Life @ Work coaching
- Adam Timlett
- 11 hours ago
- 5 min read
Is optimising everything getting you down?
Are you looking for new ideas or for inspiration?
Then it's time to try something a bit different....
De-optimise your work routine to give your work a new lease of life.
De-optimising life at work is counter-intuitive.
It is commonly accepted that optimisation is merely a definition of some target to improve, and some method to reach the target. Surely improvement is what we're after, so what is really meant by this?
The answer is that it's about finding new, innovative ways to reject/avoid a false optimisation that you didn't realise is false but which is not a true optimisation, in the process putting life back into work that was squeezed out by false optimisation.
Credit: Art by www.jamesrobertwhiteart.com
Filling time
A simple example, is where you fill your time and your calendar so that you are always busy, but this is an optimisation that is easy to show can be a false optimisation. The mind needs downtime to think and to find connections without any particular focus on a given task (see Maria Cano in references). By removing all the downtime you become less effective rather than more, squeezing the life out of work.
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.
Tighten up/Loosen up
Others have argued, most people need advice to 'tighten up' (see Chris Williamson in references). To optimise more, to work harder, think more, etc. Only the obsessive optimisers need advice to 'loosen up' to de-optimise their lives and focus only on optimising the essential things. But for work, everyone is being told to optimise their job, and the job is important. But, it doesn't mean that we are actually effective at optimising our jobs, just because we try. False optimisations are everywhere, they are traps that you fall into without realising it; or you realise it, but you have no alternative strategy, squeezing the life out of work in the process.
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/created the project/defined the scope & requirements. Then other 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, you can't get away without one. Agile methods just pretend that the problem of needing to plan doesn't exist.
This work life coaching 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.
Deeper reasons for de-optimisation
The deeper reasons for de-optimisation are that life itself, in other words, biological systems, are not governed by an algorithm, and yet organisms deal with uncertainty and risk very effectively through evolved strategies and methods that introduce what looks like inefficiency and messiness, but is actually a sophisticated strategy to deal with uncertainty.
By leveraging cutting-edge research in biology we can understand:
The crucial difference between machines that are optimised and organisms which ultimately go beyond false optimisations.
How this applies to businesses where we treat our work life as a machine to be optimised (e.g. business processes) when in fact, in the longer term, it is not a machine/cannot simply be optimised, and some messiness and lack of structure is essential.
We can learn and apply very valuable lessons, and distinct strategies and models that allow us to avoid the traps of false optimisations, or optimising for the wrong things.
We can generate new ideas and an edge in our own workplace, offering a new lease on our work life with sources of new ideas and potential innovation.
To de-optimise your work life there are 6 main topics that illustrate the background knowledge of de-optimisation in biology, and the specific strategies which can be coached 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 coaching assumes no background knowledge of biology or science and uses educational methods such as constructivism and the ideas of Jean Piaget, to coach these ideas into examples you can use, familiar in everyday work.
This list below merely illustrates the technical background and examples from the scientific literature, to demonstrate that this coaching distils knowledge and cutting-edge ideas from complexity science and systems biology. This is why it has concrete outputs and is consistent and logical.
De-optimise your work life. 6 types of technical topic explained using everyday examples with concrete results.
Using imagination/fantasy in a practical way (a la Barry O'Reilly's Residuality, see references) to de-risk your own or adopted models of your job.
Continuous variety problems – no optimal solution exists – work from what is exhausted inwards, seen in biology due to 'Red Queen Races'.
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 options – incommensurable, no way to compare them, meta-precision trades off with meta-accuracy (one reason why evolution is not an algorithm), see the book 'On the Origin of Risk'.
Slow down de-optimise what you can see and currently know, access later epochs, 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 very large topic explained simply, using everyday examples from work and life.
Reach out to adam@turingmeta.org today to discover how to de-optimise for the better and get a new lease on your life at work.
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 fantasy, and why agile is not enough)
