WTF is an Applied Microeconomist?

Episode Transcript
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Ernest (00:03):
Hello and welcome to Learn Make Learn where we share
qualitative and quantitativeperspectives on products to help
you make better.
My name is Ernest Kim, and I'mjoined by my friend and co-host,
Joachim Groeger.
Hey Joachim, how's it going?

Joachim (00:18):
I am good.
I feel like I say that everytime.
I don't, I I wanna mix it up,but we're okay.
Yeah.
I think my little, one of mykids has got a mild cold and I
think he's giving it to all ofus.
So we're all feeling a, a littlebit under the weather, but not
enough to, to call in sickproperly and especially not
cancel a podcasting session.
How are you doing, Ernest?

(00:38):
How are things at your end?

Ernest (00:39):
Good.
Yeah.
Yeah, there.
That definitely does seem to begoing, something going around
our region here.
So if I sound a little bit extragravelly today, it's uh, just
because, uh, I've had a bit of asore throat.
Same with my wife, but,similarly I think we're getting
over it.
So not quite enough to call insick, but, feeling better.
Um, but.

Joachim (01:00):
Adding character, with your voice, you know, getting
extra gravel, more serious.

Ernest (01:05):
Yeah, it wasn't just out smoking.
all right, well this is episodeseven and our topic today is
what the bleep is an appliedmicro economist?
It's the first in a series oftwo episodes in which Joachim
and I will interview each otherwith a focus on our careers for
the benefit of anyone interestedin pursuing our respective

(01:26):
career paths.
And today I'm going to interviewJoachim about his career as an
applied micro economist.
But before diving into ourinterview, let's start with some
follow ups to our previousepisode, experimentation in
product innovation.
Joachim, you wanna get usstarted?

Joachim (01:43):
Yeah, I have a quick follow up.
It actually touches oneverything that we've talked
about up to this point, really.
but it's a book recommendationthat I think, is really tightly
connected to our discussionaround Pixar's culture of having
the, brain trust and a trusttrustworthy space where they can
discuss their ideas openly.

(02:05):
And so Margaret Heffernan has abook called A Bigger Prize,
which is all about buildinginnovative business cultures.
and her focus in that book isabout reducing pecking orders.
So not having stronghierarchies, but having flat
structures.
And she interviews variouspeople.
But the one that really struckme, was the company Gore of
Gore-Tex fame.

(02:27):
They have traditionally kepttheir entire structure super,
super flat, and the chapters onthat in this book are really
great.
Um, but there's a interestingpiece here about the innovation
process.
Um, and so let me just read asmall section from it so you get
a sense of what's going on here.
At Gore, associates are expectedto share ideas early and widely.

(02:50):
Instead of hanging onto aproject in order to defend
credit and power, goreAssociates are encouraged to put
their ideas out where colleaguescan see them, add to them,
refine them, and challenge them.
If an idea elicits no interest,that says a lot If it provokes
debate and discussion.
That says even more whether ornot people want to contribute to
an associates idea.
It depends a lot on how muchthey like working with that

(03:11):
person and how generous andhelpful he or she has been to
others.
None of this has anything to dowith how power is wielded in a
formal hierarchy.
Um, and so I think that's areally interesting piece because
she's getting at so many aspectsof the innovation process,
right?
It's flat structure, opendialogue, being safe enough to

(03:34):
be challenged in your ideas, notto have to worry that this
somehow undermines yourcredibility in the organization,
and importantly, the sharing ofthat.
Precious resource, which is timewithin your organization.
If someone has proven to behelpful and engaging with your
ideas, taking them seriously andtrying to get, make them better,

(03:54):
then that will be reciprocatedby the organization and people
will show up.
There are a couple of littleanecdotes in there where some of
the associates mentioned peoplecoming in from other companies,
and essentially forgetting thatthe company is flat and they,
they call a meeting and theyexpect everyone to show up, and
then they get angry when no oneshows up because the meeting has
no meaningful agenda.

(04:16):
So that's a great example of,setting up an agenda around an
idea, not around a peckingorder, which is what Heffernan's
Point is about.
So I really recommend that book.
I think there are a lot ofreally great tidbits in that
about how to create innovativeculture.
But the theme really that runsthrough it is safety and, you
know, flat hierarchy.
Another way of thinking about aflat hierarchy is we're all safe

(04:38):
here.
no one can outrank someone anddestroy your chances of pro
promotion'cause there is nopromotion.
Maybe you'll get paid morebecause you did a good job, but
that doesn't mean you get morepower over other people inside
of the organization.
I think that's quiteinteresting, right?
it's saying, I will compensateyou for your successes, but that
doesn't buy you the right tostart crapping on everyone
else's ideas and then becoming achoke point for the entire

(05:00):
organization.
Your value in the organizationis determined by how you
interact with everyone else.
So, really interesting thing.
So I recommend that book.
It's Margaret Huffman's book, ABigger Prize.

Ernest (05:11):
That's super interesting.
I imagine that that's probablybeen a big part of their
success, you know, as a smallcompany managing to stay
successful for all these, Imean, how, however long it's
been, I think decades now,That's, I didn't realize that
that was how they werestructured.
That's really cool.

Joachim (05:28):
There's a really great anecdote about how they picked
the new CEO you.
They have a CEO that is the onlyperson with a title.
And so when it came time to pickthe new CEO, everyone threw a
name in the hat.
I mean, that's how they decidedeveryone from the, the most
junior person to the most seniorperson, you know, by tenure was
throwing a name in the hat.
And then, a person emerged whowas the best leader that they

(05:52):
could have picked it and was amajority vote so, that was an
interesting tidbit as well.

Ernest (05:57):
Oh, that's a great one.
on my end I have, two followups, both corrections actually.
So the first is regarding thatminimum viable product concept
that we talked about last week,or MVPI had said that it was
created by Eric Ries, but theterm and concept were actually
pioneered back in 2001 by aperson named Frank Robinson.

(06:20):
In subsequent years, Eric Riesand Steve Blank, who were both
key figures in the Lean startupmovement, did help to popularize
the MVP concept, but it wasFrank Robinson who created it.
So, um,, apologies for that one.
And then my second one is, in mydiscussion of Ed Catmull and his
excellent book, Creativity, Inc.

(06:40):
from Pixar, um,, that you talkedabout a second ago as well, I
misremembered and consequentlycompletely mischaracterized his
concept of ugly babies.
As I described the concept inour last episode, part of the
role of Pixar's Brain Trust wasto help weed out the ugly
babies.
In other words, ideas that aproject team gets attached to

(07:02):
over the course of a film'sdevelopment, but they don't
actually make the end productbetter.
Now, weeding out bad ideas is akey function of Pixar's brain
trust, but this responsibilityhas nothing to do with what Cat
mul describes in the book asugly babies.
Instead, ugly babies are howCatmull describes ideas in their
nascent form.

(07:23):
As he writes in a chapter titledThe Hungry Beast and the Ugly
Baby, quote, originality isfragile and in its first
moments, it's often far frompretty, this is why I call early
mockups of our films, uglyBabies.
Then he goes on to explain thatthe role of Pixar's Brain Trust

(07:43):
when it comes to ugly babies isto protect them from being
judged too quickly.
He writes, quote, when someonehatches an original idea, it may
be ungainly and poorly defined,but it is also the opposite of
established and entrenched.
And that is precisely what ismost exciting about it.
If while in this vulnerablestate it is exposed to naysayers

(08:05):
who fail to see its potential orlack the patience to let it
evolve, it could be destroyed.
Part of our job is to protectthe new from people who don't
understand that in order forgreatness to emerge, there must
be phases of not so greatnessunquote.
So pretty much the exactopposite of how I characterized
the concept, my apologies forthat in my defense, it has been

(08:30):
sometimes since I last readCreativity Inc.
And this error has, uh,, givenme a good excuse to read it
again, because it really is verygood.
I'm generally not a big fan ofbusiness books, but I think
Creativity Inc.
is worth the read.
Alright.
All right.
with those follow ups out of theway.
Let's get to our main topic,what the bleep is an applied
micro economist, and I'll startwith an easy one.

