Cognitive Shortcuts: Models, Visualizations, Metaphors, and Other Lies (Cascadia Ruby Conf 2014)

COGNITIVE SHORTCUTS:
MODELS,VISUALIZATIONS,
METAPHORS, AND OTHER LIES

SAM LIVINGSTON-GRAY

Programming is hard. It’s not [ADVANCE] quantum physics...
4

http://fathom-the-universe.tumblr.com/post/56500448933/who-is-schrodingers-cat-no-doubt-schrodingers
PROGRAMMING IS HARD
{{ IMAGE: cat in a box }}
!
...but neither is it falling off a log.
!
If I had to pick just one word to explain why programming is hard, that word would be [ADVANCE] "abstract".
5

ABSTRACT
Existing in thought or as an idea
but not having a physical or
concrete existence
As software developers, we deal in abstractions.
!
I asked Google to "define abstract", and here’s what it said: [REVEAL] "existing in thought or as an idea but not
having a physical or concrete existence".
!
I usually prefer defining things in terms of what they are, but in this case I find the negative definition extremely
telling. Abstract things are hard for us to think about precisely because they don't have a physical or concrete
existence.
6

“The best programmers I know all have some
good techniques for ... conceptualizing or
modeling programs they work with. And it
tends to be sort of a spatial/visual model ... but
not always. ... our brains are geared towards
the physical world and dealing with our senses
and integrating ... sensory input ...”

!
-GlennVanderburg, Ruby Rogues #77
I got the idea for this talk when I was listening to the Ruby Rogues podcast episode with Glenn Vanderburg. I edited
this a little bit, but basically, Glenn said: [REVEAL]
!
"the best programmers I know all have some good techniques for conceptualizing or modeling programs they work
with. And it tends to be sort of a spatial/visual model but not always. What's going on is that our brains are geared
towards the physical world and dealing with our senses and integrating sensory input."
7

“... But the work we do as programmers is all
abstract. And it makes perfect sense that you
would want to ﬁnd techniques to rope the
physical, sensory parts of your brain into this
task of dealing with abstractions. But we don’t
ever teach anybody how to do that, or
even that they should do that.”

!
"But the work we do as programmers is all abstract. And it makes perfect sense that you would want to ﬁnd
techniques to rope the physical, sensory parts of your brain into this task of dealing with abstractions. But we don’t
ever teach anybody how to do that, or even that they should do that."
!
[PAUSE]
8

1. Brains are awesome! You can take shortcuts
through the hard work of programming with
specialized modes of thought.

2. Brains are horrible! They lie to us all the time!
But if you watch out for cognitive biases...

3. ...you just might be able to pull off an amazing
hack.
When I heard Glenn say this, I got really excited, and I started thinking, [REVEAL] “yeah! YEAH! Brains are awesome,
and we should be teaching people that this is a thing they can do!”
!
And then I thought... “wait, no! [REVEAL] Brains are horrible, and they lie to us all the time! Teaching this stuff
would be completely irresponsible if we didn’t also warn people about cognitive bias!”
!
And then [REVEAL] I thought about an amazing hack that we won’t ﬁnd unless we actively work to counter our
biases.
9

1. BRAINS ARE AWESOME
Our brains are extremely well-adapted for dealing with the physical world. Our hindbrains, which regulate
respiration, temperature, and balance, have been around for half a billion years or so.
!
But when I write software, I’m leaning hard on parts of the brain that are relatively new in evolutionary terms, and I’m
using some relatively expensive resources.
!
Over the years, I’ve built up a small collection of shortcuts that engage some specialized structures in my brain.
10

VISUAL/SPATIAL REASONING
BOXES & ARROWS
There are a lot of visual tools that let us leverage our spatial reasoning skills.
!
I’ll list a few examples, because I think most developers are likely to encounter these tools, either in school or on the
job, and they all have the same basic shape: they’re boxes and arrows.
11

http://en.wikipedia.org/wiki/Entity%E2%80%93relationship_model
ENTITY-RELATIONSHIP DIAGRAM
There are Entity-Relationship diagrams, which help us understand how our data is modeled...
12

http://en.wikipedia.org/wiki/Self-balancing_binary_search_tree
DATA STRUCTURE DIAGRAM
We often draw diagrams to describe data structures like linked lists, binary trees, and so on...
13

