Open Thinkering


Tag: Donald Schön

TB872: Schön’s swamp and ‘ideas in good currency’

Note: this is a post reflecting on one of the modules of my MSc in Systems Thinking in Practice. You can see all of the related posts in this category

It’s a delight to be introduced to thinkers who are not only new to you, but who have a sizable body of work you can go back through. It’s even better when they were alive at a time to have been recorded on video, as is the case with Donald Schön.

Schön was the Ford Professor of Urban Studies and Education at MIT from 1972 until his death in 1997. Before this, he worked at an agency on ideas which complemented Thomas Kuhn’s Structure of Scientific Revolutions and was invited to give the BBC’s Reith Lectures in 1970 on the topic of going beyond the ‘stable state’.

In this post, I’m going to reflect on the first lecture (transcript) of his Reith Lectures as well as an extract from a video of a lecture given at Iowa State University in 1989, in which Schön discusses the nature of design practice and education. My aim is to give my understanding of some of the concepts which he discusses. But first, let’s look at a famous metaphor he uses:

…I’d like to set the stage… by talking more broadly about the professions, and suggest to you that every profession, both major and, as they’re sometimes called, minor professions, is now confronting what might be called a dilemma of rigour or relevance. And one way to conceptualise that is to think about a kind of topography of practice in which you have, first of all, a very high, dry cliff, and underneath it a swamp. And on the high ground you can complete the work of your PhD dissertation, you can make econometric models, you can model inventory control systems.

The problem is that, on the high ground, the problems that you’re working on are relatively trivial. In the swamp below, you can work on what you take to be the really important social and technological problems of the age, but you don’t know how to be rigorous in any way that you can name. And so your choice is to be rigorous and trivial on the high ground or to be working on really important problems but not in any way that you can define as rigorous in the swamp – high ground or swamp.

And I think that issue confronts most of my students who’ve entered into professional work. It confronts people on the leading edge of most professions. I think it confronts people in the design professions as well.

(Lecture at Iowa State University, in The Open University, 2021)

The Stable State

Schön’s concept of the ‘stable state’ refers to the deeply held belief in the constancy, predictability, and unchangeability of certain central aspects of our lives and society. It embodies the notion that there exists a state of equilibrium or permanence in personal identities, organisational structures, societal norms, and technological advancements. Significant significant changes to these are seen as rare or can be effectively managed to maintain stability.

Belief in the stable state is belief in the unchangeability, the constancy, of certain central aspects of our lives, or belief in the attainability of that kind of constancy: it’s deep and strong within us. We institutionalise it in every social domain. We do this in spite of our talk about change, our apparent acceptance of change, our approval of dynamism. Language about change is for the most part talk about very small change—trivial in relation to a massive, unquestioned stability—which nevertheless appears formidable to its opponents by the same peculiar optic that leads a potato chip company to see a larger bag of potato chips as a new product. Moreover, talk about change is as often as not a substitute for engaging in it.


Belief in the stable state is central, because it is a bulwark against the threat of uncertainty. Given the reality of change, we can maintain belief in the stable state only through tactics of which we are largely unaware. Consequently our responses to attacks on the stable state have been responses of desperation, largely destructive, and our need is to develop institutional structures, ways of knowing, and ethics, for the process of change itself.


The feeling of uncertainty is anxiety, and the depth of the anxiety increases as the threatening changes strike at more central regions of the self. In the last analysis, the degree of threat presented by change depends upon its connection with self-identity, and against all this we’ve erected our belief in the stable state.

(BBC, 1970)

Highlighting the limitations of this belief in the face of the dynamic, complex, and often unpredictable nature of modern societies, Schön argues that clinging to the idea of a stable state prevents individuals and institutions from effectively responding to, and engaging with, the continuous and often rapid changes that characterise contemporary life. These include technological advancements, social transformations, and environmental challenges. This is even more true in the 2020s than the 1970s.

Public Learning and ‘Ideas in Good Currency’

The process by which societies, organisations, or groups learn collectively is what Schön calls ‘public learning’. This is often in response to changes or challenges in the environment, more recently due to having to confront the loss of the ‘stable state’. This requires a collective learning process to adapt to new realities that result from this loss. Public learning is a concept that emphasises the importance of shared understanding and adaptation to navigate uncertainties and encourage resilience in the face of change.

[W]hen our diffusion curves get below 30 years, below 20 years, then the social impact generated by that technology lies well below the limit for generational adaptation, and you and I within our own lifetimes have to handle the kind of adaptations that used to be handled as one generation passed into another. These are real human limits and technology transgresses them.

(BBC, 1970)

Ideas that are widely accepted and used within a society or professional community are described by Schön as ‘ideas in good currency’. This is often because they offer effective solutions to current problems or resonate with prevailing values and beliefs. Thomas Kuhn suggests that science progresses almost generationally as scientists retire or die, due to the way that ideas are framed. Schön suggests that, due to the end of the ‘stable state’, we require new learning systems that allow societies to learn intra-generationally. This allows the circulation of new, valuable ideas to address contemporary challenges.

