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Speaker 1: Humans disagree on many political fronts, but we're finding ourselves

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Speaker 1: globally in a period of higher polarization, and the question

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Speaker 1: is is there anything to be done about that. Presumably

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Speaker 1: we're not going to get humans to not have conflict,

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Speaker 1: but is there any such thing as better conflict? What

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Speaker 1: would that mean? And what does any of that have

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Speaker 1: to do with brains or with the fact that modern

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Speaker 1: humans only spread out across the globe sixty thousand years ago,

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Speaker 1: or with social media recommender algorithms, or the Iroquois Native Americans.

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Speaker 1: Welcome to Inner Cosmos with me David Eagleman. I'm a

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Speaker 1: neuroscientist and an author at Stanford and in these episodes,

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Speaker 1: I examine the intersection of our brains and our lives.

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Speaker 1: And today's episode is about something that you know, I'm

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Speaker 1: obsessed with from the point of view of brainience, which

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Speaker 1: is issues of conflict and empathy and in groups and outgroups.

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Speaker 1: We find ourselves in a time of conflict. Now. The

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Speaker 1: things on everyone's mind are the war in Ukraine and

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Speaker 1: the war between Israel and Hamas. And even though those

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Speaker 1: have sucked up most of the media's attention, it's critical

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Speaker 1: to note that we have lots of other conflicts also

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Speaker 1: going on around the globe right now. There's ongoing internal

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Speaker 1: conflict in Myanmar. There's an Islamist insurgency in the Maghreb

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Speaker 1: region of Africa. There's the Boko Haram insurgency in Nigeria.

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Speaker 1: There's a civil war between rival factions in Sudan. There's

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Speaker 1: a multi sided conflict of the Syrian Civil War. There's

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Speaker 1: the ongoing civil wars in Somalia and Ethiopia, and Afghanistan

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Speaker 1: has been in near continuous armed conflict for essentially as

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Speaker 1: long as I've been alive. Now, if you've been listening

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Speaker 1: to this podcast for a while, you know that I'm

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Speaker 1: very interested in why conflict happens between humans and why

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Speaker 1: it happens so readily and consistently. So in this light,

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Speaker 1: there are three main things to note from the neuroscience

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Speaker 1: point of view. The first thing is that we all

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Speaker 1: have the same brains on the inside. So despite cultural

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Speaker 1: differences and geographic differences, your pink brain looks exactly the

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Speaker 1: same as someone else's anyone else's anywhere around the globe.

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Speaker 1: And I don't mean that as a feel good statement.

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Speaker 1: That's just a biological fact. Anatomically modern humans radiated out

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Speaker 1: of Africa only about sixty thousand years ago, and some

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Speaker 1: turned left and became European, and others turned right and

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Speaker 1: became Asian, and those who stayed remained African. But the

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Speaker 1: timescale that we're talking about some tens of thousands of years.

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Speaker 1: That is simply not enough time on the evolutionary timescale

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Speaker 1: for brains to change in any meaningful way to change

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Speaker 1: their operation or their algorithms. So we're all running the

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Speaker 1: same version of the biological operating system, despite superficial differences

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Speaker 1: in size and shape and culture and the costumes that

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Speaker 1: we wear and how much melanin we have in our

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Speaker 1: skin and so on. We can end up seeming pretty

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Speaker 1: different as a result of these surface properties, but we

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Speaker 1: have the same cognitive and emotional engine on the inside. Now. Unfortunately,

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Speaker 1: one of the things that operating system does is it

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Speaker 1: gives us all a very strong drive toward having in

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Speaker 1: groups and outgroups. This tribalism is something that characterizes our species.

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Speaker 1: We belong to groups based.

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Speaker 2: On our neighborhood or the way we look, or our

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Speaker 2: religion or our family or whatever it is, and we

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Speaker 2: take to feel closer to our in groups and treat

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Speaker 2: them better and listen to their opinions more strongly.

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Speaker 1: Our outgroups not so much. When it comes to someone

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Speaker 1: from the other side of the railroad tracks, or who

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Speaker 1: happens to have grown up under a different deity, or

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Speaker 1: who sings different songs or uses different language sounds to

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Speaker 1: transmit information, or whatever it is, we tend to treat

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Speaker 1: them with more suspicion. And in decades of neuroscience experiments,

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Speaker 1: including those from my lab, we've been able to show

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Speaker 1: that your brain simply does not care as much about

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Speaker 1: people in your outgroup. At the extreme, your brain will

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Speaker 1: view somebody in an outgroup the same way it will

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Speaker 1: view an object, not like a fellow human the way

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Speaker 1: it does with people in your in group, but instead

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Speaker 1: as something that's not particularly worthy of empathy, something that

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Speaker 1: you have to deal with in the same way that

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Speaker 1: you would deal with a vice, rusts, or a cockroach

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Speaker 1: or a rat. And if you're interested in more detail

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Speaker 1: on this point, I dove deep into examples of this

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Speaker 1: in episode twenty and one of the things I talked

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Speaker 1: about was an experiment that we did in my lab

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Speaker 1: that got at this in group outgroup difference, where all

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Speaker 1: we needed were single word labels. So I'll just tell

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Speaker 1: you the short version here of the experiment. We put

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Speaker 1: you in brain imaging in fMRI, and you're looking at

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Speaker 1: six hands on the screen and one of them gets

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Speaker 1: stabbed by a syringe needle. Now, seeing that activates a

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Speaker 1: network in your brain that we summarize as the pain matrix.

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Speaker 1: Your brain is showing a big spike, and what that

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Speaker 1: represents is you are feeling the other person's pain even

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Speaker 1: though it's not your hand. The eras in your brain

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Speaker 1: that care about pain are coming online. This is the

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Speaker 1: basis of empathy, of feeling someone else else's pain. But

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Speaker 1: now we label the six hands with one word labels Christian, Jewish, Muslim, Hindu, Scientologist, atheist,

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Speaker 1: And now we measure people of all religions in the

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Speaker 1: scanner as these different hands get stabbed. And what we

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Speaker 1: found is that everyone has a larger brain response when

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Speaker 1: it's their own group that's getting stabbed, and their brain

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Speaker 1: shows a smaller response when it's any of the other

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Speaker 1: five out groups. And this was true across religions and

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Speaker 1: even for the atheist group, which cared more when the

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Speaker 1: atheist hand gets stabbed than the other hands. So this

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Speaker 1: is not an indictment of religion, but more broadly, it

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Speaker 1: highlights your brain's exquisite skills at distinguishing in group from

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Speaker 1: your outgroups and the fact that your brain simply does

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Speaker 1: not spend as much energy on simulating what it is

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Speaker 1: like to be someone who is in your If you

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Speaker 1: want more details on that, please listen to episode twenty,

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Speaker 1: which is called why does your brain care about some

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Speaker 1: people more than others? And beyond the fact that we

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Speaker 1: are naturally tribalistic, there's a third point to note, which

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Speaker 1: is that every one of us feels like we know

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Speaker 1: the truth, and it's not clear to us why other

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Speaker 1: people don't see the truth as clearly as we do.

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Speaker 1: They must be stupid or ill informed, or trolls or

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Speaker 1: stubborn or who knows why they don't simply admit to

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Speaker 1: what is so obviously the truth. And deep down most

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Speaker 1: people have an intuition that if you could just simply

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Speaker 1: shout loudly enough in all caps on social media, that

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Speaker 1: everyone would agree with you, everyone would see the light.

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Speaker 1: And this is true of everything, not just political positions.

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Speaker 1: Take religion. There are two thousand active religions on this

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Speaker 1: small planet, and many people feel that if they could

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Speaker 1: just share with all the infidels why their religion is

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Speaker 1: right and everyone else is wrong, that everyone would agree

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Speaker 1: with them. Everyone would come to know that the religion

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Speaker 1: you happened to grow up in is the correct one. Now,

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Speaker 1: if there actually were one correct religion, we might expect

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Speaker 1: it to spread everywhere equally, But obviously that's not the case.

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Speaker 1: Religions remain clustered around where they started, and you're not

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Speaker 1: going to see a blossoming of Islam in Boise, Idaho,

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Speaker 1: just like you won't expect to see a blossoming of

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Speaker 1: Protestantism in Mecca. But here's the interesting part. Whether we're

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Speaker 1: examining one's political preferences, or religious affiliation or whatever, people

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Speaker 1: are generally reluctant to change to consider the perspectives of

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Speaker 1: other people. So, given the deep wiring that we have

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Speaker 1: for having our in groups and for feeling we know

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Speaker 1: the right answer because of our very limited internal models,

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Speaker 1: this leads to the critical question about whether there would

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Speaker 1: be any way for eight billion brains to find peaceful coexistence. Now,

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Speaker 1: if you've been a regular listener to the podcast, you

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Speaker 1: know that I'm generally highly optimistic about everything, but I

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Speaker 1: would suggest the answer to the question of whether we

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Speaker 1: will achieve peace is probably not. We're probably not going

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Speaker 1: to find ourselves ever in some magical moment where all

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Speaker 1: the newspapers say, Wow, nobody is fighting, we're taking a

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Speaker 1: day off today, and we're not going to find some

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Speaker 1: extraordinary presidential candidate where everyone says, yeah, that person's great.

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Speaker 1: I think we can all agree presidential elections are always

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Speaker 1: fairly close to fifty to fifty. Why this results from

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Speaker 1: the enormous variety of internal models inside everyone's heads. There

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Speaker 1: is no perfect candidate because no one can fit all

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Speaker 1: the constraints at once of all all the different models. Okay,

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Speaker 1: so if we take this all as our starting point

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Speaker 1: for today, the question is what does this all mean

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Speaker 1: for the future of conflict? Are we facing a future

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Speaker 1: where wars and polarization will stay as they are or

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Speaker 1: even grow worse. Well, that's certainly a reasonable fear. But

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Speaker 1: a little while ago I discovered there was a movement

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Speaker 1: of political scholars around the idea of how to have

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Speaker 1: better conflict. Now, what does better conflict mean? We'll get

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Speaker 1: into that in a second, but first I just want

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Speaker 1: to say I've been moved and inspired by this movement.

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Speaker 1: It feels like it's the opposite of what we see

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Speaker 1: online every day, where people pick aside and scream and

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Speaker 1: yell for it or fight and die for it at

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Speaker 1: the cost of not meaningfully considering the complexity of human situations.

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Speaker 1: Just take what's happening in the middle least. Typically, if

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Speaker 1: someone points out that the history there is extraordinarily multi

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Speaker 1: layered and complex, people will often say, no, this is

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Speaker 1: actually quite simple, and then they will give their version

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Speaker 1: of the history. But the extraordinary part is that people

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Speaker 1: on both sides will deliver that same line. They will say,

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Speaker 1: it's actually very simple, And to me, that's a barometer

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Speaker 1: that tells me the situation is not so simple. But instead,

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Speaker 1: like most human conflict, it's full of different paths that

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Speaker 1: you can take through the history to come to a

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Speaker 1: variety of conclusions. And we witness this sort of complexity

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Speaker 1: whenever you have millions of people with all personalities involved

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Speaker 1: in conflict on all sides. You have sinners and saints,

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Speaker 1: and you have psychopaths and peace seekers, and no one

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Speaker 1: can hope to derive a meaningful solution without doing the

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Speaker 1: hard work of digging in and trying to understand what

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Speaker 1: happens between groups of millions of humans, rather than imagining

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Speaker 1: there's a good guy side and a bad guy side. So,

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Speaker 1: as I said, I was very moved to discover that

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Speaker 1: some people really take on this sort of political intellectual

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Speaker 1: humility and try to figure out what do we do

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Speaker 1: with humans? What do we do about the fact that

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Speaker 1: they're never going to agree, There's always going to be conflict.

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Speaker 1: How do we make conflict more productive rather than simply

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Speaker 1: being about bloodshed? So to explore this, I called up

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Speaker 1: my colleague Jonathan Stray. Jonathan is a former journalist who

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Speaker 1: is now a senior scientist at the Berkeley Center for

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Speaker 1: Human Compatible AI, where he works on the algorithms that

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Speaker 1: feed up content to online and how their operation affects

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Speaker 1: things like polarization. And he writes a terrific newsletter called

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Speaker 1: The Better Conflict Bulletin. So here's John Jonathan. So you

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Speaker 1: are a conflict researcher. Tell us about what that means.

