Mario has a Soul

Yes, "It's-a-me Mario" from 1985 Nintendo NES.

Here's the gist:  Having a soul is when somebody on one dimension temporarily controls someone on another dimension.   When the character in that other dimension "dies," the original one lives on.  Consider this:  If you've ever played any Super Mario Brothers, then Mario had a soul while you were playing him.  YOU were his soul.  When he died, part of him went with you.

Reaching In
Consider when you type out an email.  You are, in a manner of speaking, "reaching in" to the computer to type it.

Reaching in is, of course, the wrong metaphor.  I'm not sure what to call it.  But you're interacting through the computer.  Like, if someone would to ask "Who wrote that email?" we'd be hard pressed to say that the computer wrote it, even though, all you did was touch the keys and worked the mouse.

Why is that, exactly?  Because the control was from you -- operating from a different ...dimension, as it were.  (Ooh, I love saying the English "as it were."  It's such a classy expression, ten grammar points Brett!)

Inter-Dimensional Signal   (Very Nerdy Segueway, Please Skip)
If you want to get more technical:  You typing into Google is the very definition of an inter-dimensional interaction, even though people don't usually think of it as that.  Reading is, too -- transforming a one-dimensional signal (if you think about it, reading is just a long ribbon: information in one dimension), temporarily translated through a four dimensional signal, and then (depending upon how you want to think of it) possibly into a weird uni-dimensional signal which is consciousness, in which we reside.  Television is a two dimensional signal.  Radio is a one dimensional signal, translated into a three dimensional signal.  (Note: You can add another dimension to each if you wish to include time as a dimension.)

 Back to Google
 So consider this:  When you wrote something into Google, clearly that was you and not the computer.  What about if you play a game online?  A game like Mario Brothers?  Is that "you"? Are those "your" actions?   Or are those the actions of that character?  It's both.  Mario is himself, but he's also you. You live on, even after you totally led him into one of those goddamn biting flowers that come out of the pipes.

Well, it has been demonstrated (in several ways, in fact) that the universe is a richly detailed rendering.   (There are enormous consequences to that, which I've written about lots on this site -- four links, right there.)    If so, then the possibility remains that we are being controlled from without.  Just like how you control Mario.

That could be.

Screw You, Religion
Now, technically, there are a lot of people who would say something quite similar to this.  Indeed, many Orthodox religions would. "Yes, that's Heaven.  Are you ready to welcome into your heart now?"  

Yes, maybe it's real.  But I'm pret-ty sure (and that "pret-ty" implies an ever-so-slight pause between the t's, which is unbelievably annoying if you're on the wrong end of it) that it's not some goddamn Middle Eastern text (or Chinese or Tibetian or whatever) text.  And even if there ARE parallels, that demographic's credibility is total crap because until super-recently (historically speaking) they believed in Adam and Eve and Satan.  They fail the logic test, out of the gate.

The only one I'd give a pass to is Taoism.  They're cool.

What About Us?  Do We Have A Soul?
All of this, of course, begs the question:  Do we have a soul?

Well, I've though about this.  I've thought about it a LOT, from as many angles as I could.  And in the end, I reached a conclusion that I've actually quite confident in:   I have absolutely no idea.

I could really go either way on this one.  I could totally see that when we die, that we simply discorporate and that's that.  Or I could see when we die, that we discorporate here, but on some level our awareness pops out on some cosmic Playstation 4, and goes to get a sandwich.

However, what I have concluded is that if there is another dimension as such, that curiously the laws of religion apply:  You can't take material things with you.*  But clearly if you helped someone here, they would appreciate it up there.  Goodness passes through, even though material things don't.  And if you want to get serious, it wouldn't surprise me if you can Find Out What Happened.  Boy would that suck for, like, every person on the planet.


So if you're a colossal asshole down here, and you wake up there, they might still think you're a colossal asshole.  But if you're kind down here, maybe they'll buy you a cosmic beer.  So I'm going to try to be nice, if only to be the first trans-dimensional ingratiatingly nice person -- and I bet the beer is way better.

The Ultimate Analogy

Do you know how you understand things?  Do you know what your brain is doing, to take a concept and really get it?

Partly, the answer is rote memorization -- there's plenty of that.  But to the extent that you can intuitively grasp a concept, the answer is simple:  Your brain makes comparisons.

It's like this.  It's like that.  It's like a ball.  It's like a rope.  It's like running alongside a moving train.

Put simply:  You make analogies.

What the heck is an analogy, anyway?  
Most people think that an analogy is just a colorful example of some phenomenon or dynamic.  It's just a more vivid picture.  And indeed, it is that, without a doubt.  But it's more than that, too.  Or, at least, the process of finding an analogy is more than that.

Finding an analogy requires grasping the nature of something, and then seeking out something else with a similar nature.  Matching patterns.

It's actually the process of understanding something, laid bare.  You first observe, and then you "try on" (i.e., apply) various analogies:  You hold them up to the light, look them both from a variety of perspectives, and assess how faithfully they match.  Quite often, you discard a potential comparison, because there's a flaw:  the analogy does not sufficiently hold.

Oh here's another tidbit about analogies:  They cannot be perfect.  The only perfect analogy would be to compare something to itself, which wouldn't technically be an analogy -- it would be something known as a "tautology."

Nevertheless, some analogies hold up only from very limited perspectives, and other analogies go deep -- where even under varied circumstances the patterns continue to match.  I'm always delighted when I find a particularly deep analogy -- it happens so rarely, though, that I can't even think of a good example. 


I'm The Analogy "Type"
I'm a fan of the Meyers-Briggs psychology tests, and am an ENTP.  That means:

• Extroverted (vs. Introverted)
• iNtuitive (vs. Sensing)
• Thinker (vs. Feeler)
• Perceiver (vs. Judger)

See that "iNtuitive" up there?  Yeah, those're the people who use analogies.  Sensers (the other type) don't.  While Intuitives seek hidden patterns, Sensers are concrete observers.  They live in the clarity of the now, and pay close attention.

Indeed, Sherlock Holmes is the quintessential Senser: he had a preternatural ability to observe the world around him, at all times, to the finest detail.  Albert Einstein would be the quintessential Intuitive -- grasping the hidden laws of the universe, while no doubt forgetting where he parked his car.

