Debugging your mind
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NDC Oslo 20134 / 150
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00:00
Task (computing)Total S.A.CountingUnit testingNumberEmailTwitterDisk read-and-write headMultiplication signWebsiteProcess (computing)19 (number)Software bugMathematicsNeuroinformatikIncidence algebraCalculationPhysical lawObservational studySign (mathematics)Selectivity (electronic)Total S.A.QuicksortMessage passingSoftwareDecision theorySampling (statistics)Goodness of fitContext awarenessComputer virusTrigonometric functionsSineFunctional programmingC sharpTask (computing)Execution unitXMLUMLComputer animation
03:18
Mountain passVideoconferencingGame theoryMathematicsDigital photographyTask (computing)File viewerDemo (music)Process (computing)Error messageReduction of orderGenetic programmingSemiconductor memoryFundamental theorem of algebraAlgebraic closureExistential quantificationSemiconductor memoryTrailSoftwareHard disk driveRight angleMultiplication signDifferent (Kate Ryan album)Group actionQuicksortBitConnectivity (graph theory)CountingWord1 (number)Bookmark (World Wide Web)Information securityNumberComputer programmingRegular graphGraph coloringDemosceneDevice driverVideoconferencingSystem identification2 (number)Message passingPoint (geometry)MathematicsIncidence algebraEvent horizonWeb 2.0Projective planeInstance (computer science)Pattern languageContext awarenessLattice (order)Real numberIdentifiabilityAuthorizationGame theoryGoodness of fitError messageStudent's t-testDigitizingComputer configurationRevision controlPersonal identification number (Denmark)Computer animation
12:19
Error messageFundamental theorem of algebraAlgebraic closureManufacturing execution systemDrum memorySound effectJava appletScripting languageComputer configurationTheory of relativityExecution unitInsertion lossObservational study1 (number)DigitizingNumberFreewareAuthorizationAlgebraic closureMultiplication signDecision theoryFitness functionError messageMereologyFundamental theorem of algebraContext awarenessEquivalence relationSpreadsheetProjective planeAttribute grammarProduct (business)Information technology consultingPlanningSoftware developerPrime idealInternetworkingCodeGoodness of fitPhysical systemComputer fontCuboidDecision support systemDisk read-and-write headSet (mathematics)SummierbarkeitSound effectServer (computing)Game theoryPosition operatorEvent horizonWater vaporEndliche ModelltheorieExecution unitObject (grammar)Student's t-testTheory of relativityRight anglePhysical lawSelf-organizationBitUsabilitySpeciesComa BerenicesType theoryProcess (computing)TrailComputer configurationBit rateCore dumpOffice suiteCycle (graph theory)Computer animation
21:19
Insertion lossReduction of orderExecution unitFocus (optics)Demo (music)PredictionGame theoryEvent horizonNormal (geometry)Asynchronous Transfer ModeDecision support systemLogicInsertion lossSymbol tableObject (grammar)Lattice (order)Representation (politics)outputDisk read-and-write headFrequencySpectrum (functional analysis)Different (Kate Ryan album)Multiplication signDecision theoryMathematicsContext awarenessCurvePredictabilityHorizonQuicksortReduction of orderWordWeb 2.0Entire functionMotif (narrative)Core dumpMereologyEvent horizonGame theoryFiber bundleJava appletFreewareSet (mathematics)Goodness of fitState of matterCountingEndliche ModelltheorieRight angleNumberShape (magazine)Basis <Mathematik>ModemLibrary catalog19 (number)1 (number)MIDICellular automatonChecklistProcess (computing)Office suiteVideo gameProgrammer (hardware)CASE <Informatik>Water vaporBitObservational studyElectronic mailing listWritingRing (mathematics)Cycle (graph theory)Division (mathematics)Computer animation
30:20
LogicSoftware developerOperator (mathematics)Group actionData integrityType theoryLocal GroupEvent horizonSemiconductor memoryStability theoryFamilyFingerprintProcess (computing)Machine visionSurvival analysisBoom (sailing)Execution unitRadio-frequency identificationControl flowFocus (optics)QuicksortFocus (optics)Electric generatorMathematicsDegree (graph theory)Point (geometry)Event horizonCycle (graph theory)Propositional formulaInheritance (object-oriented programming)BitMultiplication signType theoryVideo gameFamilyDifferent (Kate Ryan album)Semiconductor memoryMachine visionFacebookProcess (computing)Game controllerElectronic mailing listSet (mathematics)Group actionAnnihilator (ring theory)Host Identity ProtocolChaos (cosmogony)Pattern languagePower (physics)CausalityCivil engineeringAngleRight angleCombinational logicBoom (sailing)Number40 (number)2 (number)Similarity (geometry)1 (number)WeightBackupStability theoryCASE <Informatik>Network topologyComplex (psychology)ComputerFile archiverRow (database)Survival analysisObservational studyFitness functionDecision theoryPeer-to-peerComputer animation
39:20
Boom (sailing)Type theoryNumber theoryFingerprintProcess (computing)Focus (optics)HierarchyData modelMereologyContext awarenessRule of inferenceLevel (video gaming)InformationTask (computing)Dependent and independent variablesEndliche ModelltheorieExpert systemTerm (mathematics)Software frameworkDevice driverLevel (video gaming)Call centreGroup actionSoftware developerElectric generatorEndliche ModelltheorieHierarchyPoint (geometry)Computer programmingLattice (order)Focus (optics)Process (computing)PredictabilityInformationMultiplication signSource codeOrder (biology)ProgrammschleifeSoftware frameworkDomain nameInteractive televisionMereologyCompilerAreaError messageStress (mechanics)Context awarenessSoftwareDecision theoryParallel portExpert systemPlastikkarteLine (geometry)Service (economics)Artificial neural networkNeuroinformatikFamilyWordNetwork topologyFundamental theorem of algebraTouchscreenTask (computing)BitDifferent (Kate Ryan album)Punched cardRule of inferenceCycle (graph theory)Library (computing)Euler anglesRecursionComputer iconSpacetimeView (database)FreewareField (computer science)Open sourceProgramming languageShooting methodRight angleMixed realityMoment (mathematics)Formal languageStudent's t-testMetropolitan area networkSoftware testingInstance (computer science)Maxima and minimaObservational studyEvent horizonBus (computing)Computer animation
