We talk so much about the need to respond to changing environments in business and yet there is often little attention paid to differentiating the different types of change that can so adeptly frame how we should respond. I’m a big fan of situational awareness and the importance of understanding the contexts of the challenges that we face, and have referenced Simon Wardley’s work on mapping, and Dave Snowden’s Cynefin thinking in my books.
I’ve also written before about how making smart decisions about the application of technology is about understanding the opportunity in optimising existing approaches (getting bigger, better, faster at what you currently do), and where it might be necessary to unlearn existing approaches, to redesign and reinvent because technology has created entirely new possibilities. Another way of thinking about responses to these different contexts is the difference between first-order and second-order change. The former involves doing more (or indeed less) of something that you are already doing. It’s reversible, you can adjust and course correct as you go. The latter requires doing something fundamentally different from what you’ve done before:
There’s a big difference between these responses. In my work with clients around organisational agility I’m always keen to stress the importance of understanding how you can apply agile principles and ways of working to different contexts. In situations where optimisation is required, workflows, processes and journeys can be mapped out and iteratively improved. In situations where much more significant change or innovation is appropriate, a more emergent ‘think big, start small, scale fast’ approach can enable a team or organisation to test and learn and prototype new ideas in ways that minimise risk but maximise learning.
In both approaches the objective is always to learn about how best to solve a challenge but the subtlety of how the team works to find solutions and build understanding is all important. We might frame this as the distinction between ‘single loop’ and ‘double-loop’ learning. The former seeks to make adjustments within a defined set of parameters but doesn’t change those parameters. The latter involves more fundamental responses, expressed here like this:
‘Double-loop learning refers to the distinction between learning that keeps a behavioral system operating within a field of constancy and learning that changes what the system seeks to achieve or to keep constant.’
English psychiatrist and cybernetics pioneer W. Ross Ashby described single loop learning as like a thermostat that can be set at a certain temperature. The heating will then be switched on or off by the thermostat to keep the temperature the same and reduce variance. Changing the temperature on the thermostat however, engages double-loop learning.
Similarly when teams are tackling challenges, or in wider organisational change, this distinction is key. Single loop learning is all about reducing variables – you may change your actions but you are optimising against a known outcome. Double loop learning needs a more elemental or systemic level of change – a revised mindset that can then lead to a change in actions derived from that way of thinking. The subtlety in this is that in a journey of continuous learning or improvement teams need to appreciate when it’s right to focus on single-loop learning and optimisation, and when a bigger shift is needed which requires the team to change how they’re thinking about a problem or context.
Donald Schön and Chris Argyris helpfully defined double-loop learning in an organisational context as behavioural learning that changes the governing variables (which might be norms, expectations, values, or objectives). How we understand a situation or make sense of it is central to this. Schön’s concept of ‘reflective practice’ sets out the need for individuals, teams and organisations to continually learn through a feedback loop of experience, learning and practice but also to incorporate as broad a perspective as possible. There are three lenses to this:
- Governing variables: the dimensions that people are trying to keep within acceptable or accepted limits
- Action strategies: the plans and activities used to keep governing values within the acceptable range.
- Consequences: the intended or unintended results of an action.
Teams may more easily default to single loop learning, and look for solutions that will work within existing governing variables. It’s easier because it enables the team or organisation to continue current policies in order to achieve its objectives. But in double-loop learning, we may need to re-frame how we define a situation, the desired outcomes and the understanding we have of our role in acheiving those outcomes. This is harder since it involves questioning the governing variables themselves:
‘Single-loop learning is like a thermostat that learns when it is too hot or too cold and turns the heat on or off….Double-loop learning occurs when error is detected and corrected in ways that involve the modification of an organization’s underlying norms, policies and objectives.’
Good reflective practice should therefore not only focus on single-loop error and correction, but more fundamental consideration of new and improved ways of working, and challenging and changing established norms.