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Towards Data Science

Cassie Kozyrkov
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Aug 2, 2019

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14 min read
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INTRODUCTION TO DECISION INTELLIGENCE


A NEW DISCIPLINE FOR LEADERSHIP IN THE AI ERA

Curious to know what the psychology of avoiding lions on the savannah has in
common with responsible AI leadership and the challenges of designing data
warehouses? Welcome to decision intelligence!



Decision intelligence is a new academic discipline concerned with all aspects of
selecting between options. It brings together the best of applied data science,
social science, and managerial science into a unified field that helps people
use data to improve their lives, their businesses, and the world around them.
It’s a vital science for the AI era, covering the skills needed to lead AI
projects responsibly and design objectives, metrics, and safety-nets for
automation at scale.

> Decision intelligence is the discipline of turning information into better
> actions at any scale.

Let’s take a tour of its basic terminology and concepts. The sections are
designed to be friendly to skim-reading (and skip-reading too, that’s where you
skip the boring bits… and sometimes skip the act of reading entirely).


WHAT’S A DECISION?

Data are beautiful, but it’s decisions that are important. It’s through our
decisions — our actions — that we affect the world around us.

We define the word “decision” to mean any selection between options by any
entity, so the conversation is broader than MBA-style dilemmas (like whether to
open a branch of your business in London).

> It’s through our decisions — our actions — that we affect the world around us.

In this terminology, appending a label such as cat versus not-cat to a user’s
photo is a decision executed by a computer system, while figuring out whether to
launch that system is a decision taken thoughtfully by the human leader (I
hope!) in charge of the project.


Decisions, decisions, decisions. Image: SOURCE


WHAT’S A DECISION-MAKER?

In our parlance, a “decision-maker” is not that stakeholder or investor who
swoops in to veto the machinations of the project team, but rather the person
who is responsible for decision architecture and context framing. In other
words, a creator of meticulously-phrased objectives as opposed to their
destroyer.


WHAT’S DECISION-MAKING?

Decision-making is a word that is used differently by different disciplines, so
it can refer to:

 * taking an action when there were alternative options (in this sense it’s
   possible to talk about decision-making by a computer or a lizard).
 * performing the function of a (human) decision-maker, part of which is taking
   responsibility for decisions. Even though a computer system can execute a
   decision, it will not be called a decision-maker because it does not bear
   responsibility for its outputs — that responsibility rests squarely on the
   shoulders of the humans who created it.


MAKING A CALCULATION VERSUS MAKING A DECISION

Not all outputs/suggestions are decisions. In decision analysis terminology, a
decision is only made once an irrevocable allocation of resources takes place.
As long as you can change your mind for free, no decision has been made yet.


DECISION INTELLIGENCE TAXONOMY

One way to approach learning about decision intelligence is to break it along
traditional lines into its quantitative aspects (largely overlapping with
applied data science) and qualitative aspects (developed primarily by
researchers in the social and managerial sciences).


QUALITATIVE SIDE: THE DECISION SCIENCES

The disciplines making up the qualitative side have traditionally been referred
to as the decision sciences — which I’d have loved for the whole thing to be
called (alas we can’t always have what we want).


Image: SOURCE

The decision sciences concern themselves with questions like:

 * “How should you set up decision criteria and design your metrics?” (All)
 * “Is your chosen metric incentive-compatible?” (Economics)
 * “What quality should you make this decision at and how much should you pay
   for perfect information?” (Decision analysis)
 * “How do emotions, heuristics, and biases play into decision-making?”
   (Psychology)
 * “How do biological factors like cortisol levels affect decision-making?”
   (Neuroeconomics)
 * “How does changing the presentation of information influence choice
   behavior?” (Behavioral Economics)
 * “How do you optimize your outcomes when making decisions in a group context?”
   (Experimental Game Theory)
 * “How do you balance numerous constraints and multistage objectives in
   designing the decision context?” (Design)
 * “Who will experience the consequences of the decision and how will various
   groups perceive that experience?” (UX Research)
 * “Is the decision objective ethical?” (Philosophy)

This is just a small taste… there are many more! This is also far from the
complete list of disciplines involved. Think of the decision science side as
dealing with decision setup and information processing in its fuzzier storage
form (the human brain) rather than the kind that’s neatly written down in
semi-permanent storage (on paper or electronically) which we call data.


THE TROUBLE WITH YOUR BRAIN

In the previous century, it was fashionable to praise anyone who stuffed a fat
wad of math into some unsuspecting human endeavor. Taking a quantitative
approach is usually better than thoughtless chaos, but there’s a way to do even
better.