(08:53):
Joachim.
What does an applied microeconomist do?

Joachim (08:57):
The easy one.
Um, so I've.
You would think having been ateacher at a university for like
over seven, eight years, I wouldhave a really good spiel that
summarizes it.
But I think the problem is thatit's so misunderstood what
economics is and what aneconomist does.

(09:19):
There's so many people out therewho call themselves economists.
It's tricky.
So I'm gonna try my best to comeup with a quick strip down
explanation for what aneconomist does, and then how I
fit into that.
And then what I'd like to alsodo as I answer these questions
is just really paint a pictureof what's possible because of
what's happened in ourprofession is kind of lame to be

(09:40):
honest.
You know, it's very, veryconservative.
So maybe it's time to shakethings up a bit.
Fundamentally, economics isabout mathematically
representing human behavior andhuman behavior specifically when
it comes to down to choices thatthey have to make.
And choices are importantbecause there's a limit.

(10:01):
There's always a trade off.
The podcast means I can't dosomething else with my time.
So there's this constant tensionbetween the different things
you'd like to do and what'sactually feasible.
And economists do their best totry and use mathematics to build
a causal chain of events thathelps you understand how people
come to those choices.

(10:23):
One of the misunderstandings isthat people think we're trying
to understand what's going on inpeople's minds.
The kind of the neuroscience ofhow and why people are choosing
things, eh, economics doesn'tcare about that.
The true definition, ofeconomics doesn't care about
that.
What it cares about is whatchoices you make and not why you
make those choices.

(10:44):
You pick what you want to pickand you remain relatively
consistent in those choices.
And consistency has a veryspecific technical meaning in
it.
but that's basically it.
It's saying reason andrationality to quote David Hume.
Reason is the slave of thepassions, meaning what it is
that you want to do.
I don't care why you want dothat, I don't care.

(11:05):
You might, um and David Hume'sexample is you might prefer the
destruction of the world toscratching your thumb.
There's no reason that will helpyou understand why that's better
than the other thing.
All we know is you prefer onething over the other thing.
And I just have to accept thatas the who you are.
And from that, I can startbuilding a mathematical
framework that allows you toanalyze those choices.

(11:26):
And so, what is thismathematical framework?
What does this all mean?
It's very abstract, butultimately it means being able
to represent.
Numerically how you rankdifferent things.
So we want to come up with somesort of ranking and number space
that helps me understand how youpick A over B and B over C and C
over a.

(11:47):
We can only infer it fromobserved choices.
And this is where a lot ofstudents get stressed out when I
was teaching.
They're like, what is the pointof this?
We're going into this weirdabstract number space to
represent the rank of things andhow is this useful?
You know?
And when you're in the lectureroom, you have to say, it's
super useful.
Trust me, we're gonna see allthese cool examples.

(12:09):
But that gut instinct of justwhat is the point of this is far
more accurate of a response thatyou should have.
The mathematical models arepretty untethered from reality.
They are little toys that weplay around with in theory, and
sometimes some fun littlestories come out of that.

(12:31):
In the words of a famous,theorist, Ariel Rubenstein, he
refers to economic models asfairytales.
They're essentially logicalarguments that use the language
of mathematics to maintainconsistency and to be logically
coherent.
But they're just stories., andanyone who claims that a theory
from economics is more than thatis just wrong.

(12:53):
Okay?
These are not like physicsmodels.
Physics models are theoreticalconstructs that allow you to
explain physical phenomena.
They're guided by thephysicist's intuition.
I think this is what is going onin the system, and I'm gonna
write down the mathematicalequations that describe that
intuition.
But inside of that equationsystem are parameters that are
measurable.
there are many physicalconstants that, tether and

(13:15):
anchor those models to reality.
And then you can test them inthe lab or in a gigantic lab
like the large hadron collider,right?
You have these very clearimplications that have physical
representations.
In econ, it's incrediblydifficult to even get to the
point where you can say, thismodel is the right model, this
model is the wrong model, and soon.
My works begins, is when I say,okay, I've written down a model

(13:37):
now, and now I'd like to matchit to data.
That was what I was doing inacademia, was trying to match
deep theoretical models to thedata and find a way to get
parameters that drive thebehavior, that match what's
happening in the data.
So that's a very, very specificniche area of empirical work
that I was working in.
And, ultimately just a way of.
Looking at human behavior,finding a mathematical model, I

(14:01):
could explain that.
And then using the data ofobserved choices to figure out
what those little weirdnumerical values have to be to
rationalize the observed data Sothat was my academic work, and
that's my training is.
but like every, well maybe notevery good economist, but a lot
of economists in Europe, we allstart reading Marx.

(14:21):
He has his supporters and, uh,opponents, but fundamentally his
program of trying to put realfoundations to economics and
understanding the fact thatsociety evolves as an
interaction between religiousstructures, economic structures,
and all of those thingstogether, creating what we call
society, that idea of his isvery powerful and profound, and

(14:44):
I think still remains true tothis day.
He talks about, the theorists ofhis time who were the
philosophers, and he says quote,philosophers have hit two, only
interpreted the world in variousways.
The point is to change it.
And, econ up to this point thatI've described is very
descriptive and ultimately it isa descriptive thing.

(15:05):
It says, this is what's going onand that's it.
But really, why do we do this?
We want to.
Get in there and fix stuff anddo things.
And so thankfully econeventually started realizing
that that's something veryuseful as well.
And a formal mathematical theoryemerged from that called
mechanism design.
And this was a response to thequestion of, do we want a

(15:27):
capitalist structure?
Do we want free markets?
Do we want communism?
Do we want socialism?
Turns out, if you follow thewords of Leon Hurwich, who was a
founding father of this area ofresearch, he said, well, these
are all the same thing.
They're just examples ofmechanisms or protocols, and
they take inputs and then theydecide something.
And everything is just on thatspectrum of these different

(15:49):
systems.
Like a capitalist system has aspecific mechanism, socialism is
a specific mechanism.
And then once you think of it inthose times, you start asking
yourself, well, I.
Why should I only pick betweentwo?
I can now design everything thatgoes in between that.
And that's mechanism designs.
Power is saying, don't worryabout naming these things and
any isms, ultimately you willhave a goal in mind that you

(16:11):
wanna achieve for society.
And let's design rules thatactually let that emerge from
our system.
And so there's a lot of deepthings in there but that's the
architecting piece of economicsthat I think is really
interesting.
So as an applied microeconomist, I define myself as
someone who does deepmeasurement in service of then
intervening in a system to makeit better.

(16:32):
And, making better has adifferent interpretation in
different settings.
But you know, in the productinnovation business space, it is
of course about generating astream of revenue that's bigger
than what, what would've been inyour absence.
So that's kind of the highlevel.
As much longer than I wanted to.
I'm sorry on, but you know,

Ernest (16:51):
No.
No,

Joachim (16:51):
I think we needed to kind of dispel a couple of those
things.
It's important to set that up.

Ernest (16:56):
Now this is, I think, good in that, I wanted to
approach this from theperspective of a lay person,
know, for the benefit of ouraudience who maybe has, doesn't
have a lot of familiarity witheconomics.
And the good thing is that I'min that boat, so I can very
honestly play this role.
So forgive me if I ask stupidquestions, but one thing you
mentioned, was this assumptionof rationale, the assumption

(17:21):
that we're rational actors and,as a, you know, someone with not
a lot of familiarity with, uh,,,economics, that always struck me
as a, a bad assumption, uh,given what I've seen of human
behavior.
And I was curious how do youaccount for the irrational
behaviors that we often exhibit.

