There are more things to talk about than Donald Trump, though I doubt that Donnie agrees with me. But we have to get on with our lives which, at least in my case, means getting on with my reading. Where does all the crap I write here come from but reading, talking to people, and waiting in line at Starbucks? Nowhere else! And if you want to be like me you may choose to read a new book by Michael Lewis, The Undoing Project: A Friendship That Changed Our Minds. Of course the book is very good and it’s very well-written and it will tell you a lot about how decisions are actually made. But if we are looking forward instead of backward here, the book and its content don’t really matter that much because we don’t decide nearly as much as we think we do. We don’t decide as much as we used to. In fact I’m about to argue that we’re well into the Post-Decision Age. It’s pretty much out of our hands.
Lewis’s book explains. He’s not breaking new ground but rather rediscovering old ground and explaining why it matters. In this case his earlier book Moneyball explained how the Oakland Athletics baseball team used statistics to win baseball games while this new book essentially takes the other side and explains why most of us (including many baseball managers) are not like the Oakland A’s.
From a content perspective this turns out to be a book about a book. Lewis explains and puts in a dreamy bromantical context the work of two academics, Daniel Kahneman and Amos Tversky, that was already shared with the world in Kahneman’s 2002 book Thinking, Fast and Slow. Read both books. If you can’t or won’t do that, then at least look at this charming video review of Kahneman’s book that explains the basics. If that gets you excited you can watch an entire hour of Kahneman discussing the same subject at Google. Finally, if you want a taste of the Lewis book here’s an excerpt from Vanity Fair.
What Kahneman and Tversky figured out is that we have ancient brains that generally don’t do the math because math has only been around for 5,000 years or so while we as a species have been walking the Earth for 2+ million years. Our decision-making processes, such as they are, are based on maximizing survival not success. Early hunter-gatherers weren’t so bothered with optimization as just not being eaten. This put an emphasis on making decisions quickly.
We see fast-versus-slow decision-making in many aspects of life. I used to teach archery — yes, archery, who would have thought it? — and fast-versus-slow is at the very heart of that sport. In archery there are sight shooters and instinct shooters. Sight shooters are the archers you see in the Olympic Games. They take up to a minute to very carefully release one arrow at the exact correct moment. Instinct shooters release the arrow when it feels right and shoot many times as many arrows as a result. Sight shooters win all the medals. Instinct shooters save your ass in a battle. It’s all about maximizing survival.
Back in the early 1980s I wrote about a retired engineer who played the horses at Bay Meadows, a thoroughbred race track in San Mateo, California. He had developed an expert system to guide his betting, crunching out the results on his Cromemco computer. That engineer was the horse racing equivalent of a sight shooter where nearly all the other bettors worked entirely on instinct. And because horse betting is a parimutuel system where the bets immediately affect the odds, he wasn’t so much betting on the horses as betting against the other people at the track. If he bet correctly and the horde of bettors bet incorrectly he could make a lot of money. His betting system worked well and the guy was miserable as a result.
Here’s one reason why he was miserable. His personality was that of an instinct shooter but he was forcing himself to be a sight shooter. It made sense, he understood it, but couldn’t he just once give-in to a whim and increase his bet on Nobody’s Fool in the 4th race? After all, just look at that beautiful horse, and the jockey is wearing my favorite color! Nope. One emotional bet could wipe-out an entire day’s results. His system was conservative and made a consistent eight percent per day so don’t screw with it.
The other reason he was miserable was that very eight percent per day. His optimal approach would have been to bet fairly large sums, keeping them just small enough to not seriously impact the parimutuel odds, then re-invest his winnings to gain what the lady at the bank called “the miracle of compound interest.” He figured the track was good for up to $500,000 per year in easy winnings based on about four hours of work per day, 100 days per year ($1,250 per hour!). But taking $5000 per day every day at the betting window would gain the notice of unsavory characters who would want to either steal his winnings or his system. So he bitterly kept his winnings down to $500 per day ($125 per hour). His need to survive forced down the engineer’s winnings.
The engineer’s expert system predated off-track betting, so I’m guessing he or some descendent is making great money today sitting on a bar stool in Vegas. The variables he used haven’t changed so the system should still be good. Which is to say the other bettors haven’t got any smarter. Why should they? Our brains haven’t evolved.
Lewis presents a very interesting example of decision-making tradeoffs based on an actual example. Henry Kissinger was trying to achieve peace between Israel and Syria and the Israeli government asked Kahneman and Tversky to recommend possible decisions based on likely outcomes of Kissinger’s work. For example, they estimated that a failure by Kissinger would increase the likelihood of a new war by about 10 percent. Kissinger fails and war is 10 percent more likely.
“Foreign Minister Allon looked at the numbers and said, “Ten percent increase? That is a small difference.” Danny was stunned: if a 10 percent increase in the chances of full-scale war with Syria wasn’t enough to interest Allon in Kissinger’s peace process, how much would it take to turn his head? That number represented the best estimate of the odds. Apparently, the foreign minister didn’t want to rely on the best estimates. He preferred his own internal probability calculator: his gut. “That was the moment I gave up on decision analysis,” said Danny. “No one ever made a decision because of a number. They need a story.” As Danny and Lanir wrote, decades later, after the U.S. Central Intelligence Agency asked them to describe their experience in decision analysis, the Israeli Foreign Ministry was “indifferent to the specific probabilities.” What was the point of laying out the odds of a gamble if the person taking it either didn’t believe the numbers or didn’t want to know them? The trouble, Danny suspected, was that “the understanding of numbers is so weak that they don’t communicate anything. Everyone feels that those probabilities are not real—that they are just something on somebody’s mind.”
