Many types of systems mapping also force us to structure our ideas using some set of rules or symbols (ie., creating boxes and lines to represent concepts and their relationships). This will introduce simplifications and abstractions, but it will also make explicit our mental models.

2. Helping us orient ourselves: a systems mapping process will often also help us orient ourselves to a system or issue. Whether a map helps us see our, and others’, positions in the system, or whether it helps us quickly develop a fuller understanding of an issue, we will be better oriented to it. This helps people navigate the system better, be aware of what else to think about when considering one part of a map, or know who is affected and so should be included in discussions.

3. Helping us synthesise and connect information: the more flexible types of mapping are particularly good at bringing together different types of data, evidence, and information. They can all be used to inform the development of a map, making connections that would not otherwise be possible. Different types of visualisation, hyperlinking, and map structure can also be used to help people return to the information underlying a map.

4. Helping us communicate: whether we build maps in groups, or alone, and then share them, all system maps should help us communicate our mental models and representations of systems. The process of mapping with others, and the discussions it generates, unearths a multitude of assumptions which can then also be challenged and unpicked. The end product of a mapping process can also help us communicate our ideas about a system. Maps can become repositories for our knowledge which can be accessed by others, and updated, becoming a living document.

5. Helping us extrapolate from assumptions to implications: systems mapping approaches which can be turned into simulations, or which can be analysed in a formal way, also allow us to follow through from the assumptions we have embedded in them, to their implications.

Concluding questions

Are there other methods that you use to develop causal maps of systems and that can be used in participatory ways? What’s the main value that you have found in systems mapping? Do you have other lessons to share from your experience of systems mapping?

To find out more:

Barbrook-Johnson, P. and Penn, A. S. (2022). Systems Mapping: How to build and use causal models of systems. Palgrave-Macmillan: Cham, Switzerland. (Online – open access): https://link.springer.com/book/10.1007/978-3-031-01919-7

Biographies:

1. Pete Barbrook-Johnson PhD is a social scientist and complexity scientist working on a range of environmental and energy policy topics, using systems mapping, agent-based modelling, and other related approaches. He is a Departmental Research Lecturer at the University of Oxford in the UK and a member of the Centre for the Evaluation of Complexity Across the Nexus (CECAN) hosted by the University of Surrey in Guildford, UK.

2. Alexandra S. Penn DPhil is a complexity scientist working on combining participatory methodologies and mathematical models to create tools for stakeholders to understand and ‘steer’ their complex human ecosystems. She is a Senior Research Fellow at the University of Surrey and a member of the Centre for the Evaluation of Complexity Across the Nexus (CECAN) hosted by the University of Surrey in Guildford, UK.

Article source: Seven methods for mapping systems. Republished by permission.

Header image source: Pete Barbrook-Johnson, Smith School of Enterprise and the Environment, University of Oxford.