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Speaker 3: Well, I try to study what causes people to disagree

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Speaker 3: and then how that disagreement transforms into polarization and eventually

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Speaker 3: violence if unchecked, And how to build computer controlled media

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Speaker 3: systems like social media that will prevent that.

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Speaker 1: I know that you think about conflict in terms of

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Speaker 1: good and bad conflict. So what does that mean?

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Speaker 3: Well, we don't want no conflict in society. Conflict is

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Speaker 3: so first of all, it's inevitable. We all disagree, we

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Speaker 3: want different things unavoidably. But sometimes when we disagree, it

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Speaker 3: comes to something productive, you know, we talk about it,

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Speaker 3: we figure something out, and sometimes it doesn't right, Sometimes

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Speaker 3: it escalates to some sort of zero sum game or

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Speaker 3: even violence. The idea is, when we think about conflict,

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Speaker 3: we're not trying to prevent people from fighting. We're trying

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Speaker 3: to have them fight in some sort of productive way.

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Speaker 1: So what does that look like? What does that mean?

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Speaker 3: Well, the simplest example might be violent versus nonviolent, right,

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Speaker 3: so we can all understand the difference between you know,

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Speaker 3: people trying to get their way by using force or

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Speaker 3: getting their way by discussion, yes, but also non violent

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Speaker 3: conflict tactics, you know, protests. You know, there's famous historical

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Speaker 3: figures like doctor Martin, Luther King or Gandhi who really

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Speaker 3: developed these techniques and showed that you could achieve you know,

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Speaker 3: large political victories without using physical force. So that's that's

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Speaker 3: maybe the most obvious way but there's a bunch of

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Speaker 3: other stuff when you start thinking about this.

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Speaker 4: So maybe you think about this at a personal level.

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Speaker 3: You know, we've all had the experience of an argument

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Speaker 3: where you know, we said some ugly things that can't

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Speaker 3: be unsaid, and you feel worse at the end of it.

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Speaker 3: And then I think most of us have had the

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Speaker 3: experience of an argument where you know, there were some

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Speaker 3: hard truths, but it was honest, it was carrying, it

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Speaker 3: was empathetic, and even if we couldn't solve the problem

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Speaker 3: at the end of it, we still have a positive

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Speaker 3: regard for each other.

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Speaker 4: And so that would be an example of good versus

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Speaker 4: bad conflict.

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Speaker 1: So one of the things that has always intrigued me

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Speaker 1: is that we all have this illusion that we know

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Speaker 1: the truth, that we have a complete story of what's

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Speaker 1: going on, and therefore other people that disagree with us

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Speaker 1: seem like they're trolls, or they're uninformed, or they're disingenuous.

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Speaker 1: And so the question is, how do we address that

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Speaker 1: illusion that we have. How do we get ourselves a

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Speaker 1: little bit outside our own fish bowls to see the

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Speaker 1: other side.

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Speaker 3: Yeah, no, that's a great question. So there's a bunch

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Speaker 3: of ways of answering that. So there's I was at

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Speaker 3: a conference yesterday on the topic of intellectual humility, the

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Speaker 3: idea that actually we don't know the answer, there's always

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Speaker 3: something we're missing. And this is important not just as

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Speaker 3: an epistemological practice, that is, to be more correct in

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Speaker 3: what we believe, but as a relational practice, meaning it

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Speaker 3: changed is how we relate and how we can relate

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Speaker 3: to other people. And I think part of this is

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Speaker 3: we always have a partial picture, and we tend to,

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Speaker 3: in conflict situations, believe that the other person actually holds

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Speaker 3: more extreme views than they do. So there's really good

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Speaker 3: evidence of that. This is the case in the American

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Speaker 3: Culture War, where if you ask one side what the

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Speaker 3: other side thinks, they will imagine the other side is

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Speaker 3: actually more extreme than they are. So conflict in particular

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Speaker 3: is prone to these misperceptions of the other.

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Speaker 1: So give us an example of how we know that

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Speaker 1: that each side thinks the other side is more extreme.

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Speaker 4: Yeah. Yeah, there's tons of work on this.

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Speaker 3: So there's a group called the Perception Gap which has

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Speaker 3: done a bunch of research from this. And so, for example,

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Speaker 3: if you ask Republicans how many Democrats would support completely

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Speaker 3: open borders, they'll say something like seventy five percent. If

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Speaker 3: you ask Democrats how many of them would support completely

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Speaker 3: open borders, it's something like, so they're twenty or twenty

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Speaker 3: five percentage points off. And this pattern is consistent across

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Speaker 3: a range of issues, and it's bidirectional as well. So

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Speaker 3: you know, if you ask Republicans or if you ask

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Speaker 3: democrats something like, you know, how many Republicans would say that,

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Speaker 3: you know, everyone should have access to as many guns

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Speaker 3: as they want, Democrats will guess that number a lot

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Speaker 3: higher than Republicans will say.

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Speaker 1: So, speaking of Democrats and Republicans, here's the question. Are

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Speaker 1: we more polarized currently in America? Is that just an

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Speaker 1: impression or is that backed up by statistics?

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Speaker 3: No, Unfortunately, it's real, and you can measure it a

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Speaker 3: lot of different ways. You can look at where people

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Speaker 3: are on political issues, you can look at how people

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Speaker 3: feel about each other. You can look at congressional voting patterns,

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Speaker 3: whether you know members of Congress will cross the aisle

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Speaker 3: to vote on each other's builts, and all of these

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Speaker 3: measures show the same basic pattern, which is that polarization

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Speaker 3: has been increasing since the the mid seventies or maybe

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Speaker 3: the early eighties and is now at historically high levels.

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Speaker 3: And it's not just America, it's several other countries in

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Speaker 3: the world as well.

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Speaker 1: Right now, I know you and I have independently made

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Speaker 1: arguments about why this is not entirely about social media,

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Speaker 1: and that, of course seems like a good thing to

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Speaker 1: point to is the fact that this started in the seventies.

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Speaker 1: But tell us your sense of the involvement and the

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Speaker 1: non involvement of social media in this polarization.

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Speaker 4: Yeah, well, there's definitely some involvement.

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Speaker 3: There are a number of ways that social media, in

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Speaker 3: particular social media algorithms, that is to say, the systems

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Speaker 3: that decide what we see.

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Speaker 4: And in what order, can have some involvement.

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Speaker 3: So probably a lot of your listeners have heard of

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Speaker 3: the idea of the filter bubble, the idea that, you know,

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Speaker 3: we're each trapped in this sort of algorithmically produced reality

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Speaker 3: where we don't see the other side. The evidence tends

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Speaker 3: to be against that. We actually do see quite a

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Speaker 3: lot of cross cutting content, you know, even if it's

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Speaker 3: you know, rage clicking on an article that has a

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Speaker 3: different viewpoint, but there's a bunch of other things going on. So,

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Speaker 3: for example, most social media and other systems which select

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Speaker 3: content for us, like news recommenders and so forth, rank

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Speaker 3: things based on basically the number of clicks they get engagement,

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Speaker 3: And we really do pay more attention to things that

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Speaker 3: are valuable to us, but also we pay more attention

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Speaker 3: to things that are threatening, offensive, fearful. So this approach

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Speaker 3: of ranking things by how much attention we give them

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Speaker 3: also tends to amplify more extreme or scarier items.

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Speaker 1: Now, I did an episode a few episodes ag Go

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Speaker 1: where I mentioned for the Iroquois Native Americans up in

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Speaker 1: essentially Wisconsin and Canada, some hundreds of years ago, they

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Speaker 1: were having bloody battles between these different tribes all the time,

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Speaker 1: and they had a new leader come in who came

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Speaker 1: to be known as the Great Peacemaker because one of

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Speaker 1: the things he did is assigned everybody in the tribes

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Speaker 1: to different clans. So you might be a member of

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Speaker 1: the Turtle clan and I'm a member of the Heron clan,

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Speaker 1: even though we're members of the same tribe. And it

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Speaker 1: turns out these allegiances were cross cutting, so that each

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Speaker 1: person had their allegiance to their tribe and also to

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Speaker 1: their clan. And these weren't equivalent, and that ended up

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Speaker 1: bringing peace because now it wasn't so simple to have

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Speaker 1: a clear in group and out group because they were mixed.

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Speaker 1: And I know that you in your work with social

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Speaker 1: media have been looking at something similar to this, which

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Speaker 1: is this issue of how an algorithm should rank posts.

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Speaker 1: So tell us about that.

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Speaker 3: Yeah, so that is a great example. I've heard of

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Speaker 3: this Ariquais example as well. And what is happening is

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Speaker 3: conflict is tied up with identity. We fight basically against

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Speaker 3: people who are different than us. And in the last

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Speaker 3: few decades in this country, identity has become more and

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Speaker 3: more collapsed into a single dimension of you know, left

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Speaker 3: versus right, red versus blue, Republicans versus Democrats.

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Speaker 4: It didn't actually used to be this way.

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Speaker 3: People didn't identify with their political parties in the same

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Speaker 3: way a generation ago. And also there was much more

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Speaker 3: mixed identification at the national versus local level. So you know,

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Speaker 3: you would have some town where, you know, you had

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Speaker 3: a Republican mayor, but you had, you know, progressive politics

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Speaker 3: because that's what the people there believed.

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Speaker 4: In and wanted.

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Speaker 3: Politics has become very nationalized. There's less local news, there's

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Speaker 3: less split ticket voting.

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Speaker 4: Every issue takes place.

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Speaker 3: At this huge scale where there's only two ends in spectrum.

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Speaker 3: So how you would have to reverse this is you

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Speaker 3: would have to have some sort of sense of not

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Speaker 3: necessarily local, but community level values.

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Speaker 4: Right, it would have to be.

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Speaker 3: Okay for you know, specific groups online or perhaps discussion

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Speaker 3: to have opinions that just don't quite fall along this

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Speaker 3: left right axis.

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Speaker 1: And what's the reason that we have seen more sorting

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Speaker 1: into these groups. What's the reason that particular issues go

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Speaker 1: together that don't necessarily belong together.

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Speaker 3: Well, again, it's an identity thing. It's one of the

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Speaker 3: things that makes all of these issues sort of one

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Speaker 3: big blob where you only have two choices is merely

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Speaker 3: exposure to people who are far away from us, either

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Speaker 3: physically or social. So you know, maybe you only talk

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Speaker 3: to your neighbors in your town, or you only read

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Speaker 3: the local newspaper, and so you could have this sort

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Speaker 3: of quixotic local politics in a way that was healthy.

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Speaker 3: But now we are exposed to people all across the

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Speaker 3: country and indeed the world on a daily basis. So

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Speaker 3: now we're comparing our point of view to someone you

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Speaker 3: know that will never meet, who's far away from us.

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Speaker 3: And there's simulations that show you don't need any antagonism

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Speaker 3: for people to sort themselves into two big tribes.

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Speaker 4: All you need is when you talk.

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Speaker 3: To someone and they're you know, near enough like you,

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Speaker 3: you get a little bit closer to them, right, which

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Speaker 3: is a natural human thing. When two people talk, they

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Speaker 3: tend to you know, converge a little on their on

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Speaker 3: their views and identity. But because that those interactions are

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Speaker 3: now happening all across the country, we're coalescing into sort

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Speaker 3: of two big groups.

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Speaker 4: It's it's kind of a paradox, right.

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Speaker 3: We want to be able to talk to people who

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Speaker 3: are very far away from us, you know, that's the

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Speaker 3: promise of these online networks, and.

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Speaker 4: Yet it seems to make our con clicks global.

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Speaker 1: And there are network science models on this right that

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Speaker 1: demonstrate how groups end up dividing like this. And what

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Speaker 1: is the evidence that these network science models actually cash

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Speaker 1: out in real life?

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Speaker 3: Yeah, So these models developed actually from models built in

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Speaker 3: the nineteen sixties to study physical racial segregation. And what

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Speaker 3: they showed is that you don't need racism in the

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Speaker 3: sense of not wanting to live near people who are

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Speaker 3: a different race than you. All you need is a

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Speaker 3: slight preference, you know, maybe a few percent to live

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Speaker 3: near people who are more like you, and that is

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Speaker 3: enough for entire neighborhoods to eventually segregate. And so those

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Speaker 3: same models are now being applied to online networks and

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Speaker 3: they show very similar results. People if they have a

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Speaker 3: slight preference for people who are maybe a little more

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Speaker 3: like them in their politics, will spend.