Which brings us finally to the Post-Decision Age.
Thinking about decision analysis and how it simply isn’t compelling to decision makers, there’s one place where I believe that’s not true — Google. At Google it is all about the algorithm and the algorithms are (deliberately, I’m beginning to think) so complex that the whole issue of Kissinger’s failure leading to a 10 percent increase in peril is avoided. It’s avoided because the probability relationship is too complex to be stated in a single sentence and so nobody involved even bothers to decide whether the analysis is actionable or not: they just do it. At Google they do what the algorithm tells them to do. So the algorithm is, itself, in charge until enough time passes that a preponderance of data makes it clear the algorithm has failed. But even then they don’t reject the algorithmic approach, they just revise the algorithm.
This, I believe, is the trend. As humans we’re pretty much all instinct shooters but the optimization of complex systems requires sight shooters. If we can’t become those we use machines to do the work. And if the machines fail we don’t reject them, we improve them.
On October 5, 1960, the U.S. nuclear command center NORAD received signals from its early warning radar in Thule, Greenland, indicating that a massive Soviet nuclear attack on the U.S. was underway—with a certainty of 99.9 percent. What the radar was actually seeing was the Moon rising over Norway. Luckily, nuclear armageddon was somehow averted but we didn’t throw out the computer, we taught it about the Moon.
The print version of this gig began for me 29 years ago in 1987. Reagan was President and you could still buy a new IBM PC-AT. And that fall Wall Street suffered its first Flash Crash. “Black Monday” is what they called October 19, 1987 when the Dow dropped 22 percent in one day. That was 508 points back then, equivalent to a 4,180 point drop tomorrow. Are you ready for a drop like that? Nobody is.
The crash was primarily caused by program trading — computers deciding to sell stocks in order to minimize losses and conserve portfolio value. No humans were really involved until it came time to fix the mess. The problem was every trading program was ignorant of the fact that there were other trading programs. The result was that stocks would drop, programs would sell some shares, stocks would drop even more because of those sales and the sales of other programs, so the programs would sell even more. Wash, rinse, repeat. They were selling against each other and would have taken the market to near zero if humans hadn’t intervened to stop trading. There were changes made to program trading but it wasn’t eliminated. In fact program trading today supplies most of the volume on Wall Street.
So we can make policies but we can’t implement them at any scale without help. And with the rise of machine learning, Big Data, and the relentless advance of Moore’s Law, computers have become so fast and so smart that we think they can sight shoot at instinct speeds. And maybe they can, but it’s for sure that normal humans no longer understand the underlying algorithms and may be unable to regain control.
We still make decisions. We decide what to wear and what to eat and maybe where to work or go to school, but most of the decisions that are made about us are made by machines. (Is the IRS going to audit you this year? Will your kid be accepted to Ohio State University?) And this trend has so deeply infected society that I think it can never change.
Let me give two backward examples — Apple and IBM. IBM has screwed itself by blatantly ignoring technological realities that are obvious (and machine derived) at Amazon, Google and Microsoft. IBM is Old School in its decision-making and suffering dearly for it. Apple, on the other hand, appears to be paralyzed. Fortunately Cupertino is for now locked on a profitable path, but are those guys making decisions at all? Not really, not much, and it will eventually bite them.
Welcome to the Post-Decision Age. What do you think?
I’m not so convinced that that system is working that well, if at all, for Google. Every new product that they have released ever since android has been a failure – Wave, Buzz, Glass, Plus… you name it – the last announcement was particularly depressing, it’s like they have alll but given up, or at least “the algorithm” has: “Here’s our version of the iPhone, our version of Amazon’s Alexa, and our version of Samsung’s Gear VR” (…or am I missing something?). I keep having the feeling that they are turning into Microsoft / IBM – slowly alright, but steadily (in the sense that most of their income comes from products increasingly… old?).
I think that Google was founded on “gut” instinct and has since been moved by the money to trying to “calculate” solutions – so they are currently another example of the issues that Bob is discussing.
Consider this: with Google YOU are the product.
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It’s not an accident that Android was open-sourced and other manufacturers were encouraged and enticed to design phones using it. The last thing Google needs is to be on the wrong side of a walled garden.
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So, yes, Google is all about the algorithm.. applied to lots and lots of data about us.
Huh? I didn’t follow how the preceding part of your essay led to Apple paralysis. Are you saying they seem to be utilizing neither instinct shooting nor sight shooting? They are not making decisions by either gut or calculation? I doubt that. Cook has been telegraphing their intended move to augmented reality and they seem to be finally taking machine learning seriously. The next step is an ever present smart agent that augments your reality to provide real time, contextualized information by knowing you and your needs. That device (Apple Glass?) is still years off but part of its smarts will come from the iCar OS they are still working on, an OS that needs to provide real time, contextualized information by knowing where you are and where you want to go and knowing its ever changing surroundings to help you get there.