http://commons.wikimedia.org/wiki/File:Finite-state-transducer-comments.png
STATE MACHINE DIAGRAM
And for state machines of any complexity, diagrams are often the only way to make sense of them.
!
I could go on, but like I said, most of us are probably used to using these kinds of tools as at least an occasional part
of our jobs.
!
Diagrams are great, because they let us analyze systems that are larger than what we can hold in our heads. They let
us associate ideas with things in space—and our brains have a lot of hardware support for keeping track of where
things are in space. This lets us free up some room in our working memory.
!
Also, our brains are really good at pattern recognition. So using diagrams to visualize our designs can give us a
chance to spot certain kinds of problems before we ever start typing code in an editor—just by looking at their
shapes.
14

VISUAL/SPATIAL REASONING
THE SQUINT TEST
Here’s another way to use your spatial skills to work with code. This one is called the squint test. You can use this to
get oriented in a new codebase, or to zero in on high-risk areas.
!
It’s very simple: you open up some code and either squint your eyes at it, or make the font size smaller than you can
comfortably read. The idea is to look past the words and notice things about the shape of the code.
15

class InstallationsController < ActionController::Base!
def schedule!
desired_date = params[:desired_date]!
if request.xhr?!
begin!
if @installation.pending_credit_check?!
render :json => {:errors => ["Cannot schedule installation while credit check is pending"]}, :status => 400!
return!
end!
audit_trail_for(current_user) do!
if @installation.schedule!(desired_date, :installation_type => params[:installation_type], :city => @city)!
if @installation.scheduled_date!
date = @installation.scheduled_date.in_time_zone(@installation.city.timezone).to_date!
render :json => {:errors => nil, :html => schedule_response(@installation, date)}!
end!
else!
render :json => {:errors => [%Q{Could not update installation. #{@installation.errors.full_messages.join(' ')}}] }!
end!
end!
rescue ActiveRecord::RecordInvalid => e!
render :json => {:errors => [e.message] }!
rescue ArgumentError => e!
render :json =>!
{:errors => ["Could not schedule installation. Start by making sure the desired date is on a business day."]}!
end!
else!
if @installation.pending_credit_check?!
flash[:error] = "Cannot schedule installation while credit check is pending"!
redirect_to installations_path(:city_id => @installation.city_id, :view => "calendar") and return!
end!
begin!
audit_trail_for(current_user) do!
if @installation.schedule!(desired_date, :installation_type => params[:installation_type], :city => @city)!
if @installation.scheduled_date!
if @installation.customer_provided_equipment?!
flash[:success] = %Q{Installation scheduled}!
else!
flash[:success] = %Q{Installation scheduled! Don't forget to order the equipment also.}!
end!
end!
else!
flash[:error] = %Q{Could not schedule installation, check the phase of the moon}!
end!
end!
rescue => e!
flash[:error] = e.message!
end!
redirect_to(@installation.customer_provided_equipment? ? customer_provided_installations_path : installations_path(:city_id
=> @installation.city_id, :view => "calendar")!
end!
end!
end
Here are a few things you can look for:
- Is the left margin ragged, with lots of nesting?
- Are there any ridiculously long lines?
- Are there areas where code is formatted into vertical columns?
- Is the ﬁle mostly regular, but with one big gnarly area? Or is it a mess from top to bottom?
- What does your syntax highlighting tell you? Do certain colors cluster together, or spread themselves out in a
regular pattern?
16

LINGUISTIC REASONING
I also have a couple of techniques that involve the clever use of language.
!
The ﬁrst one is very simple, but it does require a prop...
17

http://en.wikipedia.org/wiki/Rubber_duck
LINGUISTIC REASONING:
RUBBER DUCK
[PROP: BRAIN]
!
Here’s how it works: you keep a rubber duck on your desk. When you get stuck, you pick up the rubber duck and
put it on top of your keyboard, so that you can’t type. Then you explain your problem, out loud, to the duck.
!
It sounds silly, but there's a good chance that, in the process of putting your problem into words, you’ll either realize
what’s wrong, or at least think of something else to try.
!
One of my coworkers has an interesting variation on this technique, which is to start writing an email describing the
problem. I like this one because I think differently when I’m writing than I do when I’m speaking.
18

http://en.wikipedia.org/wiki/Bicycle_gearing
LINGUISTIC REASONING:
“PLEASE, MR. GEAR...”
The other linguistic hack I got from Sandi Metz. In her book, “Practical Object-Oriented Design in Ruby”, she
describes a technique she uses to ﬁgure out what object a method should belong to.
!
She says…
19

