Decoding Academia: Matt on his ENTIRE research career *Patreon Sample*
So, what even IS the deal with Matt? Is he a proper psychologist or does his past conceal something darker, aside from his bronzed skin-tone? Until now, he's been a mystery hidden in a enigma and wrapped within a svelte Australian shell. Well, enquiring minds need to know, so that's all about to change! Here it is - The Story of Matt. The ins, the outs, and the 'what-have-yous'. The false starts, the missed opportunities for fame, the many entry roles as a research 'shit-kicker', and his final glorious ascension to his ultimate form as a white haired tenured Professor.You'll learn why being an English language teacher is not a real job, how Matt could have been a contender in the massively lucrative and prestigious field of artificial intelligence, where all the fish live (under the sea, mostly), the powers and ideologies at play in gambling research, why Matt isn't impressed by Taleb's claims about fat tails, and so much more.You'll be left wondering, "How can one man, even if he is very ancient, do so much? Is he a polymath? Or does he just have a short attention span, and trouble holding down a job?" And finally, as an exercise for the listener, like Chris, you will be left to wonder "is convolve a real word?"Here it is: the backstory of Matt.LinksMatt's prolific research outputInside Gambling article on Matt's research'Two for Tea' podcast episode with Matt on his research
Welcome to decoding the academics, decoding academia, instantiation number 2. That's, you know, just French words we sometimes use in academic parlance.
Well, I feel like the pilot has been done.
The pilot is out there and this is episode 1. It was commissioned on the basis of how well it was received.
Yes, that's accurate.
I see that you're competing with your Zoom background too.
I have a large set of books here and behind me, whereas you've gone for quantity over quality.
My image is ancient tomes of knowledge and you're just like, you know, an Englishman's parlor where he's got his show books for when he's entertaining guests.
Yes, most of those are...
Copies of the Guinness Book of Records for various years.
I really enjoyed those when I was a kid.
So yeah, I have to come around.
But the fact is we're discussing fake Zoom backgrounds and I know that we shouldn't be comparing fake Zoom backgrounds because they're not real, Matt.
They don't mean anything.
We know they're not real.
We're not stupid.
We know that.
It's just an image.
That's right.
And to demonstrate that we are not stupid, we're going to focus This week on an academic who it's fair to say he's been around a very long time.
He's built up a substantial body of research.
He's now a bit long in the tooth.
You know, the young dogs are looking to take him down.
But still, you know, we got a humor and we got to talk about how it used to be in the old days.
So it's one Matthew Bryan.
Look at that.
Yeah, the nice one.
The nice one and the cool one, according to the diagram that somebody made.
Nice and cool.
Nice and cool.
Yeah, no, it's true.
It's true.
I've been around for a while.
I'm feeling it now.
I actually recently got an email from the university congratulating me and inviting me to an awards ceremony.
And it's my 10 years service award.
So I've been there for 10 years.
How long have you been there?
About 10 years.
So I didn't like that, Chris.
Like if I see any golden watches or something, I'm out of there.
You won't see me for dust.
And it's not just because you steal golden watches at the first sight of them.
It's the symbolic length of your career that that represents.
The hot young thing.
I used to be...
What's the word for the young brilliant one?
The Chris?
Not the Chris.
But yeah, no, I'm not that anymore.
No, there's a better word.
Young Buck.
Well, yeah, these are all synonyms for what I'm thinking of.
Yeah, okay.
Yeah, but I'm not there anymore.
Rising star.
Yeah, still not that.
That's okay.
Ladies and gentlemen, the word we are searching for is wunderkind.
Wunderkind.
Let's see.
Where shall I begin?
Let me say we have a problem, right?
Because in my case, my research career is so shallow that it's not hard to find which topic I should talk about.
But in your case...
You have such an impressive back catalogue, so many topics of expertise that it's actually hard for us to narrow down what we should talk about.
So what we thought might be a good approach would be for you to take an overview for your research career and then I can bother you with questions about various aspects and stuff that comes up, right?
Yeah, right.
And hopefully some of it's...
You can find something interesting in it.
Because I look back through what I've done and it's all very technical and so on.
And I'm worried that there might not be the nice, interesting hook.
Like with you, you've got religion and rituals.
It's, you know, that's...
It's sexy.
It's the thing the kids are talking about these days.
Jordan Peterson lectures about Jesus.
That's what they want to hear.
What did the Buddha say?
The sense I was getting from looking at the Reddit and stuff, which is that actually people...
On one hand, it feels super self-indulgent to like, okay, this is the story of my career and all the things I've done.
On the other hand, I could think there are a lot of people who might be considering getting into research, might be considering studying psychology, might be interested in the kinds of places that can lead you.
And yeah, maybe my story...
Disabuse them and tell them all why they should immediately cash in those dreams and go into private industry.
Go into dentistry.
That's where the money is.
Just get in there.
All right.
Shall I begin?
Shall I tell you?
Yeah.
Take us from the start, Matt.
And one thing to say also, all academics, to some extent, think that their own research is not interesting.
I mean, some people's research isn't interesting.
What they're often is that the people are actually doing interesting research also don't think their research is interesting.
So you're just a stereotype.
There's no way to tell.
We'll have to listen and let people decide for themselves.
Because if your research was fascinating, you would say the same thing.
This is what I'm saying, Matt.
You can't be judging this subjectively yourself.
All right.
I'll just tell you.
I'll let you.
And the audience be the judge.
So I finished my PhD in 2002.
I ended up doing a PhD because right after I studied psychology or behavioral science, they're called over here, I were in a recession in Australia and couldn't find a job.
Didn't know what to do.
So I was unemployed for like a while.
And at the time you could, if you had a university degree, you could get a sweet gig teaching English in Japan.
So that's what I did after a while.
And so I did that and I was teaching English in Japan.
And that's a really pleasant job.
It paid well by the standards at the time.
And it's such an easy job.
It's just conversational English.
You didn't need any skills.
And all the students are really nice.
They're Japanese and they're there.
It's like, you know, they're doing it as a hobby.
