A ‘system’ is an interconnected set of elements coherently organized in a way that achieves something. It is more than the sum of its parts: a body is more than an aggregate of individual cells; a university is not merely an agglomeration of individual students, professors, and buildings; an ecosystem is not just a set of individual plants and animals.
A defining property of human systems is complexity; because of the sheer number of relationships and feedback loops among their many elements, they cannot be reduced to simple chains of cause and effect. Think of a crowd on a city street, or a flock of starlings wheeling in the sky at dusk. Even with supercomputers, it is impossible to predict the movement of any given person or starling, but there is order; amazingly few collisions occur even on the most crowded streets.
In complex systems, change results from the interplay of many diverse or apparently unrelated factors. Those of us engaged in seeking change need to identify which elements are important and understand how they interact.
Unfortunately, the way we commonly think about change projects onto the future the neat narratives we draw from the past. Many of the mental models we use are linear plans – ‘if A, then B’ – with profound consequences in terms of failures, frustration, and missed opportunities [when the plan is thrown out by unexpected consequences within the plan, or by things that were never in it]. As Mike Tyson memorably said, ‘everyone has a plan ’til they get punched in the mouth.’
Let me illustrate with a metaphor. Baking a cake is a linear, ‘simple’ system. All I need to do is find a recipe, buy the ingredients, make sure the oven is working…
Baking a cake is also a fairly accurate metaphor for the approach of many governments, aid agencies, and activist organisations. They decide on a goal (the cake), pick a well-established method (the recipe), find some partners and allies (the ingredients), and off they go.
The trouble is that real life rarely bakes like a cake. Engaging in a complex system is more like raising a child. What fate would await your new baby if you decided to go linear and design a project plan setting out activities, assumptions, outputs, and outcomes for the next twenty years and then blindly followed it?
Deng Xiaoping said, “We will cross the river by feeling the stones under our feet, one by one.”Duncan Green – How Change Happens (amazon)
Think about it [systems thinking] through the lens of a new tech product, which is kind of the centre of what we do [at Andreesen Horowitz]… If you’re not a systems thinker basically you say “I’m going to build a really great product, and then I’m going to have a really great product, and it’s going to be great, because it’s a really great product.”
The systems thinking more is that that’s just the first step because it’s not just about the product. It’s about okay, now the product is going to enter into the marketplace, and there are going to be customers that are going to have a point of view on your product, and there are going to be competitors that are going to be trying to take you out with a better product. And you’ll put your product in retail, and the retailers are going to try to gouge you on price, and make your product uneconomic to manufacture, and the press is going to write a review of your product, and maybe the reviewer is going to have a really bad day and he’s going to say horrible, horrible things… and your employees are hard at work and they build the first product, and you assume they’re going to be with you to build the second product, and maybe they will, or maybe they won’t, because maybe someone else will hire them.
And so with any kind of creative endeavor, with anything we do in our world – and this is for products or for companies – they’re launching into technically what is called – there’s actually a mathematical term – a complex adaptive system – the world. And inherently it’s not a predictable system, it’s not a linear system, it doesn’t behave in ways that you can expect, kind of by definition. So they say “complex” because there are many, many dimensions and variables, and then “adaptive”, like it changes. Things change. So the introduction of a new product changes the system and then the system recalibrates around the product.
And so as a consequence, to launch a new tech product and have it succeed you have to have a keen awareness of all the different elements of the system. You have to have a willingness to engage in the entire system, and it’s a gigantic problem generally if you’re in denial about that, if you’re not willing to think in systems terms.Marc Andreessen on The Moment with Brian Koppelman*
*Full transcript here.
I’m a sucker for filling up spare moments with reading, listening, messaging. I like being in touch with people, and I love learning, so empty moments can feel like moments wasted.
But – and it’s a big but – it’s often in empty moments that your thoughts settle and clear. You make a connection between two thoughts, or have a new idea, or remember what it is you were really supposed to be doing today, or who it is you should be in touch with. You see the things around you more clearly.
You empty moments add value to your full ones by helping you to clarify, simplify and focus. And even if they don’t, they’re worth it on their own terms:
In people’s eyes, in the swing, tramp, and trudge; in the bellow and the uproar; the carriages, motor cars, omnibuses, vans, sandwich men shuffling and swinging; brass bands; barrel organs; in the triumph and the jingle and the strange high singing of some aeroplane overhead was what she loved; life; London; this moment of June.Virginia Woolf – Mrs Dalloway*
*Which I never thought I’d actually quote, and certainly not in an actual place.