Joachim (17:39):
It is a bad assumption in so many settings, and
sometimes it's a really greatassumption to lean on.
When you look at the reallymodern definition of
rationality, it's so flexible.
Rationality, as I said, meansyou choose what you want to
choose the thing that you like,and you stay consistent in those
choices so you don't contradictyourself in those choices.

(18:02):
there are a couple of otherdeeper consistency requirements
that come in there.
I'll put notes that you couldlook through in the show notes
and a good reading for that aswell that will help paint a
picture of what this exactlymeans.
But when you start thinkingabout you take rationality
seriously, then you start askingquestions around, well, what are
the, why does a funky,irrational behavior emerge?

(18:26):
If you turn it around fromeveryone does what they wanna do
and it's best for them in thesense that they perceive it to
be the best thing forthemselves, then you start
asking questions around what arethe constraints that are
preventing them from makingbetter choices?
Let's talk a little bit morecontroversial, right?
You can ask why are there somany people joining gangs?
This typical nineties fear ofcrime running rampant in the

(18:48):
inner city, all these kids arejoining gangs.
My goodness, they're so moronic.
All they should do is work ontheir grades and get better
grades and everything will befine, right?
These kids don't know whatthey're doing.
They're irrational.
They have no sense, and all Ineed to do is knock them into
shape and then they'll do theright choice.
Now.
Instead, if you say, now, hangon a second, they're doing the

(19:09):
best that they can given theconstraints that they face, then
you start asking the question,what are the constraints that
are on these people?
And then you start actuallylooking at the context in which
they operate in.
And that's the magic of thisrational framework is actually
saying, look, you are doing thebest that you can given the
constraints.
So now let me illuminate what'shappening in your immediate

(19:29):
neighborhood, right?
Well, what's the set of rolemodels look like?
Well, how do you form beliefsabout what your future path
looks like?
If it's in a constrainedenvironment, there's maybe only
a few paths available to you andbecoming a neurosurgeon is
completely ludicrous to say thatthat's feasible.
Given where you are right now,you know, it doesn't make sense.

(19:50):
And so.
Instead, you're gonna ask, well,what, what is the set of
feasible things someone canattain?
And what is it that we can nowdo now intervene to actually
free up some of thoseconstraints?
It could be something as simpleas, there's no clean water,
okay?
Yeah, no clean water.
No way to make good decisions.

(20:11):
If you're always hungry andyou're always thirsty, it
doesn't matter.
You can say to someone, pullyourself up by your bootstraps
and go off to school and learnsomething, but you haven't fixed
the problem.
Right?
iT's also, related very deeplyto a kind of notion of free will
versus determinism.
And this is getting a little bitfunky, but the truth is, if I
put you in this situation, itcan only evolve one way, and I

(20:31):
put you in a differentsituation, it will evolve
another way.
And so.
If I took any person and Iplunked them into a very tough
situation, it's unlikely thatthey will say, oh yeah, I
remember what it's like to be astockbroker and I'm gonna figure
out how to do that.
This is the ridiculousness ofthe pulling yourself up by the
bootstraps, idea.
But ultimately when you'regiving people rational choices,

(20:57):
what you're ascribing to them isthe fact that they are doing the
best that they can given thesetting that they're in.
And instead of saying, well,those are terrible choices,
you're a terrible human being.
You're saying no, these are thebest choices.
You can, given the constraintsand the set of choices you can
actually access and given theinformation that you've been
able to gather about yourenvironment.
You can think of rationality asbeing a very respectful

(21:17):
assumption because it's saying,I'm giving you agency.
And I understand that it's toughout there when there's situation
and when you have a good time,you make great choices.
'cause it's easy.
You have access to so many coolthings that you could do, right?
So, I, I like to think ofrationality in that way as well.
And then there's, of course,there's a deeper problem that's
separate, which is to do with,how much can a human being

(21:38):
actually compute and how cleverare they There's a lot of
interesting literature that Ican point our listeners to, some
evolutionized psychologists thathighlight that we are really
good, human beings are really,really good at doing a lot of
stuff.
We're just not very good atexplaining how we come up with
those things.
But our heuristics areincredibly powerful because
we've evolved them over many,many millions of years, and we
have the ability to learn fromother people, and we have

(22:01):
systems that augment ourabilities as well.
So we know that we can't domental arithmetic really well.
This is a favorite example of alot of people who say that
irrationality is rife and that'strue.
Yeah.
We make mistakes all the timewith math and we make mistakes
with probabilities and we don'tknow how to gamble.
But we have computers that canaugment that and they can tell
us, okay, well how do I use thiscalculator now to figure out the

(22:22):
right odds?
There's a role there for theEconomist is there something
that I could give you that wouldhelp you see the landscape of
what's available as well?
Can I give you some informationthat will help you make better
decisions?
And I don't know what's a betterdecision for you, but I know
that if I gave you all thisinformation, maybe you'll do
something differently.
So that's also important.

Ernest (22:42):
That seems actually really powerful.
I hadn't considered that.
I think, as a lay person, I,think we tend to go into these
sorts of situations, in a waythat's very quick to judgment
values-based judgments.
and the way you've described it,it sounds like you, you know, as
an economist or a microeconomist, you've developed this

(23:02):
ability to, look outside of thatand, assess things in a much
more, rigorous way that, um,,doesn't make value judgements, I
guess, which does sound reallypowerful, a powerful way look at
problems.

Joachim (23:17):
Yeah, that's the ideal.
I mean, that's the ideal I holdmyself to, I hope everyone else
tries to do that as well.
It's difficult of course,ultimately also from a product
innovation perspective, let'sbring it back to what we always
like to talk about.
Let's talk about jobs to be donejobs to be done as a framework
that is trying to represent thecustomer journey and asking the
fundamental question, what is itthat you're trying to achieve?

(23:38):
I see a mirror of the jobs to bedone framework and this
framework of trying to model andrepresent the person as
neutrally as possible withoutjudgment, as you were saying,
which is a very, it's a, it'sthe right way to say it.
It's softer because you're,really trying to represent the
person that's being analyzedhere.

Ernest (23:56):
Right.
I have another very basic highlevel question for you, which
is, what is the differencebetween microeconomics and
macroeconomics?

Joachim (24:08):
So the, there's a joke about macro economists that they
say that they've accuratelypredicted seven of the last five
recessions perfectly.
so there's kind, there's a pieceof that.
Fundamentally we're buildingfrom the same starting point.
The mathematical frameworks arepretty comparable.
But the difference is the macroeconomist is really trying to

(24:31):
capture these global variablesthat affect the whole economy.
Inflation, GTP employment, verymuch aggregated.
Now, what a micro economistwould try and do, or an applied
micro economist would like todo.
Especially in the niche areathat I was active in, where I'm
trying to build these highlyhigh fidelity models that

(24:52):
represent agents' choicesaccurately is they would go and
zoom into exactly individuallevel data and try and figure
out how is this personnavigating, let's say
unemployment.
unemployment's a great one.
A macro economist says there'sthis number that's unemployment,
it's 2%, 3%.
And then they have someaggregate models, they can.

(25:14):
Throw onto this problem thatthey believe tells the story of
unemployment, where you haveessentially the equivalent of
what I'll call a representativeagent.
There's some like average personthat they can lean on to help
describe what's going on in thesystem and then what macro
economists have been doing.
Well, I shouldn't speak aboutwhat they've been doing lately,
but the last I heard is muchmore interest in trying to allow

(25:37):
a little bit more variety withinthat representative agent.
Now an applied micro economist,and there are many who've worked
on unemployment questions, theyactually will try and access
data on the lifecycle of aworker, of a single worker, and
then they'll try and rationalizethe path that that person is
taking from employment tounemployment employment at the
individual level.

(25:59):
And they will then do that overmillions of people to then try
and find the model that matchesthe data.
So it has something to do withthe aggregation level, is it the
whole economy and you're justgonna use a representative
agent.
Or are you gonna try and reallyget into the weeds and
understand how that singleperson is navigating this
challenge of work or not work.