> Strategies based on pure mathematical rationality are relatively naïve and
> tend to underperform.

Strategies based on pure mathematical rationality without a qualitative
understanding of decision-making and human behavior can be pretty naïve and tend
to underperform relative to those based on joint mastery of the quantitative and
qualitative sides. (Stay tuned for blog posts on the history of rationality in
the social sciences as well as examples from behavioral game theory where
psychology beats mathematics.)

> Humans are not optimizers, we’re satisficers, which is a fancy word for corner
> cutters.

Humans are not optimizers, we’re satisficers, which is a fancy word for corner
cutters who are satisfied with good enough as opposed to perfect. (It’s also a
concept that was enough of a shocker to our species arrogance—a punch in the
face of rational Man, godlike and flawless — that it was worth a Nobel Prize.)


Aristotle thought that the brain was a glorified air conditioner for the heart.
I guess the brain looks less impressive when it’s on the outside… Image: SOURCE

In reality, we humans all use cognitive heuristics to save time and effort.
That’s often a good thing; working out the perfect running path to get away from
a lion on the savannah would get us eaten before we’ve barely started the
calculation. Satisficing also reduces the calorie cost of living, which is just
as well, since our brains are ridiculously power-hungry devices as it is,
gobbling up around a fifth of our energy expenditure despite weighing
approximately 3 lb. (I bet you weigh more than 15 lb in total, right?)

> Some of the corners we cut lead to predictably suboptimal outcomes.

Now that most of us don’t spend our days running away from lions, some of the
corners we cut lead to predictably rubbish outcomes. Our brains aren’t exactly,
er, optimized for the modern environment. Understanding the manner in which our
species turns information into action allows you to use decision processes to
protect yourself from the shortcomings of your own brain (as well as from those
who intentionally prey on your instincts). It also helps you build tools that
augment your performance and adapt your environment to your brain if the poor
thing is Lamarckably slow to catch up a la Darwin.

> If you think that AI takes the human out of the equation, think again!

By the way, if you think that AI takes the human out of the equation, think
again! All technology is a reflection of its creators and systems that operate
at scale can amplify human shortcomings, which is one reason why developing
decision intelligence skills is so necessary for responsible AI leadership.
Learn more here.


Image: SOURCE


PERHAPS YOU’RE NOT MAKING A DECISION

Sometimes, thinking through your decision criteria carefully leads you to
realize that there’s no fact in the world that would change your mind — you’ve
selected your action already and now you’re just looking for a way to feel
better about it. That’s a useful realization — it stops you from wasting more
time and helps you redirect your emotional discomfort while doing what you were
going to do anyways, data be damned.

> “He uses statistics as a drunken man uses lamp-posts… for support rather than
> illumination.” -Andrew Lang

Unless you would take different actions in response to different still-unknown
facts, there’s no decision here… though sometimes training in decision analysis
helps you see those situations more clearly.


DECISION-MAKING UNDER PERFECT INFORMATION

Now imagine that you’d dealt very carefully with setting up a decision that is
sensitive to the facts and you can snap your fingers to see the factual
information you need for executing your decision. What do you need data science
for? Nothing, that’s what.

> The first order of business should be figuring out how we’d like to react to
> facts.

There’s never anything better than a fact — something you know with certainty
(yes, I’m aware there’s a gaping relativist rabbit hole here, let’s move along)
— so we always prefer to make decisions based on facts if we have them. That’s
why the first order of business should be figuring out how we’d like to deal
with facts. Which of the following uses would you want to put your ideal
information to?


Your author particularly enjoyed this wall in Jamaica. Image: SOURCE


WHAT CAN YOU DO WITH FACTS?