“How can you determine if the Gear class
contains behavior that belongs somewhere
else? One way is to pretend that it's sentient
and to interrogate it. If you rephrase every one
of its methods as a question, asking the
question ought to make sense. ...”

!
-Sandi Metz

Practical Object-Oriented Design in Ruby
!
[READ QUOTE]
20

“... For example, "Please Mr. Gear, what is your
ratio?" seems perfectly reasonable, while "Please
Mr. Gear, what are your gear_inches?" is on
shaky ground, and "Please Mr. Gear, what is your
tire (size)?" is just downright ridiculous.”

!
!
-Sandi Metz

Practical Object-Oriented Design in Ruby
[FINISH READING]
!
This is a great way to evaluate objects in light of the Single Responsibility Principle, and I’ll come back to that in a
moment. But ﬁrst...
21

•Rubber Duck

•“Please, Mr. Gear, what is your ratio?”
LINGUISTIC REASONING
I described the rubber duck and “Please, Mr. Gear...” as techniques to engage linguistic reasoning, but that doesn't
really do them justice. Both of these tools force us to put our questions into words... but words themselves are tools.
!
We use words to communicate our ideas with other people.
!
So while these techniques do involve the language centers of our brains, I think they go beyond language to tap into
our [ADVANCE] social reasoning.
22

•Rubber Duck

•“Please, Mr. Gear, what is your ratio?”

•asking the question ought to make sense
LINGUISTIC SOCIAL REASONING
The rubber duck technique works because putting your problem into words forces you to organize your
understanding of the problem in such a way that you can verbally lead someone else through it who doesn’t have all
of your implicit context.
!
And by anthropomorphizing an object and talking to it, "Please, Mr. Gear..." lets us discover whether it conforms to
the Single Responsibility Principle.
!
To me, the key phrase in Sandi's description of this technique is: [REVEAL] "asking the question ought to make
sense."
!
Most of us have an intuitive understanding that it might not be appropriate to ask Alice about something that is Bob’s
responsibility. Interrogating an object as though it were a person helps us use that social knowledge. It gives us an
opportunity to notice that answering this question isn’t really the job of any of our existing objects, which in turn
prompts us to create a new role and give it its own name.
23

METAPHORS
Now for the really handwavy stuff.
!
Metaphors can be a very useful tool in software.
24

http://commons.wikimedia.org/wiki/File:TurtleGraphics1.png
SPATIAL METAPHOR:
TURTLE GRAPHICS
The turtle graphics system in Logo is a great metaphor.
!
Most of the rendering systems I've used are based on a Cartesian coordinate system, which is all very formal. But
Logo encourages the programmer to imagine themselves as the turtle and use that understanding to ﬁgure out what
to do next. One of the original creators of Logo called this [ADVANCE] "body syntonic reasoning"...
25

http://commons.wikimedia.org/wiki/File:TurtleGraphics1.png
BODY SYNTONIC REASONING
...and speciﬁcally developed it to help children solve problems.
!
But the turtle metaphor works for everyone, not just kids. Cartesian grids are great for drawing boxes.
26

CSS
is
awesome
http://www.codeproject.com/Articles/117957/Turtle-Graphics-and-L-systems-with-F-and-WPF

http://docs.python.org/3/library/turtle.html

http://smackerelofopinion.blogspot.com/2013/12/infinite-snowflake.html

http://programmingpraxis.com/2012/01/03/turtle-graphics/
Well, mostly great.
!
But it can take some very careful thinking to figure out how to compute the set of (x, y) pairs you need to draw:
!
- A spiral.
- Or a star.
- Or a snowflake.
- Or a tree.
!
Choosing a different metaphor can make certain kinds of solutions easy, where before they seemed like too much
trouble to be worth bothering with.
27