I probably wasn’t clear enough about Apple. My overall point is here are two companies that — for very different reasons — are trying to wing it in a world where their competitors are mainly trying to CALCULATE it. In Apple’s case there’s a lack of vision. Remember Apple has to be thinking in terms of $40 BILLION opportunities and deciding if one product or service or another has that potential (AND WHEN — you can jump too early or too late) has kept them from doing much lately. Steve Jobs didn’t think about the size of the opportunity and that’s the main difference. To Steve anything insanely great was also destined to be huge, but Steve’s world was also smaller and simpler.
I have worked in a large corporation for most of my career. All of management has technical degrees, but they are not currently technical. They having chosen to enter management or project management, and tend to value people that follow similar paths. Therefore, the majority have a hard time telling the difference between a good idea and a good story (BS). They could reach down to those that are technical in the organization, but they don’t.
On the flip side there are about 20%-30% of the engineers that are good at building stuff. They are generally very good in one area such as software, digital hardware, RF design, etc. However, rarely a person comes along that can span multiple skills. When these people lead projects, magic happens. They see the simple solution to complex problems, and can be truly visionary in an organization. However, nobody is looking for them, and they are hard to recognize, because you really have to be one to recognize them.
I could write a book on management stupidity and how they overlook the creative, but retiring looks more promising.
I think Lewis talks about this – I haven’t read the book, but I did see him interviewed on Charlie Rose – highly recommended at:
https://charlierose.com/videos/29545?autoplay=true
It’s the bests friggin interview I’ve seen in a long, long, long time.
Near the end he talks about how to deal with limitations: listen to other people’s input.
I used to be highly successful project manager of technical people. You have to know something about the technoloyg, how it works – at least at a conceptional level – but the key is to tap into everyone’s intelligence when laying out a project. I started doing this instinctively even before I left school. I made a list of what I though needs to happen. Then I called the team together and asked what’s missing, what can be subtracted etc…
The project plan becomes the accumulation of everyone’s collective knowledge.
However that’s not the only benefit of it.
By tapping into people’s knowledge you get their buy-in, their ownership of the project.
By talking it through together you create a team with esprit de cour and cohesion.
You then ask who is going to do what. People volunteer for the stuff they are good at or have an interest in doing.
Then you negotiate timing. In general people under estimate the difficulties so you try to double the amount they commit to. Conversely, if the schedule is tight you throw out the deadlines as a challenge with the question “can we do this? Can we make this?” If it’s doable people step up.
Then you just keep the communication easy and open and tollerant of mistakes. You want people to inform you of a problem earlier rather than later. It’s best to let them see you make a mistake and watch you go about fixing it so they know they can do the same. Find the most eccentric but yet still tolerable personality and celebrate it. If people know that being an eccentric is okay, then they being themselves are okay, and if they know mistakes won’t get their head bitten off, they’ll come to work every day anxious to chase the problems they’ve bitten off.
In this kind of atmosphere people will work their tales off and you don’t have to do much of anything beyond that. I’ve gotten projects done in half the expected time, have team mates come up to me and say they’ve never worked harder nor had a better time in their lives.
The real problem is, being a successful project manager attracts politically ambitious dilettantes with long knives. Good &/or rutheless politicians and mediocre or worse managers get perpetuated. Good project managers end up in Korea teaching english to middle school students at one fifth their former wage.
Proves your point Bob.
https://www.bloomberg.com/news/articles/2016-11-21/how-renaissance-s-medallion-fund-became-finance-s-blackest-box
Years ago, in the 70’s, I spent several years in an Ashram following a spiritual path – I have left the Ashram for many years now but never forgotten the lessons that I learned – that our lives are guided by either our “heart” or our “mind” – and that peace comes when we follow the path with a “heart” and that confusion comes from following the path that our “mind” would have us follow.
Living a good life seems to be, these days, balancing the two conflicting guides – neither is purely good or bad – we have to balance one against another.
Unfortunately all the really big decisions are still made on the basis of gut feeling, not logic or algorithms.
Do we go to war? Do we double down in Afghanistan or pull the troops out? Are trade barriers a good or a bad thing? Even, Continue quantitative easing? Increase/decrease interest rates?
Most people in power make most of their decisions by first looking carefully at all the data, and then ignoring it and going by their gut feeling. This is not going to change.
What a bullshit article. Sight shooting in all it’s forms (whether business, tech or war) requires a long period of practice and study. Unfortunately, the world doesn’t stop so you can take 6 months to analyze every little detail of life. You cannot be a sight shooter and not miss 99% out of sheer negligence. We call this analysis paralysis.
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Sight shooting is fine for repetitive tasks like archery, or computer chess. Algorithms only do simple tasks that a human expert can explain in code. They are NOT innovative. They cannot quickly add variables as in a war/diplomatic situation. I would certainly roll my eyes at someone who stated Henry Kissinger’s work would have a 10% impact on war. A number like that is totally fake. It’s 10% plus or minus 90%.
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Great leaders and innovators like Steve Jobs are intuitively brilliant. This is a combination of business experience and social knowledge. Without Steve, Apple is being run by bean counting sight shooters (i.e. missing opportunities by being too conservative or slow). They cannot make good instinctive decisions because they don’t have the social knowledge Steve had.