(26:21):
And then I could be morescathing.
I could say, well, macroeconomists just make everything
up and try and, you know,unreasonably aggregate
everything up and, you know,they didn't see the credit
crisis coming.
There's lots of things that youcould say, but I don't wanna
insult them all, all once.
But, um.
You know, some of the morepuzzling behaviors in the
aggregate, I think really wouldbe more illuminated if we had

(26:43):
that very micro perspective.
So, we all kind of build on thesame ideas, but then we veer off
in terms of how much we want toaggregate up to some bigger unit
of observation.
Maybe that's the easiest way tothink about

Ernest (26:59):
Kind maybe building on that somewhat.
I was curious, is microeconomicsalways applied or is there a
theoretical version?

Joachim (27:07):
If you go back to my description right at the
beginning, I was talking aboutthis idea that we take human
behavior and we have amathematical model, and then we
try and map those behaviors intoa mathematical framework.
And that mathematical frameworkneed not be tethered to any
existing, data or behaviors.
It could just be something as,oh, I noticed that people tend

(27:30):
to do X, Y, Z, so I'm gonna tryand write a story about how, why
they do X, Y, Z.
So those.
Models are theoretical.
They don't necessarily gettethered back into the data
where they're holding themselvesaccountable to explain the exact
patterns that emerge in thedata.
So let's make this clear.
Theoretical microeconomics alsohas a spectrum of applied to

(27:52):
very theoretical as well.
And so there are very deeptheoretical problems that are
essentially pure applied mathproblems.
and then there's more appliedthings where someone looks at
the world and sees a phenomenonand they're gonna tell you a
fairytale or a story about howthat behavior emerges
mathematically.
So those guys are kind of.

(28:12):
Uh, this is gonna sound unfair,but they're untethered from the
data, right?
This is very much just, here'ssome cool framework.
There's some interesting thingsthat emerge from that, and those
stories will help you navigatethe world.
They build intuition about whatis happening in the world, but
you can neither prove ordisprove them because you're not
holding yourself accountableagainst the data.
So there is a theoretical group,but it's not the same thing as

(28:35):
like a theoretical physicist.
A theoretical physicist's goalis to build a model that can
then be tested in anexperimental setting.
Theoretical economists don'treally have to do that.
They don't need not worry aboutthat.
So you get very obscure, papersthat are so focused on really
bizarre stuff that peoplewouldn't even care about or even
understand that that matters insome form, you know?

Ernest (28:59):
That's interesting.
Well, actually, maybe so takingthe flip side of that, you.
Mentioned this earlier, but Iwas curious how is or how can
applied microeconomics berelevant to making products and
services?
You know, getting to the veryconcrete.

Joachim (29:15):
applied economists in industry have traditionally
focused on the measurementpiece.
And as you could tell, that'swhat I was focusing on.
And that's what allowed me toenter the, to enter industry and
measurement in a very specificway, which is, how do I know
that when I launch this changein experiment, it will actually
generate the, impact that Ithink it will.

(29:35):
And, how are there differentways of measuring it?
Some of the measurements fromI've been involved in have to do
with how do I know from a veryshort experimental window what
happens one year out to thefuture?
I only have two weeks of data ofan experiment is there something
that we can do to figure out, asignal on what the long run
impacts for the company are ofthis one small experiment?

(29:57):
A lot of applied economistseventually become data
scientists, but their wholespiel is that they're able to
get at causal explanations ofwhat's going And I also say that
my measurement is always drivenby causality.
I'm trying to get to theunderlying mechanism that
generates the outcome.
And so that's why I center thehuman in all of my analysis as
well.
I'm trying to understand how isthe customer.

(30:20):
Experiencing this product andhow can I represent that
mathematically and using thedata that we have.
So a lot of my work has beenunpicking from our data signals.
What are the moments where aperson is making a choice and
how is our system moving theminto a different state of
happiness or unhappiness or opento the next exploration or so on

(30:44):
and so forth.
I have been trying, and this iswhere the aspirational piece
comes in more and more.
I've been trying to find thatintersection between, the design
of economic mechanisms and thedesign of product as well, which
is a, it feels like a big chasm.
One of these things is about,designing like a marketplace or

(31:06):
helping the platform economicsperform better or getting the
prices right as people like tosay.
And then there's this otheraspect, which is designing
products that people use at theend of the day.
But if you think about it,ultimately design is about
influencing people to dosomething, right?
It's, it's either to get them touse something or think about
something in a certain way.

(31:27):
Think about social mediaplatforms.
They're all in, you know,they're built on the notion of
influencing you to do something.
and if you think about theeconomics piece, that's exactly
what we're doing, right?
We're trying to build systemsthat influence people to do
what's right for themselves, forthe platform and so on.
So the piece that I think ismore compelling now is
essentially thinking about.

(31:47):
Designing in tandem withproduct, systems that take
advantage of economic designprinciples and product design
principles together.
Digital platforms are theperfect example of that, right?
Platforms are essentiallyintermediaries that bring
multiple parties together ontheir platform, on that screen,

(32:09):
on that surface that they haveon the app.
So think about any ride sharingapp that's bringing customers,
bringing drivers, and then theykind of do stuff in the middle.
How do I make sure that thisperson wants to be on my
platform, generates value forthemselves and the other party
that they're exchanging with.
And then on top of it,generating value for the
platform for the long run.

(32:31):
I would like to make the productcompelling enough for you to be
on the platform.
And then, once you're on theplatform, I want you to behave
in a way that is based on theprinciples of the platform.
Like you will be a safe driver,you will be courteous,
passenger, uh, you will treateveryone with respect.
You will tip accordingly.
You know, all of the things thatwe treat as, nice to haves on a

(32:53):
platform.
If you buy into those things,then you can kind of see a
little bit more closely theproduct and the incentive design
coming together there.
Uh, I hope, I'm still in theprocess of exactly defining what
that path looks like.

Ernest (33:06):
I could actually think of an example and, you know, it
was when we worked together atNike Night, so we can't talk
about it in any detail, but, andthis was before we really knew
each other.
but, I recall that you hadpresented this concept in a way
that really.
Led me to change the way Ithought about this product that
we were working on.
And that was, I found that to beso impressive, um, that you took

(33:29):
an idea that was, you know,fundamentally very complex, but
expressed it in a way that madeit simple for people who aren't
economists, to understand it andthen to be able to act on it.
But I was curious, has that beena challenge for you to, you
know, as you've moved intoindustry, to story tell in a way

(33:50):
that non non econ people couldunderstand and act on?

Joachim (33:55):
Yeah, I think that's, I think that's been the hardest
thing personally, emotionallyreally to get over is when I was
an academic, I really stronglyheld onto this delusional idea
that facts, facts would win outand data will tell the story.
There's no need to add any colorto it, just represent what's

(34:17):
happening, as cleanly aspossible.
And the motivation for whatyou're doing will be clear.
You are illuminating something.
And then I entered industry andI started seeing more of these
battles around narrative and.
It wasn't really about thenumbers of the truth or making
more money, and that was quitestressful for me.
I was really having a, a momentthere where I was confused.