Peter Morville on the unpredictability of complex systems:
In 1997 Watson’s precursor, IBM’s Deep Blue, beat the reigning chess grand master Garry Kasperov in a famous man-versus machine match. After machines repeated their victories in a few more matches, humans largely lost interest in such contests. You might think that this was the end of the story (if not the end of human history), but Kasparov realised that he could have performed better against Deep Blue if he’d had the same instant access to a massive database of all previous chess moves that Deep Blue had. If this database of tools was fair for an AI, why not for a human? … To pursue this idea, Kasparov pioneered the concept of man-and-machine matches, in which AI augments human chess players rather than competes against them…
You can play as your unassisted human self, or you can act as the hand of a supersmart chess computer, merely moving its board pieces, or you can play as a “centaur”, which is the human/AI cyborg that Kasparov advocated… In the championship Freestyle Battle 2014, open to all modes of players, pure chess AI engines won 42 games, but centaurs won 53 games. Today, the best chess player alive is a centaur. It goes by the name of Intagrand, a team of several humans and several different chess programs.
But here’s the even more surprising part: The advent of AI didn’t diminish the performance of purely human chess players. Quite the opposite. Cheap, supersmart chess programs inspired more people than ever to play chess, at more tournaments than ever, and the players got better than ever…
If AI can help humans become better chess players, it stands to reason that it can help us become better pilots, better doctors, better judges, better teachers.Kevin Kelly – The Inevitable
Yesterday’s post laid out several reasons benefits of understanding your organisation as a machine.* But many of the important parts of your organisation don’t behave in predictable or mechanistic ways. Your team’s culture, for example, is a affected by, for example:
- organisational history;
- personalities on the team;
- individual moods;
- collective morale;
- Team members’ stage of life and health;
- interpersonal chemistry or rivalry;
- changes in team members’ salaries or position;
- ‘near’ factors like the closure of a favourite neighbourhood gathering spot;
- the health of the wider economy;
- government policy;
- levels of outside interest and flows of money into or out of your sector
In short, your organisation is a complex adaptive system within the complex adaptive system of the economy and the world as a whole.
Unlike complicated problems, complex problems cannot be solved, only managed. They cannot be controlled, only nudged. This is the domain of the butterfly effect, where a small change can lead to something big, and a big change can barely make a dent.Aaron Dignan
What this means is that trying to eliminate uncertainty or narrowly control outcomes is likely to be futile. Instead you need to ask which nudges will make your system more likely to produce good outcomes. You can think of projects and things you do as seeds with a chance of bearing fruit, and so widely – and position yourself to take advantage of opportunities that crop up in surprising places but seem to strengthen the system.
And as you think about the wider ecosystem and realise that you’re scarily dependent on ‘exogenous’ factors way beyond your control, you can think about how to nurture and protect the flows that you depend on, like flows of information or resources (including people and ideas) into and out of your organisation. How might building a network (for example) strengthen these flows, or tweak the system’s disposition in a way that’s in your favour?
*If you go deeper there are compelling arguments that even our machines are part of a living ecology – see Kevin Kelly‘s What Technology Wants and The Techium for jumping off point into this way of thinking
Here are two useful ways to view your organisation: as machine, and as ecosystem.
A machine is usually a complicated system: lots of moving parts, not necessarily easy to understand… but consistent and predictable once you do understand it, with a limited number of inputs and outputs:
- Fail to put fuel in your car, and you can predict fairly accurately when it will stop.
- Let your hard-drive fill up, and your computer will slow down.
- Leave popcorn on the stove too long, and it will start to burn.
- Run out of money, and everyone goes home.
This type of ‘complicated’ is largely reserved for inanimate objects. It seems obvious that things involving people – especially groups of people – won’t follow simple rules of cause and effect, and contemporary thinking is biased towards the back-to-nature sound of ‘ecosystem’ (‘people are not machines’), but there’s still lots of mileage in taking a systematic look at your organisation as a machine.
Mechanistic ‘if this, then that’ thinking runs the danger of over-simplifying things, but it’s great for working through regularly occurring processes. You have to take the time to think logically through processes like:
- How money flows through your projects – how you make, request, receive and account for payments and expenses, and how cash flows through the organisation;
- The logistics of product or service delivery and stock control;
- How users contact you – or you contact customers, and how you make sure you respond in a timely and helpful way;
- Completing reports on time, maintaining legal registrations;
- Product development and regular (as opposed to custom or one-off) manufacturing;
- Routine tasks like cleaning and maintenance.
Michael Gerber’s The E-Myth Revisited (amazon link) is a fantastic resource for thinking through your organisation as machine. If you often find yourself desperately trying to focus on doing the ‘real work’ while everything seems to be falling apart around you, this is the book for you. In Gerber’s words, you need to spend less time working in your business and spend more time working on your business, establishing the structures and systems that will keep the wheels on with far less effort from you.