Worst.  Analogy.  Ever. 
I once worked with an extreme Senser.  Hyper-literal and concrete, he was very precise, but often hard to fully understand.  Speaking to him was like reading the 2004 Acura TSX Drivers Manual, or reviewing a summary of your federal taxes:  Factually correct yet any comprehension comes only with much concentration.

Anyway, someone suggested that this person try to use more analogies like me.  He asked me if he could try some out.  I said sure, I was delighted to help!

So he says, "Okay, let me try one:  The reporting system is like a car."  Then looks at me, eyebrows raised for feedback, and there's a pained silence.

So I said, "Um, you have to say how it's like a car."  Oh!   He hadn't know that.  Good to know.

Needless to say, his analogies greatly improved, even if I usually winced at even the best of them.

Seeking the Ultimate Analogy
Anyway, I mentally compare a lot of stuff.  Especially when it comes to the Very Big (e.g., galaxies), the Very Small (e.g., quantum physics), and the Very Profound (e.g., meaning of life).

So consider this question:  What state of matter is life?  What is life "like"? 

I mean, if you had to draw an analogy to another state of matter, what would it be?  Granted, it's a part-solid, part-liquid mishmash, but neither solid nor liquid really captures its essence.  First and foremost, those states don't die and discorporate like we do.   You might be skeptical that there's any state of matter that dies.

But there is:  Fire.

Life is a very slow burn.  This fire can be transferred to new life (e..g, having children), but once it's out, it can't be lit again.  So if we have to compare ourselves to anything else that's out there in the universe, I think that Fire is our closest relative.

I'm not sure what this tells us.  Does it mean that perhaps stars are alive?  I don't think so.  I think they are unfocused burning.  But perhaps other forms of burning could also be considered life.  I do think that any form of life must involve some sort of burning -- life requires a flame.

I think this must be what those kids meant back in high school, when they called me a flamer.   I had no idea how right they were.

How Can Someone So Smart Do Something So Stupid?

Most people I know think that I am smart, but that I do stupid things.  You could describe many of my friends the same way.  If you're part of a particular clique of super-smart WPI twenty-something engineers, I bet you could be described this way, too.  (Note:  I hear that you do jello shots on garage roofs -- I rest my case.)

Anyway, take a look at the Title Question.  Has that ever been said about you?

My wife says that phrase approximately once every six hours about me.  And she means it every time.  I've heard my friends' wives say it about them.  Hell, I've heard our wives commisserating about how we're all that way.   What the heck does that mean?

In other words:  How the hell CAN someone "so smart" do something "so stupid"?

I think I've figured it out!

Before I go any further, I don't think this is necessarily a man / woman thing.   I have little doubt that literally millions of times per year, men say that about their wives.    At the very least, we can debate that some other time.  I just want to describe the phenomenon, not start World War III.

The So-Smart-So-Stupid Answer
The answer is that there are many dimensions of intelligence, and two are breadth and depth.  (There are probably many other meaningful dimensions, but these are two.)

For a given set of raw brain-processing ability, it can be divvied up into different attention-chunks.  So two people with IQ's of, say, 140, can allot their attention in different amounts -- and either to a few topics deeply, or more topics not-quite-as-deeply.

So here is precisely what happens, which leads to the Blog Title Question.

1. A "Deep" person can demonstrate a few tasks extremely well.
2. The "Broad" person observes that this person is really good in a very precise area.  Even if she has this skill herself, she senses the depth.
3. However, the "Broad" person also sees that there's some really basic things that said Smarty is totally screwing up.
4. This seems like a contradiction.

And hence:  "How can someone so smart do something so stupid?"

Epilogue  (I wrote this later)
So I wrote this post and was feeling pretty good about the article -- then I went back a few hours later and noticed something a bit ...untoward.  (Whoa, there's a word you don't hear much.)  Anyway, if you take the picture above (of the woman) totally out of context, it looks like I'm saying "This is the stuff that broads talk about."

That is not how I meant it.  And lest anybody say, "Yeah, but I bet I that's where the term 'broads' probably came from, so you're just as bad!  We're not broads!"  I would like to say that I'm pretty damn sure that the phrase "broad" originally referred to a woman's decolletage, not her perspicacity and parallel mental processing abilities.

Brett Finds God

All of my life I have been an avowed atheist -- well, at the very least, I'm unaffiliated from any run-of-the-mill Gods. 

Yet, I have read countless times about the benefits of praying.  Praying helps you focus your thoughts, and helps you live up to your own ideals.  Nevertheless, for the longest time, I thought this was one of the downsides of atheism:  No praying.

Then, a few years ago, I decided to forge ahead with praying anyway.

Nothing In Common With "Capital-G" God
I quickly concluded that if I were going to pray, it certainly would not be to the Judeo-Christian God.  I have virtually nothing in common with that Guy.   I mean, how boring is He: totally all-powerful, all-knowing, all everything. I cannot relate to Somebody like that whatsoever.

I need a lower-case-g god:  A god who is more human, more flawed, with strengths and weaknesses, not one who checks every goddamn box in the Awesome column.  Not somebody for whom I gotta capitalize my frickin pronouns when I refer to Him.

God Shopping

I'll Pass

So I went god shopping. I inspected a few of the Hindu gods that had promise -- but nothing panned out.  I discovered that I have a natural aversion to praying to any god with any of the following:

• A trunk
• More than two arms
• A pastel complexion

Then I investigated the Christian saints: So lame.  Christian saints are to mythology what Thomas the Tank Engine is to children's toys -- they took something that could be cool and made it uptight and stodgy. 

But then I hit the treasure trove: the Greek gods!

Here's my take on each God:

Asshole Divorcee

Zeus:  The ultimate father figure, but more of a selfish, divorced dad than Mike Brady.  Too philandering and bossy -- kind of an asshole.  If he were a mortal, I have little doubt he'd drive a Corvette, date twenty-something waitresses well into his late 40s, and occasionally have chlamydia.

Hera:  Meanwhile, if Zeus is the prototypical divorced dad, then Hera is the bitter ex-wife.  It seems like she spends all of her time bitching about Zeus (not that he doesn't deserve every bit of it), rather than just getting on with her life.   Hardly inspirational.