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Speaker 4: More time, you know, in a bunch of ways, right,

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Speaker 4: hanging out.

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Speaker 3: In the same groups, or maybe you know, friending them

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Speaker 3: or fallollowing them, that sort of thing. And so we

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Speaker 3: see this this sort of global splitting.

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Speaker 1: Right, So before we drop this start, I just want

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Speaker 1: to return to this issue of what does social media

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Speaker 1: not have to do with the current polarization, And that

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Speaker 1: certainly seems like one of the issues is this, you know,

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Speaker 1: network segregation. What else do you see where you think

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Speaker 1: that social media is maybe not the sole culprit as

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Speaker 1: opposed to human behavior.

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Speaker 4: Yeah.

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Speaker 3: Well, one of the things I like to say is that,

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Speaker 3: you know, if Facebook could fix our democracy by changing

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Speaker 3: one hundred lines of code, they would have done it already, right,

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Speaker 3: if only our problem was that simple. But polarization is

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Speaker 3: one of these problems. That requires an all of society approach.

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Speaker 3: So it's you know, social media, yes, but it's also

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Speaker 3: media journalists. Journalists are going to have to learn to

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Speaker 3: cover politics in different ways, and it's the politicians themselves.

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Speaker 3: There is always an advantage to taking a view that

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Speaker 3: is more extreme because it motivates people, and so it's

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Speaker 3: kind of a devil's bargain, right. You can get people

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Speaker 3: to turn out and show up for your cause by

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Speaker 3: pressing on a divisive issue, but doing that divide society further.

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Speaker 3: So we actually need a whole bunch of sectors of

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Speaker 3: society to move in the same direction. And there's para

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Speaker 3: conflict scholars called Guy and Heidi Burgess, and they have

438
00:26:22,680 --> 00:26:28,760
Speaker 3: a wonderful article on forty different roles that people have

439
00:26:28,840 --> 00:26:31,760
Speaker 3: to do to create a healthy and peaceful democracy. And

440
00:26:31,800 --> 00:26:34,639
Speaker 3: what do they say, Well, some of them are exactly

441
00:26:34,640 --> 00:26:36,959
Speaker 3: what you'd expect, right, So we already talked about journalists

442
00:26:37,000 --> 00:26:40,720
Speaker 3: and so forth, But they also talk about issue analysts.

443
00:26:40,760 --> 00:26:43,120
Speaker 3: There are people who have to go deep on particular issues.

444
00:26:43,359 --> 00:26:47,119
Speaker 3: They talk about healers, people who present a vision where

445
00:26:47,200 --> 00:26:50,800
Speaker 3: we have emotionally healthy relationships with each other. They talk

446
00:26:50,840 --> 00:26:54,440
Speaker 3: about they call them democracy firsters, people who are concerned

447
00:26:54,760 --> 00:26:58,040
Speaker 3: first and foremost with the correct functioning of elections and

448
00:26:58,160 --> 00:27:00,560
Speaker 3: rule of law. Once you start thinking this way, you

449
00:27:00,600 --> 00:27:03,240
Speaker 3: realize there's a lot of different roles to play, and

450
00:27:03,240 --> 00:27:04,560
Speaker 3: a lot of people have to be pulling in the

451
00:27:04,600 --> 00:27:07,879
Speaker 3: same direction. And they say that what we need is

452
00:27:08,359 --> 00:27:12,479
Speaker 3: a generation of people interested in conflict who in much

453
00:27:12,560 --> 00:27:14,399
Speaker 3: the same way that we now have a generation of

454
00:27:14,400 --> 00:27:16,560
Speaker 3: people who are working on climate change.

455
00:27:16,920 --> 00:27:20,680
Speaker 1: Specifically, who are interested in doing conflict right exactly, because

456
00:27:20,680 --> 00:27:24,000
Speaker 1: we certainly have a generation interest in conflict. What do

457
00:27:24,040 --> 00:27:26,640
Speaker 1: you see when you look around at college camps? Is, Jonathan,

458
00:27:26,680 --> 00:27:29,040
Speaker 1: you're located at Berkeley, and I know that you are

459
00:27:29,080 --> 00:27:33,280
Speaker 1: a very wise person who keeps a foot in both

460
00:27:33,359 --> 00:27:38,000
Speaker 1: camps and tries to see things from all sides. That's

461
00:27:38,040 --> 00:27:41,800
Speaker 1: not the reputation that Berkeley has in particular. So when

462
00:27:41,880 --> 00:27:44,760
Speaker 1: you look around at the campus, what do you see?

463
00:27:44,760 --> 00:27:47,359
Speaker 1: And is anything different about this generation than previous ones?

464
00:27:48,200 --> 00:27:48,560
Speaker 4: Yeah?

465
00:27:48,720 --> 00:27:50,359
Speaker 3: I mean, you know that's a loaded question, right the

466
00:27:50,440 --> 00:27:51,480
Speaker 3: kids today and so forth.

467
00:27:51,560 --> 00:27:53,240
Speaker 4: But yeah, I mean, of course Berkeley.

468
00:27:52,880 --> 00:27:56,359
Speaker 3: Is a liberal campus, right, you know, it's a public

469
00:27:56,440 --> 00:27:58,760
Speaker 3: university in one of the most liberal places in America.

470
00:27:58,840 --> 00:28:00,000
Speaker 4: So it has it's own policy.

471
00:28:01,000 --> 00:28:03,560
Speaker 3: I do see, And of course it's not just Berkeley

472
00:28:04,600 --> 00:28:08,639
Speaker 3: that the current generation of students are I would say,

473
00:28:09,320 --> 00:28:11,680
Speaker 3: more involved in politics, which I would say is a

474
00:28:11,720 --> 00:28:17,000
Speaker 3: good thing, but also less open or less compromising, which

475
00:28:17,040 --> 00:28:21,520
Speaker 3: troubles me in particular when it leads to the suppression

476
00:28:21,720 --> 00:28:24,840
Speaker 3: of alternative views. Now, I don't want to say that,

477
00:28:25,280 --> 00:28:27,200
Speaker 3: you know, anybody should be able to say anything without

478
00:28:27,200 --> 00:28:32,000
Speaker 3: any consequences. I think there are real issues here, for example, inclusion.

479
00:28:32,400 --> 00:28:36,080
Speaker 3: You don't want to make people feel like they don't belong.

480
00:28:36,040 --> 00:28:37,880
Speaker 4: At an academic institution.

481
00:28:38,520 --> 00:28:41,120
Speaker 3: However, where it sort of crosses a line for me

482
00:28:41,400 --> 00:28:44,600
Speaker 3: is where saying something a little unorthodox or a little

483
00:28:44,680 --> 00:28:50,480
Speaker 3: challenging becomes impossible. People are scared to do it. And

484
00:28:50,480 --> 00:28:53,120
Speaker 3: I'm not talking about, you know, the haters here. I'm

485
00:28:53,160 --> 00:28:56,240
Speaker 3: talking about people who are trying to engage in good

486
00:28:56,280 --> 00:28:58,960
Speaker 3: faith and maybe saying something that is, you know, a

487
00:28:58,960 --> 00:29:02,120
Speaker 3: little controversial, and there's just less.

488
00:29:01,920 --> 00:29:04,120
Speaker 4: Tolerance for that than there used to be.

489
00:29:04,920 --> 00:29:07,040
Speaker 3: And I think both it's just sort of anecdotally, you know,

490
00:29:07,080 --> 00:29:09,600
Speaker 3: talking to the faculty, but also there's a bunch of

491
00:29:09,720 --> 00:29:13,600
Speaker 3: data on this. Ironically, Berkeley, which is associated with free

492
00:29:13,600 --> 00:29:16,160
Speaker 3: speech very closely. Right, we have the Free Speech Cafe

493
00:29:16,600 --> 00:29:18,320
Speaker 3: on campus, and this was the heart of the free

494
00:29:18,320 --> 00:29:22,680
Speaker 3: speech movement in the nineteen sixties, is now a place

495
00:29:22,760 --> 00:29:28,880
Speaker 3: where people are often criticizing free speech as being too permissive,

496
00:29:29,320 --> 00:29:30,560
Speaker 3: and I find that very troubling.

497
00:29:31,280 --> 00:29:33,800
Speaker 1: So how do you think about dealing with that as

498
00:29:33,840 --> 00:29:36,600
Speaker 1: you think about training the next generation, for example, with

499
00:29:36,600 --> 00:29:39,280
Speaker 1: all these different roles that we might need to have

500
00:29:39,400 --> 00:29:41,840
Speaker 1: better conflict, how do you do that?

501
00:29:42,600 --> 00:29:44,560
Speaker 3: Yeah, well, you know, I tried to do this in

502
00:29:44,400 --> 00:29:47,840
Speaker 3: my classes yesterday. I mean, it's really about sort of

503
00:29:48,400 --> 00:29:50,160
Speaker 3: I think it requires two things, right. One is you

504
00:29:50,200 --> 00:29:54,520
Speaker 3: have to create a sense of psychological safety in some way.

505
00:29:54,720 --> 00:29:58,080
Speaker 3: Right when my classes, you know, we discuss algorithm design,

506
00:29:58,160 --> 00:30:01,280
Speaker 3: we discuss racially biased algorith we discuss all this stuff.

507
00:30:01,800 --> 00:30:03,040
Speaker 3: And one of the things I say at the top

508
00:30:03,080 --> 00:30:05,880
Speaker 3: of those classes is like, okay, so you know, we're

509
00:30:05,880 --> 00:30:08,280
Speaker 3: going to talk about some issues which are very charged.

510
00:30:08,840 --> 00:30:10,600
Speaker 3: I understand that some of you are going to have,

511
00:30:11,480 --> 00:30:16,040
Speaker 3: you know, real upsetting personal experience with this stuff. But

512
00:30:16,880 --> 00:30:18,760
Speaker 3: you know, I asked that you you give it a shot,

513
00:30:19,640 --> 00:30:22,440
Speaker 3: and that you know, you try to engage in a

514
00:30:22,480 --> 00:30:26,120
Speaker 3: spirit of curiosity. And we're not always going to get

515
00:30:26,120 --> 00:30:29,080
Speaker 3: things right, But I don't want anyone to ever accuse

516
00:30:29,160 --> 00:30:32,560
Speaker 3: us of not being thoughtful or careful or are empathetic.

517
00:30:33,080 --> 00:30:34,959
Speaker 3: And I think that's the spirit you have to approach this,

518
00:30:34,960 --> 00:30:38,360
Speaker 3: This is spirit of curiosity. So for example, why do

519
00:30:38,400 --> 00:30:41,120
Speaker 3: we disagree about this? What is it about you know,

520
00:30:41,520 --> 00:30:45,240
Speaker 3: this student experience and that student's experience that leads to

521
00:30:45,240 --> 00:30:50,960
Speaker 3: to such dramatically different conclusions on say, affirmative action, the

522
00:30:51,360 --> 00:30:55,480
Speaker 3: you know, media bias, you know, how we should deal

523
00:30:55,520 --> 00:30:57,800
Speaker 3: with crime, how should we should do with immigration that

524
00:30:57,920 --> 00:31:02,080
Speaker 3: came from somewhere? And very often what we find when

525
00:31:02,120 --> 00:31:04,960
Speaker 3: we have the conversation that way is either there's some

526
00:31:05,080 --> 00:31:08,800
Speaker 3: intense personal experience, family background. You know, my mother came

527
00:31:08,840 --> 00:31:12,680
Speaker 3: from El Salvador, my father was deported, you know, I

528
00:31:12,720 --> 00:31:16,040
Speaker 3: had to grow up in economic uncertainty, you know, something

529
00:31:16,120 --> 00:31:21,360
Speaker 3: like this. Or we find that people shaped their ideas

530
00:31:21,400 --> 00:31:23,480
Speaker 3: from their social network and never really thought about it.

531
00:31:23,800 --> 00:31:26,680
Speaker 3: And this is part of what polarization is, is that

532
00:31:26,760 --> 00:31:30,920
Speaker 3: all of our political ideas end up sort of collapsing

533
00:31:30,960 --> 00:31:35,000
Speaker 3: into this left right access. There's no logical reason why

534
00:31:35,440 --> 00:31:39,240
Speaker 3: if you are pro choice you should also be concerned

535
00:31:39,240 --> 00:31:42,920
Speaker 3: about climate change and yet here we are. So there's

536
00:31:42,960 --> 00:31:48,120
Speaker 3: some sort of social process that makes everybody split intoto

537
00:31:48,120 --> 00:31:50,840
Speaker 3: camps in this way, and we can counteract that by

538
00:31:50,960 --> 00:31:54,200
Speaker 3: thinking carefully for ourselves and having discussions with people who

539
00:31:54,360 --> 00:31:55,480
Speaker 3: might have a different view.