I’m sorry you see my work as being totally without merit (that’s my interpretation of the term “bullshit article” — please correct me if I am wrong). But even if I am an idiot I think you are wrong about the trend in how decisions are being made. Nobody stops for six months to decide, for example, if someone is a terrorist preparing an attack. Every developed country has built a largely automated intelligence gathering system that is intended to locate such folks as quickly as possible by modeling a society without them then looking for change. Once the algorithm is built into the decision process then deciding becomes a more-or-less continuous process. One offshoot of all this computational intensity, for example, is the answer to the simple question “are we as a nation at war?” Twenty years ago the answer might well have been “no,” but today the answer is inevitably and unvaryingly “yes.” And that “yes” comes from our very dependence on automated systems and the time it takes to spin them up. So once we spin them up, we never spin them down.
Robert, where you start to lose me is when you make statements like “Every developed country”. That deserves the term “bullshit” because you are pretending to be an expert on things you CANNOT know anything about. You are making the case that something is obvious, not to be debated — when it is really a guess.
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In your reply to my comment, you argue that there is a “trend” in decision making. I do not see evidence of a trend presented in the article. You merely discuss the 2 types of decision making, using anecdotes to illustrate. Perhaps the article just needed more polishing to make this point clearer.
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Just because there is a computer on every desk, does not mean algorithms are replacing human decision making. Algorithms take a very long time to build, and once built are relevant only for a short time. The world changes. There is entropy and an uncertainty principal at work. So your algorithm is always fighting the last war. Decision makers quickly learn their expensive program is garbage in, garbage out.
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If you doubt me, write an algorithm to trade the stock market. Should be simple. I predict it will work (be profitable) only for a short time.
With Gartner studies such as “Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016” I think it’s pretty clear there’s a trend. Given that, I think it’s also fair to say that companies are becoming increasingly reliant on decision making systems. So yes, for better or worse, algorithms are now replacing human decision making.
Garbage In Garbage Out will always be true – but now some of the big decision makers are the algorithms; fix them or die with them.
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High Frequency Trading – Guaranteed profit and lasting longer than most human strategies?
In my 30 years in IT, Gartner research more times than not epitomizes the phrase “garbage in, garbage out” – or at least the second part.
“Everyone has a plan until they get punched in the mouth.”
– Mr. Michael Gerard “Mike” Tyson
And here is how to take advantage of this “flaw” in our mental abilities.
https://www.influenceatwork.com/book/
But I have a question for you Bob, There is all this talk about how machines will eventually do everything smarter, better, faster. But what if the human ego can’t handle this and we develop technologies to “enhance” human cognition so that humans become even smarter, better, faster than the machines? Maybe a combination of sight shooter and instinct shooter that is only achieved in human enhanced form. If the technology is there for just a machine, why can’t it also be there for a human cognition enhancement that out-performs the machine?
Humans have many advantages but Moore’s Law is working against us in many ways. Spend any time with Ray Kurzweil and you’ll soon end up talking about the Singularity, which is either the beginning or ending of humanity as anything other than consumers or attendants at frozen yogurt shops.
Am I deciding to write this response?
Interesting subject and helps explain why math is so hard to so many people, but perhaps not so hard to computer nerds. Are we therefore more evolved? (Strokes my ego.)
I think we’re too early into this “surrender of will” Bob seems to be describing. The moon rising over Norway did not trigger Armageddon. We are designing our machines to be our slaves and like all masters we evaluate what our slaves put forward and then “decide.”
The approaching problem I sense, along the lines of what this article maintains, is when those slaves are more “like us.” So when speech recognition & artificial intelligence passes most folks internal Turning test, then that’s when the trouble will start. We will lose the strangeness associated with a cylindrical bluetooth speaker and it will become our friend, perhaps mentor. Then we will be more susceptible to its decision-making. I ask Alexa what is today’s weather, but not how much time should I spend with my kids today. Alas when that day comes the dangerous vision Bob outlines also will come to pass. Until then, keep shooting arrows as fast as you can.
You ARE deciding to write your response as I am, but you AREN’T deciding to sign a peace treaty or attack a neighbor. Which decision is easier.
Gut instinct, supporting your 21-day kickstarter project. Your last project update promised shipments this week. I’m sure the actual odds are against that happening.
We still have a shot at shipping this week, Everything is done except some sysadmin work relating to our networking hack. It’s a matter of getting time from our new networking partners who are, unfortunately, also trying to ship a major release of their bread-and-butter product. We’ll do the sysadmin work if they’ll explain what needs doing. It’s a matter of getting attention, so Fallon and I are headed there to be a friendly but hard-to-ignore presence in their office. It’s all we can think to do.
Then why don’t you update your Kickstarter page? Or didn’t you think of that?
Little updates like this would do a world of good and be much appreciated on your Kickstarter page.
It would appear that “little updates” on the Kickstarter project site aren’t going to happen since that would be another admission of failure on their part. They have never understood that the project backers would rather have an update that we are now working on “X” as opposed to “Good News … we are ready to ship… we just need to rebuild our network stack.”
Agreed!!!! Updates don’t have to be positive “we’re shipping” messages. Things goof up. But LET US KNOW. It’s been A YEAR. I have had kickstarters go over a year before. Even 2 years. But they COMMUNICATE. Here it is Friday with no promised Thursday update. We can forgive last Thursday, being the holiday, but promises fail and fall and fail again. 🙁
A kickstarter that shipped http://fiveninjas.com/slice/
I don’t understand the point of Slice. It’s just an external hard drive holding media. How does it differ from a NAS?