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Level (video gaming)Context awarenessInformationTask (computing)Software frameworkSource codeRule of inferenceExpert systemFocus (optics)Data modelMereologyInstallable File SystemFormal verificationProcess (computing)Metric systemStatement (computer science)RootException handlingInformationDecision support systemExpert systemLine (geometry)Source codeRight anglePerturbation theorySoftware design patternBinary fileRule of inferenceComputer programmingFactory (trading post)PurchasingInformation technology consultingFocus (optics)Software testingLevel (video gaming)MereologyPhysical systemCoefficient of determinationChainQuicksortLink (knot theory)Vulnerability (computing)Open sourcePlastikkarteProjective planeTwitterLoginEmailNumberFormal languagePattern languageMathematicsTraffic reportingBitGreen's functionComputer-assisted translationStatement (computer science)CodeFeedbackHTTP cookieJava appletMultiplication signMetric systemRootObservational studyComa BerenicesDivisorInheritance (object-oriented programming)Address spaceGraph coloringOracleInternetworkingPosition operatorReading (process)Image resolutionFacebookCalculationComputer animation
57:22
XMLUML
Transcript: English(auto-generated)
00:06
All right, hello. How is everyone? Thinking about lunch already? Yeah, yeah, okay. Dynamite. You having fun so far?
00:20
Okay, that's good, that's cool. All right, well, so if you're anything like me by now, you've been to a whole bunch of talks and you've heard stuff about C Sharp and functional programming and Agile and this and that and your head's about to explode, right? Good, cool. Well, this will help. This will really make it just burst out.
00:40
So I love this background picture here. I would if my clicker worked. Come on, clicker, you can do it. There we go. Do you know what this is? This is, in fact, the first bug. You've heard the story about when they were debugging the, what was it, the Mark II relay calculator, September 1945, and they had this problem.
01:06
This was a relay calculator. They're going through running a sine and cosine unit test, bless their hearts. They're running it and there was a bug and they found there was a moth trapped in the relay. They took the moth out, taped it to the log book,
01:21
and that was indeed the first incident of debugging a computer. 1945, an old joke. So we've had bugs in computers for a long time, sometimes literally so, but as it turns out, we've got bugs in the very way that our brains work.
01:41
We've got quite a few of them, in fact, so I want to go through today and just show some of the more interesting or amusing or common bugs that affect our decision making, how we write software, how we do agile processes, all these sorts of things. The first thing I want to talk about is how we're creatures of habit.
02:05
So here we've got the policeman out to give tickets behind the sign that says, slow down, the cop hides behind the sign. Clearly he's still hiding there, just a creature of habit. But it's kind of funny, they do a lot of studies on selective awareness
02:24
and gradual change and it turns out we're really not nearly as observant as you might think we are. And I've got a couple samples here just to show how that works. The first thing I want to show is a fairly difficult counting task.
02:43
So what's going to happen is you're going to see a bunch of kids passing a basketball, some wearing white shirts, some wearing black shirts. And the idea is you've got to really focus on the players in the white shirts and count how many times they pass the basketball.
03:01
Now it's not impossible, this isn't like one of those trick site questions. You can actually do it, but you just have to focus very closely and count the total number of passes. All right, you ready? Okay, Twitter down, email down, here we go. We're going to count the number of passes. Count how many times the players wearing white pass the ball.
03:56
The correct answer is 16 passes.
04:00
Did you spot the gorilla? For people who haven't seen or heard about a video like this before, about half miss the gorilla. If you knew about the gorilla, you probably saw it. But did you notice the curtain changing color or the player on the black team leaving the game?
04:22
Let's rewind and watch it again. Here comes the gorilla and there goes a player and the curtain is changing from red to gold.
04:40
When you're looking for a gorilla, you often miss other unexpected events. And that's the monkey business illusion. Learn more about this illusion and the original gorilla experiment at theinvisiblegorilla.com. So I used to show the original version of this, just the regular gorilla
05:02
experiment, where you've got the kids passing the basketball and the gorilla walks in. And everyone watches the video and they're like, I got 15, I got 17. It's like, great, did you see the gorilla, the what? And you show it again and it's like, how can you miss it? You see it the second time and it's like, how do you possibly miss this?
05:21
But we do. So this has been around the interwebs. People got used to it and it's like, oh, I know this one. I know about the gorilla. Yeah, great, did you catch the curtain changing color? Did you catch the player leaving the scene entirely? Right out the edge. But that's OK. This is a very busy video.
05:42
There's a lot to keep track of, a lot of motion. You're trying to keep track of the passes. So let's try something a little simpler, perhaps. I'm just going to show a couple of single, still photographs. Nothing's moving, but something's changing. Raise your hand when you spot the change.
06:23
How'd you do? Ooh, oh, dear. That whole bit of greenery went away. OK, let's try the next one, then. Now you got warmed up, now you know what to look for. We'll try the next one.
06:55
OK, a little better. OK, only a little better.
07:18
OK, this is an easy one. You'll definitely get this.
07:37
Yeah, the entire side goes away. So it's interesting.