540
00:31:56,000 --> 00:31:59,080
Speaker 1: So you address this in your class, but how do

541
00:31:59,120 --> 00:32:02,880
Speaker 1: you think about taking to a larger world. And before

542
00:32:02,880 --> 00:32:05,160
Speaker 1: we get into social media algorithms, which I want to do,

543
00:32:05,680 --> 00:32:08,160
Speaker 1: you know, one of the things that struck me. Let's

544
00:32:08,240 --> 00:32:11,840
Speaker 1: take the Israel Hamas conflict going on right now. Whenever

545
00:32:11,880 --> 00:32:15,320
Speaker 1: it comes up about Okay, which side launched that rocket,

546
00:32:15,440 --> 00:32:19,040
Speaker 1: it turns out that no amount of evidence sways anybody

547
00:32:19,080 --> 00:32:22,800
Speaker 1: on that. People have their side and they generally stick

548
00:32:22,840 --> 00:32:24,400
Speaker 1: with it. As far as I can tell. This is

549
00:32:24,480 --> 00:32:27,520
Speaker 1: just a you know, view from surfing a lot of

550
00:32:27,520 --> 00:32:30,080
Speaker 1: social media on this stuff. The question is how do

551
00:32:30,160 --> 00:32:33,000
Speaker 1: you expand beyond your class to get people to ask

552
00:32:33,080 --> 00:32:36,760
Speaker 1: these questions about changing their point of view, not even

553
00:32:36,760 --> 00:32:39,960
Speaker 1: necessarily changing it, but just being willing to examine other

554
00:32:40,120 --> 00:32:40,960
Speaker 1: pieces of evidence.

555
00:32:41,360 --> 00:32:43,960
Speaker 3: First, let's talk about facts. I believe that facts matter.

556
00:32:44,040 --> 00:32:45,920
Speaker 3: I believe that they're deeply important. I used to be

557
00:32:45,960 --> 00:32:49,040
Speaker 3: an investigative journalist. We can't know everything, but we can

558
00:32:49,160 --> 00:32:54,080
Speaker 3: know some things. The problem is what facts mean depends

559
00:32:54,080 --> 00:32:57,000
Speaker 3: on who you ask. So in the you know Israel

560
00:32:57,040 --> 00:33:00,680
Speaker 3: Palestine conflict, the fact that Palestinians are living there before

561
00:33:00,760 --> 00:33:04,120
Speaker 3: nineteen forty seven is a fact that matters deeply to

562
00:33:04,160 --> 00:33:07,200
Speaker 3: a lot of people. The fact that Jews were living

563
00:33:07,200 --> 00:33:09,400
Speaker 3: there three thousand years ago is a fact that matters

564
00:33:09,440 --> 00:33:12,040
Speaker 3: deeply to a lot of people. So these facts are

565
00:33:12,080 --> 00:33:14,480
Speaker 3: not in dispute. What is in dispute is the meaning

566
00:33:14,560 --> 00:33:18,880
Speaker 3: of these facts, which points to the problem is fundamentally

567
00:33:18,920 --> 00:33:22,160
Speaker 3: relational in many cases rather than factual, and people can

568
00:33:22,200 --> 00:33:25,160
Speaker 3: be misinformed. I'm not disputing that, but often what you

569
00:33:25,240 --> 00:33:28,200
Speaker 3: find is it is about how people feel about each

570
00:33:28,240 --> 00:33:30,120
Speaker 3: other and about how they are relating to each other.

571
00:33:30,240 --> 00:33:32,680
Speaker 4: Now, so you ask what we can do.

572
00:33:33,240 --> 00:33:35,760
Speaker 3: So this is probably the moment to mention that I

573
00:33:35,800 --> 00:33:38,959
Speaker 3: write a newsletter called the Better Conflict Bulletin, and we

574
00:33:39,000 --> 00:33:40,360
Speaker 3: are news and analysis for.

575
00:33:40,360 --> 00:33:41,200
Speaker 4: A better culture war.

576
00:33:41,640 --> 00:33:45,560
Speaker 3: And the idea here is that many people have this

577
00:33:45,840 --> 00:33:50,800
Speaker 3: gut sense that we're fighting ugly, and we try to

578
00:33:50,840 --> 00:33:53,960
Speaker 3: explore in this newsletter what would it be to fight better?

579
00:33:54,400 --> 00:33:59,600
Speaker 3: So we cover conflict research, the science of how people

580
00:34:00,520 --> 00:34:05,120
Speaker 3: misunderstand and misperceive each other, and we cover people who

581
00:34:05,160 --> 00:34:08,319
Speaker 3: are successfully navigating the culture in a more productive way.

582
00:34:08,920 --> 00:34:12,279
Speaker 3: So there are people exploring these topics, there's not a

583
00:34:12,280 --> 00:34:17,360
Speaker 3: lot of coverage for it because, as they say in

584
00:34:17,400 --> 00:34:21,480
Speaker 3: the peace building field, sometimes peace has no natural constituency.

585
00:34:21,680 --> 00:34:24,399
Speaker 3: It's very easy to get people excited about winning. It's

586
00:34:24,400 --> 00:34:27,520
Speaker 3: harder to get people excited about living together harmoniously.

587
00:34:27,800 --> 00:34:29,960
Speaker 1: That's right. That's why when I met I just met

588
00:34:30,000 --> 00:34:32,239
Speaker 1: you very recently, Jonathan, just a few weeks ago, and

589
00:34:32,280 --> 00:34:35,399
Speaker 1: I was so excited by the kind of work you're doing.

590
00:34:35,440 --> 00:34:38,200
Speaker 1: Because I come from a neuroscience angle. I study a

591
00:34:38,200 --> 00:34:41,279
Speaker 1: lot about in groups and outgroups and empathy and how

592
00:34:41,320 --> 00:34:45,920
Speaker 1: we so easily relegate others to a different group. I

593
00:34:46,000 --> 00:34:47,839
Speaker 1: was so excited to learn that you and others are

594
00:34:47,840 --> 00:34:51,719
Speaker 1: doing the boots on the ground work of trying to

595
00:34:52,120 --> 00:34:56,040
Speaker 1: bring groups of people together. And so I still want

596
00:34:56,040 --> 00:34:58,719
Speaker 1: to drill in on this point though. So you've got

597
00:34:58,719 --> 00:35:01,880
Speaker 1: the newsletter, which is to I'm a paid subscriber to that.

598
00:35:02,440 --> 00:35:04,720
Speaker 1: What are the things that you think about though, besides

599
00:35:04,760 --> 00:35:06,440
Speaker 1: social media, which we'll get to in a second, And

600
00:35:06,480 --> 00:35:09,799
Speaker 1: that's obviously a big leverage point. But are there any

601
00:35:09,840 --> 00:35:12,080
Speaker 1: other things you can do when you think about how

602
00:35:12,080 --> 00:35:13,720
Speaker 1: do we get people to fight less ugly.

603
00:35:14,520 --> 00:35:17,319
Speaker 3: Yeah, so there's a bunch of sort of immediate things

604
00:35:17,360 --> 00:35:20,600
Speaker 3: that you can do. So, first of all, there's this

605
00:35:21,600 --> 00:35:25,520
Speaker 3: perception gap, so misperceptions, so we tend to be both

606
00:35:25,600 --> 00:35:29,200
Speaker 3: misinformed about what the other side actually believes and we

607
00:35:29,280 --> 00:35:33,280
Speaker 3: tend to stereotype them, meaning you know, if a Republican

608
00:35:33,320 --> 00:35:35,960
Speaker 3: thinks about a Democrat, or a Democrat thinks about a Republican,

609
00:35:36,000 --> 00:35:39,919
Speaker 3: they think about the most extreme version of that right.

610
00:35:40,400 --> 00:35:41,920
Speaker 3: And you can see this in the data when you

611
00:35:41,920 --> 00:35:44,440
Speaker 3: ask people, you know, you know, what is the distribution

612
00:35:44,600 --> 00:35:48,239
Speaker 3: of how you know, many you know, the opposite side

613
00:35:48,280 --> 00:35:50,279
Speaker 3: with support let's say violence if they don't get their

614
00:35:50,280 --> 00:35:52,680
Speaker 3: way in election, right, and they you get these like

615
00:35:52,880 --> 00:35:56,279
Speaker 3: very extreme distributions, where in the reality there's actually much

616
00:35:56,320 --> 00:35:59,720
Speaker 3: more more overlap. So yeah, first, you can inform yourself

617
00:35:59,719 --> 00:36:02,480
Speaker 3: about what other people actually believe in.

618
00:36:02,560 --> 00:36:03,560
Speaker 4: Honest in its way.

619
00:36:04,040 --> 00:36:09,000
Speaker 3: Second, there is a set of techniques for how to

620
00:36:09,040 --> 00:36:15,160
Speaker 3: have conversations with someone who has not just like you know,

621
00:36:15,280 --> 00:36:18,600
Speaker 3: a polite conversation, but like genuinely has different values than you.

622
00:36:19,400 --> 00:36:22,200
Speaker 4: And it's got to start with curiosity. It's got to

623
00:36:22,200 --> 00:36:22,960
Speaker 4: start with listening.

624
00:36:23,520 --> 00:36:26,239
Speaker 3: And I would suggest don't go into those conversations with

625
00:36:26,280 --> 00:36:27,719
Speaker 3: the goal of changing someone's mind.

626
00:36:27,800 --> 00:36:29,480
Speaker 4: You wouldn't want them to do that with you.

627
00:36:29,920 --> 00:36:32,040
Speaker 3: Go into those conversations with the goal of trying to

628
00:36:32,120 --> 00:36:36,200
Speaker 3: understand how they got to that place. Because when we're

629
00:36:36,200 --> 00:36:40,080
Speaker 3: talking about the American conflict, we're talking about you disagree

630
00:36:40,120 --> 00:36:43,480
Speaker 3: with half of America. Well half of America, you know,

631
00:36:43,600 --> 00:36:48,360
Speaker 3: isn't stupid. You know, they're not like fundamentally broken or

632
00:36:48,440 --> 00:36:51,319
Speaker 3: evil or something. They're going to be pretty average, just

633
00:36:51,360 --> 00:36:53,560
Speaker 3: like the other half of America. So how is it

634
00:36:53,640 --> 00:36:57,280
Speaker 3: that smart and kind people can end up believing something

635
00:36:57,320 --> 00:37:01,200
Speaker 3: completely different than you. That's the thing to get curious about.

636
00:37:01,760 --> 00:37:04,279
Speaker 3: And the third thing I would say is watch for

637
00:37:04,360 --> 00:37:08,399
Speaker 3: your own emotional reactions. Watch for where you know, you

638
00:37:08,440 --> 00:37:13,560
Speaker 3: get you know, angry or let's just say uptight, or

639
00:37:14,000 --> 00:37:15,799
Speaker 3: you know, really have this feeling that you have to

640
00:37:15,840 --> 00:37:21,000
Speaker 3: defend something or protect something, because those reactions will lead

641
00:37:21,040 --> 00:37:24,880
Speaker 3: you to first, what is it that you're scared of

642
00:37:24,960 --> 00:37:25,800
Speaker 3: that you're worried about it?

643
00:37:25,840 --> 00:37:27,320
Speaker 4: Where are your fears and concerns?

644
00:37:27,560 --> 00:37:32,560
Speaker 3: And second, if you can notice those and not be

645
00:37:32,800 --> 00:37:35,680
Speaker 3: overtaken by them, you can have much better relationships with

646
00:37:35,680 --> 00:37:38,400
Speaker 3: people who disagree with you. And actually, one of the

647
00:37:38,520 --> 00:37:43,240
Speaker 3: practices that we teach is speak them aloud, right, don't

648
00:37:43,320 --> 00:37:46,600
Speaker 3: don't project your anger onto the person across from you,

649
00:37:46,840 --> 00:37:49,880
Speaker 3: who you're probably stereotyping, who you know, never did you

650
00:37:49,920 --> 00:37:52,520
Speaker 3: personally any wrong. Just say, you know, when you say that,

651
00:37:53,800 --> 00:37:56,720
Speaker 3: I get very upset, I have reaction. I feel anger

652
00:37:56,840 --> 00:37:59,760
Speaker 3: thinking about that, without directing it towards the other person.