 * You can use facts to make a single important pre-framed decision. If it’s
   important enough, you’ll need to lean heavily on the qualitative side of
   things to frame your decision wisely. Psychologists know that if you allow
   yourself to be ambushed by surprise information, it can manipulate you in
   ways you wouldn’t like, so they (and others) have lots to say about how to
   approach selecting the information you’ll accept in advance.
 * You can use facts to make a special kind of pre-framed decision, called an
   impact (or causal) decision. If your decision is framed in terms of taking an
   action to cause something to happen, then you need facts about
   cause-and-effect relationships to make your decision. In such cases, facts
   about effects (e.g. “people recover from this illness”) are useless to you if
   they don’t come with facts about causes (e.g. “because of antibiotics”). The
   way to get your hands on cause-and-effect information is to do a controlled
   experiment. On the other hand, if you are making an execution decision framed
   as a response to a non-causal fact (e.g. “if I have at least x in my savings
   account, I will treat myself to new shoes”), then an experiment is not
   necessary.
 * You can use facts to shore up opinions (“I expect it’s sunny outside” becomes
   “I know it’s sunny outside”).
 * You can use facts to make a single important existence-based decision.
   Existence-based decisions (“I just found out there exists a case of ebola
   right next door, so I’m getting out of here pronto…”) are decisions where the
   existence of a formerly unknown unknown shakes the foundation of your
   approach so much that you realize in hindsight that your decision context was
   sloppily framed.
 * You can use facts to automate a large number of decisions. In traditional
   programming, a human specifies the set of instructions for converting fact
   inputs into appropriate actions, possibly involving something like a lookup
   table.
 * You can use facts to reveal an automation solution. By seeing the facts about
   the system, you can write code based on them. This is a better approach to
   traditional programming than coming up with the structure of a solution by
   thinking really hard with no information. For example, if you don’t know how
   to convert from Celsius to Fahrenheit, but you could use a dataset to look up
   the entry in Fahrenheit that goes with the Celsius input… but if you analyze
   that lookup table itself, you’ll discover the formula that connects them.
   Then you can just code up that formula (“model”) to do your dirty work for
   you and lose the clunky table.
 * You can use facts to generate an optimal solution to an automation problem
   that is perfectly solvable. This is traditional optimization. You’ll find
   many examples in the field of operations research, which covers, among other
   things, how to wrangle constraints to get the ideal outcome, such as the best
   order in which to complete a series of tasks.
 * You can use facts to inspire how you’ll approach future important decisions.
   This is part of analytics, which also belongs in the section on partial
   information. Hold that thought!
 * You can use facts to take stock of what you’re dealing with. This helps you
   understand the kinds of inputs you have available for future decisions and
   design how to curate your information better. If you’ve just inherited a big,
   dark (data) warehouse full of potential ingredients, you won’t know what’s
   inside until someone looks at it. Luckily, your analyst has a flashlight and
   rollerblades.
 * You can use facts sloppily to make unframed decisions. This is efficient when
   decisions have sufficiently low stakes that they do not warrant the effort
   required to approach them carefully, such as, “What should I eat for lunch
   today?” Attempting to be rigorous all the time on all decisions gives
   suboptimal long-run / lifetime outcomes and falls into the category of
   pointless perfectionism. Save your effort for the situations that are
   important enough for it, but please don’t forget that even if it’s efficient
   to use a low-quality low-effort approach, the optimal decision approach is
   still of low quality. You shouldn’t thump your chest or be overconfident when
   that’s your method… If you cut corners, you’re holding something flimsy.
   There are situations where flimsy gets the job done, but that doesn’t
   suddenly make your conclusion sturdy. Don’t lean on it. If you want
   high-quality decision-making, you need a more rigorous approach.

With training in the decision sciences, you learn to reduce the effort that it
takes to make rigorous fact-based decisions, which means that the same amount of
work now gets you higher-quality decision-making across the board. This is a
very valuable skill, but it takes lots of work to hone it. For example, students
of behavioral economics form the habit of setting decision criteria in advance
of information. Those of us who took a beating from sufficiently demanding
decision science training programs can’t help but ask ourselves, for example,
what the maximum that we’d pay for a ticket is BEFORE we look up the price.


DATA COLLECTION AND DATA ENGINEERING

If we had the facts, we’d be done already. Alas, we live in the real world and
often we must work for our information. Data engineering is a sophisticated
discipline centered on making information available reliably at scale. In the
way that going to the grocery store for a pint of ice cream is easy, data
engineering is easy when all available relevant information fits in a
spreadsheet.


Image: SOURCE

Things get tricky when you start asking for the delivery 2 million tons of ice
cream… where it’s not allowed to melt! Things get even trickier if you have to
design, set up, and maintain a huge warehouse and you don’t even know what the
future will ask you to store next — maybe it’s a few tons of fish, maybe it’s
plutonium… good luck!

> It’s tricky to set up a warehouse when you don’t even know what you’ll be
> asked to store next week— maybe it’s a few tons of fish, maybe it’s plutonium…
> good luck!

While data engineering is a separate sister discipline and key collaborator to
decision intelligence, the decision sciences include a strong tradition of
expertise involved in advising the design and curation of fact collection.