http://commons.wikimedia.org/wiki/File:Maury_Geography_005A_compass.jpg
METAPHOR:
“EAST-ORIENTED CODE” 
-JAMES LADD
James Ladd has a couple of interesting blog posts about what he calls “east-oriented code”.
!
Imagine a compass overlaid on your code.
28

class Invoice!
def total!
line_items_total + taxes!
end!
!
# ...!
!
def line_items_total!
@line_items.sum(&:amount)!
end!
!
end
class LineItem!
!
def name!
!
def description!
!
def quantity!
!
def amount!
!
end
In this model, messages that an object sends to itself go [REVEAL] south. Any data returned from those calls goes
[REVEAL] north.
!
Communication between objects [REVEAL] is the same thing, rotated 90 degrees: messages sent to other objects go
[REVEAL] east, and return values go [REVEAL] west.
!
What James Ladd suggests is that, in general, code that sends messages to other objects—where information ﬂows
east—is easier to extend and maintain than code that looks at data and decides what to do with it—where information
ﬂows west.
!
Really, this is just the design principle “Tell, Don’t Ask”, but the metaphor of the compass recasts it in a way that lets
us use our background spatial awareness to keep the principle in mind.
29

http://www.ﬂickr.com/photos/sherwood411/9052151857/
METAPHOR: CODE SMELLS
Code smells are an entire category of metaphors that we use to talk about our work. In fact, the name “code smell” is
itself a metaphor for anything about your code that hints at a design problem.
!
I guess that makes it a… [REVEAL] meta-metaphor?
30

http://www.ﬂickr.com/photos/sherwood411/9052151857/
META-METAPHOR: CODE SMELLS
…okay, maybe not.
31

METAPHOR: CODE SMELLS
•Duplicated Code

•Long Method

•Large Class

•Too Many Parameters 
•Feature Envy

•Refused Bequest

•Primitive Obsession
Some code smells have names that are extremely literal: “Duplicated Code”, “Long Method”, and so on.
!
But some of them are delightfully suggestive: [REVEAL EACH]
“Feature Envy." "Refused Bequest." "Primitive Obsession."
!
To me, the names on the right have a lot in common with “Please, Mr. Gear”: they’re chosen to hook into something
in our social awareness, to give a name to a pattern of dysfunction, and—by naming the problem—to suggest a
possible solution.
32

COGNITIVE SHORTCUTS
•Boxes & Arrows

•SquintTest

•Rubber Duck

•"Please, Mr. Gear..."

!
•Metaphors:

•Turtle Graphics

•East-Oriented Code

•Code Smells
These are some of the shortcuts I’ve accumulated over the years, and I hope that this can be the start of a similar
collection for some of you.
33

2. BRAINS ARE HORRIBLE
[PAUSE]
!
Evolution has designed our brains to lie to us.
!
Brains are expensive. The human brain accounts for about 2% of body weight and about 20% of our caloric intake.
That’s a huge energy requirement that has to be justiﬁed.
!
Evolution does one thing, and one thing only: it selects for traits that help an organism stay alive long enough to
reproduce. Evolution doesn’t care about getting the best solution—only one that’s good enough to compete.
Evolution will tolerate any hack as long as it meets that one goal.
34

HUMAN VISION
To illustrate, let’s talk about how we see the world around us.
35

PHOTORECEPTOR CELLS
•~120m rod cells 
(monochrome, low light, peripheral vision)

•~6-7m cone cells 
(color, more light, central details)
The human eye has two different kinds of photoreceptors.
!
[REVEAL] There are about 120 million rod cells in each eye. These play little or no role in color perception and are
mostly used for night and peripheral vision.
!
[REVEAL] There are also about 6 or 7 million cone cells in each eye. These require a lot more light, and they’re what
lets us see in color.
!
The vast majority of the cone cells are packed together in a tight little cluster near the center of the retina. This area
is what we use to focus on individual details, and it’s smaller than you might think: it’s only about 15 degrees wide.
36

http://www.cambridgeincolour.com/tutorials/cameras-vs-human-eye.htm
As a result, our vision is extremely directional. We have a small central area of high detail and high color, but outside
that, our visual acuity and our color perception drop off very quickly.
!
When we look at [REVEAL RIGHT] this… our eyes see something like [REVEAL LEFT] this.
!
[PAUSE. REALLY. AT LEAST FIVE SECONDS.]
!
In order to turn the image on the left into the image on the right, our brains are doing a lot of work that we’re mostly
unaware of.
37