(34:37):
I thought companies were clean,it's just make more money and we
don't have to even argue aboutit.
Human beings are narrativedriven creatures.
We tell stories about our lives.
I'm telling a story now about,how I navigated the things I
did.
It's, it is a narrative thatI've created so that, I can pass
my experiences.
And similarly, storytelling andnarrative is the way we get our

(34:57):
ideas across.
and I've embraced that more andmore now as a thing.
So just being happy to tell thestory and the.
I want people to really like thestory and get the, the narrative
arc.
And so yeah, having powerfulimages, analogies are very
useful, but I think if the workitself is being built on the

(35:18):
foundation of trying torepresent the customer, help the
customer, the story should tellitself, you know?
And, if you're in a quantitativefield and you're having trouble
coming up with a story, whyyou're doing what you're doing.
But maybe you're kind of on thewrong track.
You found something that istechnically interesting, but

(35:38):
it's maybe not that useful.
But don't throw it away, writeit down, but keep it as a
technical piece of documentationfor yourself, because it might
have application in anothersetting where the story lines up
with exactly the, the technicalinnovation that you have.
So, yeah, you have to be able totell, tell a compelling story.
And I have to say, companieslike Nike are companies that are

(35:58):
really driven by story.
You just grab any of thedesigners like Tinker Hatfield,
he's got a great story about howthe exposed airbag in Nike Air
comes about.
And he's, oh, I went to SanPompidou in Paris and I was
inspired by this architecture.
And you go, okay, what afantastic story.
I get it.
The in guts of the building areon the outside.
Fantastic.
Now apply it to the shoe.

(36:18):
But I'm glad that I was able todo that in our setting.
Ernest.
It, it was a, it is a trickydomain when you are building
data-driven product and, I thinka lot of people get into the
habit of just talking to othertechnical people because it's
much easier, and saying, oh, I,I have this new algorithm.
It does this and it does that,and you go, that's cool.
it doesn't really get at thecustomer need.

(36:39):
And I think a lot of the thingsI was thinking about at Nike
was, what is the customer need?
I think that was, that was kindof the starting point, was just
asking that very fundamentalquestion, um, and having a good
team around you that werewilling to listen to this very
fuzzy idea.
I want this thing to be able to,hug you and help you across the

(37:00):
line.
And then you go, okay, does thatmean?
But then from that comes.
A need and you can come up withmodels and designs and so on
that come from that.
So I hope that answers it,honest.
I, tricky one.
Yeah, it's a tricky question.

Ernest (37:16):
Well, now, you've talked about some of the challenges of
being on the industry side.
I was curious, you started outin academia, so what have been
the biggest differences for youbetween teaching and working in
industry?

Joachim (37:30):
Teaching, I think I said this again at this in the
first episode, we were talkingabout, experience creating and
making products.
Teaching is the closest to, tomaking a product, right?
I need to convince you of theimportance of this thing and I
need to transmit it to you, andI need to then also iteratively
update it so that you find itcompelling and you want to have
it, and you want to be in theroom with me and you want to

(37:52):
have this conversation with me.
And that's, The piece that feelsvery much transferrable into,
into the area of industry.
And I think the hardest thing isas well, when you walk into a
meeting, you're not theprofessor.
And, when you're the professor,you get to stand in front of the
class, right?
You have this lecture room, youget to control the room in a way

(38:13):
that is very rare in industry,right?
Unless you are Steve Jobs, youare not gonna get a dimly lit
stage with a background andimages, of cool innovations.
It's not gonna happen.
and so being reminded that youhave to build that rapport
individually with people and youdon't automatically show up with

(38:35):
people respecting yourperspective is.
tricky.
And I think it comes back tohaving compelling narrative,
compelling story about what itis that you're actually bringing
to the game.
Maybe that helps, but also Ithink the environment that you
operate in, right?
You have to know that there's anenvironment where, not
necessarily that people assumeeverything you're gonna say is
incredible, but that there's acertain healthy amount of

(38:58):
respect that you have beenbrought in to bring your
perspective.
And you need to find thoseenvironments where you're able
to say, this is my perspective,take it or leave it.
This is why I was hired.
that's taken me a while to alsoembrace that.
that approach as well.
'cause you always wanna pleaseeveryone, which is, which is
also important.
But sometimes that means youstart losing your identity

(39:20):
because you're trying to matchsomething that you think a
person wants.
And then eventually, ah, thiscomes back to our other topic of
selling out, right?
You start feeding the beast, youstart saying, well, this is what
they want.
So I'll give them more of that.
And maybe that's easy for awhile, but it's also tricky.
You start asking, well, what'sleft over of me?
When I step away from that?

(39:41):
Have I got anything that I canhold onto that was a real
meaningful, contribution or,insight?
'cause at the end of the day inthese fields, it's really just
insights that you can hold ontoand bring with you.
So actually useful tool is writeeverything down.
I was talking about theimportance of narrative and
story.
I've been using a lot of thetime recently to just write my
story of what I've figured outand make a little book for

(40:04):
myself so I can look back andsay, these are the things you
figured out.
Yeah, you have to do thatyourself.
In academia, they expect you towrite papers and so that's
different.
There's a external pressurethat's telling you to do those
things.

Ernest (40:16):
I was just curious, do you ever see yourself going back
to academia?

Joachim (40:21):
Oh, wow.
I really miss teaching.
When you exit academia, unlessyou've exited as a tenured
professor, which I did not,you're kind of done, you're not
gonna be able to get back.
You have to keep publishing.
And I haven't done that, but Iwould relish the opportunity to
do some teaching again, because,when I think about where you
have the most impact as anacademic, it is in teaching,

(40:45):
which is the thing that everyresearcher hates.
You know, they treat it withsuch disdain.
Most people, it's always ahassle.
And if you treat it as a hassle,the students think it's a waste
of time because you're treatingit like a hassle.
So you're in this terrible cyclewhere they go, oh, look, the
students don't care either.
Because you're telling them it'sa hassle.
They can read your bodylanguage.
When you come into the room, youdon't want to be there.

(41:06):
You're thinking about writingthat cool paper, right?
And you're just trying to getyour paycheck.
So I, I always took a verydifferent perspective to
teaching as being one of the fewspots where, your insights can
be transmitted.
And actually the coolest thingis if you are willing to let it
happen, you can drop your,professorial status and meet the

(41:27):
students in the mix.
And a lot of the students willbe asking fundamental questions
that challenge your narrativeabout why you're doing what
you're doing and how you'redoing it.
They're gonna come in and go,why the, why the heck is this
important?
And you go, oh, crikey, you'reright.
I've just been blindly doing it.
'cause I'm, I've, I've boughtinto this.

(41:48):
I'm in the cult of economics.
You know, you, you are sittingon the outside and, I can see
myself through your eyes andthat's kind of scary.
I don't wanna be in thatposition.
And that's exactly when yourteaching moment comes in, is
when you have to meet them thereand I had so many interactions
like that with students thatjust challenged me on basic
things that I took for granted.
And I would have to go off and Ihad to sit down and read and
think and sometimes a cool newexplanation, a new way of

(42:13):
representing this idea comes outthat is new to me, new to the
students obviously, and far morecompelling than what they had
before.
And so those are wonderfulmoments that I still enjoy.
And in, in the corporation thatsometimes happens as well,
right?
When someone gets it, it'sreally, really enriching when
that happens.
I think those moments aremoments where I.
A sense of community andcommunion comes, right?

(42:34):
You're all on the same level andyou've all figured out, at the
same time.
So yeah, teaching teaching wouldbe nice, you know?

Ernest (42:41):
Yeah.
Well, speaking of irrationality,it does seem strange that the
most revered institutions tendto be where the least teaching
happens.
That seems, like

Joachim (42:55):
Yeah.
I,

Ernest (42:56):
of doing things.

Joachim (42:57):
it is a strange way of doing things.

Ernest (42:58):
Well, kind of taking a step back, I was curious to know
what led you to Appliedmicroeconomics as a career path?
Was there a, precipitatingevent, some experience, or some
professor that inspired you topursue this?

Joachim (43:12):
The full story is maybe more ridiculous.
When I was a teenager, the bandRage Against Machine was a very
big deal, and they had CcheGuevara on their T-shirts I had
idea who that, this, this guywas.
I just thought, you know,handsome chap, nice goatee,
revolutionary.
and thankfully I asked.
My dad was like, what is thisguy?