QUANTITATIVE SIDE: DATA SCIENCE

When you’ve framed your decision and you look up all the facts you need, using a
search engine or an analyst (performing the role of a human search engine for
you), all that’s left is to execute your decision. You’re done! No fancy data
science needed.

What if, after all that legwork and engineering jiu-jitsu, the facts delivered
are not the facts you ideally need for your decision? What if they’re only
partial facts? Perhaps you want tomorrow’s facts, but you only have the past to
inform you. (It’s so annoying when we can’t remember the future.) Perhaps you
want to know what all your potential users think of your product, but you can
only ask a hundred of them. Then you’re dealing with uncertainty! What you know
is not what you wish you knew. Enter data science!

> Data science gets interesting when you’re forced to make leaps beyond the
> data… but do be careful to avoid an Icarus-like splat!

Naturally, you should expect your approach to change when the facts you have
aren’t the facts you need. Maybe they’re one puzzle piece of a much bigger
puzzle (as with a sample from a larger population). Maybe they’re the wrong
puzzle, but the best you have (as with using the past to predict the future).
Data science gets interesting when you’re forced to make leaps beyond the data…
but do be careful to avoid an Icarus-like splat!

 * You can use partial facts to make a single important pre-framed decision with
   statistical inference, supplementing the information you have with
   assumptions to see if you should change your action. This is Frequentist
   (classical) statistics. If you’re making an impact decision (framed in terms
   of taking an action to cause something to happen, e.g. “you’d only want to
   change your logo color to orange if that causes more people visit your
   website”), then it’s best to use data from a randomized controlled
   experiment. If you’re making an execution decision (e.g. “you’d only want to
   change your logo color to orange if at least 25% of your users consider
   orange to be their favorite color”), a survey or observational study is good
   enough.
 * You can use partial facts to reasonably update opinions into more informed
   (but still imperfect and personal) opinions. This is Bayesian statistics. If
   these opinions involve cause-and-effect relationships, it’s best to use data
   from a controlled randomized experiment.
 * Your partial facts may turn out to contain facts about existence, which means
   you could use them in hindsight for existence-based decisions (see above).
 * You can use partial facts to automate a large number of decisions. That’s
   traditional programming using something like a lookup table where you convert
   something you haven’t seen before into the closest thing you that you have,
   then proceed as usual. (That’s what k-NN does in a nutshell… and it usually
   works better when that nutshell has more things in it.)
 * You can use partial facts to inspire an automation solution. By seeing the
   partial facts about the system, you can still write code based on what you’re
   seeing. This is analytics.
 * You can use partial facts to generate a decent solution to an imperfectly
   solvable automation problem so you don’t have to come up with it yourself.
   This is machine learning and AI.
 * You can use partial facts to inspire how you’ll approach future important
   decisions. This is analytics.
 * You can use partial facts for understanding what you’re dealing with (see
   above) and to accelerate the development of an automation solution with
   advanced analytics, for example by inspiring new ways to blend information
   together to make useful model inputs (the jargon for this is “feature
   engineering”) or new methods to try in an AI project.
 * You can use partial facts sloppily to make unframed decisions, but be aware
   that the quality is even lower than when you use facts sloppily, because what
   you actually know is one step removed from what you wish you knew.

For all of these uses, there are ways to integrate wisdom from a variety of
previously-siloed disciplines to approach decision-making more effectively.
That’s what decision intelligence is all about! It brings together diverse
perspectives on decision-making which make all of us stronger, together, and
gives them a new voice that’s free of the traditional constraints of their
originating fields of study.


To return to the kitchen analogy for AI, if research AI is building microwaves
and applied AI is using microwaves, decision intelligence is using microwaves
safely to meet your goals and using something else when you don’t need a
microwave. The goal (objective) is always the starting point for decision
intelligence. Image: SOURCE

If you’re curious to read more, most of my articles here on Medium.com have been
written from a decision intelligence perspective. My guide to starting AI
projects is probably the least subtle, so I’d recommend diving in there if you
haven’t already chosen your own adventure by following the links in this
article.


THANKS FOR READING! HOW ABOUT AN AI COURSE?

If you had fun here and you’re looking for an applied AI course designed to be
fun for beginners and experts alike, here’s one I made for your amusement:


Enjoy the entire course playlist here: bit.ly/machinefriend



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CASSIE KOZYRKOV


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Chief Decision Scientist, Google. ❤️ Stats, ML/AI, data, puns, art, theatre,
decision science. All views are my own. twitter.com/quaesita


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