You're disparaging the English teacher community in Japan.
That's a huge portion of our audience.
It's not a real job.
Matt, I don't sign off on this English teaching in Japan.
Don't harass me.
I harass him on Twitter.
So teaching English was nice, but it was really, really, really boring.
Like really boring.
I wanted to eat my brain, nor off my arm or something.
And so I would go to Kinokunya and I would grab these increasingly heavy going technical books about...
Artificial neural networks and mathematics and just weird stuff.
Kinopuni is a bookshop in Japan.
That's probably context that people need to know.
Carry on.
Yes.
Thank you.
So I made that decision.
I said, right, okay, well, what I need to do is get a more interesting job.
I didn't want to be unemployed again.
So I thought, okay, I'll do a PhD.
So I went back to Australia, went back to my old uni, did a PhD in psychophysiology.
So that's the EEG.
Sorry, you're smiling.
It sounds like, you know, after watching Foundation, that you're like saying psycho history or whatever it is.
When you said it, I was like, huh?
You did?
You started that?
It's not a made up word.
It's a real thing.
That's just using basically EEG, the electroencephalogram, and recording event-related potentials, which is just EEG that's time-locked to a particular stimulus or a behavior.
Isn't that similar to like James Hellers, the like reform researcher guy?
I thought he was like somebody that focused on like biofeedback and measurement.
Is that his field?
Just out of curiosity.
I actually don't know about him specifically, but that biofeedback and stuff is definitely a subset.
It's not really...
It is based on the EEG, but it's not really...
You seem very keen to get into it, so I'll accept that answer and move on.
Um, but I, I didn't find like the problem with the EEG and this is a problem that you've talked about in the podcast, Chris, which with things like, like, you know, fMRI and things like that, you know, you could do this various signals from the brain, whether it's electrical or blood flow or whatever,
magnetic resonance imaging.
The problem is, is like how you've measured this sort of blobs of activity and trying to connect it with some observable behavior.
Or stimulus and it's all a bit like going through the tea leaves and so on.
So I didn't end up loving EEG that much, but I did.
I was interested in the statistical and mathematical signal processing tools that you could use to kind of extract the signals from the noise and do classification and all of those things.
So I ended up doing quite a technical PH, publishing a bunch of very technical.
Things in sort of medical and biological engineering and clinical neurophysiology and that kind of thing.
So I ended up being doing this very sort of geeky, dorky stuff, which I could explain, but it would involve describing things like wavelets and time frequency transforms and so on.
So I'm not sure if I should.
You don't need to.
I don't need to, do I?
But that sort of technical background brought me back to Japan for a postdoc to work in a lab in Japan, which was affiliated with the German National Research Center, the Fraunhofer Gesselschaft, which had a lab there.
And we were working with mobile robotics.
And in particular, I was focusing on the sort of image or video processing and basically trying to...
Make intelligent systems, to make autonomous robots that could process the video and make intelligent decisions about that.
So it's kind of like an embodied cognition in a way, but very applied.
Are you the man responsible for Pepper, the robot?
Is that you?
You made him?
No, I didn't have anything to do with Asimov.
You knew him, of course.
That little robot just walking around and, you know, everyone knew him, but you weren't directly involved with him.
I understand.
You know, in Star Wars, on the Death Star, there's those little black boxes on wheels that would go...
And Chewbacca would scare you.
That's what our robot looked like.
Okay.
Well, that's, yeah, slightly less humanoid, but maybe more functional in any case.
I'm not sure what they're doing in Star Wars, those little, like, moist droids.
I don't think it's ever made clear.
Yeah, maybe they're pets.
I don't know.
Yeah, yeah.
Well, the droids in Star Wars can also, like, as shown, they can feel pain on their feet.
So they're just a very weird thing which they've put into the design brief there.
So, yeah.
There's not to delve into it too deeply.
Just don't think about it.
Don't think about it and enjoy it.
Okay.
So we were working with artificial neural networks, which are really cool.
They're really a fun thing to talk about.
We were working with these things called convolutional neural networks, which were quite deep neural networks with many layers.
And they actually operated via these what's called convolutional filters that would kind of convolve over.
An entire image and extract features.
Is convolve a real verb?
Is that a real verb?
Yeah, look it up, Chris.
Look it up.
You'll find it.
I'm learning things.
I'm already learning things today.
So you were involved in convolutional research with convolving rays.
Carry on.
All the listeners with a background in signal processing, please join me in mocking Chris at this moment.
Anyway.
I'm realizing, Matt, that you have the technical depth that were you to put our expertise in guru-ology to nefarious purposes, you would be able to draw a massive storage box of jargon,
the likes of which Eric Weinstein only...
Only dreams about it.
It's a terrible power that I choose not to exercise.
It's such restraint.
So carry on.
Sorry, I completely interrupted your discussion of convolutions.
Anyway, so there was a particular sort of framework for these things that was created by a guy called Lecun, a French guy called Lecun, L-E-C-U-N.
And he is since has become sort of the lead chief technical officer or whatever at Google or Facebook or one of those places, Apple or whatever.
And why I hear you ask?
Because these little things, along with some other stuff that was happening with Hinton and so on, became a whole deep learning paradigm.
So we were actually implementing these deep...
Neural networks.
They weren't recurrent.
That was something special that happened later on.
That was a question that was just bubbling in my...
Were they recurrent?
No.
I want to quash those rumors right now.
It's good to keep that clear.
But here's the thing.
This is the moral of the story.
I could have been a contender, Chris, if we'd just stuck with it, right?
Because these guys became super famous, and rightly so, because this is the sort of super AI that was the new revolution.
But at the time, back in the early 2000s, mid-2000s, everyone had given up on artificial neural networks, right?
They were in the cold.
And the only people, I swear to God, as far as I know, the only people doing anything with these deep...
LeCun, who nobody heard of, right?
His paper had like about 13 citations.
We'd stumbled across it and we'd implemented it for our little robot and it worked great.
It worked fine.
It worked really well.