http://www.leahstahl.com/connected.html
SACCADES
We compensate for having such highly directional vision by moving our eyes around a lot. Our brains combine the
details from these individual points of interest to construct a persistent mental model of whatever we're looking at.
!
These fast point-to-point movements are called [REVEAL] saccades, and they're actually the fastest movements the
human body can make: the shorter saccades you make when you’re reading typically last for 20 to 40 milliseconds.
Longer ones might take up to 200 milliseconds, or a ﬁfth of a second.
!
What I ﬁnd so fascinating about this is that we don’t perceive saccades. During a saccade, the eye is still sending
data to the brain, but what it’s sending is a smeary blur, so the brain just edits that part out.
!
This process is called [ADVANCE] “saccadic masking.”
38

“Due to saccadic masking, the
eye/brain system not only 
hides the eye movements 
from the individual but also

…
SACCADIC MASKING
You can see this effect for yourself next time you're in front of a mirror: lean in close, and look back and forth from
the reﬂection of one eye to the other. You will not see your eyes move. As far as we can tell, our gaze just jumps
instantaneously from one reference point to the next.
!
Now, I hope you like conspiracy theories, because I’m about to give you one you’ll have a hard time forgetting.
!
When I was preparing this talk, I found this sentence in the Wikipedia entry on saccades: [REVEAL]
!
"Due to saccadic masking, the eye/brain system not only hides the eye movements from the individual but also
[ADVANCE] hides the evidence that anything has been hidden."
39

“Due to saccadic masking, the
eye/brain system not only

hides the eye movements 
from the individual but also

hides the evidence that
anything has been hidden.”
SACCADIC MASKING
[PAUSE] Put that in your tinfoil hat and smoke it.
!
Our brains lie to us. And then they lie to us about having lied to us. And this happens multiple times a second, every
waking hour, every day of your life.
!
Of course, there’s a reason for this. Imagine if, every time you shifted your gaze around, [ADVANCE] you got
distracted by all the pretty colors.
40

You would be [ADVANCE] eaten by lions.
!
41

#Not All Lions
[PAUSE]
!
But in selecting for this design, evolution made a tradeoff. The tradeoff is that we’re effectively blind every time we
move our eyes—sometimes for up to a ﬁfth of a second. And we might still get eaten by lions because of this, but…
[REVEAL] not as often.
!
#NotAllLions
!
I wanted to talk about this partly because it’s just a really fun subject, but also to illustrate how our brains are doing a
massive amount of work to process information from our environment and present us with an abstraction.
42

http://www.cambridgeincolour.com/tutorials/cameras-vs-human-eye.htm
And as programmers, if we know anything about abstractions, it’s that they’re hard to get right.
!
Which leads me to an interesting question: does it make sense to use any of the techniques I talked about earlier to
try to corral different parts of our brains into doing our work if we don’t know what kinds of shortcuts they’re going
to take?
43

BIAS
.
According to the Oxford English Dictionary, the word “bias” seems to have entered the English language in the 1520s.
It was used as a technical term in the game of lawn bowling, and it referred to a ball that was made in such a way that
it would roll in a curved path instead of in a straight line.
!
Since then, it’s picked up a few additional meanings, but they all have that same basic connotation of something
that’s skewed or off.
44

COGNITIVE BIAS
Actor-observer bias

Ambiguity effect

Anchoring or
focalism

Attentional bias

Availability cascade

Availability heuristic

Backfire effect

Bandwagon effect

Base rate fallacy

Belief bias

Bias blind spot

Bizarreness effect

Change bias

Cheerleader effect

Childhood amnesia

Choice-supportive
bias

Clustering illusion

Confirmation bias

Congruence bias

Conjunction fallacy

Conservatism
(Bayesian)

Consistency bias

Context effect

Contrast effect

Cross-race effect

Cryptomnesia

Curse of knowledge

Decoy effect

Defensive attribution
hypothesis

Denomination effect

Distinction bias

Dunning-Kruger
effect

Duration neglect

Egocentric bias

Empathy gap

Endowment effect

Essentialism

Exaggerated
expectation

Experimenter's or
expectation bias

Extrinsic incentives
bias

Fading affect bias

False consensus
effect

Focusing effect

Forer effect 
(aka Barnum
effect)