653
00:38:00,239 --> 00:38:03,160
Speaker 3: We can't hide our emotions. If we're going to relate better,

654
00:38:03,200 --> 00:38:04,360
Speaker 3: they have to be on the table.

655
00:38:20,520 --> 00:38:23,480
Speaker 1: If you were suddenly assigned to be the educations are

656
00:38:23,520 --> 00:38:27,239
Speaker 1: for the nation, how would you build junior high or

657
00:38:27,320 --> 00:38:32,080
Speaker 1: high school classes to teach kids about this, about how

658
00:38:32,120 --> 00:38:35,080
Speaker 1: to relate, about how to let's say, stealman each other's

659
00:38:35,239 --> 00:38:37,959
Speaker 1: arguments so that they can understand them better.

660
00:38:38,760 --> 00:38:43,879
Speaker 3: Yeah, so what we need to do is give young

661
00:38:44,000 --> 00:38:49,799
Speaker 3: people the ability and the experience of relating successfully across

662
00:38:50,520 --> 00:38:53,279
Speaker 3: value divides. And there's actually a bunch of organizations that

663
00:38:53,360 --> 00:38:56,000
Speaker 3: do this both at the K through twelve and the

664
00:38:56,080 --> 00:38:59,399
Speaker 3: university level. You know, these programs are some of them

665
00:38:59,400 --> 00:39:03,000
Speaker 3: are in class teacher led where the last students to

666
00:39:03,640 --> 00:39:05,160
Speaker 3: you know, what is a what is the thing you

667
00:39:05,160 --> 00:39:08,760
Speaker 3: feel very strongly about and either try to find students

668
00:39:08,800 --> 00:39:12,319
Speaker 3: who have a different opinion and teach them to constructively

669
00:39:12,400 --> 00:39:16,120
Speaker 3: talk about that, or do things like there was a

670
00:39:16,160 --> 00:39:19,440
Speaker 3: recent New York Times piece about a writing teacher who says,

671
00:39:19,960 --> 00:39:22,400
Speaker 3: come up with a character who you think is a

672
00:39:22,480 --> 00:39:27,120
Speaker 3: terrible human being or something that no one should absolutely

673
00:39:27,120 --> 00:39:30,520
Speaker 3: ever say, and write a story where they say that

674
00:39:30,600 --> 00:39:33,560
Speaker 3: thing in context in a way which is sympathetic or

675
00:39:33,600 --> 00:39:36,640
Speaker 3: humanizing towards them. And I think it's this is the

676
00:39:36,680 --> 00:39:40,520
Speaker 3: fundamental skill to be able to see the humanity in people,

677
00:39:40,640 --> 00:39:45,919
Speaker 3: even in moments of profound disagreement. And I don't mean

678
00:39:45,920 --> 00:39:51,000
Speaker 3: to minimize the actual stakes of these types of conversations, right.

679
00:39:51,080 --> 00:39:53,920
Speaker 3: You know, if you are an immigrant who's talking to

680
00:39:53,960 --> 00:39:56,719
Speaker 3: someone who says, you know, we shouldn't allow any more

681
00:39:56,719 --> 00:40:00,440
Speaker 3: immigration to this country, that has a deep personal impact

682
00:40:00,480 --> 00:40:02,120
Speaker 3: on you because it may means you never get to

683
00:40:02,120 --> 00:40:03,120
Speaker 3: see your family again.

684
00:40:03,600 --> 00:40:03,799
Speaker 4: Right.

685
00:40:04,080 --> 00:40:06,480
Speaker 3: So I'm not saying that, you know, we should all

686
00:40:06,480 --> 00:40:09,000
Speaker 3: talk until we get along. I'm saying we have to

687
00:40:09,040 --> 00:40:14,000
Speaker 3: see our political adversaries as human. And there are a

688
00:40:14,040 --> 00:40:16,480
Speaker 3: bunch of organizations who try to train people to do

689
00:40:16,520 --> 00:40:19,719
Speaker 3: this and also give people the experience of I had

690
00:40:19,760 --> 00:40:23,240
Speaker 3: a conversation with someone who disagrees with me and it went, okay.

691
00:40:24,040 --> 00:40:28,400
Speaker 3: We are aversive to these conversations. They're hard, they're emotionally taxing,

692
00:40:28,960 --> 00:40:31,560
Speaker 3: there's no guarantee they're going to come out well. So

693
00:40:31,600 --> 00:40:34,560
Speaker 3: we have to give people the confidence to engage in

694
00:40:34,600 --> 00:40:35,040
Speaker 3: this way.

695
00:40:36,280 --> 00:40:38,719
Speaker 1: So I'm so glad to hear you use the word humanizing,

696
00:40:38,760 --> 00:40:41,920
Speaker 1: because that's really the key from a neuroscience point of view.

697
00:40:41,960 --> 00:40:44,440
Speaker 1: The issue is that people in our out group, we

698
00:40:44,520 --> 00:40:48,560
Speaker 1: actually analyze them with our brains in a different way,

699
00:40:48,719 --> 00:40:51,520
Speaker 1: such that they are more like an object than a

700
00:40:51,600 --> 00:40:55,680
Speaker 1: fellow human. And that's how that opens the door to

701
00:40:56,640 --> 00:41:00,480
Speaker 1: you know, genocide of various sorts, or a lower level

702
00:41:00,600 --> 00:41:03,480
Speaker 1: you know, violence, or even just insulting or whatever the

703
00:41:03,520 --> 00:41:05,640
Speaker 1: thing is. We just don't care about them in the

704
00:41:05,640 --> 00:41:07,720
Speaker 1: way that we care about the people that we consider

705
00:41:08,160 --> 00:41:11,080
Speaker 1: in our in group. And yet obviously we're all made

706
00:41:11,080 --> 00:41:13,480
Speaker 1: of the same biology. We all come about from our

707
00:41:13,520 --> 00:41:17,799
Speaker 1: genetics and our experience, and it feels like humanization is

708
00:41:17,840 --> 00:41:20,080
Speaker 1: really the key. So you were just telling me about

709
00:41:20,120 --> 00:41:22,160
Speaker 1: what could be done with high school kids. Tell me

710
00:41:22,520 --> 00:41:25,239
Speaker 1: what you and others are doing with adults in terms

711
00:41:25,280 --> 00:41:28,240
Speaker 1: of helping them bridge the gap.

712
00:41:28,920 --> 00:41:31,480
Speaker 3: So there's a bunch of bridge building organizations. There's a

713
00:41:31,480 --> 00:41:33,880
Speaker 3: big one called braver Angels. And what this is is

714
00:41:33,920 --> 00:41:38,440
Speaker 3: an organization which organizes they call them Red Blue Conversations.

715
00:41:39,200 --> 00:41:41,799
Speaker 3: There's a bunch of local chapters, plus they do an

716
00:41:41,800 --> 00:41:44,120
Speaker 3: online so you can actually sign up and say, you know,

717
00:41:44,680 --> 00:41:47,319
Speaker 3: I'm in you know, this town in Indiana, and I

718
00:41:47,360 --> 00:41:49,040
Speaker 3: want to have a conversation with people who.

719
00:41:48,880 --> 00:41:49,440
Speaker 4: Disagree with me.

720
00:41:50,000 --> 00:41:53,239
Speaker 3: And they have a particular way that they mediate and

721
00:41:53,280 --> 00:41:57,120
Speaker 3: facilitate these conversations to make them productive. And so if

722
00:41:57,120 --> 00:41:59,520
Speaker 3: you want to have that encounter, you can have it.

723
00:41:59,520 --> 00:42:00,920
Speaker 3: They are a group that will set that up for

724
00:42:00,960 --> 00:42:02,120
Speaker 3: you and show you how to do it.

725
00:42:02,360 --> 00:42:04,239
Speaker 1: Do the people who sign up for that are they

726
00:42:04,600 --> 00:42:06,920
Speaker 1: are Some of them just trying to win, and they think, yes,

727
00:42:07,000 --> 00:42:08,759
Speaker 1: I want to meet people from the other side, so

728
00:42:08,800 --> 00:42:09,920
Speaker 1: I can convince them.

729
00:42:10,600 --> 00:42:16,200
Speaker 3: Possibly, But the moderation format is designed to prevent that.

730
00:42:16,520 --> 00:42:18,480
Speaker 3: They don't let people bludge in each other. And they

731
00:42:18,560 --> 00:42:21,120
Speaker 3: say right up front, the goal here is not to

732
00:42:21,200 --> 00:42:24,799
Speaker 3: change someone's mind. The goal is to increase understanding.

733
00:42:25,320 --> 00:42:27,160
Speaker 1: Oh that's lovely, Okay, great, So you're about to tell

734
00:42:27,160 --> 00:42:28,240
Speaker 1: me about another organization.

735
00:42:28,760 --> 00:42:31,239
Speaker 3: Yeah, so there's a bunch of organizations doing this kind

736
00:42:31,239 --> 00:42:34,759
Speaker 3: of bridging work. I'm also involved in an organization called

737
00:42:34,800 --> 00:42:37,960
Speaker 3: the Dignity Index, And what this is is they've created

738
00:42:37,960 --> 00:42:42,880
Speaker 3: a scale from contempt to dignity to rate political speech.

739
00:42:43,200 --> 00:42:47,520
Speaker 3: So when a politician talks about their opponent, are they saying,

740
00:42:47,719 --> 00:42:50,080
Speaker 3: you know, those people are evil, we have to destroy

741
00:42:50,120 --> 00:42:53,760
Speaker 3: them to save America. Or are they saying, I respect

742
00:42:53,840 --> 00:42:56,360
Speaker 3: what they believe. I believe something different. I think my

743
00:42:56,440 --> 00:42:58,160
Speaker 3: plan is better and if I win, we're going to

744
00:42:58,200 --> 00:43:01,640
Speaker 3: work together. Those are really different things, and the scale

745
00:43:01,640 --> 00:43:04,680
Speaker 3: actually goes it's an a point scale. It goes from

746
00:43:04,960 --> 00:43:08,719
Speaker 3: literally calling for genocide to literally saying that you see

747
00:43:08,719 --> 00:43:11,839
Speaker 3: no a difference between self and other. And so what

748
00:43:11,880 --> 00:43:14,480
Speaker 3: they're using this for is a couple different things. First

749
00:43:14,640 --> 00:43:18,080
Speaker 3: is they're putting together sort of scorecards in the upcoming election,

750
00:43:18,760 --> 00:43:22,440
Speaker 3: just rate different candidates, especially at the local level, on

751
00:43:22,480 --> 00:43:24,960
Speaker 3: whether they speak with contempt or dignity.

752
00:43:25,040 --> 00:43:25,839
Speaker 4: And the second thing.

753
00:43:25,719 --> 00:43:30,440
Speaker 3: They're doing is groups student groups in universities to train

754
00:43:30,560 --> 00:43:34,759
Speaker 3: people to rate political speech on this scale, which is

755
00:43:34,840 --> 00:43:38,440
Speaker 3: less about producing the ratings and more about getting people

756
00:43:38,560 --> 00:43:41,880
Speaker 3: to think in this way and to notice when people

757
00:43:41,920 --> 00:43:47,120
Speaker 3: are engaging in let's say, constructive versus destructive disagreement.

758
00:43:47,880 --> 00:43:51,520
Speaker 1: Excellent. Okay, so those are things on an individual level

759
00:43:51,640 --> 00:43:55,680
Speaker 1: that can be done on a societal level. Tell me

760
00:43:55,760 --> 00:43:58,480
Speaker 1: how you think about, for example, journalism and what might

761
00:43:58,520 --> 00:43:59,080
Speaker 1: be done there.

762
00:44:00,040 --> 00:44:00,239
Speaker 4: Yeah.

763
00:44:00,280 --> 00:44:03,600
Speaker 3: So, as I mentioned, I used to be a professional journalist.

