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Gutless Wonders and the Control Illusion

Gutless Wonders and the Control Illusion

Ian Davis, previously worldwide MD of McKinsey, once said, “Long-gone is the day of the gut-instinct management style. Today’s business leaders are adopting algorithmic decision-making techniques and using highly sophisticated software to run their organisations.”

An astounding example of the control illusion. Nothing sits better in a crisis than intense rationality. Trouble is, we’re deluding ourselves.

For one thing – as neuroscientist Antonion Damasio proved by studying people with damage to the part of the brain where emotions are generated – decisions are driven by emotions. With our rational brain alone, we’ll just weigh pros and cons until the cows come home.

Although our subconscious is in charge, we insist on living an illusion of conscious choice – simply because it feels so comfortable and alluring. The more chaotic and bewildering the world around us, the more that neat illusion beckons. We duck down, distrust (our people and ourselves), flick the switches to ‘tight control’ mode and abort anything risky, like experimentation.

Now I’m all in favour of data and analytics – in fact metrics are key to agility – but only when the numbers aid creative, whole-brain decision-making. Certainly not at the expensive of good judgement and gut feel.

The world is rife with easy lies that satisfy our control-illusion-fueled thirst for order: putting more people in prison reduces crime; financially incentivising staff improves performance; increasing the price of alcohol reduces drinking; markets are driven by rationality…

It’s yet another lie that gut-instinct management style is replaceable by algorithmic decision-making software.

The truth? The extreme usefulness of technology and data is being massively compromised by misuse. Spending money on software won’t solve our problems, unless we overcome the control illusion and exercise some solid emotional judgement. Half-assed attempts to retrench when the going gets tough hasn’t rendered our leaders any wiser nor more effective.

Has it?

After all, the average life expectancy of a company in the S&P 500 has dropped from 75 years in 1937, to 15 years today. What’s more, the 3/2 law of employee productivity demonstrates that tripling your number of employees causes productivity to drop by half. The reality is that corporate performance has worsened as digital technology has penetrated the economy.

It’s about time we recognised that cultural and structural changes in business are fundamental to making our technology useful. Blindly pumping out numbers is a wasteful business for gutless wonders. Muscle-bound chart-wielders are unable to move.

Instead we need to arm intuitive people with data that aids emotional decision-making, set them free within loose structures and replace ‘if you can’t measure it, don’t do it’ with ‘if you can’t make a decision on it, don’t measure it’.

Then focus on fine-tuning your organisation to enable rapid, decentralised decision-making.

Purposeful experimentation = innovative leaps.


From machines to ecosystems

From machines to ecosystems

When we talk about thriving in the digital age, we tend to revert to discussing how to leverage social media, mobile and other cool channels. There’s nothing wrong with that (and I do it myself!), but it can be useful to consider the bigger picture now and then.

The challenges we face in business are not related to technology, they’re related to human beings. The industrial revolution brought us machines; and with it linear, machine-age thinking, articulated in machine-age language that in turn makes us think more like machines. This machine metaphor shaped the 20th century. We viewed biology as a big machine, we searched for machine-like predictability in economics and physics – and you could argue that it served us fairly well when we lived in a world that was changing less rapidly, with fewer choices.

However lately we’ve started to realise that our rigid financial forecasts, waterfall development methods and other attempts at predicting what’s likely to work or not work in business and product design are very flawed.

A more useful metaphor for the 21st century is nature. Instead of technology and nature being enemies, I believe our most successful innovations will be like living things. Concepts like self-organisation, co-evolution, emergence and feedback loops are coming to the forefront.

If you look at thriving companies – like Facebook – you can see the characteristics that make them fittest. They’re thriving within millions of systems and sub-systems (i.e. markets). The structure of what they’re creating is all about fluidity, feedback loops, interlinking; people, applications, APIs – lots of iterations and replications.

We need to start focusing on developing traits that make us more likely to be fittest within any given system. Traits include: agility; the ability to replicate; the ability to get undistorted, accurate feedback and a fine balance – between impulse and restraint, competition and cooperation, chaos and order.