Achilles:  Sure, Achilles is a great warrior, but in kind of a single-dimensional way.  He doesn't really represent my values. Plus, do you know why Achilles sat out the first half of the Trojan war? It was because Agamemnon filched his most attractive slave (Brisies) for himself! I could hardly worship somebody who took sex slaves in the first place, much less was happy to stand by while a war raged, because his harem was a bit light.

Hercules:  Great, you're really strong, we get it.  Maybe I'll pray to him if the lid gets stuck on the pickle jar again, or if I want to carry three boxes up from the basement in a single trip.   But otherwise I'll keep looking.

Attractive Dingbat

Aphrodite:  She's the stereotypical brainless hot chick, who almost got herself killed by a mortal (Diomedes) because she wasn't paying attention.  Put on some decent clothes and get a job, you trollop.

Hephaestus:  Not cool himself, per se, but he has all sorts of neat stuff:  he was the armorer for the gods, and makes some really slick weapons. But he himself is weird and moody.  Worshiping him would be like hanging out with a creepy kid just so that you can play his Xbox One.  Nope.

Paris / Helen of Troy:  Fuck no.  Choosing them would be like worshiping Jason Priestly or Shannon Doherty.  Out of the question.

Apollo / Ares:  I could never quite figure out Apollo, except for the fact that he was typically pissed about one thing or another. And Ares was even worse.  A bunch of meatheads, if you ask me.

Hades:  He's like a creepy goth kid, with whom nobody wants to hang out.  So he gets this angry dog (Cerebus) to make some sort of statement. Get a life.

God Found!
So far, I had no luck.  I needed a god who was strong, yet clever. Someone who could not rely on power alone, because in the real world, nobody can.  Someone who was cunning at times, but clearly had a very good heart.  Someone who did important things.  Someone I could relate to, and be inspired by.

At long last, I found my god -- or, as it turned out, goddess: Pallas Athena.

About Pallas Athena
Pallas Athena is the goddess of Just Warfare, which is awesome.  In other words, she fights, but she fights nobly.  Also, while she's not quite strong enough to go toe-to-toe with Ares (who is the primary god of war), she typically gets the best of him anyway.  

Totally Awesome

She's kind to mortals, and isn't always bitching about sacrifices, like some other gods I could mention.  Generally, she wants to avoid bloodshed, and tries to help the mortals to get along.

Her symbol is the owl, which is an awesome creature in its own right:  An owl is a quintessential predator, yet is known for its wisdom.  (Plus, have you ever seen an owl up close?  Man, those things are cool.)   It captures her spirit perfectly.

Oh,  and she's not some hussy.  Aphrodite or Hera were always tramping around with the other gods, or seducing mortals, but not Athena.  Hephaestus tried to throw himself at her once, but he was no match for her, and just ended up embarrassing himself.  She very well might be a lesbian.

Even how she was born is awesome: She sprang from Zeus's head in full battle armor, and pretty much had a not-to-be-fucked-with demeanor from the get go.


Worship 101
I'm still figuring out how to best worship her.  I'm treading a bit lightly -- I certainly don't want to piss her off.  But I hope she doesn't hold me to ancient Greek standards, as I can hardly afford to be sacrificing hecatombs, like they did back then:  I looked it up: a hecatomb is 100 cattle.   My wife would be pissed if I bought 100 cattle and then slaughtered them in the back yard.

But I did buy a statue of her decked out for battle, with this awesome owl.  I pray to it now and then, when I need strength or focus -- like before a big presentation at work.  I like to think that she's rooting for me, too.

However, if you ever hear that I've been struck dead by a bolt of lightning, you can conclude that I either offended her, or one of the other gods heard I was talking smack.  For my sake, please do NOT forward this blog to any dieties.

How to Think About Forecasting

When I graduated college in 1995, I got a job working in Data Warehousing -- setting up reporting systems using a variety of relational and multi-dimensional databases.  It required a balance of financial reporting (i.e., what to report) and data design skills (i.e., how to report it).

Yet, if you asked my grandmother, you'd hear a slightly different story.  As far as she was concerned, "Brett works in computers."   Now, people often said such things in the 1980's, and it's probably acceptable septuagenarian-speak today.    But for most of us, it no longer cuts it.

Working "in Computers"
Why is that?  How is it possible that a phrase that was totally legit in the 80's can now sound so ridiculous? 

The answer:  As a society, we have learned things, and as we learned, language got more precise.   The phrase "in computers" was fine, back when few people did.   But today, it's just a catch-all for a broad array of specialties -- networking, security, graphics, databases, software development, etc. etc. 

Here's the kicker:  As our language evolves, even some of these terms will become too generic.  I already know term that's bound for the trash heap:  Forecasting.

Forecasting isn't really a thing.
These days, I tell people that I do forecasting, and they nod approvingly.  Quite often, they exclaim, "You should talk with So-and-So."  Sometimes I do talk with So-and-So, and find that we have very little, if anything, in common.

Why this?   The answer is simple:  Forecasting isn't one thing.  People picture it as a specialty, like something specific, like database administration or UI development.   People think that it surely has some common base of skills that all forecasters share, the equivalent of a DBA's schema or accountant's general ledger -- but it doesn't.

If you choose any two forecasts, they're likely to be wildly, crazily different.  They can use radically different techniques, different math (or as my Oxford-trained friend puts it: different "maths"),  different error tolerances,  different outcome distributions, etc. etc.

How to Think About Forecasting
I've dreamed up many analogies and descriptions for forecasting, to try to convey to people just how broad forecasting really is (and consequently how devoid of meaning the term is).

Here's the best one I've come up with:  Forecasting is just guessing what's going to happen next.   That's it.

If you think of it in those terms, a lot of what I've written becomes obvious.  You might presume that forecasting is a formal skill -- but what about "guessing what's going to happen next"?  Is that a formal skill?  Not quite so much.

Also, if someone at a party says that they do "Forecasting", you might not your head.  But what if they said, "I guess what's going to happen next."  ...There, you'd probably give them a blank-eyed look, because it's such a meaningless thing to say.

But that's all forecasting really is.

Brett's TSX

Anybody who knows me is aware that I love my car.  It's not an impressive car of anyting -- it's not an Audi RS5, a Porsche Cayman, or a Tesla.  In fact, it's eleven years old -- I bought it in November 2003.  It's a glorified thin-person's Honda Accord, but I love it.