Framing effect

Frequency illusion

Functional fixedness

Fundamental
attribution error

Gambler's fallacy

Generation effect

Cognitive bias is a term for systematic errors in human cognition: patterns of thought that diverge in measurable,
predictable ways from the answers that pure rationality would give.
!
When you have some free time, go have a look at the Wikipedia page called “List of cognitive biases”. [REVEAL]
There are over 150 of them, and they’re fascinating reading.
!
This list of cognitive biases has a lot in common with the list of code smells I showed earlier. A lot of the names are
very literal, but there are a few that stand out, like “Curse of knowledge” or the “Google effect”.
!
The parallel goes deeper than that, though: this list gives names to patterns of dysfunction, and once you have a
name for a thing, it’s much easier to recognize it and figure out how to address it.
!
I do want to call your attention to one particular item on this list. It’s called [ADVANCE] “the bias blind spot.”
45

BIAS BLIND SPOT
The tendency to see oneself as
less biased than other people,

or to be able to identify 
more cognitive biases in others
than in oneself
This is [REVEAL, READ ALOUD]
!
...sound like anyone you know?
!
There’s a big part of tech and geek culture that gloriﬁes rationality. We often want to see ourselves as beings of pure
logic.
46

HERE IS A PICTURE OF MR. SPOCK.
YOUR ARGUMENT IS INVALID.
https://ﬂickr.com/photos/39039799@N04/3589716749/in/gallery-48528847@N04-72157628424835507/
[PAUSE]
!
I hate to break it to you, but ain’t none of us Mister Spock. Even Spock would bend or break the rules when it suited
him and make up some bullshit excuse later.
!
As humans, we’re all biased. It’s built into us. Pretending that we aren’t biased only allows our biases to run free.
47

LOOKING FOR BIAS
“HOW IS THIS BIASED?”
I don’t have a lot of general advice for how to look for bias, but an obvious and necessary first step is just to ask the
question: [REVEAL] “how is this biased?”
!
That first word is crucial! If you only ask “is this biased?”, it’s way too easy to let yourself say, “eh, seems fine.”
There is always a bias. Your job is to figure out what it is.
!
Asking the question is a great way to start. Beyond that, I suggest that you learn about as many specific cognitive
biases as you can, so that your brain can do what it does, which is to look for patterns and classify and make
associations.
!
If you’re not checking your work for bias, you can look right past a great solution, and you'll never even know it was
there.
48

3.AMAZING HACK
I have an example of a solution that is simple, elegant, and just about the last thing I ever would have thought of.
49

http://www.spriters-resource.com/nes/pacman/sheet/15839
PAC-MAN
Let’s talk about Pac-Man. If you’ve never played it, this is a very simple game where you run around a maze trying to
avoid four ghosts.
!
Now, playing games is fun, but we’re programmers; we want to know how things work. So let’s talk about
programming Pac-Man.
50

http://www.spriters-resource.com/nes/pacman/sheet/15839
PROGRAMMING PAC-MAN
For the purposes of this discussion, we'll just consider three things:
!
[REVEAL EACH]
the Pac Man, the ghosts, and the maze.
!
- The Pac Man is controlled by the player, so that code is basically hardware events. Boring. [HIDE]
- The maze is there so that the player has some chance at avoiding the ghosts. Boring. [HIDE]
!
But the ghost AI is what’s going to make or break the game, and that’s where we get to have some fun.
51

GHOST AI
To keep things simple, let’s start with one ghost. How do we program its movement?
52

GHOST AI
CHOOSE RANDOM DIRECTION 
AT EVERY OBSTACLE
We could choose a random direction and follow it until we hit a wall, then choose another random direction. This is
fairly easy to implement, but it’s not much of a challenge for the player.
53

GHOST AI
MINIMIZE LINEAR DISTANCE
We could compute the distance to the Pac Man in the X and Y axes, and pick a direction that makes one of those
smaller... but then the ghost will get stuck in corners or behind walls, and again, it'll be too easy for the player.
54