Effective Learning Systems

To be considered ‘effective’ according to Schön, learning systems need to be capable of self-transformation, adaptability, and of handling uncertainty. They must enable individuals and institutions to reflect, learn from experiences, and continuously innovate. These learning systems should bring about an environment where questioning, experimentation, and the application of new knowledge are encouraged and supported.

If Schön’s learning systems are characterised by their adaptability, reflexivity, and capacity for continuous learning, then the focus is on an ability to incorporate new insights into policy and practice. These systems thrive on diversity of thought and flexibility in response to new information and challenges.

Dynamically Conservative Social Systems

Schön suggests that even in the face of necessary change, social systems often exhibit a dynamically conservative nature, striving to maintain core identities and values while adapting to new conditions. We can see this with the climate emergency. His model implies a balance between preserving essential characteristics of the system and incorporating innovative practices for evolution and growth.

In a situation of uncertainty, the problem that you face is the problem of constructing a problem because you don’t know what the problem is. And the problem of constructing a problem is not a technical problem. In fact, the opposite is true. You have to construct the problem before you can carry out any technical activity.


So problems of uncertainty, situations of uncertainty, are not technical problems. Because you have to frame the problem before you get to a technical problem. Situations in which you’re dealing with a unique case are not technical problems because you can’t apply the rules to them. By definition, they fall outside the rules. And situations of conflict are not technical problems because we have no clear and self-consistent set of ends to try to meet. You have to reconcile ends before you can solve the problem. These indeterminate zones of practice have become more and more powerful over the last 20 years, I think.

(Lecture at Iowa State University, in The Open University, 2021)

Policy formation and implementation is complex, particularly within the context of uncertain and constantly-changing environments. Effective policy-making therefore requires a learning system approach, where policies are formed through iterative processes of experimentation, reflection, and adaptation, rather than through static, one-time decisions. This reminds me somewhat of DAD vs EDD.

Governments as Learning Systems

In an ideal world, governments would be proactive in identifying societal needs, thinks Schön, as well as being responsive to changing conditions, and capable of experimenting with innovative solutions. He suggests that governments need to develop mechanisms for continuous learning and adaptation to effectively address complex, evolving societal challenges.

Constructive responses to the loss of the stable state must confront the phenomenon directly. They have to do it at the level both of the person and of the institution. If our institutions are threatened with disruption, how can we invent or modify them in such a way that they are capable of transforming themselves without flying apart at the seams? If we are losing stable values and anchors for identity, how do we preserve self-respect while in the process of change?

(BBC, 1970)

Schön argues that governments should shift from a model of static governance, based on maintaining a ‘stable state,’ to a dynamic learning system approach. This change is necessary to effectively address the increasingly complex, uncertain, and rapidly evolving challenges of modern societies. By embracing a role as a facilitator of public learning, experimentation, and adaptation, governments can better serve the needs of their citizens. This should lead to more resilient, responsive, and innovative societies.


Image: Midjourney (“Cartoon style image with NO TEXT NO HUMANS NO ANIMALS showing a natural landscape with some high ground on a cliff overlooking a swamp. The scene on the high ground looks arid and boring. The swamp looks like it’s complicated and teeming with plant life. There should be a big difference in height and vibe between the high ground and low ground, including a cliff. –ar 16:9 –v 5.1”)

Are organizations like brains?

Images of OrganizationAs part of my Ed.D. course through the University of Durham I had to take some taught modules. One of them that I took back in 2006 was entitled Management, Leadership & Change. It was an excellent course from which I gained a lot. Unfortunately, unlike many of my classmates, I wasn’t then at a time where I could use this knowledge being then only just finished my second year of teaching. Now that I’m in a position that carries more responsibility, management responsbilities and leadership opportunities, it’s time to revisit that course and related reading.

One of the books I read for the Management, Leadership & Change module was Gareth Morgan’s Images of Organization. I found it a revelation, especially being so fond as I am of metaphor. Morgan uses eight metaphors as a lens through which to view organizations:

  1. Organizations as Machines
  2. Organizations as Organisms
  3. Organizations as Brains
  4. Organizations as Cultures
  5. Organizations as Political Systems
  6. Organizations as Psychic Prisons
  7. Organizations as Flux and Transformation
  8. Organizations as Instruments of Domination

Each of these perspectives teaches the reader something about organizations; it’s a very clever and interesting way of presenting insights.

Having just come across this neat overview of Daniel Goleman‘s idea of the various leadership styles, I wonder how much overlap/synergy there is between the two?

Goleman - Leadership Styles

I’m especially interested in the idea of organizations as ‘organisms’, ‘brains’ or ‘cultures’ as I believe these lenses to be the most powerful for effecting positive change. The remainder of this post will look at organizations as ‘brains’.