But it hasn't always looked so nice:  I maintained it meticulously for the first few years, but eight years on, it was really showing its age:  All that time, I had to park it outside, and the net effect was that the outside got dulled (due to the icy winter) and oxidized (due to the blazing summer).  Its cherry gloss red dulled to a milky matte red; the wheels got a fair share of scrapes; the headlights fogged. 

By 2011, I was looking for something new, and had a good shot at convincing my wife to let me get something a bit pricier.

Quest For The New
So I went on a quest for an new car.  Here are the front-runners, in no particular order, that I was considering:

I fell into a pattern of falling in love with a car based upon pictures and reviews, and spending endless cycles building and re-building the car online.  Ultimately, I'd go see the car in person, and for one reason or another, I'd find myself happy to get back into my own car.   Some were too loud (Cayman), some felt a bit heavy (S6 and 6-Series), some simply left me flat (Jaguar XK, Mercedes SL), and some weren't compellingly different from what I currently drive (Lexus IS350, Infiniti Q50 and Audi S4).

Fun Facts About the TSX

• Acura is the bling version of Honda, much like Kraft Macaroni and Cheese Deluxe is the bling version of Kraft Macaroni and Cheese:  Technically, yes, it's better, but you're still not impressing anybody.
• The TSX is the sports version of the Honda Accord sold in Europe.  Honda sells a different model as the Accord in the United States, because Americans are fat.  This is true.
• It sports a naturally-aspirated (i.e., non-turbo) four cylinder, with 200 horsepower and 168ft/lbs of torque.  To those who don't already know, these numbers compare quite favorably to many mopeds.

Brett's Bet and the Result
Around then, I happened to see my mechanic (a wonderful guy) and I asked him how much life was left in my TSX.  "This car here?", he said.  "This car is not even halfway through it's life yet.  You could drive this over 250,000 miles." 

That's when I decided to take the plunge:  Instead of buying a new car, I'd fix up the one I had.  I tracked down Pasquale Bruno, a passionate Italian autobody expert, and we set to work:

• A high-end paint job.
• New headlights.
• New trim.
• New wheels.
• All worn pieces on the inside replaced.
• Cold-air exhaust and re-spec'ed ECU (both to bump up horsepower)
• Stainless steel brake lines.
• Rear-view camera
• Bluetooth and iPhone integration

What's more, I cleaned out my garage and hardly ever leave it outside (especially not in the winter).

The result:  I'm ecstatic.  I preen over it constantly -- washing, waxing, polishing.  As a result, it looks and runs great, and I still get compliments (including this past weekend from a valet).

Here are some other pics of it (click to expand them):

All told, I've probably put $12,000 into it so far.  I know it's a waste of money, but on the bright side, it's much less of a waste than the depreciation of a new car!   Or, at least, that's what I keep telling myself.

Frequently Asked Questions
Okay, time to dive into my mailbag, and answer questions people have sent me about my car!  Let's see what's on peoples's minds, shall we?

Q: Isn't the TSX more of a girl's car?
A:  Shut up.

Q: The only way that car would break 100mph, is if you dropped it out of an airplane.
A: That's not even a question. 

Hm, I guess there aren't a lot of questions.  Okay, that's all for now!

Epilogue
After I wrote this, my friend Rachel took this picture and sent it to me.  She can go jump in a lake.

Into The Next

In this post, I'm going to give an idea of where I think we're headed, and where to look for alien life.

Here's my Wave of Refinement theory:  On some cosmic level, we're at the crest of a wave -- a wave that started at the beginning of the universe.  It's a wave of refinement.  It started off with a vast sea of energy (immediately following the Big Bang), transforming into matter, into galaxies, into solar systems, into life, into intelligent life, into computers, into the next, into the next.

Each of these phases is considerably faster than the one before it.  Technology cycles (like adoption rates of fire, language, written language, printing press, electricity, then phone, then television, then cable, then internet, then cell phone, then Facebook, then Twitter) are collapsing from millenia to centuries to decades to years.

Take a Good Stare

Now ponder this: Where will we be in one thousand years?  Personally, when I really try to look ahead, and then I ponder this question, it's the cognitive equivalent of staring into intense direct sunlight -- only instead of intense light, it's intense chaos.  But the result is the same -- I cognitively have to avert my gaze, it's too intense to keep looking.

The world will be totally unrecognizable in a thousand years.  I'm not sure it will be recognizable in a hundred.  Things are transforming.  We'll be inside the computers soon, whatever that means.  I don't think we have the imagination to KNOW what that means, but it's what's going to happen.  Experts can agree on scant few details, aside from the porn being amazing.

But I think I see where this is generally headed, even after we merge with computers.  Into tinier bits of energy, with quicker processing times.  A year to us now will be an absolute eternity to future manifestations of this wave of refinement.  Much like a century now is an eternity compared to caveman days (e.g., 100,000 years ago), and that was but a fraction of the time before that.

As a result, if we seek other intelligence, perhaps we're looking for it at the wrong level.  Perhaps the majority of intelligent life has, for all intents and purposes, refined itself so much that it is woven into the universe as a form of energy. ...but not energy like photons and joules, something far more subtle and essential.

Don't Bet On It

For them, our life would be ridiculously slow.  We're slower than the slowest tortoise, and any attempt at communication would be tantamount to shaping the rings on a tree that after ten thousand years spells out the "h" in "hello".  To them, we are a mountain range or a scultpure:  we're just matter.    Probably the closest analogy would be a reef, which people treat as a thing, but it is (or at least has the potential to be) alive.

So that sucks.  No Martian babes, apparently.

Brett Endeavors a Grammar Maneuver

Hello!  Today I have made a momentous decision which I must call out and justify for the public record.

I'm no longer saying "his or her".  

I hate that damn phrase.  Starting immediatley, I am saying "they."   I realize this is a second-order grammatical faux pas; I forge ahead nonetheless.

Grammar Snobbery

Brett, Grammar Dandy

If you know me, you are likely aware that I am a ferocious grammar snob.  The rest of you will surely lose respect for me when you read this post.  It's pathetic, and I am not a proud man.

But the truth is:  I delight over grammar and its rules like a foppish aristocratic manchild.

