More from Alchemy: The Dark Art and Curious Science of Creating Magic in Brands, Business, and Life by Rory Sutherland (Part 1 is here):
When every function of a business is looked at from the same narrow economic standpoint, the same game is applied endlessly. Define something narrowly, automate or streamline it – or remove it entirely – then regard the savings as profit. Is this, too, explained by argumentative thinking, where we would rather win an argument than be right?
Especially egregious in large institutions engaging in budgetary arbitrage. A hospital I work at saved money a few years ago by outsourcing a lot of its IT abroad. I guarantee they didn’t actually save money for the organization despite spending less on IT.
Today, the principal activity of any publicly held company is rarely the creation of products to satisfy a market need. Management attention is instead largely directed towards the invention of plausible-sounding efficiency narratives to satisfy financial analysts, many of whom know nothing about the businesses they claim to analyse, beyond what they can read on a spreadsheet. There is no need to prove that your cost-saving works empirically, as long as it is consistent with standard economic theory. It is a simple principle of business that, however badly your decision turns out, you will never be fired for following economics, even though its predictive value lies somewhere between water divining and palmistry.
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The problems occur when people try to solve ‘wide’ problems using ‘narrow’ thinking. Keynes once said, ‘It is better to be vaguely right than precisely wrong’, and evolution seems to be on his side. The risk with the growing use of cheap computational power is that it encourages us to take a simple, mathematically expressible part of a complicated question, solve it to a high degree of mathematical precision, and assume we have solved the whole problem.
There’s a line from Tony Fadell’s Build that I love: “Data can’t solve an opinion-based problem.” So many meetings and dashboards, and so much of that data is used as a proxy to somehow measure the real question: Is this all really working? Can we do this better? Not just the one part that’s easy to measure—but the whole thing?
In the event, the platform has improved since we adopted it, but the fact that a cost-saving decision could be made without any consideration of the hidden risks to efficiency was nonetheless alarming. Why are large commercial organisations adopting this ideological approach to business? That was supposed to be the weakness of communism.
At least in many healthcare and other large organizations, it’s departmental siloing and abstracting both strategy and tactical decision making to layers of managers with little or out-of-date domain expertise. I have seen even good initial decisions with involved stakeholders ruined by implementation decisions by suits.
People’s motivations are not always well-aligned with the interests of a business: the best decision to make is to pursue rational self-justification, not profit. No one was ever fired for pretending economics was true.
Moral hazard is potent, especially when combined with the Peter Principle.
We fetishise precise numerical answers because they make us look scientific – and we crave the illusion of certainty. But the real genius of humanity lies in being vaguely right – the reason that we do not follow the assumptions of economists about what is rational behaviour is not necessarily because we are stupid. It may be because part of our brain has evolved to ignore the map, or to replace the initial question with another one – not so much to find a right answer as to avoid a disastrously wrong one.
It’s comfortable but wrong to wave away unavoidable uncertainty with a blanket of data.
[Herbert] Simon used satisficing to explain the behaviour of decision makers under circumstances in which an optimal solution cannot be determined. He maintained that many natural problems are characterised by computational intractability or a lack of information, both of which preclude the use of mathematical optimisation procedures.
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Consequently, as he observed in his speech on winning the 1978 Nobel Prize, “decision makers can satisfice either by finding optimum solutions for a simplified world, or by finding satisfactory solutions for a more realistic world.
I’ve included Simon’s work on “bounded rationality” in a bunch of my talks, but surprisingly not much on this site. Simon’s work set the stage for much of behavioral economics and modern decision science. It is of both incredible personal and professional value.