07:42
We're really just not well equipped to notice slow moving things. Gradual change creeps up on us and we just don't notice it. This is a real problem. If you're on a software project, what's changing? Everything, right? The requirements are changing, the technology is changing,
08:01
there's stuff changing all around us, and we're not really well equipped to see that or to experience it. So that's kind of the first interesting thing. But I like to go on, and that's just sort of fun. We're blind as bats. OK, fine. But I want to talk about some actual sort of processing
08:21
errors, if you will, in how our brains operate. If you look on, say, Wikipedia, and you look up cognitive biases, there are some 90 common known cognitive biases. I've certainly met people who can exceed that number by a good bit.
08:43
But there's 90 common ones. And I'm just going to go through some of my favorites here and just talk about them for a little bit. The first is relying on memory. Our memory is so bad. We tend to think of memory as like a hard drive.
09:01
Something happened, and it gets written to disk, and we've got it. Maybe we'll forget it, but at least what we remember is accurate, right? Now, it doesn't work that way. In fact, the way that memory works, every time you access a memory, you change it just a little bit.
09:22
You access it enough over the course of a lifetime, it can change quite a bit to the point where that incident that you think you remember when you were six years old probably didn't happen that way. Maybe it didn't even happen at all. It's very easy to implant false memories into people
09:42
and to suggest things were very suggestible. And every time you access a memory, that rewriting takes place in your current context, how you currently understand the world, your current age, so every time you're accessing it, it changes a little bit. This can get really difficult on a software project.
10:04
You're sitting there, it's months and months into it, it's like, why did we decide to do X, Y, Z again? Oh, well, because, and you're going to get 15 different answers, because everyone will remember it differently. This is why, it used to be good practice
10:20
to keep like an engineering log or something, where you just scribble some notes to yourself, you know? Well, we had this meeting, we decided to use this component because X, Y, Z. That's a great thing to do, because then months later, you can go back and look, why did we decide to do that? Oh, yeah, we thought that at the time.
10:41
Okay, makes sense. This is different now, now we can do this, whatever and ever. But if you rely on memory alone, even communal memory in the team, it's very foggy, not reliable at all. Some other things that can throw us off the track a bit. There's this great experiment
11:01
related to a phenomenon called anchoring. The way this works is, our brain is always seeking patterns, trying to find stuff to compare, but it's not always so picky about what it compares, and it can get confused really easily. So for instance, if I started talking about
11:21
the fact that my publishing company, our publishing company, the Pragmatic Bookshelf, that we have hundreds of books for you to peruse, we've got hundreds of authors, and I keep tossing out this word hundreds in various contexts, and then I say, here's a nice leather-bound edition of Programming Ruby for $85. Your brain somewhere says, oh, that's less.
11:42
That's a bargain. It has nothing to do with it. They're completely unrelated. Doesn't matter. Your brain seizes on that. So they did an experiment with undergrads doing bids in an auction, and before the experiment, they prepped the group and said,
12:00
okay, if your social security number, your national identification number, your driver's license number, whatever you have, take your number out, and write down just the last two digits. That's all. And now we're gonna go ahead and bid on this auction. And what they found was people in the audience who had relatively high last two digits
12:24
tended to outbid the other students by anywhere between 60 and 120%. They bid twice as much on the objects. They perceived their value to be twice as much solely because the last two digits
12:41
of their ID number happened to be high. We do this kind of thing, unfortunately, all the time. The marketing people know this. The politicians know this, and they use this to their advantage. You anchor on something, and then you make the wrong decision. We'll talk more about that in a bit.
13:02
Another interesting thing is the fundamental attribution error. And I just love the picture. The problem here is lack of context. We have an innate system. Maybe it's some kind of ego prevention, but if I've done something terrible to the team
13:21
or something bad to the code or whatnot, that's because I had a bad day. I missed breakfast. I didn't get a good night's sleep, whatever. You have kind of an excuse for it. If you do something bad, it's because you were born that way. It's because you are from this part of the country or that, or from this country or the other,
13:40
or this political party, or this race, or this species, well, whatever it is, it's because you are you that this happened, whereas if I did it, oh, that was just context. That was just whatever. So we have this mistake of attributing something to the fundamental behavior of a person instead of the context that a person is in.
14:02
Something to bear in mind. You can see this a lot on internet troll fests when the comments start flying. It's not that your position is wrong. It's you are an idiot and your mother has had sex with farm animals or whatever. It degrades pretty rapidly. So just something to bear in mind.
14:21
It's context. It's all about context. Now, the next one is particularly bad for agile projects and agile adoption, and that is a built-in need for closure, and Dilbert sums it up quite nicely. Dilbert says, I didn't have any accurate numbers,
14:40
so I just made this one up. Studies have shown that accurate numbers aren't any more useful than the ones you make up. How many studies showed that? 87. You laugh. The problem is, and this one is really pernicious because every project starts off and some bean counter in accounting says,
15:04
there's a spot in the spreadsheet I've gotta put a number in. How much is this gonna cost me? And you give the agile answer, well, we don't know yet. It's exploratory. We'll find out. No, no, no, no. I need a number to put in the box. I don't have a number.
15:20
I need 87. Good. Doesn't matter. Doesn't matter. And this is a curious thing. All parties concerned, including the requester, are well aware the number you just pulled out is rubbish. It's garbage. They know that. It's okay. It's a number. I have satisfied my need for closure.
15:41
I have a number. I know it's wrong. I don't care it's wrong. I have it. I can get my hands on it. I'm happy. This happens unfortunately all the time. You say, well, what technology are you gonna use here in this project? Well, we haven't decided yet. I gotta fill that out.
16:00
I have to know. Okay, fine. We'll use SQL Server. We're not really. But fine, I write it down. Oh yeah, we changed our mind. We're using Amnesia instead. We're using MySQL or we're using what have you, Mongo. But you've gotta have that need for closure. This flies in the face of Agile development where we want to leave all our decisions open as long as possible,
16:21
defer decisions and make them late in the game. This is a really hard one to get by. Some folks have an easier time with this than others. A lot of folks, this is really hard. They have to have closure even knowing that the number's wrong. Confirmation bias, another popular one. You see this a lot on the TV news cycle.