It's quite similar to how I feel when reading The Economist, where I'm all like "Mmm yes, Belarus, do implement those reforms." or "Tut tut Zimbabwe!"

So here's one of the symptoms:   I cringe when I hear a first-order offense  like "irregardless,"  "could care less," or "supposibly," and internally recoil like I've been bitten by a snake.  I mean, you could never tell by looking at me; but inside, I'm aflutter like an aging schoolmarm. 

Another symptom:  I fawn over parallelism, and bestow silent accolades to myself for my skill:
*   Incorrect: I want to do some creating, then learn something, then have grown. 
*   Correct:  I wish to create, to learn, to grow.

Violating parallelism will get you dinged on the GMAT, and plus, it sounds terrible.  Seriously, if you replace "have grown" with "have, like, grown and shit." in the "Incorrect" sentence above, it flows just as well.   (Parallelism says, when listing verb clauses, if one verb ends in -Ing, all must end in-Ing; if you use the Infinitive once, you use the Infinitive every time.)

I also secretly delight myself at distinguishing between the use of that and which.  "That" is a required phrase, "Which" is optional.  Hence, one should use "that" only if you kinda gotta know it for the sentence to make any frickin' sense whatsoever, but if you're charming people with delightful side details, then it's which.

I preen over such details, and give myself little blue ribbons for getting things like this correct.  I am generally, if for fleeting moments, quite the dandy.

I'm Not Normally A Tight-Ass
You might wish to say "Yes, Brett, you are anal retentive.  Get over it."  But I'm NOT!   I swear, I do more than my fair share of cursing and occasionally dabble in some rather questionable (if quite amusing) effrontery.

Like, last year, I was on a conference call with twelve people, and the project lead said, "Okay, so we've got a number of issues to discuss.  Number one is...."   While we were discussing that, I raised another concern, and she says, "So Brett just put a big Number Two on the table."  and I immediately interjected, totally straight-faced, "I'd like to clarify that I did NOT just put a big Number Two on the table.  That's gross." before going on mute and weeping with laughter.  Total silence on the line. Truly, one of the finest moments of my life.

On another call, our India team kept saying that they "have to take a [data] dump" or "who took the last dump" or "I look forward to that dump."  Each time it would make you wince slightly.   After a while, I had had enough, and so I said, "Well, if you need any help, [co-worker] would be happy to help you take a dump.  Wouldn't you [co-worker]?"  Then I ran over to his desk (we're good friends) and we both cackled on mute, while our Indian colleagues remained oblivious.  Another life highlight.

Back to My Grammar Faux Pas
Okay, so now that you know that I'm a grammar dandy (even if I am occasionally vulgar) hopefully you understand that I don't lightly violate the grammar rules by which I derive my fragile self-worth.

But I'm willing to give it all up, because I'm sick of "his and her."  It makes all sentences sound awkward:   "If each student could take his or her seat".   Oh please.

What's worse, this is a brand-new, self-inflicted problem!  Until the 1970's, people wrote "if each student could take his seat".  See how much nicer that flows?  Unfortunately, it comes an unpleasant side effect of sounding like (if not arguably, being) a sexist asshole.  So society changed it.

Total aside: I super-cringe when someone  uses her for the generic, instead of "his" or "his or her."  It's like some failed offering to the Gods of Progressivism.  Like, "if each students could take her seat" (when speaking to a mixed group).  This sounds forced and weird, like, every goddamn time.

Anyway, I think a gender-neutral phrasing sends a healthy message to men AND women.  Plus, I read that women comprise over 15% of the total population.  I bet a lot of people don't realize that there are quite so many women.   So we should be, like, nice to them.

Screwed The Replacement
So I love the spirit, but  our implementation sucked:  We should have chosen "their", not "his or her." "His or her" sounds like total crap.  And even THEN women get kind of screwed because they're still  in second place!  Arrgh!  I have yet to hear someone say "her or his," but I imagine that the term exists in copious feminist diction.

So screw it.  I'm making the bold decision to switch to "theirs".  Like so:

Current:  If each student could take his or her seat.
Brett's New Way: If each student could take their seat.

Current:  I'd appreciate it if he or she would call me back.
Brett's New Way: I'd appreciate it if they'd call me back.

Please Don't Judge The Grammar
I understand if you don't agree with me, but don't lump me in with the grammatical riff-raff -- as if I had confused "their" with "there", or wrote "you're" for "your."   I couldn't bear it.

I'm begging you.   Grammar snobbery is all that I have --  I couldn't...  I couldn't bear to lose it...  I'm feeling faint just thinking about it.  Where is my silk fan?   Where is it?  Where is my fan?

Ah yes, here it is.  Okay... One sec.

...Okay, I'm feeling better now.  Where was I?

Conveying Forecast Error

Whenever you forecast, sometime you'll want to see where you're off a little.  And sometime, you'll want to see where you're off a lot.  You'll need a functional way to express these errors, that let you analyze them in a variety of ways.

I can pretty much step you through what most first-time forecasters do, and then explain where you'll sooner-or-later end up: Using a logarithmic scale.

Bad Method 1:  Using a Linear Scale

Crappier than it seems

At first glance, you'll be tempted to just do it like the picture to the right.  ...So, in this chart, the 130%-140% bucket would contain a forecast of $6.65 for an actual of $5 (6.65/5 = 133%), and the 60%-70% bucket would contain a forecast of $3.25 for an actual of $5 (3.25/5 = 65%).  

This works, right?   Sort of, but it has two big drawbacks:

Static Level of Precision

You probably don't want all of your error buckets to be the same size;  this gets ridiculous pretty quickly.

I mean, reporting upon a 100-110% bucket makes sense -- but what about the 170-180% bucket (are these sufficiently different from 180-190% to warrant their own bucket?).   And it makes zero sense to report a 780%-790% bucket or (even more ridiculous) a 8,920%-8,930% bucket.  And that's what you're gonna get with this approach.

Ideally, your buckets will grow along with your error:  You need fine precision when your forecasts are close, and coarser precision for your big misses -- which you don't get.

Asymmetrically Bounded

On a percentage chart like the one above, the smallest error bucket is fixed:  0-10%.  Your forecast can't get any lower than 0% of actual.  However, your forecast could be a bazillion times higher than actual.  