Organizations as brains

Morgan starts off the chapter comparing brains to holographs where ‘everything is enfolded in everything else’, there is not centre or point of control and, most importantly,

Pattern and order emerge from the process – it is not imposed. (Morgan, 1998:73)

The philosopher Daniel Dennett, someone who I read fairly widely at university during my undergraduate degree in Philosophy, suggests that our highly-ordered stream of consciousness is actually the result of ‘a more chaotic process where multiple possibilities are generated as a result of activity distributed throughout the brain.’ (ibid.) Competing parallel activities can make complementary and competing contributions into a coherent pattern.

‘Just In Time’ and perceived chaos

Morgan gives the example of ‘Just In Time’ (JIT) manufacturing as being a process that is highly organized yet without ‘boundaries and patterns of membership’:

To an outsider, it may be impossible to distinguish who is working for whom. The fundamental organization really rests int eh complex informaiton system that coordinates the activites of all the people and firms involves rather then the discrete organizations contributing different elements  to the process. (Morgan, 1998:75)

Clay Shirky - Here Comes EverybodyThe above leads Morgan to wonder whether it is better to refer to a ‘system of intelligence’ rather than an ‘organization’ when describing such states of affairs. These systems break what Herbert Simon, Nobel laureate, called the ‘bounded rationality’ of human beings. To my mind it’s Morgan picking up on the start of what Clay Shirky has shown to be completely revolutionary in his excellent Here Comes Everybody (which I’m currently reading).

Understanding how organizations can become capable of learning in a brain-like way is similar to understanding how robots and other objects in the study of Cybernetics are able to ‘learn’. The latter discipline involves negative feedback. That is to say error-detection and correction happens automatically to maintain a course towards a desired goal. In order to be able to self-regulate, systems must be able to:

  1. Sense, monitor, and scan significant aspects of their environment.
  2. Relate this information to the operating norms that guide system behavior.
  3. Detect significant deviations form these norms, and
  4. Initiate corrective action when discrepancies are detected. (Morgan, 1998:77)

This negative feedback system is only as good as the procedures and standards that underlie it. So long as the action defined by these procedures and standards is appropriate dealing with the changes encountered, everything is fine. The ‘intelligence’ of the system breaks down, however, when these are not adequate leading to negative feedback attempting to maintain an inappropriate pattern of behaviour.

In order to prevent the above happening (so called ‘single-loop learning’) the model of ‘double-loop learning’ has been promoted by Donald Schön and Chris Argyris. This builds in a self-review ‘loop’ to the learning process:

Double-Loop Learning

Image cc-by-sa Ed Batista

There are three major barriers to double-loop learning: budgets, bureaucracy and accountability. One of the most famous examples of double-loop learning and organization being thwarted by these three barriers came with the US Challenger space shuttle explosion.

Learning organizations

So, how are ‘learning organizations’ created? Insights from cybernetics would suggest the following:

  • Scanning and anticipating change in the wider environment
  • Developing an ability to question, challenge and change operating norms and assumptions
  • Allow appropriate directions and patterns of organization to emerge (Morgan, 1998:82)

Morgan follows this with stressing the importance of ‘framing and reframing’ which reminds me of Lord Bilimoria’s discussion of the value of regular SWOT analyses (see this post). ‘Many organizations,’ says Morgan, ‘become myopic, accepting their current reality as the reality.’ (Morgan, 1998:84)

Organizations that embrace double-loop learning sound like the type of places I want to be part of:

For successful double-loop learning to occur, organizations much develop cultures that support change and risk taking; embrace the idea that in rapidly changing circumstances with high degrees of uncertainty, problems and errors are inevitable; promote an openness that encourages dialogue and the expression of conflicting points of view; recognize that legitimate error, which arises from the uncertainty and lack of control in a situation, can be used as a resource for new learning; recognize that since genuine learning is usually action based, organizations must find ways of helping to create experiments and probes so that they lear through doing in a productive way. (Morgan, 1998:85)

Emergent organization

Coming back to the metaphor of brains, the intelligence of the brain is not predetermined. It is not centrally driven. It is emergent. A top-down approach to management leads to single-loop learning and therefore is the opposite of such a model of emergence. To prevent chaos and incoherence targets should take the form of vision and value-sharing.

Morgan continues on to articulate a vision of ‘holographic organization’ based on five principles:

  1. Build the ‘whole’ into the ‘parts’ (i.e. ‘networked intelligence’)
  2. The importance of redundancy
  3. Requisite variety (i.e. ‘internal complexity must match that of the environment’)
  4. Minimum Specs (i.e. don’t define more that is absolutely necesssary)
  5. Learn to learn (i.e. ‘double-loop learning’)


After fleshing out these princples, Morgan concludes this chapter with listing the strengths and limitations of the brain metaphor.


  • Gives clear guidelines for creating learning organizations
  • Shows how IT can support the evolution of organizations
  • Gives a new theory of management based on the principles of self-organization
  • Addresses the importance of dealing with paradox


  • There could be conflict between the requirements of organizational learning and the realities of power and control
  • Learning for the sake of learning can become just another ideology

I can live with these limitations. I think the ‘organization as brain’ metaphor has a lot going for it. What do YOU think? 😀