And we were like, oh, that's nice.
Okay.
And we, you know, wrote up some little papers and we just went to conferences and presented them.
Hey, you know, this thing, look, it's got these layers and stuff and it worked really well.
And then forgot about it and moved on and did other things.
Later on...
I find out that, yes, this is the computational artificial revolution, discovery that is, you know, so I missed out, Chris.
I was this close.
This is our version of Brett and Eric talking about what happened, right?
And like, as far as I can tell, Matt, you were the single researcher that had the possibility to unlock.
Neural networks and machine learning, was it really, you know, just chance and you, you know, the sands of fate?
Or was it some nefarious force was like, yeah, this guy doesn't, he's not going to get the credit.
And if you were credited on one of those papers, Matt, this would be a very different podcast now.
That's right.
I'd be getting eight-figure salaries at Google right now.
But they stole my ideas and they ran away with no.
Of course, the truth is we were just one of the also-rans.
We didn't persevere.
We didn't keep going.
And good luck to them.
I'm happy for them, Chris.
I can tell.
I can tell.
Not better at all.
See, that's just Lester.
It's just a note.
This is the difference when people say, you know, you guys are gurus, right?
No, we know we're cogs in the machine.
We know that.
We accept our clunking position in that mechanism.
So that's the difference.
One of the differences.
It's not the only one.
That's right.
The other difference is we're funnier.
We're funny.
Yeah.
We're funnier than Brett and Eric.
I'm thinking of Brett and Eric.
So what happened next?
What happened next?
I know, the tension is killing it.
So you missed the book in one field of research, and then what happens?
So I went back home to Australia, blissfully unaware that I'd missed the innovation of the century.
Because I was homesick, basically.
And my wife, who's currently my wife now, very kindly agreed to come back with me.
That's nice.
Yeah, that's nice.
So, you know, I got something, Chris.
I didn't leave Japan empty-handed.
Actually, that now makes sense because I find it strange that you were married to a Japanese woman who you met in Australia when you lived in Japan.
But now, obviously...
This makes much more sense.
Yeah.
I mean, I learned about the Australian-Italian community, so there might be an Australian-Japanese community as well.
There is.
My wife avoids it.
Okay.
So then I worked at CSIRO.
So yeah, okay.
So it's basically now like a stats and maths and engineering type AI guy.
You were a boffin.
I was a boffin.
I was a quant.
I should kick a quant.
This basically was where I was at in my career.
Was that what they said on your CV?
So I got a job at a university here as a postdoc in actually ocean stuff.
So that field called coastal engineering, where they sort of study the waves and the wind.
I thought you meant like ocean, the personality, like, you know, the big five.
So actual ocean.
The oceans.
So we did stuff like this is global wind wave models and we did these spectral artificial neural network and sort of like various simulated models of how that works.
And, um, yeah, you know, I was like, you actually have technical scientific competence.
Like fields of research that are not social science related.
I'm glad we had this discussion.
I know, I know.
I have very little skills in social sciences really.
Can you run a regression model?
This is why I'm more like, oh no, you know, I got to watch what I say.
I didn't realize what I've been sitting next to.
I feel very inadequate.
I've been judging you the whole time for your lack of understanding of things like...
That's right.
Multi-level models!
I do those in my sleep.
Yeah, yeah.
So I was publishing stuff like weird, just weird statsy stuff that no one's going to care about, like multi-scale polynomial filters for smoothing.
Nobody cares.
Oh, you care.
Thank you, Chris.
I'll dip my toe into the technical stuff, right?
So there's one thing I did, which was a geometric approach to nonparametric density estimation, right?
Which sounds a bit...
I know parametric distributions.
Yeah, that's right.
I know geometric unity.
Listen to me, Chris, I'm not going to baffer you with bullshit.
I'm not going to pull an Eric on you.
You will understand what I'm talking about, right?
Okay.
So you know that normally you've got some data, you've got some points scattered about on a plane, on a space.
I'm with you so far.
And you could put a normal distribution, the bell-shaped curve.
You know, it's like a Mexican hat.
Can you imagine it, Chris?
I kind of wear one every weekend.
So, yes, I've got it.
I've got it.
In my mind's eye.
Exactly.
And there are lots of other distributions with different shapes.
And they're all parametric distributions, right?
Because they involve some parameters that describe it.
But, you know, you can do non-parametric.
What about if you threw away those parametric models because they're so constraining?
Yeah, that's right.
Throw it away.
Just like, take it.
It's gone.
It's like, you know, discarding clothes and going to a nudist beach.
And what if you just let the data...
Speak to you and don't have any model, don't have any parametric model or try to avoid having a model.
Yeah, I know.
Okay.
Right.
And so there are ways to do it.
So there are things called tessellations.
There are Delaunay tessellations and Voronoi tessellations, which basically just...
These should be our words.
These should be our words.
Okay.
We'll skip over this bit, right?
No, Gary, look.
Carry on.
I'm following that.
I've got it.
I've got it.
Carry on.
To sort of cut to the chase, there are just ways to generate an estimate of a smooth, sort of curvy type distribution, like the good old normal distribution, that covers the whole space, that fits the data points that you've got in the space, but doesn't really have any assumptions about the shape that it's supposed to have.
So this is handy for stuff like astrophysics, where you've got all these stars up there and they have all these filaments and all these weird sorts of things.
And they want to kind of have a smooth kind of density that describes, you know, where the stars are really, you know, where they're most likely to be.
So yeah, I had some ideas about how to do that kind of thing.
Sir, I have a question.
Is this why whenever Taleb is trying to baffle you and various other people?
With his fat teal distribution malarkey.
Yes.
That you find it less impressive than some others seem to do because you have actual expertise in distributions and how to model them and whatnot.
So what he's saying is not, you know that it's not as revolutionary as he portrays it to be.
Yes, absolutely.
And I'm not including my own stuff in that.
Like, you know, most of the stuff I did.
In fact, all of it, just say all of it, has been totally superseded by these people in statistics that are far, far smarter than me and have done things like these distribution-fitting things and curve-fitting things that make none of those assumptions and they have amazing qualities.