(43:33):
And my dad came up with some,half-baked answer, but he did
also buy me a book.
And then that got me startedwhat is going on here?
Who are these people?
And then economics comes out asa key feature because of course
it's about capitalism,communism.
And then I started digging andreading more.
And thankfully in high schoolwas really the moment that it,

(43:55):
it clicked for me.
I had a econ high school econteacher, Andy c Husen, he's
still a teacher.
And he was one of the few peoplethat gave me permission to
engage with these big ideas.
And he was very encouraging.
And I, I came up to him with abit of marks, I think actually
that quotation that I sharedfrom is from the thesis on,

(44:16):
which is Marx's response to thisother philosopher that no one
remembers anymore.
But, it seemed very importantback then.
and so this, Mr.
Husen and I had these littleconversations during lunch where
I was like, I'm confused aboutthis.
And he just nudged me in theright way and gave me permission
to think about these big ideas.
And I think that was the when itreally clicked for me.
Oh, you can engage with bigideas that involve society and

(44:39):
the structure of society and theeconomy.
You as a 16-year-old high schoolstudent, have a right to think
about this stuff as well.
And so that allowed me to push alittle bit deeper into that
domain.
And I think it sustained methroughout the whole thing.
I did an undergraduate ineconomics.
And that was really greatbecause finally I was starting
to see a little bit more of themathematical structure that

(45:01):
allowed people to model thosethings.
And then, I worked, after myundergraduate, I worked at a
consultancy where I was, juststraight out of school.
I I had very little idea what Iwas doing, but it was the first
time I was surrounded by a, alarge number of PhDs in
economics.
and this is gonna soundterrible, but I just realized,
oh, they're just normal people.

(45:23):
And, and, actually one of themore junior PhDs that was very
open about that, I said, oh, youcan just do it.
You just go and write a paperand stuff.
And I thought that that doesn'tsound right.
Anyway.
I went to grad school, I went, Idid a master's and then I, did a
PhD.
and then during that period, thekey moments were just figuring
out that there was a way to takethese abstract models and anchor

(45:45):
them in the data.
And thankfully, I had just reada really cool paper, and one of
the authors was at the schoolthat I'd already applied to and
gotten into for grad school.
we'll link to that paper in theshow notes, guys.
'cause this is, I think it's areally, it's so telling that
something so obscure andesoteric can be very, very
impactful to the right mind inthe right moment.

(46:05):
But he wrote this paper calledThe Estimation of a Dynamic
Auction Game.
And, it was.
Pretty seminal.
and it blew my mind.
And that, and that set me onthis path.
And then, yeah, I wanted to bean academic.
and then the industry piece camemuch, much later.
It, it seemed, I was approachedby some people, and it made
sense in my career at that timeto make that switch.

(46:26):
I have to say it was quite aprocess.
It's still a process.
And I think the piece that'scome out lately is much more
embracing the slightly moretouchy feely aspects of things.
Even this idea of narrative and,and drawing inspiration from
non-technical areas, I'm gonnariff on everything that we've
said in the past.
one of the first things that youshared, as one of your

(46:47):
recommendations was UrsulaLewin's award speech.
a lot of her writing imaginesdifferent worlds, including
different economic systems.
The dispossessed is all aboutthat.
There's so many ways of knowing,and she just displayed such
depth in her thinking, eventhough it was all verbal and in
the form of a novel.
It's deeply inspiring.
And, uh, so again, I drawinspiration now from more

(47:09):
places, but I think really the,the pattern is just keep
learning.
There's no, there's no end insight.
There was a path, I was on apath, You just keep learning and
grabbing all the differentthings that, uh you encounter.

Ernest (47:22):
I'm on that topic of learning and continuous
learning.
I was just curious, what in yourexperience is something that you
find people consistently tend toget wrong about economics more
broadly, or microeconomicsspecifically?

Joachim (47:37):
In the last 10, 15 years, one of the things,
everyone's gotten quiteknowledgeable about economics
from a very specific angle,thanks to podcasts.
One of them is this, thisbehavioral economics was a
really big trend that peopleloved, and because of its potent
narrative, just took hold of thecollective consciousness.

(47:57):
And it's one that I get verystressed out about, because like
I said at the beginning,rationality is such a powerful
tool because it puts you in thisvery neutral position and you're
trying to represent that personcleanly.
Whereas behavioral economics,which is all about the
behavioral biases that we have,the cognitive biases that we
have, there's this, this ideathat we are fundamentally flawed

(48:18):
human beings.
Our brains are just not verygood at stuff.
And as a result, we need todesign rules that constrain
human beings.
I, that's a really sad outcome.
Like, you're just, you're alldumb and you got to, I have to
take control of your life andthen you're gonna make good
choices.
So, one of my favorite biases orfallacies that people like to

(48:40):
talk about is the hot handfallacy.
It's a sports one if you've gota hot hand of basketball, you're
gonna keep shooting.
meaning.
They're these clumps of scoringthat occur.
And some early behavioraleconomists amongst them, Ky
said, well, that's baloney.
That doesn't exist.
There's no such thing as a hothand.

(49:01):
So people are really dumb whobelieve it, that that was kind
of the judgment.
A couple of years ago, someonejust sat down and did the math
on, you know, the hot handfallacy.
Like, what does it look like ifyou are, um, trying to make a
prediction on.
Multiple sequences of shotsbeing made or not made.
And it turns out there'sactually a bias in the way the

(49:21):
math works out and that bias canexplain some of this stuff.
And then there's a follow uppaper where they go even deeper
into, actually it's reallydifficult to test whether there
is a hot hat in the data.
So if I just gave you a sequenceof shots, I can't even tell.
Like there's no way to even, dothat intuitively.
You need to build a really deepstatistical testing framework to

(49:42):
get at that.
I think people forget is thatyou could have a cognitive bias,
but then you know how to do mathand measurement and then you can
check that bias and make surethat it doesn't persist.
so I get very sad when peopleare convinced human beings are
so dumb.

Ernest (49:56):
Well, I have to follow up with, is there consensus on
whether there is a hot hand ornot?

Joachim (50:03):
So there is evidence that it could still exist, that
there is a hot hand.
So if you trust that people maybe like a coach who's seen so
many players understandsomething about the game on a
visceral level,, let's take thatseriously.
Let's take that seriously andtry and figure out, well, how
could a hot hand emerge?
Right?
So now you're gonna start askingfor rationalizations for how

(50:25):
clusters of scores could happen,well, could be just it's luck
and you just keep feeding it tothe right guy.
But then there's also thepossibility of intimidation,
right?
Oh, he is scored two already.
What, what's the point?
Don't, don't bother.
Right?
There are all human responsesthat are very complicated in
high dimensional.
Another human being watching thegame might understand, right?

(50:45):
They could say, keep feeding itto Michael.
he's gonna do it.
He's gonna intimidate them into,doing something.
Um, so it starts opening up afar more interesting
conversation about the nature ofwhat this game is that we're
looking at, right?
If there is a hot hand.
You need to explain why there'sa hot hat and what drives it.
And, and now we're in a verydifferent world of exploration

(51:06):
and we'll maybe understandsomething more deeply about the
game of basketball, theinteraction between strategy,
shooting, all those things.
It's a fascinating thing whenyou start taking someone's
expertise a little bit moreseriously.
I think it's also why in sportsanalytics and, and data sites
haven't really taken off to theextent that you'd expect with
all of the data because.
I think a data scientist'sapproach is to say, you're kind

(51:29):
of dumb and I have all this dataand I see it if you watch
Moneyball, it's kind of thatidea, right?
That you're all stuck in thestone age and I've got all this
data and I can figure this stuffout.
Is that really the way toconvince someone to start using
data or should you be trying toaugment their ability is, should
be saying, I don't know moreabout the game than you do, but

(51:51):
I can create signals for youthat will be even more powerful,
that will allow you to makebetter decisions.
And I think that's kind of themagic bit that comes up when you
start looking at people as, goodsignal processes.
and human computer interactionis part of that mix as well.
So that was a longer excursioninto more topics.
But again, it's one of thosethings that is hard to avoid

(52:11):
when you start pulling on one ofthese threads.