As a result, when you plot out your error, you'll get a very short "low-tail", and a very long "high-tail".  Among other problems, this makes it impossible to visually scan for forecast bias, because your too-high and too-low forecasts aren't reported in a consistent fashion.  

 

Bad Method 2:  Using a Custom Scale

From there, most people move on to creating customized buckets.  So you'll write statements like this:

If err = 0 then "Match"

else if err > 0 and err <= 1% then "1%"

else if err > 1% and err <= 5% then "1-5%"

else if err > 5% and err < 10% then "5-10%"

else if err > 10% and err < 25% then "10-25%"

...But you'll probably discover that your buckets are always somewhat arbitrary and not quite  pleasing.  And, of course, any time you want more or less-granular buckets, you gotta mess with your equation.

Proper Method:  Use a Log Scale

Reporting upon forecasting error on a logarithmic scale solves all of these problems.

 

But before I jump into using  log-based error groups, let me remind you about logarithms.  (For the rare few of you who are already savvy with logs, feel free to jump ahead.)

Logs 101

Easy, once you get the hang of it.

Logs are the opposite of exponentials, just like subtraction is the opposite of addition, or division is the opposite of multiplication.  So, ya know how since 5 * 2 = 10, then by definition 10 / 2 = 5?  Well, if 5³ = 125  then log₅(125) = 3.  ...Btw, that five in subscript is called the log's "base."

Logs are much easier to understand if you just see them in action.  The chart to the right shows the log values for certain bases. As you can see, for log base 5, each value represents five times the previous value.   For log base 2, each value represents two times the previous value.

To convert your forecasting error to a log scale, just take the log of the forecast/actuals.  Any base is fine.  With base 2, if your forecast were double your actual, log(200%,2)= 1.   If your forecast were half of your actual, log(50%,2)= -1.  If your forecast matched your actual, log(100%,2)=0.  

What does this buy us?  A lot!

Dynamic Level of Precision

First and foremost, each log value represents a bigger group, which can go from very constrained to enormous.  

Let's say that your forecast is often off by 30%, but you're occasionally off by upwards of 100,000%.  (This especially happens when your actual value unexpectedly drops near zero, where the forecast/actual ratio can be sky-high even for modest forecasts.)

On a linear scale, you can't meaningfully show 30% and 100,000% in the same chart, without the chart being enormous.  Yet on a log base 2 chart, the 30% gets a value of .37, and the 100,000% gets a value of 13.2. 

Amazingly, a log scale allows for fine-tuned precision for accurate forecasts (i.e, you can see the difference between a 5% and a 10% miss), even when reported right next to a 100,000% miss!

Symmetrically Bounded

As I described earlier, on a linear scale, too little precision for forecast that are very low (they're all lumped into the 0-10% bucket), and too much precision for forecasts that are too high (each one gets its own useless bucket, like 8050-8060%).

Logarithms don't have that problem: They represent high and low values on an equal terms.  If you were ever 100x too high, using a log base 2, you'd get a log value of 6.6.   If you were 100x too low, you'd get a log value of -6.6. 

Working Example:

Here, I used Excel to generate 1000 random numbers (each from 1 to 100), and then "forecast" each with another set of semi-random numbers -- based partly upon the original number, and partly upon another random number.  

For kicks, I gave my forecast a subtle positive bias -- it's higher than the actual number a bit too often.

Now, let's plot the error both linearly and logarithmically, and see what we find.

Linear Scale

Ugh, what a disaster. 

First, for one thousand data points, we have 629 buckets.  

Second, ten "low" buckets (0-10%, 10-20%, etc) averaged 28 entries each, while six hundred and nineteen "high" buckets averaged just one entry each.  

Third, because the chart is naturally lopsided, we can't visually see the overt bias in the forecast.

Logarithmic Scale

This is more like it!

First, our chart has only 100 points, instead of six hundred and twenty nine.  Yet our precision was precise where it mattered (i.e., forecasts close to 100% of actuals), and rough when it didn't matter.

Second, you can see that both our high and low values slope away, roughly in symmetry.  Instead of our low forecast crammed into ten groups, and our high values spread out over 619 groups, they're much closer to equal.

Third, you can see instantly that there is a positive forecast bias.  (Focus on the center bar, which represents forecasts near 100% of actuals.  Now compare the bars immediately to its right and left, and then bars two to the right and left.  See how the right bars are always higher?  That's a tell-tale sign of positive forecast bias.)

Conclusion

I probably could have written this entire article in a single sentence:  When reporting forecast error, use a logarithmic scale.

The trouble is, other people had told me that in the past, and I never quite understood why I was doing it, or even knew if I was doing it right.  Hopefully, by describing the drawbacks of the more conventional linear approach, you'll have a bit better understanding of why you should embrace logarithmic error reporting sooner, rather than later!

Makin' It

I'm kind of ashamed of my finances.   I should be saving more.  I buy too many toys and spend too much on needless shit.   I fear my friends are saving WAY more than I am.  Plus, I gotta think about retirement and college accounts and all that.

It's not that I'm not saving anything, but I'm probably not saving as much as I could.   That's why I'm ashamed.

But it's all good.  I'm pretty sure 80% of my friends feel this way, too.  We're the demographic that frets over this sort of thing.  We're Makin' It.

Makin' It.
As far as I can tell, people fall into one of several demographics: Children, Students, Paycheck-to-Paycheck, Makin' It, and Have It.

My Co-Workers: the Makin' It Demographic

I'm in the Makin' It class, which is roughly comprised of people who can afford to purchase a luxury car, or some other large-ish indulgence -- but will wince a bit doing it, because there are far better things for them to be spending money upon.

We have career plans and debate the merits of Masters degrees: either lusting after them, as a faux blue ribbon on our resumes, or resenting them as a faux blue ribbon on other people's resumes.  We save.  We dabble in the stock market.  We have 401Ks and retirement plans and SEPs and Roth IRAs.   Sometimes we spring for Brooks Brothers no-iron shirts.   We fret about buying a house, or adding an addition to our house, or upgrading to a bigger house, even though we can't afford it quite yet.

...And we occasionally assess our total assets and think, "Shit, really?  Is that it?  Well...I guess if I count the house...that's something."