And the stuff that Taleb is arguing against is like 1950s statistics.
It's like a caricature.
It's like a straw man of statistics which doesn't reflect anything that's happened since 1970.
The thing that sort of surprises me about this is like, this seems like it would be relevant information to mention to people when we discussed Taleb, which you didn't mention.
And then we got emails from people saying, has Matt seen that Taleb has, you know, like books about fat tail probabilities and stuff?
So I'm just saying, Matt, your modesty, it caused us to get emails.
You're dragged unnecessarily.
So that's that.
After that, I went to CSIRO, which is Australia's science and technology government organization.
So not a university, but sort of government science sort of thing.
Yes, working for the government on their secret projects.
That's right.
And I can't say too much about that because it's all very...
Hush, hush.
And now I can.
So we're doing interesting stuff like, again, all stats and maths, but for the marine and atmosphere people.
And that was fun.
So we did things like estimating the distribution of species across the Great Barrier Reef.
So, you know, like thousands and thousands of species and all these different geophysical conditions.
And, you know, it's a challenge to figure out where everything is because it's hugely complicated.
And like, where are the Plants and the benthic species and the corals and the fish and the prawns and so on.
Under the water.
That can help you.
I could have done it.
Mostly under the water, yes.
Some are under the sand.
That's an important qualification.
Yeah, so there's some tricky stats problems there, which were fun.
And then, Chris, this is the twist.
This is the twist.
This is an exciting twist.
I was there for a while, quite happily.
Taking it easy, pretty much, really.
But I saw a nice career at CSIRO, but I thought, you know, it seemed to, it was like, I saw these old guys, these old hands, you know what I mean?
Probably my age.
They're probably my age.
The age they are now is probably what they were then.
Ancient.
Ancient, yeah.
And I looked at them and I went, look at these.
I don't want to be like that.
I want, you know, this is, I don't want my future mapped out for me.
And I quit.
I left science and academia completely to go and build stairs with my dad for a few years.
That old chestnut.
The hot shot stats, the science researcher who throws it all in to build stairs with their dad.
It's a tale as old as time.
We didn't build any old stairs, right?
Built big commercial stairs for skyscrapers and stuff.
So really fancy lawyers and stuff like that, they need to impress people when they come into their things.
You built stairs for the high-powered people to walk on.
So that was soul-destroying about it because then you realize, like, what's the point?
I remember being taken up in one of these big buildings.
We were in the process of building them like a one and a half million dollar staircase, right, to go in their foyer, which was awesome, by the way.
Completely clad in Corian and like a coiled spring.
It was beautiful.
Corian is like an artificial stone.
It did.
That's right.
That's why it had to be a spring.
It had to be a spring because originally we designed it so it was too rigid.
And apparently these huge floors with the concrete and stuff would flex with the heat and move like maybe a centimeter or two.
And it would just go like if it was too rigid.
So it had to be a spring to kind of...
So it weighed like 12 tons, but it was a 12-ton spring.
Sounds like I could design stairs.
Yeah, anyone can.
I mean, you know, you just put your mind to it.
I question them as my role as questionnaire inquisitor here.
So when you say you like build stairs, you weren't an architect, right?
So you didn't like, you know, rock up and draw a stair and then give it to someone.
So were you like a hard hat man?
Like digging, I don't know, digging rock and I don't know how people build stairs.
Whatever the way they do to build stairs.
Or like, where were you?
Were you the man telling people that they should build stairs?
Well, it was a small business, right?
Speciality type contracting business.
So it was like all hands to the wheel at any time.
So I was there grinding and sanding and stuff like that a lot of the time.
I remember one time I was there, I was going to pick up one of these massive steel members that was going to go into one of these things.
I'm not going to make a joke.
I'm not that pure.
I had my hands around this massive steel member.
And the proper tradies, right?
The proper guys that were there that actually had muscles and stuff said.
No, no, no, mate.
You're going to do yourself an injury.
That sent me away.
But I was helping, Chris.
I was helping.
You were there.
You were contributing.
No, but mainly I was doing the engineering, like in the technical drawings and stuff.
So we'd use Autodesk Inventor, which is like a technical drawing, three-dimensional drawing program.
And it's not that hard.
It's not that hard because it has all of the things.
It'll compute the stresses and all that stuff.
And we'd take it to an engineer.
A proper engineer, one with certifications and stuff, who would then sign off on it and say this won't fall down.
I'm glad you didn't go down an alley and get a budget engineer to sign off on you.
How's this look?
Yeah, yeah, yeah, it's all right.
You're cheap, bud.
It's quite interesting how the sort of super high-tech software and stuff, like he would laboriously do his hand calculations and stuff, but we already knew it was fine because software told us it was fine.
Anyway, then I learned that dad's secret plan was to retire and leave me holding the bag.
The stairs.
The stair bag.
Yeah.
And let me tell you, a little thing about the construction industry, I don't know if anyone listening has had any experience with the construction industry, but the culture is pretty rough and ready and it's all about making money and everyone's pretty, you know.
Like, they're okay, salt of the earth and stuff, but it wasn't...
Are you subtly hinting that the Australian Stare Mafia got involved with your business and that legendary organization that the Italians are there?
It's, you know, it's been below the surface, Matt, and implicitly hinted at in previous weeks.
Let me just put it this way.
Like, you know, like, woke Twitter would find it very confronting.
The kind of culture.
I know what that's like.
I grew up in Belfast.
That's right.
You're mad at the people.
You're a boy from the streets.
That's right.
Yeah.
I'm not going to make any jokes.
Belfast is nice, please.
Anyway, I changed my mind again.
Decided I didn't want to do that anymore.
What can I do?
I started applying for academic jobs.
And no one would have me, Chris.
No one would have me.
It's very hard to get back into academia when you just leave it for several years.
So eventually I found this little university called Central Queensland University.
Had to move out of the big smoke and move to a little country town in order to get the job, which was at the time seemed devastating.