Ernest (52:13):
Right.
Actually, I know this is a bitof a sidebar, but just to the
point you're making, there hasbeen a lot of debate in the
American football community.
We're recording this, just aweek after Super Bowl, the big
game.
One big moment was the game wentinto overtime and, the San
Francisco 49 ERs won the cointoss and they decided to receive

(52:38):
the ball.
and that's been verycontroversial.
Apparently the coach of the 49ERs said that the, their
analytics team told them that.
They should receive the ball ifthey win the coin toss.
and the rationale was that, theythought they'd get an additional
chance to score.
They'd get, their first chance.

(52:58):
Then the chiefs would get achance and then they thought
they'd get a third chance.
and the pushback, there's beenquite a bit of pushback.
'cause in the football world,American football world, there's
been a big adoption ofanalytics.
It's, I think, among theAmerican sports, the sport where
analytics has been reallyembraced.
but this is leading to a bit ofa pushback and people saying,
you as a coach need to applyjudgment.

(53:20):
You can't just take what theanalytics people tell you is the
decision to make because thecontext matters.
You know, maybe in terms oflooking at.
Averages overall, that's theright choice.
But in this situation, in thiscontext against this team that
you're playing, a differentdecision would've been the
better one.
But it's interesting to seeexactly what you were

(53:41):
describing, playing out, um, inthe real world as well.

Joachim (53:45):
It is, it's a never ending.
Well, because you use the worddecision.
Decision is so, if you thinkabout anything in your life and
you go back and say, oh, thatwas a good decision.
How do you know?
Because you only lived one life,which was when you took that
decision, you know?
There's a missing paralleluniverse that you never get to

(54:05):
see.
And that's also part of theproblem with analytics and
sports, is that you only see thedecisions that were taken.
Not the decisions that were nottaken, obviously, but you'd need
to see both to know which onewas the right thing.
and that's where the humanjudgment comes into it.
It reminds me of the talk thatKevin Slavin gave about, the
interaction between humans andcomputers.
I will put a link into the shownotes for that one, but that's

(54:26):
definitely worth watching.
He talks a lot about, extendinghuman intelligence through AI
systems.
and then the power that humanbeings bring to that fusion
where they can say, Hmm, this isprobably not a good choice.
You know, and, and even thoughthe system is telling me to do
that, I think my judgment and myexperience and the context in
which I'm operating right now,tell me another path, makes a

(54:48):
little bit more sense.

Ernest (54:48):
I love it.
It's great.
But actually, so speaking ofpaths, I was, I think a lot of
people might be asking how doyou become an applied micro
economist?
you've talked about math quite abit.
Like do you have to be good atmath or, and also are there
maybe adjacent career, careerpaths that might, be pretty
close in terms of the skill setsand, where you might say, Hey,

(55:10):
if you are interested in that,but don't love it.
maybe applied, microeconomicscould be something relevant to
you.

Joachim (55:17):
Let me start with the adjacent disciplines and I've,
I've touched on it before and Ionly encountered this discipline
because of my first placement asan assistant professor.
and it's human computerinteraction.
You can touch on all of thequestions that I'm talking about
in that domain as well, but alsowithin the context of, human
computer interaction.
How do I get from the machine todo something to get it to the

(55:39):
human?
How do I augment what thehuman's doing and so on.
So I think that's a veryinteresting path to actually
take where you start off when avery quantitative subfield, you
know, maybe you'll start withapplied math or stats and prob
all, all of those things.
And then you go into somethingthat.
Captures the behavioral element.
Uh, I think ultimately it's,that's economics value is that

(55:59):
it puts quantitative structureon behavioral pieces.
If you want to get into the, myparticular subfield and actually
econ in general.
Yeah.
Econ is very mathematical.
There are some bits of economicsthat are not super, super
technical.
There's still some technicalknow-how, but it varies, right?
If you wanna be, aneconometrician that means you're

(56:19):
someone who just thinks aboutstatistics, but applied to econ
problems, causal analysis andeconomics, that is essentially
just a, an applied mathematicssubfield, you know, that is
pure, pure math.
if you then go into more theorythings, again, very
mathematical, and then, thesubfield I got into is, is, is
kind of the closest I could getto, I think also as being an

(56:40):
interdisciplinary mix of things.
So yes, you should be happy towork in math and, What I think
is important about the math isthat it is just another
language, such a cliche, but itis, and it's a really efficient
language.
It's so efficient.
You can transmit an idea in afew lines, and it's very, very
powerful.
and so if you approachmathematics from that

(57:01):
perspective, you start learningthe mathematics.
That's more to do with, provingresults and mathematics.
then you start seeing the powerof what this logical framework
is, and then you can see how itcan be applied to behavioral
things using, the econframeworks.
I don't treat math as the be alland end all.
I treat it as the tool that getsme to the goal, which is a
deeper understanding of what'sgoing on in the system and what

(57:23):
needs to be designed.
But yes, it's very mathematical.
Yeah.
So you have to have an interestin wanting to understand human
behavior.
I think that's, that'sultimately it, you know, and you
want to do it with math becauseyou need to get it back into the
data.

Ernest (57:35):
Right.
Alright.
Now, my last question for youis, kind of thinking back to
your 16-year-old self before youhad that in interaction with
that professor that you talkedabout, what, if any advice would
you give to that person who,maybe even doesn't know that
they're interested in econ yet,in terms of why they might want

(57:58):
to pursue it as a career path?

Joachim (58:00):
This is such a great question.
I'm gonna, I'm gonna use it onyou next time.
Uh, but, um, it, I think itcomes down to have breadth and
have range read, consumeeverything.
Don't be a snob.
You know, when you're 16 you getsnobby, right?
You get, you want to have yourtribe and, and defend it.

(58:20):
Listen to all the music, readall the books, uh, do
everything.
Consume everything and feel thatyou have license to explore
anything that you want toexplore.
It sounds so cheesy, but it isvery, very true.
We've really focused as asociety on specialization, and I
don't think that really helps usanymore.
Breadth is so important.

(58:42):
and when you look at the worldthat we live in now, breath is
essential.
I just don't see how specialistscan thrive in an environment
that is so complex that we havenow.
So that would be the advice.
Read everything, eat everything.
Taste everything.
Try everything, consume all themedia that you can.
It's never been easier to dothat.
Explore really, really deeplyexplore, and then part of that

(59:06):
is also, find, find your people,find the people that you share
enough, enough and common andare willing to also explore with
you and make you feel safe inthat exploration together.
I think that's a reallyimportant piece.
Like this podcast.
We have a safe space here whereI'm allowed to talk and talk
about these things and, youknow, and I have Ernest here, my

(59:27):
friend, helping thisconversation go forward.
So I think that's a really bigdeal as well.
So, yeah, you need to find yourpeople that allow you to do
that, but that's the thing youdon't want narrow-minded
conservatives.
You need to have thatopen-minded range, so for sure,
just, just do it all you whenyou're that 16, right?
You have so much energy.

(59:47):
I just think about you have somuch time, you have so much
energy and you are worryingabout not the right things all
the time.
But if you read widely and watchwidely and listen widely, I feel
you will naturally not want tobe stuck in a very specific
teenager mode, which I think isunhealthy, that we've labeled
that period, it's such a, anegative thing.

(01:00:08):
I think it should be a time tojust push the boundaries on, on
knowledge broadly, I think foryourself and yeah, engage with
everything.

Ernest (01:00:16):
That's awesome.
That's a great answer.
All right, well now that youknow what an Applied micro
economist is, we want to hearfrom you, talking about Find
Your, find your People.
Um,, we want to hear, you know,do you have any follow up
questions that you'd likeJoachim to address?
Are you a micro economist and doyou disagree with Joachim's
perspectives or agree?

(01:00:37):
Um, We want to hear so let usknow at
LearnMakeLearn@gmail.com.
Now let's move on to ourrecommendations of the week.
Joachim, what has you excitedthis week?