Hi Bob,
As always, an interesting article. Only thing I wanted to mention, as you’re setting up the premise and talking sight shooting versus instinct shooting, and the premise of making decisions quickly–have you read on the Boyd Cycle (the OODA Loop). Boyd was an Air Force pilot and was interested in why the US Sabre jet in the Korean War consistently outperformed the Soviet MiG, which was arguably superior on paper. Long story short, the Sabre had a couple things going for it that allowed US pilots to go through a cycle of Observe, Orient, Decide, Act more quickly than their counterparts. Boyd argued that in a competition, the person who was slower at going through the OODA Loop would eventually be so far out of sync with what was happening that his opponent would win easily. Better to do the wrong thing quickly, learn from it, and try something else than to spend forever working out the perfect plan, only to find out you forgot something or things changed.
Don’t know that this has anything to do with the premise of this article, but I thought you might enjoy it.
So, what were things that the Sabre had going for it that allowed the pilots the upper hand?
Off the top of my head, cocktail party conversation level of accuracy, a bubble canopy (OK, I cheated and Googled, the MiG had a bubble canopy so that wasn’t one), and hydraulic controls. The MiG had wires from the stick to the control surfaces, while the Sabre had hydraulics that made the controls more responsive. I’d been told that the bubble canopy on the Sabre gave better visibility than the “greenhouse” style of the MiG, but since the MiG also had a bubble, I’m going to say armaments. The Sabre had multiple .50 caliber machine guns with a relatively large number of rounds while the MiG had a cannon with only a few rounds. So if a Sabre was hit, it would probably get shot down, but the MiG pilots had to make their shots count and it was an all-or-nothing proposition. A Sabre could afford to miss a lot and still get in a couple hits. A hit probably wouldn’t take out a MiG, but it would cause damage, making it less responsive and easier to hit again.
Again, this is all from memory so take it with a grain of salt, but the gist was that Boyd found F-86 pilots could make decisions more quickly than MiG pilots, developing the theory of the OODA Loop.
I think motivation was a key factor in the Sabres performance. Being an ace was a huge boost to a pilot’s career, especially if they wanted to make General. They took every opportunity they could to get those 5 kills.
Two things: Armament and roll response.
The MiG-15 was designed to fight US bombers, so it had a 37mm and two 23mm guns. They had different ballistics, so they needed to be installed at a slightly different angle. The two 23mm would shoot slightly up and the bullets would arc down and meet the straighter path of the 37mm bullets at the proper distance to shoot at a B-29. Which was way farther out than what was needed to shoot at a Sabre. When the MiG shot at a Sabre, the bullets would go above and below it and rarely hit.
The Sabre had 8 0.50″ guns which was ideal to shoot at fighters. While the MiG was a defensive aircraft, the Sabre was meant for attack.
Roll rate comes into play when you need to point the nose of your aircraft at an adversary. The plane which rolls faster aims faster and can shoot earlier. Again, the MiG, being designed to fight US bombers, did not need a high roll rate, so it could not respond at the same speed than the Sabre. But it could end combat at will by just climbing away.
Steve, thanks for your posting. I read about this concept years ago. All I remember about it was the idea of being faster than your opponent. There was also the idea of constantly increasing your speed. But now I can review the details of this concept and benefit from it.
This is off-topic but as the only MiG-rated pilot (15, 17 and 21) in this conversation I feel compelled to comment. The main Korean fighter was the MiG-15. Chuck Yeager flew a captured MiG-15 and determined that it was slightly slower than the F-86, but only by a few knots. The MiG-15’s cannons had more firepower and it was superior to the Sabre at high altitudes. The F-86’s boosted controls were nice and that’s exactly why they were added to the MiG-17 (nicest of the Soviet fighters of that era) but weren’t considered worth adding to the MiG-15. At the end of the day what gave the F-86 its superior kill ratio came down to two things: 1) its RADAR gunsight, and; 2) superior American pilot training.
@Steve: Funny you bring that topic up as it’s something I once did a report on years ago. So, not to get too far astray from the topic of Bob’s article, I have to disagree with your assertion:
Sabre jets most decidedly did not “outperform” MIG jets. Combat experienced U.S. pilots (with G-suits) outperformed extremely inexperienced Korean and Chinese pilots (without G-suits). The MIG and the Sabre were actually extremely evenly matched and I suspect had the experienced well-equipped U.S. pilots been dogfighting experienced well-equipped Russian pilots, the lopsided results would have been way different. Thank God that never happened!
The image of stock-trading programs reacting to the decisions of other stock-trading programs makes me think about self-driving cars. What happens when one, traveling at freeway speed, decides to make a sudden course correction that causes another self-driving car to adjust, which causes an inattentive driver to…well you get the idea.
Yikes – so true.
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Your thoughts made me think of this dark TV I watched recently where a serial killer, talking about his first kill, said “I found myself unchallenged by the law or the divine and I thought to myself – I could do this again, but better.”
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We already can’t explain exactly how the stock market is impacted by all these traders and Google admits that they can’t exactly explain how (with the data they fed it and the instructions they provided) their software can translate reasonably well between two languages where it has no pair-examples.
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As we make the algorithms better and teach them to test their own evolution by making small adjustments and assessing the response/reaction/results, we risk bigger issues if we don’t think far enough ahead of our creations, not just where they are today, but where they could be tomorrow and the next day, especially if they are allowed to work with or alongside other self-evolving algorithms.
The “gut” is a fetish, an ersatz, an object that is substituted for a competent object.
