Open Thinkering


Tag: complexity

TB871: 5 reasons why the unknown is not just a temporary or local state

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

A photograph of the vast, seemingly endless ocean, and sky.

The module materials (The Open University, 2020) outline five really useful reasons why the world is fundamentally unknowable:

  1. The overwhelming complexity of networks
  2. Processes that control stability and instability
  3. The unpredictability of developing patterns of relationships
  4. Social messes can be wicked
  5. Situations are not situations

Let’s consider at each in turn.

1. The overwhelming complexity of networks

Each person has a great number of different types of connections with humans. This exists within a vast network of close and distant relationships. So even with a relatively small number of people, number and nature of connections becomes highly complex. This increases exponentially when considering national or global populations, where a small number of individual connections contribute to an unfathomably large and intricate network. Counter-intuitively due to their size and complexity, large networks can often be resilient and maintain structural stability due to the sheer number of nodes.

The inherent stability of these networks does not mean that individual outcomes cannot be unpredictable. Emergent outcomes and behaviours can be unknowable and unexpected, for example as we’ve seen with the assault upon online social networks through misinformation, hacking, and more. Rather than anticipating specific outcomes, we live in a world where we need to focus on preparedness and reconsider what ‘stability’ looks like. Historically, ecosystems and cultures have changed rapidly due to ‘tipping points’ that we’ll consider in the next section.

2. Processes that control stability and instability

There are two types of causal loops that are used primarily by systems thinkers: balancing loops and reinforcing loops. While balancing loops help maintain stability, reinforcing loops lead to growth or collapse. The idea of ‘tipping points’ is often used to identify how these loops form, although they can be triggered by tiny changes, which makes them difficult to predict.

Reinforcing loops explain why ‘first impressions’ are often seen as so important. For example, an initial positive or negative interaction can set of an chain reaction and compound and reinforce that first impression. Similarly, ‘trophic cascades’ in ecosystems shows how complex interactions can result due to the presence (or absence) or predators; multiple potential tipping points exist, so a single ‘tipping point event’ can trigger a cascade of others.

3. The unpredictability of developing patterns of relationships

There is something called Conway’s Game of Life which I don’t completely understand, but apparently is a mathematical simulation which uses simple rules to generate complex behaviours. I prefer the example of birds flying in formation, to be honest, but the point is the same: simple, rule-based systems can produce intricate, delicately-balanced outcomes that can be difficult to predict.

This obviously causes significant challenges around strategic planning. If we fit humans into stable patterns, then we can plan base on linear, predictable responses. If behaviours deviate, then planning becomes more complex. This, I suppose, is the difference between Homo economicus which is the rational view of humans presented by economic theory and, well, how humans actually act in the real world. As a result, strategic planning needs to recognise and address blind spots, stay flexible, and be aware how assumptions influence behaviours.

4. Social messes can be wicked

A ‘wicked problem’ is a form of unpredictable social mess that I’ve discussed before as part of this module, as well as in TB872. It’s “a complex, multifaceted problem with no clear solution, often characterized by interdependencies, changing requirements, and multiple stakeholders” to quote the latter post.

As I reflected on separately in a post about the croquet game in Alice’s Adventures in Wonderland, structured problem-solving is difficult because people don’t always ‘play the game’ or even understand the rules. Real life problems are somewhat unruly. They are often embedded deeply within complex networks of relationships, and can ‘cascade’ much like the trophic cascade within ecosystems mentioned above. There are conflicting perspectives, for example relating to the climate emergency, or rewilding, or almost anything you can think of. The only way to really deal with this complexity is to involve diverse stakeholders and be open to creating entirely new approaches.

5. Situations are not situations

Following on from ‘wicked problems’ the notion that ‘situations are not situations’ is a way of highlighting that our attempts to address or even contemplate a situation alters it in some way. It’s a bit like Schrödinger’s cat in that respect, because we’re never dealing with a fixed target, but rather a construct of language. Italian physicist Carlo Rovelli writes that, “a storm is not a thing, it’s a collection of occurrences” which starts to help us out of our linguistic trap of seeing stability where there is none.