I someday hope to transfer to the Have It class, although I'm not quite sure how the hell that happens.  But for now, I'm happily in the Makin' It category.

Theme Song
Now, most of you know that each class has its own theme song.  For example, the working-class  Midwestern demographic fawns over Bruce Springsteen's Born to Run.   The former-varsity-football-playing overweight insurance salesmen demographic favors Bruce Springsteen's Glory Days.   (Note: Bruce Springsteen's songs are disproportionately reflected in demographic theme songs; sociologists cannot agree upon why.)

My demographic's theme is obvious:  The eponymous Makin' It, by David Naughton.  This song totally "gets" my cohort.  Plus, it has an awesome disco vibe, of which we unanimously approve.

This song was so awesome that it spurred its own television series, which ran for seventeen years.  Fun fact:  Makin' It is the only show to be nominated for an Emmy in the Primetime and Documentary categories.

Seriously, if you know any corporate executives, ask them and they will gaily confirm that many meetings start off with Makin' It playing on a Tivoli desk speaker.  We hum it to ourselves in Starbucks, and sometimes they play it over the loudspeaker.   We're climbin' the ladda.

Earlier today,  I passed by my colleague Jasmie, emerging from her big business presentation and I high-fived her:   An executive had nodded and smiled during her conclusion slide-- a sure sign of office success.  Jasmine is Makin' it. 

Academic Underpinnings

Even before joining the corporate workforce, some of you learned this song in corporate-minded undergraduate business and engineering schools.  Others, no doubt, ran across it in MBA programs, where verse memorization is often required.

This is more common in MBA programs than many people realize.  They certainly covered it when I got my MBA at Duke. I happen to know that the Harvard and MIT MBA programs require memorization as well.  A dear friend of mind (who does Data Warehousing at Akamai) is getting his MBA at Chicago (Booth), and expressed surprise at how much this song was reflected in its general curriculum.

The Lyrics of Makin' It
Okay, for those of you who don't know what I'm talking about, I'm just going to put the words to the song right here.  Obviously, people who already know the lyrics can skip this section.  However, it's worth a read for the rest of you.  This is the voice that speaks for my demographic.

Makin' it ooh...Makin' it
I´m solid gold, I´ve got the goods
They stand when I walk through the neighborhoods  
I´m makin' it, I´ve got the chance, I´m takin´ it, no more
.. no more fakin´ it, this time in life I´m makin´ it ooh makin´ it
Hello Uptown, Goodbye Poverty
The Top of the ladder is waiting for me
I´m makin´ it, I´ve got the chance, I´m takin´ it no more
No more fakin´ it, this time in life, I´m makin´ it, ooh
Makin´ it, Makin´ it
Listen everyone here, this coming year´s gonna be my year
I´m as fast as they come, number two to no one, I´ve got looks
I´ve got brains and I´m breakin´ these chains, make some room now
Dig what you see, success is mine, ´cause I´ve got the key
I´m makin´ it, I´m makin´ it, I´ve got the chance, I´m takin´ it no more
No more fakin´ it, this time in life, I´m makin´ it , ooh makin´ it, non stop, 
Makin´ it to the top... Makin´ it ...this time in life I´m makin´ it
Makin' it...I´m makin' it ..Makin' it....I'm makin' it
Listen Everyone here...This coming year's gonna be my year
I´m makin' it...I´ve got the chance I'm takin it in ...no more...no more
Makin' it, this time in life I'm makin' it..ooh...I'm makin' it...non stop...
I'm here...I'm here...Makin' it...to the top...ahh .. ahh..
Makin' it....non stop....right here...right now

Even when judged by contemporary lyric-writing standards, this song is a masterpiece.  Observe how "makin' it" rhymes with both "...chance, I'm takin' it" and with "no more fakin' it."  Sheer brilliance.

Word From the Boardroom
I know this might seem a bit odd, if this is the first time you're hearing about this.  I mean, people from other cultures sometimes think the Easter Bunny and Santa are a bit bizarre.  This is kind of like that.

So don't take my word for it; let's see what other people are saying.  My co-workers, the other people Makin' It.   Let's hear their tales, about how this year is going to be their year.  Are they as fast as they come?  Are they second to no one?   Let's find out:

For the last time:  I don't.  Know what.  You're talking about.  Please get out of my office.

Kate C., Senior Director

Awesome!  You go Kate -- the sky's the limit!

Yes, I remember that song.  I detest that song.

Kathryn P., Sr. Manager

Yeah Kathryn, you said it!  That corner office is as good as yours.

 Seriously, Brett, this is bordering on a behavioral disorder.

Clint H., Principal Analyst

Haaa!  Word to that, Clint.  You're on your way to the top!

Anyway, it's understandable that we all think Makin' It is our own personal theme song.  But, the truth is, everybody's really Makin' It, so it's all cool.  If someone happens to burst into song a few more times than someone else at work, or maybe if somebody plays the song a little louder in their cubicle, because they landed that big presentation, s'cool.  It's all cool.

Building Modular Forecasts

The Pain of Smarter Forecasting
Pundits love to expound about how we should use our data "smarter" -- and often they're talking about making smarter forecasts.   Seriously, is there a person alive who would publicly disagree to the importance of making forecasts smarter?   It's quite fashionable to nod our heads vigorously.

...But I wonder how many people have pondered what exactly that means.   What does it mean to make our forecasts smarter?  What does a "smarter" forecast look like?

A lot of people assume it means fancier-shmancier math.  Ya know, eigenvalues and matrix math and whatnot.  That's rarely the case.   ...Indeed (and this topic begs for a separate post), I've witnessed lots of Directors and VP's place undue faith that a statistician can solve behavioral or process problems -- which invariably ends in tears.  Math only gets you so far.

Using the data smarter usually means two things:  1. Performing more steps.  2. Using more disparate data sets.  Basically, a "smarter" forecast does more comparing, inferring, measuring, assessing, extrapolating, interpolating, checking.  A smarter forecast is a more complicated forecast.

Net-net, a "smarter" forecast isn't free; its cost is reduced system explainability.

These days, the goal of staying simple and explainable isn't even ignored, it's practically scorned.  Companies rush towards unfathomably-complicated  forecasts, as if they're the path to salvation.  On some cosmic level, executives believe that as soon as it's so friggin' complicated that nobody understands how the damn thing works, the riches will surely start rolling in.

