19 December, 2020 - 12 min read
Two heuristics stood out to me recently:
- The Game of Inches by Larry Culp at GE
- Action Produces Information by Brian Armstrong at Coinbase
Game of inches
I started purchasing General Electric (GE) stock in 2018. At this time, General Electric had incompetent management and board members. Bad acquisitions, lack of strategic initiatives, mounting debt and fraudulent activities were common. General Electric was booted out from the Dow Jones index after 110 years in 2019. It was debatable, but General Electric was likely headed towards bankruptcy. Markets would have felt the implosion of General Electric but Larry Culp came to save the giant. General Electric's major business portfolio includes electricity, power, renewables: wind turbines, airplane engines, military contracts and digital services.
Larry Culp was already a well known CEO before joining General Electric. I listened to every single one of his calls and read every single one of his letters he published while at GE. Larry Culp frequently brought up the game of inches analogy.
As the saying goes, this is a game of inches every day, not feet or miles, and I want us all to keep score together. My goals are aligned with yours. — Larry Culp, 2018 Annual Letter
I am not sure where Larry got this analogy from, but many say it was from a football movie Any Given Sunday.
We are in hell right now, gentlemen, believe me and we can stay here and get the s--- kicked out of us or we can fight our way back into the light...We can climb out of hell. One inch at a time." — Tony D'Amato, played by Al Pacino
In two years General Electric made many strides since Larry Culp took over, and they were huge in magnitude. His management style is heavily influenced by LEAN methodologies. If it weren't for these incremental steps, General Electric might not have survived during the 2020 pandemic. Today it is a much more stable organization. Larry Culp's heuristic the game of inches is important when faced with a complex and giant problem. This brings us to the next heuristic.
Action produces information
If you are a real-world decision maker such as Larry Culp, you may choose to analyze, or you may choose to act. There is a real trade-off. Studying about decision-making frameworks isn't the same thing as effective action.
If we lived in a perfect environment presented with all possible outcomes, a rational choice theory from economics would be ideal. However, frameworks and models are not immediately applicable to the real world because the real world does not share all the assumptions used in textbooks.
Decisions in the real world are time sensitive; the sooner you act, the more you realize from an action. What if Larry Culp waited for several board meetings to plan out the GE turnaround? He made decisions right away after becoming the CEO. He cut the dividend to a penny; previous management stalled for many months on whether to cut the dividend. General Electric might not have survived during the pandemic if the management had not made those decisions earlier and acted sooner. There is a cost associated to doing decision analysis.
Action generates new information which then allows to make better decisions. In the real world, the utility of each choice depends on the decisiveness with which you act on your analysis.
If the world were static, the decision-analysis frameworks would be useful. But in a dynamic and complex world, playing the game of inches and a bias towards action are much more meaningful. My investment return on General Electric was not significant, but watching Larry Culp taught me the importance of bias towards actions. A valuable lesson for me.
Bias for action from others
Brian Armstrong, the founder and CEO of Coinbase shares his thoughts action creates information.
It doesn't even matter what you do as long as you do something, because that's my other favorite quote, is “action produces information.” So at a certain point, you got to stop pontificating about this stuff and just try something, anything. You're going to be embarrassed by the V1 until you go out there, and you create. That's part of the product development process, is just dramatically scaling back kind of the ambition and the feature set and everything to rapidly iterate and prototype these things, but go do anything. The first thing you try is almost guaranteed not to work. So don't give up, just go try the next thing, and the next thing, and the next thing. That's the only way that new products and companies ever get created in the world. You got to put a lot of shots on goal to get one to eventually work.
Scott Berkun, a veteran product manager on bias towards action:
Part of the reason that perfect decision formulas can't exist is that you never know if you're buying too much or too little insurance. Did you see the right doctor for your elbow? Did you ask the right questions? You can make the correct decision in the wrong way. One risk with our plan B was that two weeks wasn't enough. We might need to spend months to improve even one weak spot. Fear of this uncertainty motivates people to spin their wheels for days considering all the possible outcomes, calculating them in a spreadsheet using utility cost analysis or some other fancy method that even the guy who invented it doesn't use. But all that analysis just keeps you on the sidelines. Often you're better off flipping a coin and moving in any clear direction. Once you start moving, you get new data regardless of where you're trying to go. And the new data makes the next decision and the next better than staying on the sidelines desperately trying to predict the future without that time machine.