If we are post-decision, then it is because we are post-truth.
If we are post-truth, then (with apologies to Bob Barker) it is because we are post-consequences.
And if we are all of the above, then we are post situational-awareness. All together now: “that doesnt end well!”
But the original goal was to evade consequences. The rest (including the glorification of the “gut”) followed.
We can’t be “post”-truth, because truth was never an issue. We haven’t moved beyond it because we’ve never been there.
Re: “If we are post-decision, then it is because we are post-truth.” Well phrased. Thanks.
[…] I, Cringely […]
I think Apple are far from paralysed. They have made a very careful analysis of the desktop, laptop and mobile platforms and interaction models and are executing a comprehensive strategy based on that analysis.
Just look at the latest MacBook Pros. The touch bar is the logical result of their conviction that the desktop/laptop interaction model is based on a horizontal interface. Rather that bolt a touch screen onto a desktop/laptop system, breaking the horizontal model, they came up with a new way to make the horizontal interface dynamic. Instead of putting a touch interface on the vertical screen, they put a horizontal screen on the same plane as the keyboard and touch pad. That’s acting decisively based on a consistent vision. See also Apple Pencil from last year. Unparalleled precision, low parallax and high responsiveness. Measure twice, cut once. As against MS which just bought in commodity digitiser tech for the Surface.
Meanwhile MS and Google are taking touch based interfaces and adding them to desktop/laptop systems. MS by pushing on with Surface and Google by adding Android apps to ChromeOS. Because if you like toasters and you like fridges, obviously what you really need is a toasterfridge. See also MS developing tech to run Android apps on Windows Mobile. They’re too busy making sure it can be done and doing it instead of making a judgement about whether they should do it at all.
Apple are doing less. But what they are doing is what needs to be done. They’re like the sight shooters. While the gut shooters are putting half a dozen arrows all over the target, they’re putting one arrow in the bullseye.
I agree with this comment. Apple is sight shooting. A small number of carefully crafted products. That does NOT mean you always succeed. I don’t think Steve would have green lighted the watch.
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Contrast that with Microsoft and Google. They seem to have so many products and beta programs you can’t even list half. It’s a strategy of throwing shit at the wall and praying something sticks. Arrows all over the place. It reeks of desperation, rather than logical thinking. Cats that freak out every 3 seconds and leap at shadows. Embarrassing.
And I think this is a fertile area of discussion that deserves a column all to itself…
Bob, you opened this can of worms: “Apple, on the other hand, appears to be paralyzed.” 🙂
I’m inclined to think “throw shit against the wall and see what sticks” is exactly how Samsung approached the smart phone market.
Apple introduced the smart phone in 2007. South Korea dragged it’s feet in allowing the Iphone into its domestic market until around January 2009, just 6 months before Samsung got its Galaxy 6 out – giving Samsung a protected market base from which to build some volume and profitability from which to stand on. Then over the next few years they produced a smart phone for practically every market segment and every size – as did LG.
Then in fall 2011 they introduced the Note – a phablet with an enormous screen and clever io capabilities and stumbled into a market: people with poor eyesight or with a desire for more intensive use of the device. I myself own a Galaxy W with a 7 inch screen (only marketed in South Korea). Samsung threw enough shitt against the wall until something stuck. The Galaxy followed the Note into becoming a bigger phone. Samsung produced the edge which to my mine really didn’t find much of a market as no one else followed them there. Eventually the market has settled on a 5.5 inch large screen.
Apple’s tiny phone was soon losing market share to the Note and for about a year Apple’s stock tumbled a bit. Then Apple started to produce phone’s with bigger screens.
But it appears fairly clear to me that Samsung kept throwing shit against the wall until they found something that worked. Ironically the Note might be dead now. Luckily for Samsung they had to first line phones, so when the Note failed they could fall back on the galaxy.
Guess which impending President of which famous country makes decisions based only on instincts?
Prepare for launch fellas – we’re gonna shoot the moon soon.
Interesting article! It reminds me of my optimal calculus classes in Engineering school. We were talking flight control command and learned the hard lesson that there’s always a trade-off between the accuracy of the optimal solution and real-time conditions: you can spend two seconds on computing the perfect flap positions but when you’re actually moving them, the conditions around your wings have changed drastically!
It is the same in business: you can spend months, even years developing the perfect business plan, while the market conditions are changing all the time, crunching your assumptions and chances of success.
My take is that we need a bit of both approaches: being a sight shooter to explore quickly/roughly the options and then move to instinct mode for shooting!
As a programmer I am concerned about the over-use of ‘Sight Shooting’ algorithms in domains that really need to be ‘Gut Shooting’.
There is too much indiscriminate applying of techniques that work in fairly predictable physical systems (eg. archery, vehicle direction) or mathematical systems (games) to social systems (finance, traffic, news) and it’ll only take a phase-transition or two and the whole thing comes crashing down. Let’s hope we notice when it does.
“the understanding of numbers is so weak that they don’t communicate anything. Everyone feels that those probabilities are not real—that they are just something on somebody’s mind.”
Reminds me of Han Solo’s reply to C-3PO when the android told him the insurmountable chances of their situation: “Never tell me the odds!”
This is right on target. A look around the market (and I’m not talking about stock prices) will clearly reveal those who are primarily sight shooting and those who are primarily instinct shooting. But there are a few who are have a blended and balanced approach which seems to be positioning them to the higher ground. GE being one which the Enterprise tech giants had better watch out for as as well as Amazon.