16:43
Any facts or events that don't fit your world view, you just throw out. So you just amplify the stuff that you agree with. And of course, the whole world thinks this way because look, everybody agrees with it and you throw out the rest of it. Entire books. Every author who writes a book with any opinion in it,
17:01
you could argue that's just a big example of confirmation bias because they're gonna throw out the other stuff that disagrees with them. That's just how we work. The exposure effect. This is a great one. The more often you see something, the more acceptable it becomes to you, the more familiar it is.
17:21
Politicians know this very, very well and that's why they will do everything to get their face out there. They will place sacks in the bar. They'll get their picture on the poster. They make sure you know their name. Even if they're a scumbag, you know the name. So when the time comes, you're like, okay, here's a name I know versus some name I've never heard of. You're gonna go with the familiar.
17:42
It's the way it works. You see something over and over and over again, even if it's horrific, even if it's horrible, even if it is the intellectual equivalent of broken glass on a toilet seat, it's familiar and you will stick with it to your detriment. Even if the plane is crashing, you will stick with it.
18:04
Familiar is not good, but we conflate the two. Just like in that anchoring experiment, we just get kind of muddled with it and we confuse them. If something is damaging your project, if it's not good, dump it, get rid of it. Doesn't matter how familiar it might be.
18:23
There's the Hawthorne effect. This is a lovely one. This is the deal where consultants come in, they're studying your team, studying your organization, and suddenly everything's working great. They've got some new giga they're trying to sell, some new process, some new tool. Everyone's using it, productivity's up,
18:42
numbers are great, everyone's happy. What happens when they leave and stop watching? Everything goes right back to the way it was before. We work differently if we know somebody's watching, at least for a little while. Relativity. So when, what company was it made the first bread maker?
19:03
I've lost track, and the font's too small. Williams-Sonoma. They made the first automatic bread maker. This thing was a marvel. You open up this little R2D2-like box, stick some flour and water in, hit the timer, and some hours later, a baked loaf of fresh bread comes popping out of it.
19:21
This was a marvel in, what, the early 90s, I suppose it was? An absolute marvel. Problem is, they were the first to market with it. So how do you price the thing? Oh gosh, I don't know. So they picked a price, set it, put it out there, heralded this technology.
19:41
This was incredible. Fresh baked bread, and all you do is hit the button. Nobody bought them. Sales were modest, at best. And they're scratching their head. They didn't know why. So they fished around, talked to some psychologist types, and said, OK, I know what we're going to do. We're going to keep this at about the same price it was,
20:02
250, doesn't matter what units. Pick a unit that's comfortable to you. But we're going to come out with the luxury model, the high-end model that does many more things, and price it at twice the amount, and put those two out there. Suddenly, no one ever bought the big one. But now they bought the middling one,
20:21
because it was perceived to be a bargain. Because you start off with the question, well, OK, is this a good value? Well, I don't know, what does a bread maker cost? I have no idea. They just invented the thing. But now, what does a bread maker cost? Oh, well, it either costs 500 or 250. Ooh, look, this 250 one is a bargain.
20:41
It's half the price. OK, right? I mean, they made these numbers up completely. These aren't even the real ones. But you make it up completely, because now you've got this false relativity to concern with. It's like, oh, this is less, I will go for it. We fall for it every time. We fall for free.
21:00
Free is not the same as cheap, however. Free is immensely valuable. Free is awesome. Free is better than one cent, or one franc, or one kroner, or what have you. In fact, when amazon.com first rolled out their Amazon Prime option,
21:20
you could pay a flat rate, and then shipping was free. And they're like, this is gonna be dynamite. They rolled it out in the US, sales took off. They rolled it out in different other countries, Asia, Pacific, Europe, sales took off. Worked great everywhere, except France. They rolled the offer out in France, nothing happened.
21:42
What was up with that? So they looked into it a bit, and they discovered, for whatever reason, the French division, when they did this, the free shipping wasn't actually free. It was cheap. It was like one franc or euro, whatever they had at the time. It wasn't free, it was very cheap, didn't count.
22:02
People did not fall for it. They changed it to free, their sales then matched everyone else. Popped right back up again. Why do we do that? Well, that's a good question. We're cheap. We like free. We have a very strong sense of loss aversion. We don't want to lose anything that we already have,
22:24
which is kind of funny. There's an example. I'm not sure if it's in this talk or not, so I'll just give it now anyway. Have you ever heard of the South Indian monkey trap? Wonderful thing. Apparently there's a lot of monkeys running around Southern India and you need to trap them, okay? So what you do is you dig a hole in the ground
22:41
with sort of a narrow entry and then a hollowed out part, and you put bananas or bait, whatever down in it. Monkey comes along, sticks his hands in, grabs the bananas, can't get back out again. The entrance is too narrow. Simple thing to do, right? Just let go of the bananas and you'd be free of the trap.
23:01
What does the monkey do? Yeah. Right, he's sitting there chattering away, stuck, because they will not let go of the bait. They are so loss averse, they will not let go of the bait and they get trapped. Happens to us in a similar manner. Sometimes you just gotta let go of it. Reasons we fall into some of these traps.
23:26
I wanna talk about symbolic reduction. So what happens here is we tend to focus even on the wrong questions a lot of the time. I had to move houses recently and as part of that, I had to clean up the back of my office.