It turned out to be a great call.
We went, you know, it's like a sea change.
We ended up here in this little, little hamlet by the ocean and rock it up to a sleepy little campus.
So I joined the psychology department.
So I came full circle.
Circle is complete.
You weren't in the psychology department to begin with though.
You were in the like psychophysiology department.
Was that in this?
That was, that was, that was in psychology.
Yeah.
Oh, is that?
Is that an area of psychology?
Yeah, psychology does that technical stuff.
You're an anthropologist.
You wouldn't understand.
I know.
There's perceptual.
I'm surrounded by cognitive psychologists.
I know what they're doing.
Okay, so you came back and they accepted you.
They accepted me, yep, as another shitkicker, as a lecturer.
There.
And that was 10 years ago, as I said at the beginning, exactly 10 years ago.
And yeah, since then, we've done a lot of research on gambling, but also on other addictive things like vaping, nicotine, podcasts, anti-vax stuff,
looking at vaccine hesitancy and just weird beliefs, conspiracy theories, that kind of stuff.
So yeah, I got back into the psychology after not having done it since forever.
Did the conspiracy belief stuff come out of the, which came first, the addiction stuff or the conspiracy stuff?
Or were they simultaneous?
Yeah, kind of simultaneous.
Like I was always more interested in the conspiracy stuff and just the weird beliefs.
Like, you know, we did a bit of work on religion and spirituality.
The kinds of personality traits and cognitive styles and whatever that might predispose you to those things.
And I've always found that stuff really interesting, but that doesn't pay the bills.
To have a career in academia, you need to get funding.
And the thing about Australia is...
Will they get me?
I don't know.
I haven't seen your CV, Chris, but I don't know.
I'm sorry.
I'm sorry if I touched a nerve.
No, you're just wrong, Matt.
It's a very false assumption, but you know, somebody studying religion and trying to get funded.
Imagine that.
Yeah.
Well, you know, the funding situation in places, well, in Australia, and I assume probably in many other places too, it's like they don't fund pure research very much.
It's all very much applied.
So it's hard.
You have to be like really brilliant.
You have to have this like stellar career and this is perfect track record and so which I never had.
So I just wasn't really in contention for that kind of thing.
So that kind of research was always like a passion project that I'd do with little bits of money here and there as a side gig in a way and with, you know, PhD students and so on.
But, you know, in Australia, we got massive gambling.
Participation is one of the biggest gamblers in the world.
And this massive amount of gambling revenue flows in.
A lot of it goes to the companies.
Most of it comes from people with problems.
And a lot of it goes to government, though, in these special taxes.
And the government spends like a tiny percentage of that on providing services and counseling treatment, that kind of thing.
And a percentage of that goes to research.
So it's a tiny percentage of a small percentage of a huge amount of money, which is still a pretty large amount of money.
So as a result, I found myself doing quite a lot of research in gambling and looking at the harms and their distribution in the population, you know, like, like what actually happens?
Like there's this weird thing with gambling where they think of it, like if you said to somebody, you know, the only people that get harmed by alcohol are clinical alcoholics.
Mm-hmm.
Apart from that, nobody gets, would get hurt by alcohol abuse, right?
If you said that to someone, they'd go, no, that's not, that's clearly not true, right?
Yeah.
But with, but with gambling, the interesting thing is, is that there's this sort of collective delusion that the only impacts from gambling are happening to this quite small percentage, about 0.7 to 1.7 to 1% of the population that have meet the clinical criteria for compulsive gambling or problem gambling.
So one of the things that I've focused on that's caused that, you know, we find ourselves providing expert testimony to royal commissions and things in various states of Australia and New Zealand, commissioning reports and things like that, to show that actually the impacts spread,
as you'd expect, more broadly than just the sort of clinical people.
And that has sort of drawn me into a lot of these, you know, where there's a lot of money at stake.
There's the politics.
It's drawn me in a little bit into the policy stuff.
So gambling, like a lot of fields, has kind of these different components.
You've got these people that are kind of, I don't want to call them industry shills, but they...
Are industry shills.
Let's just say they get money from...
We can just say there is a category of people who may be...
Chilling for certain industries.
For industry, yeah.
And in the sense that they receive money from the industry and they always seem to find conclusions that are kind of favoring more liberal gambling policies and against any kind of measures to kind of restrict it.
Have you never heard of coincidences?
That's just, you know, just chance.
And the kind of perspective on the issue that they favor is that there is a tiny percentage of people in the population who have some kind of crazy mental disorder.
That leads them to have gambling problems.
But otherwise, the products are perfectly safe and fine.
Sounds about right.
And that's not really true.
On the other hand, you do have these activist researchers, right?
People that are just gung-ho on gambling is the most evil thing in the world.
You've got to stamp it out.
I don't really feel affiliated to them either because they have more of this activist state of mind.
A pretty legitimate conclusion answer.
Yeah.
And so we try to sit kind of in the middle and try to, yeah, do what researchers are meant to do, which is, you know, evidence.
Just focus on gathering evidence and let the evidence lead you to the conclusions.
Yeah.
Point of order.
I've heard online various complaints directed at our podcast that we are shills for mainstream institutions and that we simply...
Defend the status quo or whatever government recommendations.
So I feel to see how this fits with your presentation of yourself as somebody who is not doing the bidding of the government or is arguing against industry interests.
Matt, it's almost as if you are not simply accepting whatever the mainstream status quo that the government says, but that can't be right.
Because that's not what I've heard.
The critiques are quite clear that you will not criticize anything when it comes from an official institution.
So what are you playing at?
What's this about?
I will criticize government policy and the government and many state governments in Australia would much prefer that researchers didn't rock the boat because they don't want to inconvenience things because electorally it's very difficult for them to cut off the...
Revenue that comes from something like gambling, because then they'd have to either cut services or they'd have to find the revenue from somewhere else.
Both options are electoral disasters, which is why the situation persists.
And look, Matthew here is a modest mouse, as is often the case, because his research is influential enough that you were almost deposed recently for...