Bob:
A long time ago, a skillful photographer told me about taking great photographs. He said that the photographer needed to VISUALISE the picture before hitting the shutter release. Steve Jobs visualised the product long before there was a physical product.
Now it’s perfectly possible for an algorithm (backed by some “big data”) to predict the short term future based on the immediate past. And then make some decision or another. But what about the so called “out of the box” thoughts which lead to the destruction of prior certainties? Is an algorithm likely to develop a hypothesis (say that the speed of light is a constant) AND THEN develop the consequential theory? Is an algorithm likely to wonder if all of Euclid’s postulates are true AND THEN develop alternatives to the geometry which had been held as “true” for centuries?
In both these cases, the algorithms would probably have the prior “truth” actually baked into the algorithms themselves. I will start to believe that there is no future for human thought when I hear about a “paradigm shift” in our approach to the world, a shift created completely by an algorithm!
Robert, you should check out Discovery Insights (insights.com). It’s a tool I was first given at business school and have since implemented in my businesses (and even personal relations – like my marriage!) that looks at high performance people’s behavior through a lens of their motivations and self-perceived strengths.
Your instinct vs sight shooters can also be seen as “red” vs “blue”. Each has strengths and weaknesses. Red’s are action oriented, perceiving themselves as people that get things done, and done fast. They scan a room and think, “I’m the only person that would get XXX done, no matter the challenges”. And they do. But they can seem arrogant, not as collaborative, and bossy. Blues look at the room and think “I am the only person here that will have taken the time to analyze the situation correctly, and made the right decision on my first try”. They’re perfectionists, but often it is an agonizingly slow process and often, they won’t make a decision at all because they dont feel they have enough information to proceed. There are other colors as well, as people bring other things to the table, but when it comes to high performance people, most people test in those two ranges.
clearly this minimizing of the tool is doing a disservice to the whole process! I do think it sheds a bit more light on how people act in this modern world and is more relatable than the archer explanation.
I don’t believe the theory about “ancient brains” that don’t understand numbers. Seems more likely that fast-vs-slow decision making is simply a personality difference that has always existed.
Nor do I think we’re in a post-decision age. People still either make decisions themselves or decide to delegate the decision-making to someone else. And one key factor in who gets to make decisions is trust.
How strongly do I trust the information I have? How far do I trust the decision-maker that I’m delegating to? How reliable has my gut been?
Algorithims and businesses and people have to show positive track records to gain trust. Past performance had better be an indicator of future performance if you want me to trust you. If a computer-managed algorithm is better at making decisions than a person, then I’ll trust the algorithm. But not completely. “Trust but verify” is still great advice.
Thanks for the heads-up that Michael Lewis is publishing a new book;
He is one of my favorite authors!
But how did you you get a copy of a book that has a publication date of 2016-12-06?
Amazon appears to only be taking pre-orders.
Not saying that people do the numbers, but one of the reasons that people don’t trust the numbers is that they don’t trust the models. Analysts report odds as if they are fact when they are based on an analysis of a necessarily limited set of data and it may not be the right set of data or the right set of weights. “People” don’t necessarily understand that, but they do understand that analysts get it horribly, horribly wrong often enough that they tend not to trust the predictions unless they confirm the bias they already have. I think if people had incontrovertible proof that the predictions were accurate, people would rapidly start acting accordingly, but that is unlikely to ever exist.
I read (most of) Kahneman’s book several years ago after I saw a review by … Michael Lewis. The book is an amazing description of human behavior. I was able to apply some of the concepts in my work and felt smarter for my efforts. Most people I mentioned this to didn’t get it. But comparing Apple, Microsoft and Google as “fast” or “slow” thinkers doesn’t make sense. All large companies are using data to help them make decisions, “slow” thinking. The decisions are different because the business models are different. Google sells advertising, Microsoft sells business productivity and Apple sells hardware through an emotional connection to their products. Why would you expect them to have the same approach? I think a better comparison would be a startup company with a single product and a “gut” feeling, vs an established company with tons of market data. Another interesting case would be Samsung’s decision to stop selling the Note. Was this an example of “fast” or “slow” thinking. I’m pretty sure they analyzed the data (slow) before making the decision. That data may have included the “gut” response (fast) of their customers based on actual surveys. Michael Lewis’ books are amazing. My gut response is that I will like this one 🙂
>All large companies are using data to help them make decisions, “slow” thinking. The decisions are different because the business models are different
But you can’t use data to design original products or original features. It’s like Henry Ford is reputed to have said, if he’d asked his customers what they wanted (data driven decision making) he’d have developed a faster horse. Data driven decisions like that lead to desperately misconceived decisions like supporting Android apps on Windows Phone. They don’t lead to embedding a watch OS powered touchscreen on a laptop keyboard. No amount of data would give you that. Data driven decision making lead to Steve Balmer and Blackberry dismissing the iPhone because it didn’t have a physical keyboard and all the data said that’s what customers wanted. Ten years later both platforms are dead or irrelevant. So maybe I was a bit unfair in our previous post. Microsoft and Google are using data to drive their decisions. It’s often just misleading data and poor analysis of it.
But Apples design decisions are not based on gut. They are based on deep thinking, careful weighing if options and unparalleled execution. The stunning thing is that they actualy do clearly explain the exact process that leads to these decisions. Steve Jobs was very forthcoming about the reasoning behind the design of the iPhone.