23:43
So this was a very terrifying prospect. I'm sitting there going through this mountain of cables. And in the middle of this bundle of cables, I found an old 19.2 modem. Wasn't hooked up to anything, just this ancient modem in this ball of cables. And it's like, wow, it's like archeology,
24:01
right in the office. It was awesome. But I also found a bunch of old magazines from sort of mid 1990s kind of time period. Object something or other, Java something or other. A bunch of these kinds of popular magazines at the time. And the headlines were the same on all these magazines.
24:23
The huge questions of the day. Who will win the desktop wars? Will it be Motif or OpenLook? Wrong question entirely. Missed the whole web thing entirely. And right after that big debate,
24:41
and this went on like a year, two years. These were the headlines. Another set of headlines. Who will win the middleware wars? What will be the triumphant technology? Will it be Corba or RMI? Neither, right? Dead, missed the web, missed it completely. Missed the dominance of the PC.
25:02
Totally the wrong question even to ask. And all of these, you can go through other periods of history and find the same thing. Who's going to win, A or B? And the answer is almost always E, none of the above. It's none of the contenders you think it is. So we make predictions.
25:21
This is kind of part and parcel as how our brain works is to go in and try to see something and predict what's going to happen on a daily basis. Even just reaching out, grabbing a thing of water, walking, we're constantly making predictions. But to predict anything longer range
25:41
than say even a couple of minutes, we're really bad at it. Largely because some unexpected event changes the game. In that case, the introduction of the IBM PC, changing the face of the desktop, later the Mac, advent of the web, all these kinds of things. These were more or less unforeseen events.
26:03
So there's a great book out there whose name just went out of my head. Talib wrote it. Black Swan's in the title somewhere. Google will find it for you. But the idea of a black swan is that some very high impact, hard to predict event
26:21
will change the shape of what you're trying to predict. And in Talib's book, he's like, okay, anything significant in history, this is what happens. It's always the big unexpected change that comes in. The title of the book comes from the idea that for many years, scientists thought
26:40
there was no such thing as a black swan. There was only white ones, because they'd never seen one. And one comes trotting by and they're like, oh, would you look at that? Whoops, got to change that a bit. So why does this happen to us? Well, one of the ways the brain works is to reduce much of your input
27:01
to a kind of symbolic representation. So this is coincidentally why none of us probably can draw very well, right? So you're all programmers, tech people, right? Raise your hand if you're a great artist. Yeah, don't be shy, go ahead. Come on, yeah, right? No, it's not happening. Because we're so very good at manipulating and dealing with symbols, what happens
27:22
is we're very good at symbolic reduction. So when you say draw a hand, or even mention a hand, somewhere in your brain it's like, oh, hand, I know that. It's a stick with five lines. And then you go to try to draw it, and some part of your brain is shouting, what are you doing with all these curves and shades? It's a stick with five lines.
27:43
And there you go. So of course what happens is, in this reduced state in our brain, you lose all the detail. You lose all the nuances, all the context. And that's part of why we fail to see some of these big changes coming on the horizon. We just shrink the details up, and then we don't have them to access.
28:04
So, okay, that's all kind of bad. But these are structural things. These are processing things. There's not much you can do about it except kind of be aware of it. But there's worse things, I think, perhaps. There's things that affect the very core of our judgment and decision making.
28:21
And this is largely because we still cling to this idea that this is the representation of human decision making, that we're logical, we're rational, we're thoughtful, we think things through, we make checklists. Unfortunately, that's really not a good model of human decision making.
28:41
This is a lot closer to what's really going on. You got the gibbering monkey brain inside of you going, wow, squirrel! And it's, you know, if you really want a downer, read the book Predictably Irrational, which goes through and catalogs an absurd number of ways that we just think the wrong thing,
29:02
totally, all the time. But I want to talk here about something that's maybe less touched upon is things that influence our decision making. First one's kind of easy. You know, what's your basic personality? Are you the kind that values realism and common sense?
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Probably. Or the kind that values imagination and innovation? They're different. Folks can come down on either end of that spectrum. Some folks will insist on fairness at all costs, even if it hurts them personally. Others are more into empathy and harmony. Some folks have to be quiet
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when they're thinking about it. You think about it in their head till the thought is fully formed, then it comes out at the meeting. Some folks like us, the jaw just flaps. It just comes out while I'm trying to form the thought. I sound like a gibbering moron in a meeting because it's, well, you know, all this stuff's coming out and the person sitting at the end just waiting,
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they won't say anything for an hour, then come out with some fully fledged thought that's like, whoa, that's really bright. Of course, they haven't said a bloody word for an hour, so it took some time to cook. Some folks genuinely just want to be told what to do. Just give me the step-by-step, I'll go through it,
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I'll go home and play World of Warcraft, life is good. Some of us think that's horrid. I don't want to follow a bloody list of steps. I want to figure it out for myself. All fine, all valid. It's baked in differently for each person. This is gonna affect how you approach decision-making.
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So along this line, you've got the Myers-Briggs personality types. And this is kind of an interesting look. You can be one of these four values. And I'm not gonna go through them in any detail for reasons I'll explain in just a second. But if you look at the combination of values, you see some things that they put
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in these sort of classifications. You're a planner, you're a developer, you're an analyzer, a composer, you may be your promoter or motivator, explorer, strategist, this sort of stuff. Really kind of interesting different angles on how you might approach life and what your baked-in philosophy is.
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There's only one problem with this. It's a bit like a horoscope in that you really can't fit human behavior into four bits. It's just kind of a difficult proposition. There is some value to it. And it's worth it. If you haven't taken one of those Myers-Briggs tests, it's always kind of interesting,
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but you have to realize this is a self-assessment. It's gonna depend on your mood that day. Depends whether you answer with your work persona or your home persona. Maybe they're the same for you, maybe they're different. So it's a little bit fudgy, but it's kind of interesting to look at to see where your preferences may lie.