Some review, some kind of court-based review of evidence.
I'm butchering what happened, but I understood from what you said that there was a desire which eventually got it to cross-examine you about your research to kick it apart.
We won't call them industry shills.
We'll just see industry.
Favorable people who, if they were to poke holes in some of your research, would be able to encourage more lax regulation of the gambling industry.
And you escaped doing your patriotic duty, perhaps because they were afraid of you.
I said it, Matt, not you.
They might have feared your rapier intellect.
But that does suggest that people actually pay some attention to things that you have put to print.
On this topic?
Well, you know, gambling is a very niche field.
You know, it's a very small pond.
So, yeah.
Is it?
Yeah, like as an academic discipline.
Yeah, it's not a big field.
Well, let me ask you this.
Could you, with your knowledge, turn evil?
Like, take your information and I want, like, there's two paths I see for you to become evil mind.
Well, three paths.
One's become a guru.
We've established that that would be possible with your...
Expertise and jargon.
Yeah, I know a lot of mathematical words.
And your charismatic comehiller charm.
So guru is an option.
That's always on the cards.
Second is that I feel that if you are somebody who knows the research literature well, you also know the flaws and so on well.
So if you were to flip sides, you would be able to critique your research and other people doing similar research to you in a way that...
Would be more effective, right?
I've had letters from Philip Morris and people come and talk to you at conferences and stuff like that to sound you out to see whether you'd be interested in, you know, doing, you know, doing, doing something together.
It's kind of vague.
Are they talking with the machines?
No, no, they're really nice.
They wear nice suits and they.
They're really fun.
They're always quick to shout drinks and stuff.
They're charming people.
They're a lot more charming than the activist people that are kind of not much fun.
They don't have a reputation for being the life of the party.
So that's, well, that answers one question.
There is an evil path open to you, and we can potentially use this to support the podcast that we need.
So that's great to know.
The other one, the second evil...
Path that I'm wondering if you're capable of exploiting is with your knowledge of how people encourage gambling, how they do like rewards according to algorithms, reward timings and so on, or how they pump in music and sense.
I don't know if they do this, but like to keep it going, I think they just give them alcohol and that really doesn't work.
One of the things they do do is make sure that you don't have a good view of the outside world.
So if you go to any gambling parlor or area in a club or a casino or something like that, there's no windows.
There's no sort of view of the outside.
And that's to make sure that you don't have a good sense of time, how much time has gone by.
I feel like I got this insight through my lived experience where there's a bar in London, in Soho, called The Toucan.
It's quite famous because it's like the bar that has the best Guinness in London.
I will...
Put my Irish credentialism on that to say it's 100% the case.
And the downstairs part of that bar, like you go down these stairs and you're into like the sweet alcove.
It's all red lit and the seats are big, like fluffy Guinness things and stuff.
And it's nice.
Jimi Hendrix, I think, played there once and they have some stuff.
But when you go down into the downstairs bed, there's no light and there's no kind of sense of time.
And they have a clock which says Guinness time.
And it only has a second hand, just constantly going around.
And it works.
It works.
Like when I would go in there, you know, whether it was a student or whatever, or like going around lunchtime.
And, you know, it just felt like after a couple of hours that you were just like, how long were you there?
Were you there one hour?
Was it five hours?
And then you go out and it was daylight and you feel kind of ashamed of what you've done.
Yeah.
So I felt that.
My favorite Irish bar, I mean, my favorite bar, full stop in Brisbane, happened to be an Irish bar.
This is from when I was young, you know, and it was the same.
You'd go down these steps and down, down, down, you're in the bowels of the place and it had all the Irish tat, you know, for the fake, because it's a fake Irish bar, of course, all the stuff around.
Yeah, yeah, you know the stuff.
So I can't testify to how good the Kilkenny, how authentic the pints of Kilkenny were.
But to my young impressionable taste buds, it was so good.
So we'd get like so many pints of Kilkenny and plates of chips.
It was just like carbohydrate and alcohol-fueled extravaganza.
I have such fond memories of that.
Can I also tell you, Matt, that I didn't drink Guinness growing up in Ireland.
Not at all.
I mean, I tasted it, but I didn't like it.
And then where I developed my taste for Guinness and where Guinness became like the...
Pretty much the main thing I drank after that was I worked in an Irish bar in London and I was hired for that bar precisely because I was Irish.
I went in and they were like, have you ever worked in a bar before?
And they were like, no, not really.
And they were like, but you are Irish.
I am.
Well, okay.
But I developed a taste for Guinness at O 'Neill's pub, which is like a teen restaurant in London.
And the Guinness there was good.
And I've had Guinness all over the place, you know, went back and then started drinking Guinness in Ireland.
I will say the Guinness and Neil's pub that I worked in at least was not bad.
So there you go.
I learned to like Guinness outside of Ireland.
That's my point.
It is mesmerizing.
It's the same with Guinness as with Kilkenny.
You know, they pour it and there's a little bubble.
It's all the bubbles and, you know, it gradually...
In a bar, it was the most interesting drink to pour.
I mean, there's cocktails and stuff, but there was, like, actually something a bit true, right?
Yeah, like, I've seen people fail to pour it.
You know what I mean?
Yeah, yes.
Like, screw it up and then go, shit, and then just tip it all out and start again.
Like, yeah.
And you can draw little pictures on that and all that.
I could draw, you know, shamrocks and whatnot.
So, yeah.
Nice, nice.
Very good.
Very good.
Sorry, it's a Guinness tangent.
So yeah, the question was, could you use your knowledge to manipulate people to stay at the Smith gambling emporium for untold hours?
Yeah, but I don't think I'd be any better at it and probably a lot worse at it than the professionals, right?
Because the people like Crown Casinos, Star Casino, they gather data.
They offer thousands of different pokey machines or slot machines, whatever you want to call them.
You know, thousands of different variations of them.
And they're all electronic.
They gather data from every single one of them.
Can anybody get that data?
No.
They don't release it.
Not release it, but could you break in?