Joachim (01:00:51):
I'm gonna keep it super brief'cause I've been talking
the whole time.
it's an Instagram account, byTanaka Tatsuya.
he's a Japanese artist and hemakes these miniatures, he uses
common household objects andthen he has little mini
figurines and he makes it looklike.
A real big scene somewhere else.

(01:01:12):
His most recent post is a bluewallet that he is opened up in,
in the card slots.
He's put white card and thenhe's put some yellow tape and
little people that look likethey're at the beach.
So the wallet looks like thewaves and the little people are
at the beach, enjoying the sun.
So he creates these littlescenes with household objects
that are really fun and they'rejust a, a little bright spot on,

(01:01:38):
on the social media feed thatmakes you just chuckle to
yourself and you go, that's sucha wonderful way to invert these
objects to, to do something.
So I think that's a really funlittle Instagram account to
follow.
And, that's my rec for thisweek.
How about you, Ernest?
What's, what's tickling yourfancy this week?

Ernest (01:01:53):
It is so interesting 'cause we don't, talk through
this in advance, but mine isalso related to social media,
uh, in a, a different waythough.
I guess it, it's, uh,, a blogpost titled How Threads Will
Integrate with the Fediverse bya person named Tom Coates.
Coates has worked in digitalproduct for, as the Brits would
say, for donkey's ears, and,uh,, he, uh.

(01:02:16):
Including recently, co-foundinga company called Planetary,
where their hope was to create,uh, what he described as a
radically decentralized andhumane alternative to Facebook.
Now, he's since left thatcompany, but he remains really
deeply immersed in the movementto create an open public social
network system or protocol.
And it was based on thatexpertise that he was invited to

(01:02:41):
what was called a data dialoguebetween, hosted by Meta, uh, in
San Francisco just beforeChristmas.
Christmas.
So it was hosted by Meta, butthey brought in all these people
with, um, you know,, done workand have expertise in this area
of public social networkingsystems.
And as coats describes it, theevent was designed to reach out
to people in the quote unquoteFediverse community.

(01:03:04):
So that they could share theirplans for threads and then get
some feedback about the policyand privacy implications.
And that last point aboutimplications is a reference to
Meta's stated intention tointegrate threads with the
broader Fediverse.
Now, if you're not familiar withthis, uh,, the definition of the

(01:03:25):
fedi verse as per Wikipedia isan ensemble of social networks,
which can communicate with eachother while remaining
independent platforms, users ondifferent social networks and
websites can send and receiveupdates from others across the
network.
So it's, it's this idea thatit's a network, where no one
entity owns the network.

(01:03:46):
It's, it's an open public.
Infrastructure and then privateentities can plug into it.
and a really big implication ofthe Fedi verse idea for creators
is something people refer to asaudience portability.
the Verge, the onlinepublication talked about this.
They described it as, thesituation right now as people
are looking for platforms wherethey're not stuck, where if they

(01:04:09):
wanna leave for somewhere else,they can take not just their
posts, but their entire list offollowers with them, where
they're not immediately cut offfrom all their communities just
because they delete an app.
And this is made possible by theFed verse because your content
and your followers belong toyou.
And the app that you have to beusing to, um, access the
Fediverse is really in effectjust borrowing those resources

(01:04:31):
while you use the app.
So.
and that's one of the thingsthat, Mastodon, one of the key
clients in the Fed Verse hasbuilt on this concept.
You can use different apps toaccess, the Activity Pub
Protocol, not just Mastodon, youknow, there's many different
ways to get into it.
So,, you know, it's a reallyinteresting idea, and I think
maybe people might be surprisedto learn that Meta has committed

(01:04:53):
to, to that concept for threadsthat they're going to, um add
threads to the, to the Fediverse so that you as a threads
user.
Can access, all the other, uh,people on the Fedi verse.
Uh, but that also, at least intheory, you'd be able to take
your content and your followerswith you if you decided one day

(01:05:13):
to leave threads.
And so they hosted this sessionwhere they shared their plans
for how they're gonna do that.
And on the surface it mightsound like, oh yeah, it's, it's
a very straightforward thing todo.
But, uh, coats does a great jobof talking through the many deep
implications of a platform likeThreads, uh, offering this sort

(01:05:34):
of integration with a publicnetwork.
it's a very long post, but ifyou have any interest in these
sorts of technologies and, kindof the future of social
networks, I think it's, reallyworth a read.
So it's this blog post titledHow Threads Will Integrate with
the Fediverse by Tom Coates.
And we'll share a link to thatin the show notes.

Joachim (01:05:55):
Super interesting, super interesting.
I, I think this is almosthearkening back to the early
days of the internet where, youknow, you had protocols that
everyone agreed with, and thenit, that's it, that's all that's
there as the infrastructure.
And then you, you build on topof that.
and it reminds me also kind of,of the, the early days of peer

(01:06:16):
to peer, you know, where youcould just have a cheeky folder
of MP3s and everyone can browsethrough your stuff and, you're
directly connecting with eachother.
That way It feels like this ismaybe a slightly safer version
of that.
But, um, again, the focus backon, on protocols, generic
protocols allow us to accesseach other and, it shouldn't

(01:06:38):
matter that I'm using this appand all of these walled garden
constructs.
it really, it breaks theresilience of your system.
I think even from a businessperspective.
I think this, I would like toattribute.
Deep thinking to this choicethat it's not just a cool
technical thing, but I hope thatthey understand that this is
gonna contribute to a resilientsocial media network.

(01:07:00):
Now that has, is more open andwill also help everyone get on
there and, to a certain extentkind of use social pressure when
things are getting out of hand.
being able to take your stuffwith you is a good way to
protest and say, see ya, this istoxic here.
Goodbye.
And I'm going over there.
And so as a result, you want tothen create a good, a good

(01:07:21):
space.
'cause it's very easy to leaveyour, it's just a garden now,
right?
There's no wall.
So please keep the garden going.
And then now again, the thinkingchanges.
'cause now you wanna createother protocols for your garden
that keep people there becausethey want to be there.
Back to this idea of theincentive design, right?
You wanna build a place wherepeople want to be and they have
the choice to be there.

(01:07:42):
Whereas a lot of the things thatwe have now are kind of, oh, now
I'm here.
It was good at one point, butI'm stuck now everyone's here
and I just have to put up withthe garbage.
And that's, and that's the onlyreason why we're still staying
is'cause we're all stuck.
It feels like there's so muchlock-in like that that is, it's
nice to see meta.
Trying to think of itdifferently now, so.
Yeah,

Ernest (01:08:01):
Yeah, I, I think you'd like this, um, this post, I
think you'd find it interesting'cause coats does spend some
time speculating as to why.
They might doing it.
Um, he said that question didcome up during the session and,
um, it was interesting how thefolks from Meta have responded
and then he, you know, kind ofshares his own thoughts on it.
But, you know, to your pointabout the early days of the
internet as well, the oneexample that comes to mind for a

(01:08:24):
lot of folks is email as youknow, what, what this could
enable and how powerful anddurable and resilient email has
been because it is this openprotocol that anyone can plug
into.
So it, it does, it does feelsuper exciting.
You know, if happens, that couldbe really, really exciting for
this kind of next evolution ofsocial networks.

(01:08:48):
Well, okay.
We.
Covered a lot of ground today,thank you so much for joining us
here at Learn Make Learn, and asI mentioned, we want to hear
from you.
So please send any questions orfeedback to
LearnMakeLearn@gmail.com andtell your friends about us.
In our next episode, we're gonnaturn the tables and Joachim is
going to interview me in anepisode we're calling What the

(01:09:10):
Bleep Is A Product Manager.
What does a product manager do?
How do you become a productmanager?
We'll discuss these topics andmore.
And if you have questions aboutproduct management, please send
them our way and we'll do ourbest to address as many as
possible on the next Learn,make, learn.

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