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But deeper than that comes with sort of what you value. Do you characterize yourself as a liberal, a conservative, an anarchist? Do you know how hard it was to find an anarchist logo? Just saying, that was less obvious than I thought it would be.
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But the problem is, okay, so you identify yourself one of these ways, left, right, conservative, liberal, what have you, but why? Were those your parents' values? Was that directly opposed to your parents' values? Were you just born that way? Yes, all of the above.
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This can all happen. But what's interesting is, do you know what makes the biggest difference as to what kind of values you hold and how this works? It depends on who else was born the same time as you. Your cohort, your peers, your birthmates. The researchers who look into this kind of stuff
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say that you have more in common with somebody born at the same time who lives in Mexico City or Tokyo than you do with somebody right next door who is 10, 20 years older or younger. So the power of the cohort is really quite strong because of the sort of shared memories,
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habits, styles, significant events that happen. Depending on your age, you might remember where you were when the Vietnam War ended. I remember that. When Elvis died, when Reagan was shot, 9-11. All these sorts of major events that happen that your cohort will remember the same way.
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Because what happens is, if you're 20 and one of these events happens, you'll have one outlook on it. If you're 40 or 60, you're gonna perceive that same event quite differently just because you're at a different point in your life. So you and your cohort tend to view events in a very similar way. And that sort of drives us a bit.
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I love this poster here because this is the one where the youngins are like, wait, which Star Wars was that? There's no number. So what kind of differences can this bring about? Well, different generational groups, some are gonna be much more prone to taking risks.
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Some will be very risk averse and not wanna hear about it. Some will really value individualism, others not so much. They favor more of a teamwork approach. Some value stability, others put their faith in freedom. And we'll accept a little chaos for that.
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Some focus on family, other generations more on nose to the grindstone work. So what's interesting is, according to such researchers, there's actually only four different kinds of generations and they repeat. So they did their research starting in Europe around the Renaissance and followed it over
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to the Americas. And in 20 generations of their study, there was only one gap in the pattern. And that was in the American Civil War in the 1800s cause we killed everybody. So there wasn't anyone there to kind of take their place in society as they would have normally. Other than that gap, they found this very regular
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repeating pattern of four archetypes. You have a prophet generation, a nomad, a hero and an artist. And in general, I'll give the caveat on generalities in just a second. The prophet generation values values, values vision.
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The nomad, not so much into that. They value their liberty, their survival, their honor. Or if you're in Klingon, their honor. The hero generation, much more into community, into affluence. Let's share all this together. The artist, pluralism, expertise, due process.
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Do it by the numbers. So they map this out onto the last couple generations and tacked cute little names on them cause that makes for good press. So you have the GI generation, which was a hero generation. People born back around the turn of the previous century. The silent generation after that, an artist generation.
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The baby boomers, the boom generation, a prophet. Generation X, the nomads, millennials, back to hero again. And then the newest homeland generation back to the artist. So a couple things here, first of all, a caveat. When you talk about a generation of people
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having this particular characteristic, it does not mean that everyone born at this time thinks this way. What it means is that if you take on the aggregate, a whole bunch of people, the folks born in this bucket will tend to share these values and these outlooks more than people on any other adjacent one.
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The dates are not firm. They're kind of, yeah, about then. So you can be born on a cusp year and you might feel partly this way, partly that way. These things happen, that's okay. But here's a question. Why does the cycle only go for four generations?
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Why does it then repeat? Well, that's kind of an interesting thing to ponder about. You probably knew your parents pretty well in most cases. You probably knew your grandparents, at least to some degree. What do you know about your great grandparents? The name, where they lived. What about their parents?
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Anything? Anything? If you're lucky, maybe you've got a family tree, you can pluck some names out, but you really have no experience of what their lives were like. So what happens after four generations kind of passes out of memory. Now, it'd be interesting to see if this changes. Now that you've got Facebook recording everything
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and NSA making a backup copy, you know, great. Hey, for archival, it's wonderful, really. We've got archival copies of everything. I don't have to worry now. But this kind of thing kind of falls out of human memory. Once we start keeping all that, and you can go back and look at great-grandma's Facebook posts,
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is that gonna change the cycle any? I don't know. It'll be interesting to see. So given the last couple generations that have happened here, you start off, you've got the GI and silent generations very hip on a very military metaphor. Business and military was conducted on command and control.
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The focus was more on process rather than necessarily on action. The idea is that, oh, as long as you've got a good process, as long as you follow it, it'll just turn out okay. Because the faith is in the process, not necessarily the outcomes.
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These folks are now in what? Their 60s, their 80s. These would be the elder statesmen of any industry, any generation. And this is where they're coming from. Next, you've got up the baby boomers. This was a profit generation. They wanted to save the world, collect the whole set.
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Again, less interested in outcome than approach, a tendency to what some other generations might call moralizing or preachy, because they're all about their values and do this and do that. It's like, well, yeah, okay. But then you get generation X, what some say raised by wolves.
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This was the greatest entrepreneurial generation in history, but rejects labeling. They have no logo. I want no stinking logo. When interviewed in these kinds of studies, they don't identify themselves as Gen X.
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They identify themselves as not a boomer and not a millennial, which is kind of interesting. If there's a problem, they'll quit and move on. I ain't fooling with this. Folks from other generations will take different views of it. Well, someone should fix this. Well, I oughta fix this, whatever. There's different viewpoints.
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This group will be like, sod it, move on. Next, fiercely, fiercely individualistic. Then you get down to those kids today, get off my lawn. The millennials, they plan ahead, bless their little hearts. Much more upbeat, much more team and community oriented. They're not gonna save the world so much.