GHOST AI
MINIMIZE TOPOLOGICAL DISTANCE
So how about, instead of minimizing linear distance, we focus on topological distance? We could compute all
possible paths through the maze to the Pac Man, pick the shortest one, and start down it. Then, next tick, do it all
again.
!
This works ﬁne for one ghost. But if all four ghosts use this algorithm, they'll wind up chasing after the player in a
tight little bunch instead of fanning out.
55

GHOST AI
MINIMIZE TOPOLOGICAL DISTANCE,

PRUNING PATHS THROUGH ALLIES
Okay, so each ghost can compute all possible paths to the Pac Man, and reject any path that goes through another
ghost. This means our ghosts have to keep track of each other as well as the Pac-Man, but this seems doable…
right?
!
Quick show of hands: how many people would approach this problem more or less the way I just walked through it?
I certainly would.
!
So…
56

HOW IS THIS BIASED?
…how is this biased?
!
Well, the best way I have to explain the bias in this solution is to walk you through a very different solution.
57

OBJECT-ORIENTED PROGRAMMING 
SYSTEMS, LANGUAGES AND APPLICATIONS

OOPSLA
In 2006, I attended OOPSLA as a student volunteer, and I happened to sit in on a presentation by
[ADVANCE] Alexander Repenning of the University of Colorado.
58

ALEXANDER REPENNING

UNIVERSITY OF COLORADO
COLLABORATIVE DIFFUSION:

PROGRAMMING ANTIOBJECTS
In his presentation, Professor Repenning walked through the Pac-Man problem, then presented this idea: give the
Pac Man a smell, and model the diffusion of that smell throughout the environment.
!
In the real world, smells travel through the air, but we don’t need to model each individual air molecule. What we can
do is divide the environment up into reasonably-sized logical chunks, and model the average concentration of scent
molecules in each chunk.
!
Remember when I said the maze was boring? I lied. As it turns out, the tiles of the maze already divide up the
environment for us. They’re not really doing anything else, so we can borrow them as a convenient container for this
computation.
59

METAPHOR: DIFFUSION
•Pac-Man gives floor tile a smell

•Each floor tile passes some of that smell to its
neighbors
Here’s what we do: we say that the Pac-Man gives whatever floor tile it’s standing on [REVEAL] a "Pac-Man smell"
value of, say, 1000. The number doesn’t really matter.
!
[REVEAL] That tile then passes a smaller value off to each of its neighbors, and they pass an even smaller value off to
their neighbors, and so on.
!
Iterate a few times, and we get a diffusion contour that we can visualize as a hill with its peak centered on the Pac-
Man.
60

http://youtu.be/RaDAROMGiYA
DIFFUSION CONTOUR
It’s a little hard to see here, but the Pac-Man is at the bottom of that big yellow bar.
!
[CLICK TO PLAY; WAIT 7-8 SECONDS]
!
So we’ve got the Pac-Man, and we’ve got the ﬂoor tiles passing the Pac-Man smell around. But to make it a maze, we
need some walls. The wall tiles have a hard-coded “Pac-Man smell" value of zero, which chops the hill up a bit...
61

http://youtu.be/nm2v18eN5kU
WALLS BLOCK DIFFUSION
[WAIT ~13 seconds]
!
And now, all our ghost has to do is climb the hill.
62

GHOST “AI”: HILL CLIMBING
•Sample each adjacent floor tile

•Move to the one with the strongest smell

•Ghosts cancel “Pac-Man smell”

•Intelligent cooperation 
without explicit knowledge!
We program our ghost to:
- [REVEAL] sample each of the floor tiles next to it,
- [REVEAL] pick the one with the biggest number, and go that way.
!
It barely seems worthy of being called AI!
!
But check this out. When we add more ghosts to the maze, we only have to make one change to get them to
cooperate. And interestingly, we don't change the ghost's movement behaviors at all. Instead, [REVEAL] we have
the ghosts tell the floor tile they’re standing on that its “Pac-Man smell” value is zero.
!
This effectively turns the ghosts into moving walls, so that when one ghost cuts off another one, the second ghost
will automatically choose a different path. [REVEAL] This lets the ghosts cooperate without even being aware of
each other.
!
Halfway through the conference session where I saw this, I was just...
63

http://i0.kym-cdn.com/entries/icons/original/000/009/993/tumblr_m0wb2xz9Yh1r08e3p.jpg
…what?
!
At ﬁrst, I was just really surprised by the simplicity of the approach. But then what really messed with my head was
the realization that… I never would have thought of this.
!
I hope that looking at the second solution makes it easier to see [ADVANCE] the bias in the ﬁrst solution.
64

