Open-World @ Work
- Adam Timlett
- Jul 24
- 6 min read
Updated: Jul 27
Is setting targets for everything actually working?
Are you looking for new ideas, or for inspiration?
Then it's time to try something a bit different....
De-optimise your work routine: Introducing Open-World @ Work, either 1-1 training or team workshops!
De-optimising life at work is counter-intuitive.
It is commonly accepted that optimisation is merely a definition of some target to improve against, or deadline to meet, and some method to get there. Surely improvement and goal-getting is what we're after, so what is really meant by de-optimising?
The answer is that de-optimising is about finding new, innovative ways to reject/avoid a false optimisation that you didn't realise is false, but which isn't a true optimisation. In the process this means accepting that the world of work, like life, is operating in an open environment, where the unexpected creates the potential for false optimisations, rather than a controlled closed environment, like a factory floor, in which a machine operates and is optimised for.
Credit: Art by www.jamesrobertwhiteart.com
Filling time
A simple example: You fill your time and your calendar so that you are always busy, but this is an optimisation that it's easily shown can be false. The mind needs downtime to think and to find connections without any particular focus on a given task (see Maria Cano in references) because what you should be doing at any one time is an open question. You are not in a closed environment in which the set of options like that for a machine are known and obvious. As a result of the fact that you are actually in an open environment, by removing all the downtime you become less effective rather than more, squeezing the life out of your work, by falsely thinking what you should be doing is an obvious question that requires no pause for thought.
However, there is so much more to the de-optimisation topic than just finding space for healthy downtime. This is just the tip of the iceberg of concrete de-optimisation strategies that we can learn from biology, and the way that natural biological organisms deal with open environments.
Tighten up/Loosen up
Others have already argued that most people do need to optimise more. They need advice to 'tighten up' (see Chris Williamson in references). For example, most people, in their free time, should probably try to optimise more, to 'work' harder, think more, etc., to use their free time better. In this way of thinking, it is only the obsessive optimisers who need advice to 'loosen up' to de-optimise their lives and focus only on optimising the essential things.
But, at work everyone is being told to optimise their job, and that everything in their job is important. So at work we probably are over-optimising and need to be better at 'loosening up' and choosing more carefully what to try to optimise and then also identify what are less important or 'false optimisations' that we risk wasting our time trying to 'perfect'.
False optimisations are everywhere, they are traps that you fall into without realising it. This is because re-prioritising work can be more effective than trying to optimise all of your work. This is especially true because the world of work is not a controlled environment, but an open environment where the right priorities to choose can change at short notice. What it might have made sense to optimise last week may not be worth optimising, this week.
Also, there might be longer term plans to optimise certain work that are constantly de-prioritised, despite the fact that they can have huge benefits. This happens because they can only get selected and prioritised after deeper reflection from lots of different viewpoints. It's the choice of optimisation that matters much more in an open environment. This is not important in a closed environment where the right choices of what to focus on are obvious and pre-determined.
Hence, the focus on the optimisation process more than the choice of what to optimise happens in machine-style optimisation, but leads to false optimisation in open environments, where the quality of choice matters far more.
Information is always missing from the optimisation
One big reason for false optimisations is that there is always information missing from the knowledge you had at the time you set the target, scope and method to get there. This missing info is a result of the open world that work exists in, in which new information can enter all the time.
Unlike the controlled conditions of a factory floor that a machine operates in, unexpected things come up later and suddenly the plan / target doesn't look optimal at all, and the messiness and chaos that results can be overwhelming.
However, although everyone knows this, and it happens all the time, very few people have any idea what to do about that. Agile as a method doesn't really solve this problem, as you still need to be able to plan and reflect more deeply, you can't get away without doing that. Agile methods just pretend that the problem of needing to plan and reflect doesn't exist, and so 'agile' doesn't really acknowledge all the risks involved in open environments, which are unaddressed by seeking to make 'agile' iterative improvements that are like an algorithm.
Open-World @ Work training, whether 1-1 or team workshops, shows you exactly how to address this problem, and others, by specific, practical, de-optimisation strategies grounded in biological science, introducing enough lack of structure and divergence to handle uncertainty, without chaos, enabling you to operate more successfully in open business environments.
Deeper reasons for de-optimisation
The deeper reasons for de-optimisation are that life itself, in other words, biological organisms, are not governed by algorithms, largely because they must live in open environments, and yet organisms deal with uncertainty and risk very effectively through evolved strategies and methods that introduce what looks like a controlled level of 'inefficiency' and 'messiness', but is actually a sophisticated strategy to deal with uncertainty and the open environments they can actually thrive in.
By leveraging cutting-edge research in biology we can understand and apply:
The crucial difference between machines that are optimised for closed environments and organisms which ultimately go beyond false optimisations to thrive in open environments.
We can learn and apply very valuable lessons, distinct strategies and models that allow us to avoid the 'traps' of false optimisations, or optimising for the wrong things and avoid the 'sunk costs fallacy', e.g., by curating a portfolio of complementary options to invest our energy in.
We can generate new ways of working and gain an edge in our own workplace, offering a new lease on our work life with sources of innovative ideas and potential improvements that really make a difference.
To de-optimise your work life there are 6 main topics that illustrate the background knowledge of de-optimisation in biology theory ('Life Theory'), and the specific strategies which can be trained and applied to your work by leveraging this research.
Many of these ideas are already explored and detailed in the book 'On the Origin of Risk' written by Adam Timlett, the Principal Consultant of Turing Meta.
However, the training assumes no background knowledge of biology or science and uses educational methods such as constructivism and the ideas of Jean Piaget, to teach these ideas via examples you can use, familiar in everyday work.
This list below is merely a taster, to illustrate the rich technical background and scientific literature, and to demonstrate that this training distils knowledge and cutting-edge ideas from complexity science and systems biology. This is why it has concrete outputs and is consistent and logical in its analysis. However, it's the practical examples and concrete changes that the Open-World @ Work training focuses on, not the academic training or the background literature.
De-optimise your work life.
6 types of technical topic explained using everyday examples with concrete results.
Using imagination/fantasy in a practical way.
(à la Barry O'Reilly's Residuality Theory, see references) to de-risk your own or adopted models of your job for an open environment.
Addressing 'continuous variety' problems
– no optimal solution exists – work from what is exhausted inwards, seen in biology due to 'Red Queen Races'.
Using Radial innovation
- Don't try to keep up with technology, innovate radially by keeping access to and connecting with the past, seen in acclimatisation in plants.
Conserving complementary options
– incommensurable, no way to compare them, meta-precision trades off with meta-accuracy (one reason why evolution is not an algorithm), see discussion, examples in the book 'On the Origin of Risk'.
Slowing down to de-optimise what you can see and currently know, to access later epochs, earlier.
QWERTY example, flamingos example, seen in 'complexity science'.
Damage and differential repair
– larger Gibsonian affordance classes, no algorithm accesses the 'adjacent possible'. Idea due to Stuart Kauffman (see references).
Many more ideas, and detailed examples are available on this topic explained simply, and comprehensible using everyday examples from work and life through this Open-World @ Work training offer.
Reach out to adam@turingmeta.org today to discover how to de-optimise for the better and give your work 'a new lease of life' with Open-World @ Work training.
Some selected references for a gentle introduction
Maria Carno, on de-optimising by giving space for downtime.
Chris Williamson on why some people need to 'Tighten up' (optimise) others need to 'Loosen up' (de-optimise)
Stuart Kauffman, the 'adjacent possible' (one reason/argument why evolution is not an algorithm)
Barry O'Reilly's Residuality Theory of software architecture (the practical role of imagination at work, and why agile is not enough)