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They've got this more of an attitude of, well, somebody in charge should fix this. Kids, I got news for you. You'll find out. So these things kind of go through and cycle and repeat. Now, what I find interesting, just from a software development point of view,
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is if you look at the older generations, you see a much greater emphasis on rigid hierarchies and a just hell-bent-for-leather focus on process. You gotta do these steps in this order. Then that moves on to the more Gen X-esque, oh, just do it, individualism. I'm okay, bad luck to you, but just do it.
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And then a move into the more recent generations, well, let's get everybody involved. Let's get the team involved. This is more of a community thing. Could it be that one of the reasons that open source is exploding so much is because a different generation is coming up to do it, where they treasure that kind of interaction more than us old fogies?
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Perhaps, interesting to think about, but there's a definite parallel here to methodologies as they come down and how we develop software as reflecting the values of whatever generation is kind of moving through. Now, this makes it particularly interesting because at the moment, we've got more generations in play
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in the workforce than ever before. You got everybody's got their hand up. The poor old folks can't retire because there's not enough money and the young folks are desperately trying to get money for college. You got everybody in the mix, which makes it really quite interesting. So these are things that can subtly affect your approach and your decision-making that you're probably not consciously aware of.
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There's one more topic I want to go into and that's about skills themselves. So back in the 80s, the brothers Dreyfus, Stewart and Hubert, wanted to research artificial intelligence because they wanted to build an artificial intelligence that would learn skills the same way that humans did
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and thought this would be a dynamite idea. Only one problem. They had no idea how humans learn skills. So they had to research that first. They never got around to the AI part particularly, but they did come up with this idea of the Dreyfus model of skill acquisition. And what happens is you start off anything as a novice
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with no experience and work your way up toward expert. And along the way, a lot of things change. The biggest thing they discovered was that as an expert, you're not just a really smart beginner. You fundamentally perceive the world differently. You approach problem solving differently.
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You value different things. Your decision-making process is different. The whole thing is different and gets that way as you climb up the ladder. So let's take a quick look at the stages in the Dreyfus model. Stage one, kind of predictable. You have no experience.
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You don't really want to learn. You just want to get it done. You don't know how to respond to mistakes and so are vulnerable to confusion. So if I'm learning a new programming language, I'm there. Right, this is it. I just want to get this done. What do you mean there's no loops in this language? You have to use recursion. Fine, okay. Then the compiler spits out, blah, blah, blah.
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I have no idea what that means. It could mean a permission problem on my installation. It could mean I left off a bloody semicolon. No idea, all right. I'm at a novice level at this particular skill. The only way you can succeed as a novice is if you're given context-free rules.
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When this happens, do that. And this is how call centers are able to operate with people answering the phones who really have no domain knowledge. They're novices in the field, but there's a decision tree. You know, my computer doesn't work. Is it plugged in? Yes, no. And they go down the tree, you've got rules. Note on the word experience here,
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experience in this context means I've done something and I've learned from it. So by that definition, I've filed my taxes for 30 years now. I have no practical experience at it. I'm still a novice. I still don't care. I just want to accomplish a goal.
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I don't know how to respond to mistakes. The tax service sends me this big yellow letter yelling at me, and I have no idea what they're on about. I send it to the accountant. You have to give me context-free rules like take all your money, send it in. Okay, that's, seems about right. So novices need recipes.
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They need to know, okay, I do this, then I do that. And then you can go on from there. Stage two, you grow up a little bit. You can start trying things on your own that are off the recipe card. There's still difficulty troubleshooting because you've got no sense of the big picture yet,
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no mental models to work with. You want information faster now. So you're learning a new language. It's like, okay, I want this thing out of their standard library. Where is it, where is it? That one, yeah, okay, wrong one. Okay, blah, blah, blah. It's frustrating because you kind of want to suck the information in very fast. You can begin to follow someone else's advice
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a little bit, but not great yet. Again, you don't have that overall understanding. And worse than that, you don't want the big picture. The big picture at this point in time would just be confusing to you. And you see this problem sometimes in like large corporate meetings where they have an all-hands meeting and the sales guy gets up
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and he's talking about sales things and you're like, oh, just gnaw my leg off and get me out of here. I don't care about this. Because you are a novice or advanced beginner at that particular topic. So it really doesn't make sense to you. It's just frustrating and annoying. Then we get up to competent.
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All right, now we're starting to get some traction. At this level, and now for the first time, you begin to develop conceptual models of the topic area. You begin to understand that programming language. When a syntax error comes up, it's like, oh, that's because it's parsing a line like this and it didn't see my comma, semi-colon, whatever it was.
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You can start to troubleshoot problems now that you've got a bit of a model. So the big leap when you get to step three is that you can start to troubleshoot. And that's important. Now we start getting into the real fun. You get up to proficiency. Now you really have to understand the big model.
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You want to understand the larger framework. You get very frustrated with oversimplified information. You call up that helpline and say, I'm getting a blue screen of death because of this new device driver I just compiled. And they say, is it plugged in, sir?
47:41
Punch him right through the phone. You can self-correct previous poor task performance. Now this is important. This is only at level four. And one of the things that an Agile process says is you should learn from your mistakes and correct them. That's dynamite if you're a level four practitioner. Less than that, you need help.
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You need a coach. You need a mentor. You need somebody who knows what they're doing because you don't have the skill to do it yet. Level four you do. You can learn from the experience of others. You can understand and apply maxims, proverbs, fundamental pithy sayings that make sense to you in this context.
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For instance, an XP has a saying that says, test everything that could possibly break. Dynamite, great advice if you know what it means. If you're a beginner, what can break? Hell, everything. Getters and setters could break. Log statements could break. I wrote it. Everything could possibly break.
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Up the chain a bit, it's like, okay, you're not gonna bother with the stupid stuff, but this big hairy calculation, where there's even an odd and a divide, I know I'm not gonna get that right, okay. I know my strengths and weaknesses. I know what's likely to break and what's not from my experience. If you're less experienced, you just don't know.
