Re-superstitious
Arbitrary as an advantage.
As knowledge develops, superstitions cede ground to good explanations.
Your business should add back superstitions; arbitrary or “random” decisions that cause desired output.
Your business should be re-superstitious.
Explanations
We are in the constant pursuit of better explanations.
Better explanations help us make decisions, and decisions let us change our world.
We went from:
Walking everywhere
To riding horses
To wheels
To boats
To steam engines
To flight
To space
Each of these steps required an in-depth explanation, all of which were complete before the action itself was able to take place.
The Wright brothers (or any given inventors) needed to figure out how to generate sufficient lift for a heavier-than-air object (their explanation) if they wanted to fly (their desired output).
Simply, explanations are succeeded by the ability to act.
In business this holds true.
Entrepreneurs identify an opportunity, and they “explain” it through the collection of information:
“I found this fact on a website that the web was growing at 2,300 percent per year. The idea that sort of entranced me was this idea of building a bookstore online.”
Bezos, on the growth of the internet
“Your most unhappy customers are your greatest source of learning.”
Gates, on the importance of talking to users
“Brian, I thought of a way to make a few bucks — turning our place into "designers bed and breakfast" — offering young designers who come into town a place to crash during the 4 day event, complete with wireless internet, a small desk space, sleeping mat, and breakfast each morning. Ha!”
The email that started Airbnb, solving the problem of being unable to afford their SF rent
These quotes show the seeds of business explanations being created through statistics, user research, and personal problems.
From these seeds, explanations and business decisions are made.
Now, with more and more big data, greater processing power, and better software, we have better explanations and better decisions.
The era of strong, data-backed, rigorous decisions is here.
The opposite
Enter,
This Harvard Business Review article argues for decisions made randomly.
In acting randomly, it accomplishes the underlying goals of the decision and accrues important benefits:
On speed:
For many problems, solutions are only meaningful if they are implemented quickly enough for them to matter.
On experimentation:
Launching an MVP early generates information by sparking competitor and customer reactions, which inform your next move.
On being less predictable:
By leveraging “scentless algorithms,” which introduce random delays and variations in the timing and size of orders, institutions can avoid signaling their intentions, which could be exploited by other market participants to register gains on the back of more competent traders’ analyses.
On reducing biases:
Managers often tend to replicate past successful approaches, while being less receptive to new ideas or external signals. This can lead to decline as the environment shifts around them. Well-known examples abound: Blockbuster and Nokia deferred to the “tried and true” with disastrous consequences when demand and competitive conditions changed radically.
If achieving these benefits (and others) exceeds the value earned by having a rigorously thought-through explanation, why not act randomly?
First, you have to acknowledge that the set of bad decisions is infinitely larger than the set of good decisions. True randomness is illogical, so you would need to apply randomness within a specific scope across an acceptable range of random outputs.
Even still, this proposal flies in the face of the current business decision-making regime; however, if it is able to create better outcomes in certain situations, it may be plausible to pursue.
But what happens if (or when) things go wrong?
In this case, we must understand where blame (or credit) can be assigned for such a decision.
“A computer can never be held accountable for decisions, therefore a computer must never make a management decision.”
When bad decisions are made, someone must be responsible. Blame must be assigned.
Or at the very least, you should be able to go back to the explanation and identify mistakes.
Yet, with random strategies, who and what gets blamed? Simply pointing a finger at a Magic 8-Ball is an unsatisfactory, and rather unsatisfying, explanation.
The one who chooses a strategy should also be the one who holds the justification behind it. Therefore, the finger should be pointed at whoever instituted the Magic 8-Ball strategy in the first place.
For instance, to predict the weather, a bottom-up (data-driven) strategy makes sense. A top-down strategy would not.
Why? We can come to a solid conclusion and explanation for why bottom-up is better. In choosing any strategy, including the strategy of randomness, we have to have a similarly solid explanation for its employment. For example:
“A random strategy was deployed because it is faster, is able to accelerate experimentation, is able to be less predictable, and reduces human biases.”
On why randomness was chosen, by someone getting blamed
A random strategy would not be deployed in instances where those benefits are not earned.
In fact, since explanations come before actions, and a choice of strategy is an action, we can conclude that:
Each strategy requires an explanation of how it works
And that explanation must precede the strategy’s use.
This HBR article was of course not asserting the use of random strategies as a holistic strategy, but as a practical tool in specific instances, backed up by a solid explanation.
Superstitions
Superstitions are (at least from a scientific perspective) random, or at the very least arbitrary.
They generally lack explanation.
Some superstitions make a semblance of sense:
“Don’t walk under a ladder”
Yes, generally avoiding walking under tall things that could fall on you is reasonable.
Others simply don’t:
“Step on a crack break your mothers back”
I fail to see correlation or causation, but I will cede the fact that this works like a powerful spell on many primary school playgrounds.
Regardless of the lack of merits of the explanation, these superstitions have merit in their effect.
Their effect is powerful enough to change certain behaviors.