764
00:44:03,600 --> 00:44:05,640
Speaker 3: I was an editor at the AP, I was an

765
00:44:05,640 --> 00:44:10,000
Speaker 3: investigative journalist at Republica. So I've seen the machine from

766
00:44:10,080 --> 00:44:14,040
Speaker 3: the inside. And what I will say about journalists is,

767
00:44:14,040 --> 00:44:16,760
Speaker 3: first of all, I have enormous respect for my colleagues

768
00:44:16,760 --> 00:44:19,600
Speaker 3: and journalism, and they are some of the most deeply

769
00:44:19,640 --> 00:44:22,480
Speaker 3: idealistic people that I know. Right, this isn't this isn't

770
00:44:22,520 --> 00:44:27,480
Speaker 3: a conspiracy. But most of them are pretty politically liberal,

771
00:44:27,920 --> 00:44:32,120
Speaker 3: and that's not a conspiracy either. That is because, especially

772
00:44:32,200 --> 00:44:34,840
Speaker 3: with the decline of local news, most of them work

773
00:44:35,040 --> 00:44:38,000
Speaker 3: for national outlets in big cities on.

774
00:44:37,960 --> 00:44:38,600
Speaker 4: The East Coast.

775
00:44:39,239 --> 00:44:42,920
Speaker 3: The social context in which they exist is pretty liberal,

776
00:44:42,920 --> 00:44:45,400
Speaker 3: and in particular, much farther to the left than the

777
00:44:45,440 --> 00:44:46,320
Speaker 3: Median American.

778
00:44:47,680 --> 00:44:48,719
Speaker 4: You know what that.

779
00:44:48,719 --> 00:44:53,719
Speaker 3: Means is they will have less contact with and less

780
00:44:53,800 --> 00:44:59,040
Speaker 3: understanding with conservatives, which means that conservatives will not see,

781
00:44:59,280 --> 00:45:04,000
Speaker 3: their views are identity reflected in the coverage of mainstream journalism.

782
00:45:04,400 --> 00:45:07,160
Speaker 1: And this is because conservatives tend to live in more

783
00:45:07,239 --> 00:45:08,000
Speaker 1: rural areas.

784
00:45:08,200 --> 00:45:14,640
Speaker 3: Yes, yeah, it's because demographically journalists are not like conservatives, right.

785
00:45:14,680 --> 00:45:18,359
Speaker 3: And it's not again, this isn't a conspiracy. This just

786
00:45:18,719 --> 00:45:19,839
Speaker 3: it's pure demographics.

787
00:45:19,920 --> 00:45:24,000
Speaker 4: Right. They are educated, urban people.

788
00:45:24,640 --> 00:45:27,280
Speaker 3: And you know, one of the strongest correlates of political

789
00:45:27,320 --> 00:45:29,560
Speaker 3: identity is population density.

790
00:45:29,640 --> 00:45:32,719
Speaker 4: Right, It's that simple in many ways. That's another way

791
00:45:32,719 --> 00:45:34,400
Speaker 4: you can talk about the divides in this country is

792
00:45:34,440 --> 00:45:35,399
Speaker 4: between urban and rural.

793
00:45:36,320 --> 00:45:38,960
Speaker 3: So anyway, given that that is the state of affairs,

794
00:45:38,960 --> 00:45:42,400
Speaker 3: what you get is there are only a few media outlets,

795
00:45:42,880 --> 00:45:48,200
Speaker 3: notably Fox, which speak in a language and a value

796
00:45:48,239 --> 00:45:53,359
Speaker 3: system which resonates with conservatives, and that leaves a sort

797
00:45:53,400 --> 00:45:56,280
Speaker 3: of vacuum where people who want that kind of coverage

798
00:45:56,360 --> 00:46:00,760
Speaker 3: have to end up going to let's say, less credible

799
00:46:00,960 --> 00:46:03,080
Speaker 3: or even fringe news sites. Right, so you start to

800
00:46:03,080 --> 00:46:07,600
Speaker 3: get to your Newsmax or your One America, and whatever

801
00:46:07,640 --> 00:46:11,120
Speaker 3: you can say about their politics from a pure sort

802
00:46:11,120 --> 00:46:15,719
Speaker 3: of journalism quality perspective, they're just not very good. And

803
00:46:15,760 --> 00:46:18,919
Speaker 3: so that's part of why we see, you know, higher

804
00:46:19,040 --> 00:46:22,279
Speaker 3: rates of misinformation and so forth on the political right

805
00:46:22,360 --> 00:46:28,640
Speaker 3: in the US, And my suggestion, which is somewhat controversial,

806
00:46:29,320 --> 00:46:32,680
Speaker 3: is that we need more conservative journalists. We need more

807
00:46:32,680 --> 00:46:37,440
Speaker 3: well trained people who understand the values of the people

808
00:46:37,480 --> 00:46:40,640
Speaker 3: who most journalists don't cover well.

809
00:46:41,280 --> 00:46:42,920
Speaker 1: Okay, So, by the way, you just pointed out this

810
00:46:42,960 --> 00:46:47,319
Speaker 1: difference between left and right wing journalism because of the

811
00:46:47,400 --> 00:46:51,600
Speaker 1: distribution in the country between rural and urban. But generally,

812
00:46:51,600 --> 00:46:55,160
Speaker 1: one of the things I've found so important is this issue.

813
00:46:55,200 --> 00:46:58,200
Speaker 1: I know this is something you've looked at about, for example,

814
00:46:58,280 --> 00:47:02,400
Speaker 1: conspiracy theories on the left and the right, and essentially

815
00:47:02,440 --> 00:47:05,799
Speaker 1: that they are equal. In other words, both sides, all

816
00:47:05,880 --> 00:47:09,840
Speaker 1: parties are just as subject to this sort of thinking.

817
00:47:10,400 --> 00:47:13,920
Speaker 1: And yet both parties accuse the other of this, just

818
00:47:13,960 --> 00:47:16,279
Speaker 1: in the way that both parties accuse the other of

819
00:47:16,400 --> 00:47:20,200
Speaker 1: doing book banning when both are guilty of this. Where

820
00:47:20,239 --> 00:47:22,799
Speaker 1: else do you see this sort of thing and looking

821
00:47:22,800 --> 00:47:25,360
Speaker 1: for other examples where the left and the right accuse

822
00:47:25,440 --> 00:47:27,400
Speaker 1: each other of things that they are equally guilty of.

823
00:47:28,239 --> 00:47:31,560
Speaker 3: Yeah, so you're raising the issue of symmetry versus asymmetry

824
00:47:31,600 --> 00:47:34,279
Speaker 3: and conflict, and this is a big issue, right, So

825
00:47:34,360 --> 00:47:39,080
Speaker 3: you have broadly speaking, sort of two schools of thought

826
00:47:39,280 --> 00:47:42,279
Speaker 3: or ways that people talk about conflict. One is, you know,

827
00:47:42,360 --> 00:47:46,719
Speaker 3: their side is obviously worse, that they want to destroy democracy.

828
00:47:47,160 --> 00:47:51,000
Speaker 3: They're the oppressor, they're the abuser. And you have this

829
00:47:51,040 --> 00:47:53,799
Speaker 3: other way of talking about it, which is, look, we're

830
00:47:53,840 --> 00:47:57,239
Speaker 3: all human, we are all contributing to being locked in

831
00:47:57,280 --> 00:48:02,600
Speaker 3: this escalating conflict spiral. Nobody's immune from misperceptions or mistakes.

832
00:48:03,760 --> 00:48:07,080
Speaker 3: Both of these things can be true, right. There really

833
00:48:07,200 --> 00:48:12,319
Speaker 3: are cases where one side is doing heinous things and

834
00:48:12,840 --> 00:48:15,000
Speaker 3: the other side is not, or at least some sort

835
00:48:15,040 --> 00:48:18,440
Speaker 3: of difference between the two. And I think part of

836
00:48:18,719 --> 00:48:23,280
Speaker 3: thinking about constructive conflict is bringing in issues of justice,

837
00:48:24,440 --> 00:48:28,520
Speaker 3: so you know, sometimes it really is on one side

838
00:48:28,560 --> 00:48:32,080
Speaker 3: to change their behavior, and that's where we require accountability

839
00:48:32,080 --> 00:48:36,279
Speaker 3: in various forms. On the other hand, conflict is a case,

840
00:48:36,360 --> 00:48:38,799
Speaker 3: especially conflict escalation is a case where it really does

841
00:48:38,840 --> 00:48:42,360
Speaker 3: take two to tango. I tend to think about the

842
00:48:42,400 --> 00:48:46,560
Speaker 3: symmetries more than the asymmetries in the American conflict, largely

843
00:48:46,600 --> 00:48:50,440
Speaker 3: because everyone else is focusing on the asymmetries. And so

844
00:48:50,480 --> 00:48:53,680
Speaker 3: you mentioned conspiracy theories, so that's a great example. The

845
00:48:53,760 --> 00:48:59,120
Speaker 3: sort of media narrative generally is that there's much more

846
00:48:59,160 --> 00:49:02,080
Speaker 3: sort of conspiratory thinking on the right in the US.

847
00:49:03,239 --> 00:49:07,000
Speaker 3: And if you make a huge list of conspiracy theories,

848
00:49:08,040 --> 00:49:12,160
Speaker 3: you know, everything from you know, secret Jewish cabal's controlling

849
00:49:12,200 --> 00:49:16,960
Speaker 3: the world to Holocaust denial to chemtrails, what you find

850
00:49:17,160 --> 00:49:20,799
Speaker 3: is that it's pretty bipartisan there. You know, about the

851
00:49:20,880 --> 00:49:24,719
Speaker 3: same number of conspiracy theories are more commonly believed.

852
00:49:24,440 --> 00:49:26,319
Speaker 4: On the left as opposed to the right.

853
00:49:27,200 --> 00:49:33,000
Speaker 3: However, if you look at misinformation consumption, you find that

854
00:49:33,200 --> 00:49:35,840
Speaker 3: it is definitely more of a right wing thing. And

855
00:49:35,880 --> 00:49:38,439
Speaker 3: I want to put a sort of big as risk

856
00:49:38,520 --> 00:49:41,920
Speaker 3: here and say, well, you know, doesn't this depend on

857
00:49:41,960 --> 00:49:46,839
Speaker 3: who's defining misinformation? And what we find is that when

858
00:49:46,880 --> 00:49:49,239
Speaker 3: you ask bipartisan pants, so you get a bunch of

859
00:49:49,280 --> 00:49:53,560
Speaker 3: Democrats Republicans together and you put a news article in

860
00:49:53,600 --> 00:49:56,880
Speaker 3: front of them, or a let's say, a purported news article,

861
00:49:57,280 --> 00:49:59,239
Speaker 3: and you say, you know, is this true or not?

862
00:49:59,400 --> 00:50:01,240
Speaker 3: You know, take your time, you can use any reference

863
00:50:01,320 --> 00:50:03,560
Speaker 3: materials you wants. You know, let's you know, look it

864
00:50:03,640 --> 00:50:08,160
Speaker 3: up online. You find that there is generally strong agreement

865
00:50:08,280 --> 00:50:11,960
Speaker 3: between bipartisan panels and professional fact checkers. This is the

866
00:50:12,040 --> 00:50:15,279
Speaker 3: level of evidence that I want to see to say

867
00:50:15,280 --> 00:50:16,759
Speaker 3: that there really is an asymmetry.

868
00:50:16,960 --> 00:50:17,960
Speaker 4: And I do think it's true.

869
00:50:18,000 --> 00:50:23,359
Speaker 3: There's just much more low quality information circulating in right

870
00:50:23,360 --> 00:50:24,759
Speaker 3: wing spaces.

871
00:50:24,719 --> 00:50:26,880
Speaker 1: And this is because of the journalism issuy that you

872
00:50:26,920 --> 00:50:27,800
Speaker 1: were mentioning.

873
00:50:28,440 --> 00:50:29,920
Speaker 3: Yeah, I think there's a number of things going on

874
00:50:30,040 --> 00:50:31,759
Speaker 3: right One of them is there's sort of a news

875
00:50:31,840 --> 00:50:36,440
Speaker 3: void right. There just isn't a lot of right wing journalism.