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And this is where you get into trouble with things like design patterns, for example. The Gang of Four book comes out and documents the, what was it, 24, 27, 23 design patterns, and experienced people are like, okay, I recognize this is kind of cool. These are patterns that make up for deficiencies in the C++ language that was popular at the time.
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If you had a real language, you don't need half of these, but that's fine. And everyone else is like, wow, recipes. So, true story, I knew this guy on a project. He was writing this little bit of report writer code. He just read the design patterns book, and in this hapless little piece of code,
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he wedged in something like 19 out of the 23 design patterns because he could. This also, by the way, is why you really want to do code reviews or pair programming. So proficient practitioners can self-correct. That's important.
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Now we get to the end of the line. We get to the expert. These become the primary sources of knowledge and information looking for new and better methods. Interestingly, experts work from intuition far more than reason. You know, you look at this design. Is my design good?
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No, it sucks. Why? It just does. They know it, they're correct, but they're often very inarticulate about how they arrived at decisions. The doctor looks at you, it's like, oh, you've got such and such syndrome. And they'll run the tests and you're right. How did you know? I don't know, he looked wrong. It's just, it's so ingrained, it's so baked in,
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I can't really articulate it. Also, if you force the expert to follow the rules that you set down for the novices, you drag their performance down to the novice. And they actually did this experiment with airline pilots and showed very conclusively that you wreck expertise
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by making it follow the rules because experts need to rely on their intuition. So you've got this overall movement from rules to intuition, from considering everything to being able to narrow down to relevant focus. And even such things as a novice will consider themselves
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over here and the project over there as sort of detached from it. The expert understands systems thinking and systems and realizes that they are a part of the system. Everyone on the team is a part of the system. That's a part of the company. It's all interrelated, interreacted. So they're much more aware of that kind of viewpoint.
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Whereas the beginner, not so much. So how you approach problem solving, how you view these things, the decisions you make here changes depending on your skill level. So even if you figured out, okay, I'm a left-leaning liberal who likes dogs and cats
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and whatever else from all that other stuff and my personality wants me to do this and whatnot, that's dynamite. That's all gonna change. It changes as you gain skill, changes as you get older. So I leave you in a bit of a quandary then. Your brain's really messed up. What are you gonna do about it?
52:20
Oh, my, look at the time. Okay, well, so we'll just go now. You want to, where possible, and as you get up the expertise ladder in some particular skill, you want to be able to start off with intuition. Once you have enough skill where you can rely on that, you want to start with that, but you can't stop there.
52:41
You have to verify it. And this is where some experts get into trouble. They get so used to relying on their intuition, facts change, things change underneath them. You experience that change blindness that we saw with the gradual change studies and the gorilla and the curtain and all of that. And so they begin to rely on their intuition
53:01
and forget to actually verify it and run into trouble. So we want to actually, where we can, place our trust in actual outcomes. Did the project succeed? Did the code work? Are we actually doing the right thing? And not trust to process alone or metrics
53:21
or wishful thinking, which is our most popular way. We get the wonderful Farside cartoon where they realize it's time we face reality, my friends. We're not exactly rocket scientists. They look it, they've got the lab coats, they got the little clipboards, but look at the outcome. The outcome didn't make it.
53:43
It is, he says with a pun, critically important to apply critical thinking. And I think in general, we're lazy and we just don't do this enough. We jump to conclusions, we make assumptions, and we tend to ignore actual facts.
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Actual facts become, well, I read it on the internet, it was on Reddit, it must be true. And this is a problem. We need to be better at observing something without bias, actually research statements. I wish, I honestly wish that every time somebody hit the post button on Facebook,
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it would automatically search through snopes.com and if there's a match, say, no, you just can't post that. It's stupid, it's a rumor, it's not real, stop posting it, this kind of thing. Look for credible sources, if they be any these days, and ask why.
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This is the favorite consultant's trick, right? You get into a situation and it's like, there's oil on the floor in the factory, why? Well, something must be leaking, okay, why? Hmm, now they're stumped, they go back, you get to five whys later, well, purchasing had to buy this kind of seal because of this corporate directive, and because of that, and this, and this,
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you go back and you find out the entire factory's got the wrong size seals in the ductwork because of some stupid PO memo that went through two years ago, all right? Never stop at one why. Keep going and going and going like a frustrated three-year-old, but why, Daddy? Until you get to the end and actually get to the root of the problem.
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And ask yourself, when you find yourself pontificating that, you know, Java is dead, or Oracle is dead, or everybody's programming in Ruby, or nobody's programming in Ruby, or Elixir's the next hot thing, or it's not, whatever your position, first ask yourself, okay, great, how do you know?
55:43
You know, says who? Are they credible, what are they basing it on? How specifically, what is dead, dead? No adoption, less adoption? Kick to the rubbish bin of history? How specifically, so on and so on. And there's some really interesting, penetrating questions you can ask yourself,
56:02
like what would happen if you did do something you didn't think you should do? What would happen if you didn't do something you thought you were supposed to do? Well, my personal favorite, what stops you from? And this was an actual exchange I had with someone a number of years ago, said, ask that question.
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What's stopping you? Nothing, I answered. That gets in my way a lot, too. With that, and a few minutes for questions, I want to thank you all for coming. My email address, my Twitter handle, link to my book, Pragmatic Thinking and Learning, that covers a lot of this kind of material.
56:42
Thank you all very much. And before I let you go or entertain any questions, I am bade to remind you to put your little cards in. This is a yellow card, it's a bad example, green cards.
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You want to put the green cards in the bucket outside to evaluate the talk. Feedback or comments may be written on the cards if you've got really tiny handwriting. So please do that for our good hosts, and are there any questions? Good, all right, go get cookies and coffee.
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Thanks again.