Systems thinking therefore needs to embrace the fact that situations never stop changing. This means that tradition decision-making methods are likely to be ineffective, and even incremental change approaches might be limited in success because they assume static conditions rather than dynamic contexts. We must instead account for a fluid world in which we design or organise against a background of constant transformation.


Image: Dim Hou

TB871: A Systems Thinking in Practice (STiP) heuristic

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

There are three core activities with Systems Thinking in Practice (STiP):

  1. Understanding interrelationships (uIR)
  2. Engaging with multiple perspectives (eMP)
  3. Reflecting on boundary judgements (rBJ)

These three activities can be translated into a learning system, or ‘heuristic’ which is presented in the module materials in the following way:

A heuristic diagram for Systems Thinking in Practice with a cyclical flow of five stages indicating a process for dealing with complex situations, involving understanding interrelationships, engaging with multiple perspectives, and using conceptual tools. Two silhouetted figures of people face the diagram.

Unlike TB872, this module doesn’t have such great diagrams. I’m not so good at making them, but I can’t live my life looking at this one repeatedly! So I’ve recreated it:

The same diagram as above, in a different illustrative style

There is a video associated with this task (Activity 1.13) which refers to the three areas of this diagram as:

  1. Events
  2. People
  3. Ideas

This is perhaps a more intuitive and easy-to-remember way of referring to the parts labelled Situations, People, and Tools. Here’s an extended quotation from the transcript to the video which helps explain the STiP heuristic:

Real world situations are often rendered intuitively as systems, such as the health system, or the financial system, or an ecosystem. Such renderings as systems can be a useful means for then engineering change. So for example, messy financial affairs might be more formally rendered as a budgeting system, which has clear inputs and outputs that might be more easily managed.

The danger is in fooling ourselves that such rendered systems are the actual reality. It’s confusing the map as a system for the actual territory, the reality of the situation. Like any map, much is left out.


A starting point for a systems thinking approach is working with complicated and complex issues. So systems thinking might be regarded as an endeavour to render complicated, complex, conflictual situations into bounded, conceptual construct, that is, systems for analysis and design, or more specifically, using systems for making strategy. The Systems Thinking in Practice heuristic, or a STiP heuristic as we will call it from now on, is one such learning system; a mental model or idea used as a device for learning about situations of interest and making a strategy to transform them into something better.

(Open University, 2020)

The rest of the video goes on to explain the difference between ‘complicatedness’ (which I don’t think is an actual word?) and ‘complexity’ and also defines a ‘wicked problem’. I’ve summarised these below:

  • Complicatedness refers to situations that have many parts that need to be arranged in a certain way. Although it might be tough to solve, it can figure be figured it out with enough expertise or detailed analysis. For example, fixing a broken car is complicated because it requires specific knowledge about the car’s parts and how they work together.
  • Complexity relates to a situation is one where everything is interconnected and changes can happen unexpectedly as a result of these connections. Small changes or actions can have big, unpredictable effects. For instance, the stock market is complex because many unpredictable factors can affect stock prices. See also the butterfly effect.
  • Wicked problems are tough issues that are difficult to solve because it involves incomplete or contradictory information and changes depending on how people perceive it. These problems are tricky because they are not just hard to solve; they are hard to define. For example, climate change is a wicked problem because it involves many factors and opinions, and solutions are not straightforward.

In addition, a mess is when several complicated and complex issues are all tangled up together, making it hard to see where one problem starts and another ends. Messes are chaotic and hard to sort out because solving one problem might affect another part of the mess. A city’s transportation system can be a mess because it involves roads, traffic laws, public transportation, and the behaviors of thousands of people.

I’m composing this in my local library, run by Northumberland County Council. It’s housed within the new leisure centre. Earlier this week, I was at a Design Sprint session as part of the Thinking Digital conference which was run by members of the digital team at the council. The situation we chose to address as a team was library provision, with visitor numbers going down.

Right now, as I’m trying to work, there is a group of older people meeting in the study space as part of a social group. They’ve having coffee and tea, which is not usually allowed in this space. Given the noise, I’ll probably end up decamping to a coffee shop and may not return on a Friday. This could be seen as a small example of a ‘mess’ which would also involve opening hours, underfunding, and even popular conceptions of what libraries are for.