Meanwhile, on an individual, personal level, each of us should seek the conditions and environment in which we’re fittest. Nobody is fittest in every situation, so move fluidly through different systems in seek of a place where you thrive; and if energy dissipates (which it always does, according to the 2nd law of thermodynamics!), shift to another system.

If we do things as nature does, we’ll see real progress.


Kill false assumptions & evolve

Kill false assumptions & evolve

Many of us are making decisions based on false assumptions every single day. In fact we’re underpinning our businesses, organisations, products and personal lives with false assumptions. We keep on doing things that have been proven wrong, that haven been proven not to work, despite mounting evidence that there’s a better way.

Our false assumptions are memes, i.e. viral cultural ideas we pass from human to human, brain to brain (you can read a bit more about memes in my previous post on replicators here). Sometimes we keep spreading memes that aren’t doing us any good, regardless of new information that should illuminate the fact they’re a load of crap.

For example research by MIT, LSE and loads of others confirmed several years ago that our assumption that people perform better when offered a greater financial incentive is wrong. In fact hard evidence demonstrates that when you’re dealing with tasks that require even the most rudimentary cognitive ability, the higher the financial reward you offer the poorer the performance. Surprising, but true. It’s a fact.

Yet still we keep on doing the same old things based on false assumptions, despite the evidence that we’re actually damaging our businesses and our teams’ productivity.

The facts and evidence also tell us what does actually work. What really gets the most out of people and helps them reach peak performance, is autonomy. People like to feel they’re in control of their own destiny – that they’re self-guided; and they want to feel a sense of purpose and mastery. Check out Dan Pink’s commentary on this topic.

If you take these two thoughts – 1. that we’re basing our decisions on how things should be designed on false assumptions; and 2. that people want autonomy – it isn’t difficult to draw conclusions about why platforms like Linux have been so powerful.

The thing is, when we try to design things – technology platforms, mechanisms for rewarding staff, educational programmes – lots of false assumptions come into play. This is precisely why we’ve ditched waterfall development methods in favour of agile methods. It’s risky and expensive to lock yourself in a room for years on end with a massive budget and build something you assume people want; so instead we build a little bit, show the world, learn, tweak, release, learn, tweak, release.

One way to look at optimising how you go about designing your work, life and objects, in a more agile way, is to consider very basic scientific laws and principles.

For instance, consider for a moment how far our human capabilities for designing amazing, functional structures extends. Yep, we’ve designed some pretty cool stuff. But think for a moment, what’s the best designer of all? Look around you. I’d argue that it’s very clear the best designer of all is evolution itself. We only need to look at the complexity and unexpectedness of nature to see that evolution is the ultimate designer.

In fact, organisations, markets, economics, the open source movement – they’re not just like evolutionary systems – they are evolutionary systems. We tend to think of evolution the way we were taught at (linear) school… that it’s just a biology thing; when in fact it’s the most powerful recipe for finding innovative solutions to complex problems.

As philosopher Dan Dennett said, evolution is ‘design out of chaos without the aid of mind’. It’s the act of creating a design without a designer. So long as there’s variation, selection and replication – just like the creation and spreading of human memes – you get evolution. So long as there’s variation – like staff with different abilities; selection – a process of choosing the ‘fittest’ talent; and replication – replicating the good stuff they do… you get evolution. You get the optimum way of doing things, without you having to know in advance exactly what that’ll look like.

So, you might be thinking that’s all very well, but how do you harness evolution to get things done in a better way?

Well, the first thing you need to do is stop trying to be the designer. Stop assuming you know which design will work. There’s no way you could’ve drawn a design for Linux or even Wikipedia that was an accurate picture of how it actually turned out. When you assume you’re the designer and you’ll come up with a design that’ll work, you end up spending loads of money, taking loads of time and by the time you unleash your design on the world, it’s outdated and you discover many of your assumptions were wrong and you’re screwed (ask Microsoft).

Companies who fall in love with their designs and cling onto them despite evidence they don’t work will die. Companies who embrace evolutionary principles will thrive. That’s the reason why so many start-ups find success in such unexpected places. Look at Paypal – they started as a PalmPilot app.

The really hard part is dealing with large organisation, with deeply embedded management hierarchies and industrial revolution legacy thinking.