876
00:50:36,520 --> 00:50:38,560
Speaker 3: So if people have a demand for that there, you know,

877
00:50:38,640 --> 00:50:43,560
Speaker 3: that creates an incentive for people who don't really care

878
00:50:43,600 --> 00:50:45,840
Speaker 3: about the journalism to publish things that are going to

879
00:50:45,840 --> 00:50:49,840
Speaker 3: get attention because there's no there isn't anything else in

880
00:50:49,880 --> 00:50:52,520
Speaker 3: that political space that is that is well done. So

881
00:50:52,600 --> 00:50:54,920
Speaker 3: I think I think that is real. But I want

882
00:50:55,560 --> 00:50:58,480
Speaker 3: when people say there's there's an asymmetry, right, it's it's

883
00:50:58,560 --> 00:51:00,759
Speaker 3: really those people who are the problem. I think we

884
00:51:00,800 --> 00:51:03,320
Speaker 3: should have a high bar for evidence. We should have

885
00:51:03,400 --> 00:51:06,239
Speaker 3: a high standard for saying, yes, this is real. It

886
00:51:06,320 --> 00:51:09,960
Speaker 3: isn't just a cudgel that you're using to try to

887
00:51:10,000 --> 00:51:14,440
Speaker 3: win the culture war, because the culture war is not winnable.

888
00:51:14,680 --> 00:51:18,160
Speaker 3: That's a fantasy. You can't exclude half of the population

889
00:51:18,960 --> 00:51:22,399
Speaker 3: from politics forevermore So, we have to find some other

890
00:51:22,400 --> 00:51:23,480
Speaker 3: way to approach each other.

891
00:51:24,280 --> 00:51:26,280
Speaker 1: And so one of the things that you are really

892
00:51:26,320 --> 00:51:30,120
Speaker 1: concentrating on is social media as a leverage point. So

893
00:51:30,200 --> 00:51:34,600
Speaker 1: again we talked about individual ways to help with conflict resolution,

894
00:51:34,760 --> 00:51:36,560
Speaker 1: We've talked about societal way as we were just talking

895
00:51:36,560 --> 00:51:39,640
Speaker 1: about journalism, but as far as social media goes, I

896
00:51:39,680 --> 00:51:41,640
Speaker 1: know that the way you think about this is there's

897
00:51:41,680 --> 00:51:46,960
Speaker 1: this interplay between human psychology, which cares about threats and

898
00:51:47,239 --> 00:51:50,680
Speaker 1: recommend our algorithms, with the social media companies in terms

899
00:51:50,719 --> 00:51:52,840
Speaker 1: of what they're serving up to you, and then the

900
00:51:52,960 --> 00:51:56,640
Speaker 1: content producers, who are going to do the things that

901
00:51:56,760 --> 00:52:00,560
Speaker 1: get them the views. And so human psychology we probably

902
00:52:00,600 --> 00:52:04,360
Speaker 1: can't change too much, and the content producers were probably

903
00:52:04,360 --> 00:52:08,000
Speaker 1: not going to dissuade them from producing things that get views.

904
00:52:08,320 --> 00:52:12,480
Speaker 1: So really it's the recommender algorithms that are up for

905
00:52:12,640 --> 00:52:16,040
Speaker 1: grabs there. So we touched on this before, but let's

906
00:52:16,040 --> 00:52:18,959
Speaker 1: return to that. What do you see as the possibilities there?

907
00:52:19,960 --> 00:52:22,760
Speaker 3: Yeah, so I one of the reasons that I study

908
00:52:23,400 --> 00:52:26,600
Speaker 3: recommender algorithms, which by the way, isn't just social media.

909
00:52:26,719 --> 00:52:31,320
Speaker 3: It's you know, news recommenders, it's job recommendations, it's Amazon products,

910
00:52:31,360 --> 00:52:34,959
Speaker 3: it's Netflix it's it's music, it's podcasts, it's everything. Right,

911
00:52:35,080 --> 00:52:37,480
Speaker 3: all of this stuff is picked for us by machines now,

912
00:52:38,160 --> 00:52:40,520
Speaker 3: and potentially all of it has political content. You may

913
00:52:40,560 --> 00:52:43,840
Speaker 3: not think that you know, who cares what Spotify's recommender

914
00:52:43,920 --> 00:52:47,239
Speaker 3: is doing. Well, you know this podcast is on Spotify, right,

915
00:52:47,320 --> 00:52:50,320
Speaker 3: so that that matters too. So broader than social media,

916
00:52:50,800 --> 00:52:53,760
Speaker 3: there's two reasons I think focusing on social media is interesting.

917
00:52:53,880 --> 00:52:56,920
Speaker 3: When is the direct effects and others are the indirect

918
00:52:56,920 --> 00:53:02,000
Speaker 3: effects via incentives for producers, So direct effects. So what

919
00:53:02,040 --> 00:53:05,600
Speaker 3: I would like to see is less use of engagement

920
00:53:05,640 --> 00:53:09,960
Speaker 3: signals in content ranking. So in other words, how much

921
00:53:10,000 --> 00:53:13,560
Speaker 3: somebody clicked on something, you know, how many seconds they

922
00:53:13,600 --> 00:53:17,080
Speaker 3: spent watching that TikTok video, et cetera, should have less

923
00:53:17,080 --> 00:53:20,240
Speaker 3: of an influence on whether it is shown to other people.

924
00:53:21,040 --> 00:53:24,480
Speaker 3: And to some extent, this change is already starting to happen.

925
00:53:25,080 --> 00:53:30,960
Speaker 3: So there are at least three platforms, of which Facebook

926
00:53:31,040 --> 00:53:34,320
Speaker 3: is the only one who's said this publicly. They basically

927
00:53:34,440 --> 00:53:38,880
Speaker 3: don't use resharing as a signal for civic and health content.

928
00:53:39,239 --> 00:53:41,919
Speaker 3: So maybe for entertainment, whatever catches your attention is fine,

929
00:53:41,960 --> 00:53:45,480
Speaker 3: But maybe for civic and health and politics and these

930
00:53:45,520 --> 00:53:51,640
Speaker 3: types of critical information sources we shouldn't use, you know,

931
00:53:51,920 --> 00:53:54,440
Speaker 3: whether it went viral as a signal for whether it's

932
00:53:54,440 --> 00:53:56,600
Speaker 3: any good, and so that is starting to happen.

933
00:53:56,680 --> 00:53:57,799
Speaker 4: I'd like to see more of that.

934
00:53:58,239 --> 00:54:01,759
Speaker 3: I recently pub to paper with a bunch of collaborators

935
00:54:02,160 --> 00:54:06,880
Speaker 3: where we cataloged all of the options to using engagement

936
00:54:06,920 --> 00:54:09,759
Speaker 3: as a content ranking signal. It's called what we Know

937
00:54:09,840 --> 00:54:12,840
Speaker 3: about using non engagement signals and content marking. To just

938
00:54:12,920 --> 00:54:15,320
Speaker 3: try to get this knowledge out there and to socialize

939
00:54:15,320 --> 00:54:17,400
Speaker 3: it because a lot of this stuff is sort of

940
00:54:17,440 --> 00:54:20,520
Speaker 3: very diffuse across industry. People in industry know it but

941
00:54:21,160 --> 00:54:22,600
Speaker 3: can't talk about it because.

942
00:54:22,400 --> 00:54:23,040
Speaker 4: It's all private.

943
00:54:23,520 --> 00:54:25,359
Speaker 3: So what we did is we got together people from

944
00:54:25,360 --> 00:54:29,279
Speaker 3: eight platforms for an off the record discussion about what

945
00:54:29,360 --> 00:54:31,560
Speaker 3: can we say about how to do this better, and

946
00:54:31,640 --> 00:54:37,360
Speaker 3: then we reconstructed their conclusions from public sources scattered academic literature,

947
00:54:38,160 --> 00:54:41,919
Speaker 3: old company blog posts, but also many references from the

948
00:54:42,040 --> 00:54:45,439
Speaker 3: Facebook files which were the leaks that Francis Hagan brought

949
00:54:45,440 --> 00:54:48,440
Speaker 3: out in twenty twenty one. We sort of learned what

950
00:54:48,560 --> 00:54:51,319
Speaker 3: to look for in those files. So that's the first

951
00:54:51,320 --> 00:54:53,759
Speaker 3: thing I think social media can be better. We can

952
00:54:53,800 --> 00:54:56,839
Speaker 3: build it not to optimize for outrage, And in fact

953
00:54:57,200 --> 00:55:00,640
Speaker 3: the frontier is something called bridging based ranking, and the

954
00:55:00,719 --> 00:55:06,720
Speaker 3: idea there is you find content that both sides agree

955
00:55:06,920 --> 00:55:09,160
Speaker 3: is good. So, you know, think about this, do you

956
00:55:09,200 --> 00:55:13,600
Speaker 3: want the inflammatory news article that appeals to Democrats? Do

957
00:55:13,600 --> 00:55:16,120
Speaker 3: you want the inflammatory news article that appeals to Republicans?

958
00:55:16,640 --> 00:55:19,759
Speaker 3: Or do you want the article that everybody reads and says, yeah,

959
00:55:19,800 --> 00:55:23,120
Speaker 3: that's kind of good. Now, maybe you know, psychologically you're

960
00:55:23,160 --> 00:55:26,400
Speaker 3: much more likely to click on the inflammatory headline, but

961
00:55:26,440 --> 00:55:29,799
Speaker 3: that doesn't mean our better selves actually want that. And

962
00:55:29,880 --> 00:55:32,840
Speaker 3: so I'm involved with a bunch of experiments trying to

963
00:55:33,160 --> 00:55:37,720
Speaker 3: find this bridging content and promote it. So that's the

964
00:55:37,760 --> 00:55:40,080
Speaker 3: sort of direct changes, right, There's a bunch of algorithmic

965
00:55:40,160 --> 00:55:43,000
Speaker 3: changes that you know, I and many of my colleagues

966
00:55:43,080 --> 00:55:45,719
Speaker 3: are exploring and trying.

967
00:55:45,480 --> 00:55:46,279
Speaker 4: To advocate for.

968
00:55:46,920 --> 00:55:49,720
Speaker 3: But then one of the really interesting things about doing

969
00:55:49,960 --> 00:55:53,719
Speaker 3: this in the algorithm space is that, precisely because these

970
00:55:53,719 --> 00:55:57,400
Speaker 3: algorithms decide what everybody sees, changing them can change the

971
00:55:57,480 --> 00:56:03,440
Speaker 3: incentive for producers. So if less inflammatory material is downranked

972
00:56:03,480 --> 00:56:07,240
Speaker 3: and less popular, that means it's less profitable to produce,

973
00:56:07,719 --> 00:56:12,600
Speaker 3: and therefore that changes the kind of content that journalists, politicians,

974
00:56:13,560 --> 00:56:18,200
Speaker 3: you know, think tanks, et cetera, find successful, find reaches

975
00:56:18,200 --> 00:56:22,200
Speaker 3: an audience. And so this second order or indirect effect

976
00:56:22,480 --> 00:56:26,319
Speaker 3: is very interesting because it says that, you know, maybe

977
00:56:26,320 --> 00:56:28,760
Speaker 3: if you can get ten platforms to change their algorithms

978
00:56:28,760 --> 00:56:31,880
Speaker 3: to use bridging based ranking, that could have an effect

979
00:56:31,960 --> 00:56:34,080
Speaker 3: on a much broader media ecosystem.

980
00:56:34,440 --> 00:56:35,600
Speaker 4: So it's a leverage point.

981
00:56:36,320 --> 00:56:40,360
Speaker 1: Yes, how do you convince the social media platform to change?

982
00:56:40,920 --> 00:56:45,239
Speaker 3: Well, I think there's a sort of three cases here.

983
00:56:45,760 --> 00:56:48,719
Speaker 3: So and this is why we're testing these algorithms. So

984
00:56:49,360 --> 00:56:52,360
Speaker 3: I am running something called the pro Social Ranking Challenge.

985
00:56:52,880 --> 00:56:56,040
Speaker 3: It is an open competition for better social media algorithms

986
00:56:56,080 --> 00:56:59,759
Speaker 3: where teams from around the world are competing, first of

987
00:56:59,800 --> 00:57:02,719
Speaker 3: all for a cash prize, but mostly we're going to

988
00:57:02,719 --> 00:57:07,160
Speaker 3: take the winning algorithms, as judged by a panel of scientists,

989
00:57:07,600 --> 00:57:11,960
Speaker 3: and we're going to test them on Facebook, Twitter, and

990
00:57:12,160 --> 00:57:16,200
Speaker 3: Reddit using a custom browser extension. And so we're actually

991
00:57:16,240 --> 00:57:20,760
Speaker 3: going to look to see if it changes polarization, wellbeing,

992
00:57:21,040 --> 00:57:26,960
Speaker 3: and other types of attitudes and outcomes, including engagement. Crucially,

993
00:57:27,000 --> 00:57:30,080
Speaker 3: we are testing whether it changes both short term and

994
00:57:30,160 --> 00:57:31,600
Speaker 3: long term use of these products.