I don’t walk under ladders or on cracks.
And I knock on wood.
The similarities to random strategies?
They both have arbitrary actions (actions without explanation).
Yet, we can choose to use them if we have a good explanation for the value of its effect.
The differences?
Superstitions, compared to random strategies, have a slant.
Randomness would not increase the likelihood of any one thing happening, whereas superstitions do. I have knocked on wood more times in my life than is really sensible. If I employed a strategy of randomly knocking on things, wood would come up far shorter in absolute knocks.
I’m not sure why one would want to optimize wood knocks, but some may want to create arbitrary “superstitions” that optimize things they value.
These, are causastitions.
A “superstition” intentionally created to “cause” some effect through an arbitrary system.
I’ve been writing my thoughts down as essays for a whole year now.
In my early introduction to essays before I started writing, I came across an interesting essay about publishing essays:
“So: lower your bar for what’s worth writing about! My personal standard is anything that I’ve said more than once in a conversation.”
Me, a non-writer at the time, was intrigued by this.
This causastition was a good solution to a hard problem. I always felt like my ideas had already been written about and I could not add anything of value. After reading this essay I realized that there were many ideas I had worth bringing up, evidenced by the fact that I had brought them up in conversation many more times than once!
This was a valuable solution to a problem, this arbitrary causastition, despite its inherent arbitrariness, can still solve problems.
And, as anyone who knows me can attest, I love talking. I will fill my days up with conversations, so long as they stay interesting.
So, that means the list of things that are worth writing about, for me, would be massive. I have said many things more than once in a conversation.
But, that number, and that causastition, is inherently arbitrary. Arbitrary and slanted.
We could not find a good explanation as to why an idea when expressed more than once is now worth writing about, and we could not find an explanation whether that threshold should instead be 3, 5, or 35 conversational references.
The slant exists though, as following that causastition produces more writing.
If you want to optimize for more writing, it doesn’t matter if it’s arbitrary…
it achieves the desired output.
If you want to optimize for speed, experimentation, unpredictability, and bias removal, it doesn’t matter if it’s random…
it achieves the desired output.
When working at a strategy consulting firm, we had a “causastition”:
“If you find yourself doing a process twice, ask a colleague if they have a better (or more automated) way of doing it.”
On seeking help, spoken by pretty much everyone who was my senior
This is identical to that essay writing tip, even down to the number threshold.
Similarly, the number is arbitrary, it could be 3, 5, or 35 (different numbers tend to optimize for different things but that’s less relevant to this essays’ ideas).
The slant exists though, following that causastition can produce more efficiency in our work.
Causastitions are action-centric heuristics, shortcut mental models of the world that produce a tangible output.
It’s not about threshold reaching as the two examples gave, but it is about ritualizing beneficial actions, even if how they occur is arbitrary or random.
“We encourage our employees, in addition to their regular projects, to spend 20% of their time working on what they think will most benefit Google. This empowers them to be more creative and innovative. Many of our significant advances have happened in this manner.”
On 20% time, Google’s 2004 IPO letter
There is no business explanation for 20%, and giving employees this much freedom was a nascent idea, but this was part of the Google culture. It matters less by which method you choose the arbitrary number, just choose one. And choose a number that works within your constraints (you can’t write an essay for each idea you bring up in conversation, and you can’t have employees spending 100% of their time on side projects).
This produced new ideas in a slanted way.
It was not random, and the ideas developed were not decided upon randomly.
The use of strategy had an explanation, and what ensued in practice was somewhat without explanation.
Good causastitions produce good outcomes.
The prescription for companies:
Consider more alternative strategies (random, causastitious, etc)
Think deeply about the explanations behind the use of those strategies
Deploy them within your organization
Most importantly:
Be causastitious → Bring causastitions into the culture and fabric of your organization
To start, consider an optimization problem you want to improve with your team:
I want people to take more initiative in pitching new products/product features
Or
I wish people would more often share best practices for speeding up their work
Or
I want people to review their work more diligently.
Then, decide on an arbitrary way to change those behaviors. For the first problem, maybe you could have a segment in each weekly meeting where you use a spinner to choose a team member who has to pitch the team on a new product feature.
You then need to reinforce the causastition, codify it, and embed it into the culture.
In short, follow through.
Most people find company value statements to be silly, the reason is due to how divorced these “values” are from the act of decision-making.
Causastitions are chances for you to articulate & re-focus employees on your organizational culture. By making '20% time' a codified & known causastition, you 'remind' employees to be innovative and seep that into their identity, and you also attract employees interested in that specific environment. A causastition is an actually-actionable version of 'organizational values'.
You are now better at solving this problem by making your organization causastitious.
Rather than thinking… pondering… and considering… the best explanations, you have increased the propensity of people on the team to exhibit the behavior you want.
Sometimes, a good explanation is counterproductive.
Sometimes, you knock on wood even when you know it doesn’t do anything.
Maybe, being a bit more random, a bit more arbitrary, and a bit more causastitious
is the smartest thing you can do.