Gary Klein, a psychologist who works primarily with the military on bias towards action:
If you had to compare two options, one of which is outstanding and the other of which is terrible, you wouldn’t need to do any analysis. It would be an easy choice. As the two options get closer and closer together in their attractiveness, the decision gets harder. (...) In the example of purchasing a used car, we can see that the three options are all very close—they each have comparable strengths and weaknesses. There just isn’t much that differentiates them. The options were so close together that simply flipping a coin would have been sufficient. (...) I call this the zone of indifference problem. We usually think that the goal of decision-making is always to pick the best choice. There are few decisions more important than on the battlefield or on the fireground, where lives are at stake. Yet military leaders and fireground commanders recognize that it is better to make a good decision fast and prepare to execute it well rather than agonizing over a “perfect” choice that comes too late. We can rarely know what is the best choice, and the quest for a best choice can drive us to obsess over inconsequential details. How often do we get ourselves trapped into splitting hairs, to find the very best option out of a set of perfectly good choices? Better to make your goal one of selecting a good option that you can live with. If one option emerges as the clear winner, fine. If two or more options wind up in the zone of indifference, that’s fine too—just pick one of them and move on. If you can accept the impossibility of making the “right” choice, you can free yourself from unnecessary turmoil and wasted time.
Jeff Bezos on decisions in 2015 shareholder letter:
Some decisions are consequential and irreversible or nearly irreversible — one-way doors — and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don’t like what you see on the other side, you can’t get back to where you were before. We can call these Type 1 decisions.But most decisions aren’t like that — they are changeable, reversible — they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long. You can reopen the door and go back through. Type 2 decisions can and should be made quickly by high judgment individuals or small groups.As organizations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention. We’ll have to figure out how to fight that tendency.The opposite situation is less interesting and there is undoubtedly some survivorship bias. Any companies that habitually use the light-weight Type 2 decision-making process to make Type 1 decisions go extinct before they get large.
Ken Kocienda in his book Creativity Selection shares a story of how Apple would pick a color versus Google conducting A/B testing on color optimization:
Ken explains the idea of convergence and shares the story of how Google factored out taste from its design process. When it comes to picking color, just pick one. Don't waste your time doing A/B test for colors. Use your good taste, knowledge, make the product accessible and move on. Apple always made quick choices about small details. Apple took more time on bigger questions. Always make a steady progress by showcasing demos, getting feedback, and following-up with more demos till it shapes product overtime. It is all about incremental progress. Algorithms and heuristics must coordinate to make a great high-tech product. Fast page loads on Safari or correct insertion point on a small iPhone keyboard are equally important. Finding balance was a key unlike Google trying to find an optimal blue color by running A/B tests on 41 shades of blue. Picking a shade of blue and moving on to value creation feature would've served them well. Algorithms produce quantifiable results, where progress is defined by measurements moving in predetermined direction. The best shade of blue is the one that people clicked most often in a A/B test is an algorithm. Algorithms are objective. Heuristics have a measurement of value associated with each feature. Unlike evaluating algorithms, heuristics are harder to nail down. Heuristics is question based approach. Lots of questions asked to get to a final decision. It takes effort, judgement and time to find what these things are. Apple employees put their faith in their sense of taste when picking motion and colors. Heuristics are subjective. Working at an intersection of algorithms and heuristics was such an example why there were so many demos made. It was often difficult to decide where an algorithm should end and a heuristic should take over. Hence, deciders helped bring clarity by making incremental decisions.
Theodore (Teddy) Roosevelt on who really counts during a fight:
The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood. It is not the critic who counts: not the man who points out how the strong man stumbles or where the doer of deeds could have done better. The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood, who strives valiantly, who errs and comes up short again and again, because there is no effort without error or shortcoming, but who knows the great enthusiasms, the great devotions, who spends himself for a worthy cause; who, at the best, knows, in the end, the triumph of high achievement, and who, at the worst, if he fails, at least he fails while daring greatly, so that his place shall never be with those cold and timid souls who knew neither victory nor defeat.
This story from the book Art & Fear by David Bayles and Ted Orland shows quantity leads to quality:
The ceramics teacher announced on opening day that he was dividing the class into two groups. All those on the left side of the studio, he said, would be graded solely on the quantity of work they produced, all those on the right solely on its quality. His procedure was simple: on the final day of class he would bring in his bathroom scales and weigh the work of the “quantity” group: fifty pound of pots rated an “A”, forty pounds a “B”, and so on. Those being graded on “quality”, however, needed to produce only one pot – albeit a perfect one – to get an “A”. Well, came grading time and a curious fact emerged: the works of highest quality were all produced by the group being graded for quantity. It seems that while the “quantity” group was busily churning out piles of work – and learning from their mistakes – the “quality” group had sat theorizing about perfection, and in the end had little more to show for their efforts than grandiose theories and a pile of dead clay.
Robert Hass on taking action on writing:
It's hell writing and it's hell not writing. The only tolerable state is having just written.