995
00:57:32,320 --> 00:57:33,360
Speaker 4: And so from that.

996
00:57:33,560 --> 00:57:36,360
Speaker 3: We will learn which of three worlds we live in.

997
00:57:36,760 --> 00:57:40,480
Speaker 3: If the universe is kind, we will discover that producing

998
00:57:40,520 --> 00:57:45,920
Speaker 3: a better product that reduces polarization also increases long term retention.

999
00:57:46,280 --> 00:57:48,440
Speaker 3: So you make a higher quality product, people stay on

1000
00:57:48,480 --> 00:57:51,160
Speaker 3: the platform, maybe not in the short term, but certainly

1001
00:57:51,160 --> 00:57:53,400
Speaker 3: in the long term. And then you can do well

1002
00:57:53,400 --> 00:57:55,080
Speaker 3: by doing good right, And then it's just sort of

1003
00:57:55,120 --> 00:57:57,440
Speaker 3: getting the word out. Or we could live in a

1004
00:57:57,480 --> 00:58:01,800
Speaker 3: world where you know, it has neutral but to maybe

1005
00:58:01,840 --> 00:58:07,360
Speaker 3: slightly negative effects to using algorithms that reduce polarization. And

1006
00:58:08,000 --> 00:58:11,320
Speaker 3: it's not unheard of for platforms to make, you know,

1007
00:58:11,480 --> 00:58:15,800
Speaker 3: slightly revenue reducing changes to their algorithms in the interest

1008
00:58:15,840 --> 00:58:18,000
Speaker 3: of public good. You know, I collect examples of this.

1009
00:58:18,320 --> 00:58:20,920
Speaker 3: So then it's an advocacy campaign, right, this is the

1010
00:58:21,000 --> 00:58:23,760
Speaker 3: right thing, you should do it. There's lots of groups

1011
00:58:23,760 --> 00:58:25,880
Speaker 3: that exist to put pressure on companies to do the

1012
00:58:25,920 --> 00:58:30,600
Speaker 3: right things. Or perhaps in some sense, the worst outcome

1013
00:58:30,680 --> 00:58:34,480
Speaker 3: is we discover that making algorithms which produce better conflict

1014
00:58:34,520 --> 00:58:39,240
Speaker 3: outcomes tends to reduce usage of the product in a

1015
00:58:39,280 --> 00:58:43,120
Speaker 3: way that is meaningful from a business perspective. And then

1016
00:58:43,160 --> 00:58:46,760
Speaker 3: what we have is a collective action problem. You have

1017
00:58:46,800 --> 00:58:50,120
Speaker 3: a first mover disadvantage. Who in that, whoever changes their

1018
00:58:50,160 --> 00:58:53,760
Speaker 3: algorithm to be better first loses money relatives to everyone else.

1019
00:58:54,120 --> 00:58:58,120
Speaker 3: And then you have to look at regulation because that

1020
00:58:58,240 --> 00:59:00,400
Speaker 3: can level the playing field, and very much the same

1021
00:59:00,400 --> 00:59:03,120
Speaker 3: way that environmental regulation prevents a race to the bottom.

1022
00:59:03,160 --> 00:59:06,760
Speaker 3: You know, nobody wants to be the first to use

1023
00:59:06,800 --> 00:59:10,040
Speaker 3: a more expensive process that results in less pollution, because

1024
00:59:10,080 --> 00:59:12,400
Speaker 3: then they would lose their market share. But if everybody

1025
00:59:12,440 --> 00:59:14,960
Speaker 3: has to do it, then it's okay. So those are

1026
00:59:14,960 --> 00:59:16,960
Speaker 3: the three outcomes, right, Either it's just a matter of

1027
00:59:17,160 --> 00:59:20,560
Speaker 3: spreading the good word or its advocacy or its regulation

1028
00:59:20,720 --> 00:59:22,960
Speaker 3: depending on where the science takes.

1029
00:59:22,840 --> 00:59:25,760
Speaker 1: Us, or what else. In conclusion, would you like to say.

1030
00:59:26,200 --> 00:59:30,480
Speaker 3: If we care about relating to each other better, and

1031
00:59:30,520 --> 00:59:32,600
Speaker 3: I don't just mean like kumbay oh why can't we

1032
00:59:32,640 --> 00:59:37,680
Speaker 3: all get along? But actually having a politics that functions better,

1033
00:59:37,720 --> 00:59:40,760
Speaker 3: where we get to fight what we believe in without

1034
00:59:40,960 --> 00:59:45,040
Speaker 3: dehumanizing the other side, without misperceiving what they're actually about,

1035
00:59:45,520 --> 00:59:48,960
Speaker 3: without things turning ugly and violent. That's something that we

1036
00:59:49,000 --> 00:59:54,600
Speaker 3: can do. There's many many ways to have better political conflict,

1037
00:59:55,360 --> 00:59:58,320
Speaker 3: but it's going to take a fundamentally different attitude As

1038
00:59:58,360 --> 01:00:01,760
Speaker 3: I said before, the the culture war is not winnable.

1039
01:00:02,200 --> 01:00:04,680
Speaker 3: There is no world in which you get to exclude

1040
01:00:04,760 --> 01:00:10,439
Speaker 3: your political opponents from politics indefinitely. That's what a democracy is, right.

1041
01:00:11,120 --> 01:00:14,680
Speaker 3: We accept someone winning an election because the next election

1042
01:00:14,720 --> 01:00:19,120
Speaker 3: they might lose. So there are ways to get involved,

1043
01:00:19,160 --> 01:00:20,320
Speaker 3: there are things you can do.

1044
01:00:20,840 --> 01:00:24,360
Speaker 4: The situation is not hopeless.

1045
01:00:27,720 --> 01:00:31,520
Speaker 1: That was Jonathan's strain at Berkeley. So let's take the

1046
01:00:31,560 --> 01:00:35,280
Speaker 1: work that Jonathan and others are doing to address our

1047
01:00:35,360 --> 01:00:39,520
Speaker 1: illusions that people who disagree with us are misinformed trolls.

1048
01:00:39,680 --> 01:00:43,040
Speaker 1: It's a very useful exercise to figure out how we

1049
01:00:43,120 --> 01:00:46,000
Speaker 1: can look at somebody who disagrees with us as not

1050
01:00:46,160 --> 01:00:50,200
Speaker 1: being cold and incompetent, but possibly someone who is kind

1051
01:00:50,280 --> 01:00:55,320
Speaker 1: and generous and has a different opinion. This starts with

1052
01:00:55,480 --> 01:00:59,520
Speaker 1: intellectual humility, understanding that we don't know it all, and

1053
01:00:59,560 --> 01:01:02,000
Speaker 1: I don't know that from a philosophical point of view,

1054
01:01:02,000 --> 01:01:06,000
Speaker 1: but from a neuroscience point of view. Because of brain plasticity,

1055
01:01:06,480 --> 01:01:10,200
Speaker 1: we each form an internal model of the world based

1056
01:01:10,240 --> 01:01:14,000
Speaker 1: on our very thin trajectory of space and time, and

1057
01:01:14,080 --> 01:01:17,800
Speaker 1: we form our political opinions based on just the little

1058
01:01:17,800 --> 01:01:22,200
Speaker 1: bit that we're exposed to. We shape our ideas from

1059
01:01:22,200 --> 01:01:25,360
Speaker 1: the social networks we happen to be embedded in, and

1060
01:01:25,440 --> 01:01:28,680
Speaker 1: we're not consciously aware that we're doing this. So at

1061
01:01:28,680 --> 01:01:31,040
Speaker 1: the heart of all of this is a need for

1062
01:01:31,240 --> 01:01:34,200
Speaker 1: intellectual humility, and we're going to need a lot of

1063
01:01:34,240 --> 01:01:37,640
Speaker 1: this to address the kind of polarization, the kind of

1064
01:01:37,720 --> 01:01:42,320
Speaker 1: fear and loathing that we're seeing across the globe. Meaningful

1065
01:01:42,600 --> 01:01:46,000
Speaker 1: dialogues are a great start, but also there's a need

1066
01:01:46,080 --> 01:01:50,600
Speaker 1: for scaling. How do we build this into our educational systems,

1067
01:01:50,640 --> 01:01:54,440
Speaker 1: How do we build this into the fundamental algorithms underlying

1068
01:01:54,440 --> 01:01:58,080
Speaker 1: our social media. How do we build what some scholars

1069
01:01:58,160 --> 01:02:03,880
Speaker 1: like Heidi and Guy Burgess massively parallel peace building. There's

1070
01:02:03,880 --> 01:02:05,800
Speaker 1: still a lot of work to be done on this front,

1071
01:02:06,160 --> 01:02:11,880
Speaker 1: especially as we're moving from communicating via soapbox speeches and

1072
01:02:12,080 --> 01:02:16,400
Speaker 1: hand delivered pamphlets to instant communication that allows you to

1073
01:02:16,480 --> 01:02:20,800
Speaker 1: deliver your speech or your pamphlet to everyone's mobile rectangle

1074
01:02:20,800 --> 01:02:24,400
Speaker 1: around the planet. So I suggest that an important angle

1075
01:02:24,560 --> 01:02:28,040
Speaker 1: on all this is to understand the neuroscience at the

1076
01:02:28,120 --> 01:02:30,560
Speaker 1: base of everything, why we think the way we do,

1077
01:02:31,000 --> 01:02:34,320
Speaker 1: why we behave the way we behave, and then work

1078
01:02:34,400 --> 01:02:38,120
Speaker 1: to build our societal structures like our media, our dialogue,

1079
01:02:38,120 --> 01:02:42,160
Speaker 1: our education. With that in mind, we can no longer

1080
01:02:42,560 --> 01:02:47,160
Speaker 1: make the romantic assumption that we each are just objective

1081
01:02:47,440 --> 01:02:51,840
Speaker 1: holders of truth and that ours is this single logical

1082
01:02:51,960 --> 01:02:55,600
Speaker 1: argument or position that should convince everyone. And this is

1083
01:02:55,640 --> 01:02:58,920
Speaker 1: because our internal models of the world give us different

1084
01:02:58,960 --> 01:03:03,080
Speaker 1: biases towards different groups. We have different sensitivities towards issues,

1085
01:03:03,120 --> 01:03:06,520
Speaker 1: we have different levels of knowledge, we have different emotional

1086
01:03:06,560 --> 01:03:10,200
Speaker 1: affiliations to things happening in the world. So the first

1087
01:03:10,240 --> 01:03:13,920
Speaker 1: step to better conflict is to have a more realistic

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01:03:14,040 --> 01:03:18,480
Speaker 1: understanding that we are each living on our own planet

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01:03:18,840 --> 01:03:23,920
Speaker 1: mentally and emotionally, and the important goal is to bridge planets,

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01:03:23,960 --> 01:03:27,840
Speaker 1: to set up some signaling across the vast reaches of

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01:03:27,920 --> 01:03:31,160
Speaker 1: space between us. It's easy to say that the people

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01:03:31,160 --> 01:03:35,080
Speaker 1: who disagree with you are ugly trolls, but as we discussed,

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01:03:35,080 --> 01:03:38,960
Speaker 1: it can make slightly more sense to ask how someone

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01:03:38,960 --> 01:03:43,240
Speaker 1: who is smart and kind can end up believing something

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01:03:43,440 --> 01:03:46,520
Speaker 1: different than you do. This is not a plea to

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01:03:46,880 --> 01:03:50,800
Speaker 1: agree with the other side, but to better understand their

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01:03:50,840 --> 01:03:55,720
Speaker 1: motivation and their philosophy, and fundamentally, to better understand are

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01:03:55,880 --> 01:04:03,360
Speaker 1: shared biology and therefore are shared human go to eagleman

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Speaker 1: dot com slash podcast for more information and to find

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Speaker 1: further reading. Send me an email at podcasts at eagleman

1101
01:04:10,520 --> 01:04:14,200
Speaker 1: dot com with any questions or discussions, and check out

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01:04:14,200 --> 01:04:17,680
Speaker 1: and subscribe to Inner Cosmos on YouTube for videos of

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01:04:17,680 --> 01:04:21,560
Speaker 1: each episode and to leave comments. Until next time, I'm

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01:04:21,680 --> 01:04:24,720
Speaker 1: David Eagleman and this is inner Cosmos