The good news is that the answer lies within. Management doesn’t have to come up with a crazy new design. It’s much easier than that. They just need to create an environment where the optimum design will evolve; and the way to do that is to get out of the way.

Big companies are chocful of hundreds, thousands of brains. The answers lie in there somewhere. The trouble is, traditional top-down communication and top down hierarchical management can’t extract them. These hundreds, thousands of people are desperate to self-guide, to work autonomously, to contribute great innovative leaps – just like MIT and LSE and numerous innovative companies have proved. They just need the ability to work together and to break out of the old siloed routines.

This sounds scarily like losing control to many organisations. And relinquishing control is exactly what it is. But it needn’t be scary. To remove the fear, all that’s required is a universal understanding of some basic rules… and a splash of trust. As John Whitney, a professor at Columbia Business School said, ‘More than half of a traditional organisation’s activities, including the use of time clocks that monitor workers and marketing campaigns designer to win back disappointed customers, are needed only because of mistrust’.

If everyone understands the rules and they aren’t too prohibitive and don’t hamper evolution and autonomy, it’s a recipe for success. By enabling everyone in an organisation to connect with everyone else if they need to, spreading this sort of understanding is easier than ever.

These days it’s startlingly cheap and easy to enable everyone in an organisation to connect with everyone else if they need to.

Simply by questioning assumptions – and by putting basic collaboration tools and systems in place – and by creating a culture of experimentation, of iteration – create, share, test, tweak, create, share, test, tweak – innovations will evolve naturally, teams will be happy and we have a way forward that’s altogether more fulfilling and more aligned with not only the outside world, but our fundamental human nature.


More complexity theory & humanity 2.0

More complexity theory & humanity 2.0

The discovery of complexity

The discovery of complexity

Networks are an essential ingredient in any complex adaptive system. In biology, molecules interact in cells, cells interact in organisms, organisms interact in ecosystems. As Eric D. Beinhocker points out in one of my favourite books, ‘The Origin of Wealth’:

“The economic world likewise depends on networks. The earth is girdled by roads, sewers, water systems, electrical grids, railroad tracks, gas lines, radio waves, television signals and fiber-optic cables. These provide the highways and byways of the matter, energy and information flowing through the open system of the economy. The economy also contains massively complex virtual networks: people interact in companies, companies interact in markets and markets interact in the global economy. Just as in biology, the networks of the economic world are arranged in hierarchies of networks within networks.”

BUT… traditional economics glossed over networks because they didn’t fit neatly into the equilibrium paradigm, whereby the economy was likened to an equilibrium system, i.e. behaving like a ball dropped into a bowl, rolling around until finally settling in a predictable place, until something external disrupts it. More recently the perfect sums have been ditched in favour of the idea that the economy is a complex adaptive system, i.e. a system of dynamically interacting parts in which micro-level interactions lead to the emergence of macro-level behaviour patterns. A single ant or water molecule is boring on its own, but naturally becomes an army or whirlpool as a byproduct of complex interactions. People are the same – the internet is the same. If a system reaches a state of equilibrium, it’s essentially dead.

Physics has likewise evolved to embrace complexity in favour of neat maths that doesn’t fit reality. The second law of thermodynamics states that entropy, a measure of disorder or randomness in a system, is always increasing. The universe as a whole is drifting from a state of order to disorder.

Our brains are made to deal with complexity, but we don’t make decisions by logically churning through every available piece of information. Instead we satisfice, taking the information we have and doing the best we can. Cognitive science has grown to recognise that we’re much better at inductive than deductive reasoning. We spot patterns and weave stories around metaphors and analogies.

Computers are the opposite, helping make up for our deductive shortfalls. It’s interesting that the rise of agile development follows the same pattern as new knowledge in physics, biology, economics and other advanced fields of discovery; as does the creation of new business models that embrace our inherent sociability and the complexity of networks. We’re no longer seeking the perfect, no longer adopting unrealistic assumptions to make the maths work out in the equilibrium framework we’ve been convinced explains everything for so long.

We know the energy inherent in what we’re doing renders equilibrium not only irrelevant, but impossible.

Complex adaptive system


  
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