The essential argument of the book is that you should have a clear and well documented system for every routine task in your organisation – and a system for managing and maintaining the systems, and for training people to use them. I don’t agree with his philosophy of aiming to turn your whole business into a MacDonald’s-alike franchise… but find his argument for making each part of your operation require the lowest-necessary level of skill compelling. The point is not to grow a business that can be run by robot, but rather to save time and creative and emotional energy for where it’s actually needed. If you want more time to do the ‘real work’, and/or are aiming to build something that will flourish even in your absence, you need to think like this.
Other resources for fine-tuning and automating your organisation as machine:
Sometimes people overlook… important statistics. My basketball hero, Wilt Chamberlain, who retired 56 years ago, still holds 72 NBA records, several of which are considered unbreakable, including scoring 100 points in a single game. In the 1961-62 season, Wilt set the NBA record of most field goals made (1,597). However, in that same season he also holds the record for most field goals missed (1,562). At the same time we celebrate record achievements, we need to acknowledge epic failures that make those achievements possible. Our successes make us happy, but our failures make us stronger. Michael Jordan expressed that awareness best: “I’ve missed more than 9,000 shots in my career. I’ve lost almost 3,00 games. Twenty-six times I’ve been trusted to make the winning shot and missed. I’ve failed over and over again in my life. And that is why I succeed.”Kareem Abdul-Jabbar – How do I feel seeing my NBA records get broken? Elated and inspired
GO! Play some games. Miss some shots. It’s the only way to make a few.
The engine inside a car is complicated. A complicated system is a causal system – meaning that it is subject to cause and effect. Although it may have many parts, they will interact with one another in highly predictable ways. Problems with complicated systems have solutions. This means that, within reason, a complicated system can be fixed with a high degree of confidence… here, experts can detect patterns and provide solutions based on established good practice…
Traffic, on the other hand, is complex. A complex system is not causal, it’s dispositional. We can make informed guesses about what it is likely to do (its disposition), but we can’t be sure. We can make predictions about the weather, but we can’t control it. Unlike complicated problems, complex problems cannot be solved, only managed. They cannot be controlled, only nudged. This is the domain of the butterfly effect, where a small change can lead to something big, and a big change can barely make a dent. Here expertise can be a disadvantage if it becomes dogma or blinds us to the inherent uncertainty present in our situation.
Complex systems are typically made up of a large number of interacting components – people, ants, brain cells, startups – that together exhibit emergent behavior without requiring a leader or central control. As a result, complex systems are more about the relationships and interactions among their components than about the components themselves. And these interactions give rise to unpredictable behavior. If a system surprises you, or has the potential to surprise you, it is likely complex. Software is complicated. Creating a software startup is complex. An airplane is complicated. What happens between people on board is complex. An assault rifle is complicated. Gun control is complex. Building a skyscraper is complicated. Cities are complex.
Aaron Dignan – Brave New Work
Some other complex (adaptive)* systems to bear in mind:
- Your body – and pretty much all of the parts within it
- Your thoughts, perceptions, moods
- Your family
- Your community
- A classroom / school / seminar / conference
- A team or organisation
- An airport / shopping centre / supply chain
- A forest / the climate
Conclusion: Most of the institutions that are important to us are complex adaptive systems that are themselves made up of of complex adaptive systems. The downside of this is that simple cause and effect thinking is far less useful than in a complicated system. The upside is that the right kind of butterfly could cause a wonderful storm…
The book is excellent so far. Thanks to Sharky for the tip.
*More on ‘adaptive’ in a future post
… and my forthcoming post, Machine. Ecosystem. – which has been sitting unwritten since September.
Seth Godin: The memo is only four sentences long. That’s all I needed. That a fellow traveller who knows how to do the craft of giving me feedback gave me those key lines, that’s the kind of notes that I need…
Brian Koppelman: Fellow traveller is a great expression and a great way to think about who you should enlist in your journey. And a fellow traveler doesn’t mean someone who’s already in Wyoming if you’ve starting out in New York. A fellow traveller is someone somewhere along the path that you’re going, somewhere close to where you are. Perhaps they’ve made the trip before.
SG: The whole mentor thing is way overrated… First of all, the math of it doesn’t scale, because the number of people who are successful who can mentor the number of people who need to be mentored doesn’t work.
Number two is, it’s usually an uneven exchange, in the sense that you’re asking someone who is busy and leveraged to stop that and start doing something else with you.
But the real reason is that people who are successful are almost never good at actually coaching people who aren’t successful yet. It’s a totally different skill set…The Moment, 1st January 2019
This touches on and unpacks my motivation for writing Ordinary People. Good Work. and adds a some extra ideas into the mix. Find fellow travellers.* Start talking.
*WordPress wants me to spell it one a single L.