BIAS?
•“Body syntonic” reasoning…

•Leads us to make the pursuer “smarter”

•Biases us toward “foreground” objects
For most of us, our ﬁrst instinct is to imagine ourselves as the ghost. This is the [REVEAL] “body syntonic” reasoning
that’s designed into Logo, and in this case, it’s a trap—because it leads us to solve the pursuit problem by making the
pursuer [REVEAL] “smarter”.
!
Once we’ve started down that road, it’s unlikely to occur to us to consider a radically different approach, even if—
perhaps especially if—it’s a much simpler one.
!
Body syntonicity biases us towards modeling [REVEAL] objects in the “foreground” rather than objects in the
“background”.
!
Does this mean you shouldn’t use body syntonic reasoning? Of course not. It’s a tool. It’s right for some jobs, but
not for others.
!
Let's take another look at one more technique from Part 1.
65

BIAS:“PLEASE, MR. GEAR...”
What's the bias in "Please, Mr. Gear, what is your ratio?"
!
Well, it’s androcentric, for one. [ADVANCE] But more interestingly…
66

BIAS:“PLEASE, MR. MS. GEAR...”
•“Please, Ms. Pac-Man, 
what is your current position in the maze?”

•“Please, Ms. FloorTile, 
how much do you smell like Ms. Pac-Man?”
This technique is explicitly designed to give you an opportunity to discover new objects in your model. The trap in
this technique is that it requires a name, and names have gravity. Because our brains are associative, the new
objects you discover with this technique will probably acquire names that are related to the ones you already have.
!
Here’s what I mean: how many steps does it take to get from [REVEAL]
"Please, Ms. Pac-Man, what is your current position in the maze?"
!
...to [REVEAL] "Please, Ms. Floor Tile, how much do you smell like Ms. Pac Man?"
!
For a lot of people, the answer is probably “inﬁnity.” My guess is that you don’t come up with this technique unless
you’ve already done some work modeling diffusion in some other context.
!
Which, incidentally, is why I like to work on diverse teams: the more different backgrounds and perspectives we have
access to, the more chances we have to ﬁnd a novel application of a seemingly unrelated technique.
!
[PAUSE]
67

DENOUEMENT
It can be exhilarating and empowering to ﬁnd techniques that let us take shortcuts by leveraging specialized
structures in our brains.
!
But those structures themselves take shortcuts—and if you’re not careful, they can lead you down a primrose path.
!
Here’s that quote that got me thinking about all of this in the ﬁrst place.
68

“... ﬁnd techniques to rope the physical,
sensory parts of your brain into this task of
dealing with abstractions. But we don’t ever
teach anybody how to do that, or even
that they should do that.”

!
Ultimately, I think we should use techniques like this. I think we should share them. I think, to paraphrase Glenn, we
should teach people that this is a thing you can and should do.
!
And...
69

HOW IS THIS BIASED?
I think we should teach people that: looking critically at the answers these techniques give you is also a thing you
can and should do. We might not always be able to come up with a radically different and simpler approach, but the
least we can do is give ourselves the opportunity to do so, by asking: “how is this biased?”
70

<3
ADA.cohorts[0]

Avdi Grimm

Charles Max
Wood

Chris Morris

David Brady

David Woodall

!
GlennVanderburg

James Edward
Gray II

Katrina Owen

Kerri Miller

LivingSocial

Peter Dore

Rodrigo Franco

Sandi Metz

Sara Flemming

SaronYitbarek

Sean McHugh

Sonia Connolly

Steve Jorgensen

Tim Linquist
Thank you to everyone who helped me with this talk, or with the ideas in it.
!
I look forward to hearing your ideas as well. Thank you.
71

Cognitive Shortcuts: Models, Visualizations, Metaphors, and Other Lies (Cascadia Ruby Conf 2014)

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