Just run, just leg it.
Like a really geeky thief.
I'm in the casino, not going for the vault, just going for the data repository of the payout tables and stuff.
I got some really good data from...
So I wrote a paper on...
How hard it is to figure out if you're an expert, because there is this class of expert bettors, like genuine professionals who actually make money from gambling.
And they can't make money from slot machines, obviously, because they're games of pure chance that are rigged for you to lose, right?
But you can make money if you're a genuine professional at stuff.
Well, you can make it at playing poker, depending on the quality of the people you're playing against.
Or you can make it at the races, right?
You know, dogs and horses and so on.
I wrote the statistical top paper on how difficult it is to figure out whether you are actually doing better than chance.
So figuring out whether you're doing better than chance is a little bit like if you're investing in the stock market.
Yeah, you know, your stock is up, you know, you've made whatever.
How many thousand dollars?
Were you just lucky or did you pick the right stock?
So it's very hard to tell.
You know, you need a lot of information to do that.
And it's very deceptive.
You can get the feeling that you're an expert at picking stocks or whatever without it being true.
And it's the same with gambling and picking horses.
A lot of people have this delusion.
So my paper was called Delusions of Expertise.
And it was basically based on...
The idea that you just can't track how much money you've made, even over a period of time, would take like a decade or two decades of just constant betting to actually gather enough sort of information to, if you're just monitoring your bank account, like how much money you've made,
whether you're actually genuinely any good at it, because it's the way the distributions work, right?
The way the statistics work.
So this quite famous guy, I've forgotten his name.
But he's kind of famous.
I think he's from the UK somewhere.
But he was a genuine pro.
He's a multimillionaire worth hundreds of millions of dollars.
And he made all his money at the Hong Kong racetrack.
And he did it by sending boys or employees out to collect all the data that he could, like the condition of the tracks, all the different horses.
I don't know what data he collected, but he collected every bit of information that was available and then gradually built up these predictive models and basically did all his gambling based on that using, you know, science, right?
And he did very well.
Became, you know, worth hundreds of millions of dollars.
And I think he kind of retired.
But anyway, he read my paper and he said, no, no, no, no, no.
I think you're wrong.
I could see the personal thing here.
I am an expert, right?
But the thing is, this gets back to what we were talking about before.
Where he was coming from, he was assuming these normal distributions that eventually your individual returns on these things would converge to a normal distribution.
So this is the basic statistical theory that it doesn't matter what the distribution of the variable is that you're measuring.
It could be the outcome of a horse race where it's returning 10 to 1 or 100 to 1 or whatever.
Have this really weird distribution.
But if you average over enough trials, it'll converge to a normal distribution.
And that's true.
Most of the time, that normal approximation is correct.
The distribution from the horse races, because you have these long shot type wins and stuff like that, is so perverse.
It takes such a long time to converge to a normal distribution.
He was actually wrong.
And you actually had to use the method I was using, which actually involved convolutions and things, actually.
So here's the moral of the story.
He, after exchanging many emails, we had this argument, exchanged many emails, he admitted I was right.
Yeah, I was right and he was wrong.
Well, and who is better off at the end of that?
You with the admission or him with the millions, hundreds of millions of dollars?
Which one is the wealthier man, Matt?
Which one?
I know, it was a pyrrhic victory.
I had my email from him saying, yes, you're right.
And he had his hundreds of millions of dollars.
So you could be the judge, you know?
I mean, who's to say?
That's right.
Well, it's like the gurus, right?
Because they earn significantly more than us from what they do.
But they're bad people.
So they're not bad people.
They're just doing bad things.
Maybe that's the way to put it.
Especially, you know, the ones that are promoting anti-vaccine.
Hesitancy and stuff to...
Think about Brene Brown, Chris.
Judge the behavior, but you know.
Yeah, Brene Brown's all right.
She's all right, but I'm sure she earns enough as well.
She's not doing bad.
So delusions of expertise.
It feels like if you continue down the road that we are going with looking at gurus and stuff that you can...
Easily do a part two paper of that, like delusions of expertise too.
No, it's not about gambling this time.
It's about the gurus.
That's the weird thing, Chris.
I mean, I don't know if it came out from my little potted history, but a lot of the stuff I've been doing has led me in a weird way.
It's cosmic.
It has led me in a way, all the way to decoding the gurus.
You.
You're my white whale.
You're my hologram.
All the forces of the universe were congealing to just lead you slowly to a Skype call with me one fateful night about a year and a half ago.
But honestly, it's like this weird confluence of events.
Like, I was studying anti-vaxxers just because I thought they were interesting eight years ago.
And at the time, it was like, you know, this is just a quirky...
Weird thing.
And we're studying conspiracy theories and belief in the paranormal and all these things.
And it seemed at the time like such irrelevance.
I mean, it was interesting from a psychological point of view, and that's why I was doing it.
And I had no expectations, not the faintest thought that suddenly, like, that's our news cycle now.
No, I can also, I have the exact same feeling because like, not academically, but just, you know, I was listening to stuff with ancient.
Alien people and getting annoyed.
I was listening to Joe Rogan explain that we didn't land on the moon and podcasts devoted to how near-death experiences show that there is an afterlife and so on.
And it was all fringe.
It always was fringe.
Rupert Sheldrake, Graham Hancock, it doesn't matter.
They were on Rogan or whatever, but, you know, it was still, it was niche.
And there were connections to anti-vaccine movements.
HIV, AIDS, denialism and so on, but it was generally around the fringes.
And I'm very unhappy that it's no longer the case that when I started seeing conspiracism of that variety become mainstream and politically mainstream, that's the difference because there were always conspiracies.
There always will be conspiracies, but like becoming the dominant force in politics, that was...
It was depressing.
And it still is depressing that all this stuff that I was interested in is not much more relevant because...
Yeah.
Yeah, I wish it wasn't.
No, I really wish it wasn't.
It was a fun hobby.
It was just a curious little thing and you could enjoy it.
And now it seems much too serious.
I always find the HIV /AIDS denialism hard to enjoy.