And here's the kicker:  Forecasts naturally gravitate towards incomprehensibility anyway, which leads me to my first assertion:

Left Unchecked, Forecasts Become Integrated, Inscrutable and Deteriorated.

There isn't one reason for this.  There are probably ten; here are a few that spring to mind:

1. People are more prone to add logic to, rather than remove logic from, a forecast.
2. Forecasting algorithms can work in unexpected ways, especially on volatile input data, which means that a forecast is never fully understood, even right after it is written.
3. The forecast's logic is often split over several systems and/or sets of code. As a result, changes in one area can inadvertently affect the results of the other.
4. The forecast's data inputs naturally change over time, which causes input data to become stale, inconsistent or even corrupted. 
5. It's hard to tell when there even is a bug, because it's predicting the freakin' future and past performance is never a predictor of future results.  As a result, problems can fester.
6. The people who have mastered the heady math might not be good at explaining what they're doing.  ...Worse yet, they might not fully understand how the dang forecast is even used.  As a result, problems might not be detected.

Worst case scenario (which I have seen many, many times):  The forecast integrates a myriad of different data sources, each of which slowly deviates from the forecast's working assumptions about it.  Fewer and fewer people claim to grasp how the forecast works, and nobody wants to take responsibility for (or rely upon) a forecast that they don't understand.  Finally, its performance begins to deteriorate, and nobody is sure how to fix it. 

The Proposal:  Stay Simple and Modular
At the heart of the problem is that many companies build monolithic forecasts:  Too much functionality fed into a big black box, with multiple data feeds coming out the other side.  See that picture above?  That's how it ends up. 

So what do you do about this?

Well, you can ensure your forecasts do not become too complicated by breaking them into simple and well-understood parts, that can be mixed-and-matched.  In other words, resist the natural temptation to build a big monolithic forecast, and focus upon building a series of smaller, simpler, more well-understood components that can be used independently.

I'm not 100% sure how easy/possible/feasible this will be in your circumstances.  But I've gotten it to work on many forecasts.  Let me describe the general approach I took here, using Akamai as an example.

Case Study: Revenue Forecasting

About this client...
Before I describe forecasting revenue I conducted at a client (back in my consulting days), allow me to provide some detail about the company's business model.

This company provides content delivery service, and its customers' invoices are similar to cell phone bills -- only instead of delivering minutes of phone service, they delivers internet traffic.   Just like with cell phone plans, customers can "Commit" to a certain level of usage; generally, the higher a customer's Commit, the lower the per-unit price.  If they push more than their Commit, they pay a per-GB Overage rate -- just like the per-minute Overage when you exceed your cell phone minutes.

Customers with steady traffic typically prefer high Commits (to get the lowest per-GB rate), customer with volatile traffic typically have low Commits (so that they don't pay for traffic that they don't use).  But any customer's traffic can gyrate unpredictably in any given month.

However, one way in which this company is unlike a typical cell phone plan is that the contracts can get rather complicated.  (This is a common phenomenon in B2B companies, where premier customers receive tailored contracts.)   Point being:  Even if you know a given customer's traffic, calculating their invoice amount can be very tricky.

Revenue Forecasting Data
So, when attempting to forecast quarterly revenue, what factors must be considered?  As it turns out, A LOT OF FACTORS.  However, they can generally be broken down into four categories, each with three sub-categories:

• Traffic Factors:  the levels of content that customers push
• Contract Factors: the customer contracts Akamai has, and the terms of each contract
• Invoice Factors: the amounts that appear on the actual invoices
• Revenue Factors: the amount of invoicing that Akamai recognizes (versus deferrals, rev reserve, credit memos, etc)

 Within each of these categories:

• Current Data:  The most recent month's data.
• Historical Patterns:  Patterns in how this data has changed over time.
• Known Current Adjustments: One-off events scheduled to happen within the forecasted period.

Old vs. New Forecasting Methodologies
Until 2009, the forecast took all of these quite-different inputs, and incorporated them all into a single mega-algorithm, like so:

 Since then, Akamai has transitioned to a new modular forecast.   It works like this:
1. Use only traffic information to make a traffic forecast.
2. Next, use only contract information to forecast what our contracts will look like.
3. Then input our forecasted traffic and forecasted contracts into our invoicing system, to calculate forecasted invoicing.
4. Finally, use historical revenue data to forecast how much of this invoicing we will recognize as revenue.

Benefits of a Modular Approach
You might be wondering:  "Okay, so you went from one super-complicated forecast, to a series of four less-complicated ones.  Did you really come out ahead?"

The answer: Way ahead.  Here are some of the benefits:

1. Forecast accuracy improved significantly.  As you might imagine, Revenue is really just a bunch of adjustments to Invoicing -- and in this case, we're calculating forecasted Invoicing, based upon forecasted Traffic and Contracts:  We literally use our production Invoicing system to say "Okay, if the contracts look like this, and the traffic looks like that, then how much money will we invoice?"  For a variety of confidentiality reasons, I can't cite specific accuracy levels, but believe me: nobody is complaining about accuracy.
2. Forecast intuition is very high.  Each part of the forecast (e.g., our traffic forecast, our contract forecast, etc) had sufficiently few inputs that people could wrap their heads around how it worked, and what events would affect the forecast.   As a result, the forecast hasn't turned into a black box, like many other forecasts I've observed. 
3. Forecasting modules can be swapped in and out.  Sooner or later, we'll need to revamp one of the forecasting modules diagrammed above; but because the forecasts are modularized, it won't introduce risk to the other forecasting modules.
4. Our intermediary forecasts now sing for their supper.  Originally, we modularized our forecast to yield a simpler and more-accurate model -- but we attained another benefit:  We discovered that groups within the company could use these intermediary forecasts!   Engineering, Finance and Sales use our Traffic, Contracts and Invoicing forecasts -- and even spot opportunities to improve them, which yields a more accurate revenue forecast, to boot!

Conclusion
In most organizations, forecasts naturally gravitate towards becoming monolithic beasts that nobody fully understands, and which then deteriorate over time.  However, by moving towards more modular forecasts, a company can fight this trend, and maintain understandable, accurate forecasts for much longer.