In fact, come to think of it, this might be a good topic to focus on for my assessments for this module. I shall ponder that further… 🤔


TB872: Systemic inquiry as a social technology

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.

A large arrow extending from left to right, symbolising a workflow or process. Within the confines of this arrow, a sparse collection of 3D boxes representing projects, including rectangular prisms and cylinders, are arranged to reflect different project types and stages. Small curved arrows, indicating the interconnections or links between these projects, connect the boxes in a seemingly random yet structured pattern, emphasizing the interconnected nature of project management and workflow progression.

If projects are so problematic, and we need more emotion in our decision-making, then what should we do instead? This post focuses on Chapter 10 of Ray Ison’s book Systems Practice: How to Act, which begins with a list of the kinds of things people who want to use an alternative approach need to be able to do.

Ison can be wordy, so I’ve asked ChatGPT for a more straightforward version:

  1. Comprehending the current and historical context of situations.
  2. Recognising and valuing the diverse viewpoints of multiple stakeholders.
  3. Clearly identifying and exploring the underlying purpose of actions or decisions.
  4. Differentiating between the ‘what’, ‘how’, and ‘why’, and determining the appropriate timing for each aspect.
  5. Implementing actions that are purpose-driven, systemically beneficial, culturally viable, and ethically justifiable.
  6. Creating a method to harmonise understanding and practices across different locations and over time, especially in situations where initial improvements are unclear, thus managing a dynamic, co-evolutionary process adaptively.
  7. Sustainably integrating the approach into ongoing practices without oversimplifying or misusing its fundamental principles.

Instead of setting this approach against projects, it’s more of a “meta-form of purposeful action” which provides a “more conducive, systemic setting for programmes and projects”. (See the arrow image above to get the gist.)

We understand systemic inquiry as a meta-platform or process for ‘project or program managing’ as well as a particular means of facilitating movement towards social learning (understood as concerted action by multiple stakeholders in situations of complexity and uncertainty). When conducted with others it can be called systemic co-inquiry.

Ison, R. (2017) Systems practice: how to act. London: Springer. pp.252-253. Available at:

Just because the systemic inquiry is ‘meta’ does not mean that it is necessarily bigger or longer lasting than the programmes and/or projects it contains. Nor is the ‘goal’ of systemic inquiry to create ‘a system’; it is an action-oriented approach where the intention is to produce a change.

The image below, Fig. 10.1 in Ison’s book (p.256), is an activity model of a system to conduct a systemic inquiry. It has been adapted from Peter Checkland’s work.

An activity model of a system to conduct a systemic inquiry, depicted as a series of nested loops in a flow diagram. Starting from the top, the process begins with 'set up structured exploration of situation considered problematical.' The next step is 'make sense of situation by exploring context (culture, politics) using systems models as devices.' Following this, 'tease out possible accommodations between different interests' leads to 'define possible actions to change; that are systemically desirable and culturally feasible.' The final step within the main loop is 'take action to change - creating a new situation.' This leads to a smaller loop consisting of 'monitor,' then 'take control action,' and finally 'define criteria: efficacy, efficiency, effectiveness.' Arrows between each step indicate the flow and sequence of activities within the systemic inquiry process.

If this approach creates a ‘social learning’ then this is a ‘learning system’. But what does that mean? Ison suggests that instead of thinking about it in ontological terms (e.g. “a course or a policy to reduce carbon emissions”) we should think of a learning system as an epistemic device (i.e. “a way of knowing and doing”).

This move constitutes a ‘design turn’, says Ison, away from first-order inquiry (e.g. drawing a boundary to determine what is in/out of scope) to a second-order understanding (e.g. the learning system as existing after its enactment, through human relationships). Both are necessary, it’s just a question of different levels of abstraction and “critical reflexivity”.

Although Ison doesn’t talk about it this way, I guess this is the practitioner (P) reflecting on their own place within a system, making it P(PFMS). See the diagram at the top of this post. When intervening, as an educator, policy maker, or consultant, therefore, there’s a difference between triggering a first-order response (e.g. creating a course or an ‘intervention’) versus a second-order response (e.g. creating the circumstances for people to reflect on their context and take responsibility).

Top image: DALL-E 3 (based on the bottom part of Fig. 10.1 on p.252 of Ison’s book)