But I know what you mean.
I know what you mean.
So it's an interesting path that you've wove across so many fields.
And genuinely, this is actually a useful conversation because I now have a much greater appreciation for I know that you're statistically competent,
but there's statistical competence within the psychology and social science sphere, and there is broader statistical competence than that.
That's right.
Are you going to start paying me more respect now?
Can I defer to me in any way, shape or form?
On the topic of convolutions, yes.
I'll trust your asides 5% more about statistics.
That's what my Bayesian priors have been updated with.
But no, I think this is important for the listeners because the general thing, the snarky kind of comment that you get is like, social scientists, sure.
Science.
Yeah.
You wouldn't know science if it smacked you around the face.
And very often, that's a pretty legit point of view.
But, you know, if somebody has actually been involved with engineering, building stairs, and so on, I kind of have more of this, right?
Like, I'm glad you're a psychologist now and, you know, are involved.
But it's better that you did other things as well.
Like, I feel superior to the pure psychologist because...
Even, you know, the anthropology field is crazy and it has crazy stuff going on in it.
But the one thing they do do is they go out and they hang around with normal people or, you know, interesting people.
And they're not just in a lab with undergraduate students, like, trying to plumb, you know, the depths of the universal mind.
So, like, I know there are psychologists who go out and do stuff, but like...
That is one thing that's good about anthropology and having expertise in other things.
You don't regard small experiments with undergraduate students.
There's plenty of stuff you can learn from that, but you don't automatically generalize it to be a universal model for all of mankind.
I see lots of people do that.
Anthropologists also have their own issues and limitations.
We all have limitations.
I'll tell you one more little story, which was, okay, so now you've learned about the amazing breadth and girth of my background.
So a little while after being at my current university, still a shit-kicker, and this sort of other, you know, high-flying research unit was sort of incorporated into the university from Adelaide, and they came and visited, right?
So we had the big professor, and he's kind of eager young postdocs come, and we're having a few drinks, and they said to me, okay, so...
Tell me what's your background.
What's your research?
Give me the elevator summary.
I just said, no, I'm not going to do it.
Cause I literally, I literally couldn't, like I couldn't imagine.
It would be this, it would be an hour long podcast and I, and, and they wouldn't let me off and it was became quite awkward.
And I just refused to, cause I couldn't, I couldn't even begin.
I didn't like it.
Yeah, you didn't want to blow their minds.
You did what?
With the ocean?
I know, I could go, there's the oceans, and then there's the EEG, and then there was the stairs, and there was robots, and they're like, that just looked at me like I was mad.
Look, I think this is good, Matt, because, you know, I think people should notice that, like, we've had this podcast for, what, like a year?
A year.
Over a year.
Just over a year.
Yeah.
And like, it's not like we don't talk about our backgrounds or that kind of thing, but we, we don't, we haven't focused on our research or this kind of stuff, right?
In any of this step.
And we won't do it again.
You will get like, you know, these episodes are it.
We're not doing this consistently.
End of story.
That's right.
But I think I want to be like, you know, what's that thing?
I'm back pattern.
I'm back pattern.
But just imagine if this was like, The gurus, and they had a similar history as what you have.
The chance that you wouldn't hear about that, and it wouldn't come up in conversation regularly.
Maybe convolution this and convolution that.
Yeah.
I think it's good.
I think it's a good sign that I didn't know any of this, even though we've talked.
Many times.
I mean, you know, I knew bits and pieces.
I knew you built stairs.
But that's what I promised.
It's not that interesting.
Like, it was nice to tell a story and stuff like that.
But it is, you know, it's all very technical.
It's all very specific.
It's useful.
I think one of the morals of the story is that a lot of the stuff that's quite useful is dull.
You know, it's all very dull.
Like, we're doing stuff that's, you know, it's not.
Unimportant, but it's not earth shattering or whatever.
Like I did things like developing like a review of environmental report card systems for estuaries across the world, right?
So various estuaries and you take the different samples and different things you can do.
It's not interesting.
It's not interesting.
It's not going to blow anybody's mind, but setting up those frameworks, measurement frameworks so that you can accurately measure the ecological health.
Of a riverine system and the riparian vegetation around it and the estuaries and so on.
That's important.
That's important for the fish.
If you're a fish in that estuary, you'd care.
You would, you would.
Bloody hell.
I've been waiting for someone to publish this for years.
We are decades apart in age at completing the red flags.
Hang on.
Decades. Centuries.
Almost centuries.
And then...
You're at the end of your life.
I'm at the start of my mind.
Despite this, I want to say that like this.
We're just two random academics, right?
And we have interest in conspiracy theories and all this kind of stuff.
You all know this if you're listening to this.
But this is why we are skeptical about all the stuff that you hear.
Like what Matt talked about there, right?
How much of that was about the culture war?
Zippo!
Zippo!
Maybe gambling can sort of be tied in there a bit with social justice activism, but it wasn't a big part of the story.
My research about, you know, the religion and rituals, it's not about culture war stuff.
It's not what people are debating unless Bo Wangard publishes a paper on it.
So that's what makes me skeptical about all the claims of how, you know, academia is all about that.
It isn't all about that.
It's about this boring stuff that we're telling you.
And it doesn't mean that there aren't, you know, that this doesn't have an impact in America or, you know, that...
Managerial things or whatever is going on.
It's a debate to be had about the extent to which various things are impacting academia from a political point of view.
But I, yeah, I just want to highlight that this is the kind of reason we're skeptical of those super broad narratives.
And Matt, it seems that you've frozen.
This is unfortunate because...
We're almost reaching the end.
So I'll try to sign off in case we end here, but we might have on an end.
So thanks everybody for listening.
Matt's obviously done much more important and despite what he says, interesting research than I have on my side.
But we hope you enjoyed this.
Self-indulgent waffle from Matt this week and from myself previously.
As I say, this won't happen.
Thank you.
So enjoy.
And next time for these kind of stuff, we'll be talking more about actual research and articles and that kind of thing.