The touch bar is explicitly the result of their repeatedly stated analysis that the laptop interaction model is that the interaction targets should be on the horizontal plane, so instead of adding touch to the vertical screen they put a touch screen there on the horizontal plane where they believe it belongs. No data lead to that, just clear rational thinking. Maybe they’re wrong, maybe the touch bar will prove to be a useless gimmick, but there’s no gut to that choice. It’s all sight shooting.
This column IS about Trump. Rather than give you policies he laid out a story over the course of the election.
The Wall Street crash wasn’t computer trading, but the collapse of the new Bretton Woods system that James Baker was arranging, with stable pound, dollar, and mark exchange rates, likely to expand to other currencies.. Market had been slowly rising in anticipation of this new currency stability, then a sudden end of the talks took away these gains quickly.
[…] Welcome to the Post-Decision Age […]
Well Apple put a great emphasis on design and that is intuition. I’m not aware of any attempt to use logic from data to chose design. You do make mock ups and betas and see how people like them. There is of course a whole area called the arts and humanities that has its own methods.
[…] yes, the call of nostalgia is an emotional one, not a logical one. But we are not logical beings. I think the past election is proof of that. I’ve certainly seen enough in life to […]
[…] recently read a piece by Robert X. Cringely, Previous Welcome to the Post-Decision Age, where he reviewed Michael Lewis’ latest book The Undoing Project. Lewis’ book is […]
You have to start with the right thesis or question. Data is utterly useless if you don’t know what to ask. Asking the right questions involves years of work and experience in one or more fields of study and “foresight and innovation” to look beyond what is done today to what “could” be done from where we are. Steve Jobs was special because he ignored the bean counters and executives and did it. And he did it not once, but 4-5-6 times. He had the resources and backing “most of the time” to be successful, which most of us do not have. It is a combination of expertise and attitude coupled with the ability to “talk people into” your idea. I believe the data people are out there now to say you have to “prove it”. Why is Google successful? Because more smart people are trying more things at a time at a company than at any previous time in history. Many or even most of the “algorithms” will fail and be gone in 5 years. One idea in 100 will succeed wildly and pay for all the failures. Bean counters don’t get this, and they try to get innovators to “justify” their work, and I think you do need to be able to communicate your “pitch” and the core idea/algorithm well enough for others to understand it. And you need to see if someone else did it first (but that may not matter if they did it badly or don’t have financial support). The data craze is because we all have computing to sell and we like to tell people that they need lots of metrics to prove their point. But it is useless if you don’t ask the right questions first, and ensure that your data is clean. Optimize after you find the right problem to solve.
“Lies, damn lies, statistics, and probability”
“Create a desire, then satiate that desire”
I get the focus of your article is narrow, perhaps with intent. I hope you see more than you talk about, as many do these days to my personal frustration. That said…
Some appear to have faith they can faith in the “mathmatics” to “prove it”. What I see happening is Big Data working on Big Sample to manufacture money by adding a bit/lead to people and moving them into “profitable enterprise” outcomes, in part. In very small part regarding what is real about all kinds of people.
There is false authority, authority, love (agape), but also afflicting people in order to be able to comfort them. Using a left arm to give affliction so a right arm can comfort someone is not necessarily loving but could be seen as generating “ad revenue”. Create a fake-need, then fulfill that fake-need. However, if registered with the government, kept and bared, is perhaps even “legal” these days in the country in which I am living. There is only one source for actual love and actual law, however.
This principle of “access to Big Sample” as an ability to lead people by their “preferences”, so adjustable when not based on the golden rule(s) (at the very least) is not the way I prefer to exist, but is something I look around and see with an eye to see that is not my own. Faith and testimony will overcome some of this for some, but adding not gold to gold by “rich oppressors” (James chapter 5) has been a feature of these days, and not just the ($)USD exportation of financiallization’s money manufacturing. I have noticed the output of excess USD “manufacturing” via debt-minting hiding in the increasing prices of Wall Street indices, the ability to pay for those forced out of the labor market by a worship of productivity, and supported by “shipping” overseas much of our massively increasing prices. (Apologies to any of the wise who see this better than I and can accuse me of missing the obvious.)
Hopefully many know, but few speak. I don’t run into this take on these topics often in others; perhaps this is part of my role along with a few others like me, however.
Bob, did you teach sight shooting, “instinct shooting”, or both? Just wondering,
I enjoyed your article & the discussion.
The algorithm based society was predicted in a 1991 non-fiction book titled “The Triumph of Mathematics.” In it, the author sanguinely shows the reader how divining truth and reality (and thereby making decisions about how to live one’s life) has evolved from revelation (BIblical/mythology/etc), literature and poetry (literature designed to reveal things unseen about ourselves) to scientific observation and eventually to data-driven processes. Wish I could find this book to re-read but I think it is out of print. In 1991 people like myself were still thinking that math had nothing to contribute to the truths revealed in literature, spiritual revelation, etc but it seems that math had already won the battle of truth-telling and new knowledge by that time.
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Hi Bob
Can you tell me what the name of that article is about the retired engineer who played the horses at Bay Meadows please? Thanks.
Even if you had the title and author, Cringely or Mark Stephens, it may not have been put on the Internet. I couldn’t find his article, although there are lots of hits from other sources, on the topic.
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