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Speaker 1: Why will your brain gladly flip a switch to save

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Speaker 1: five lives at the cost of one life, but it

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Speaker 1: will refuse to push one person off a bridge to

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Speaker 1: accomplish the same thing. Why do Buddhist monks and psychopaths

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Speaker 1: and patients like Phineas gage behave differently than you might?

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Speaker 1: And what happens when ancient moral instincts collide with modern

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Speaker 1: problems like pandemics and AI alignment and political tribalism. Could

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Speaker 1: a simple online game reduce polarization? And could you contribute

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Speaker 1: to charities more effectively if you understood how your moral

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Speaker 1: brain works. This week on Inner Cosmos, my colleague Joshua

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Speaker 1: Green helps us open the hood on human morality and

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Speaker 1: asks whether we can build technologies that steer us towards

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Speaker 1: cooperation in a world our brains weren't built for. Welcome

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Speaker 1: to Intercosmos with me, David Eagleman. I'm a neuroscientist and

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Speaker 1: author at Stanford, and in these episodes we sail deeply

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Speaker 1: into our three pound universe to understand how we see

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Speaker 1: the world, and, for that matter, how we see each other.

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Speaker 2: When you peer.

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Speaker 1: Into the human brain, you find a machine built on

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Speaker 1: conflict on the one hand, it's exquisitely tuned to the

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Speaker 1: immediacy of social life, reading faces, sensing fairness, feeling indignation

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Speaker 1: when someone breaks the rules, feeling compassion when someone needs help.

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Speaker 1: These emotional circuits evolved to help the oldest problem of

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Speaker 1: group living, to bind us together, to keep our small

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Speaker 1: bands cohesive, to punish the cheaters, to reward the cooperators.

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Speaker 1: These systems are fast and automatic and deeply intuitive, and

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Speaker 1: at the same time housed in the very same skullp

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Speaker 1: we have slower, more deliberative systems. This is the circuitry

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Speaker 1: that lets us step back, cool off, calculate, imagine alternative futures.

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Speaker 1: It allows us to override that first impulse and to

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Speaker 1: ask what actually leads to the best outcome, what matters

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Speaker 1: the most in this situation. Our brains can operate in

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Speaker 1: both of these modes, and most of the time we

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Speaker 1: toggle between the two without even noticing.

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Speaker 2: And as we'll see.

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Speaker 1: Today, our moral lives exist in a strange dance between

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Speaker 1: instinct and reflection. The strange part is that evolution never

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Speaker 1: anticipated that we would one day wield these moral instincts

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Speaker 1: on a planetary scale. Our emotional machinery was designed for

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Speaker 1: life in small groups of hunter gatherers, not a world

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Speaker 1: of eight billion people with global pandemics and climate changed

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Speaker 1: and polarized democracies. But we bring the same ancient intuitions

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Speaker 1: to all of it. We still divide the world into

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Speaker 1: us and them. We still experience harm differently depending on

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Speaker 1: whether it's direct or indirect. We still recoil from active

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Speaker 1: wrongdoing far more than passive neglect. Sometimes these instincts guide

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Speaker 1: us well, other times they mislead us. If you want

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Speaker 1: to understand the tensions at the heart of modern ethical life,

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Speaker 1: from trolley problems, which we'll talk about in a second,

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Speaker 1: to end of life decisions, from pandemic policy to political tribalism,

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Speaker 1: we have to understand how this dual process, this moral brain,

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Speaker 1: actually works. We have to understand why we help, why

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Speaker 1: we punish, and why certain dilemmas feel difficult even when

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Speaker 1: the math is simple. So this is why I called

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Speaker 1: my colleague Joshua Green today. Josh is at Harvard where

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Speaker 1: he's a psychologist and a neuroscientist and a philosopher, and

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Speaker 1: his lab studies how we make moral judgments, how our

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Speaker 1: fast gut reactions and our slow reasoning systems work together

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Speaker 1: and sometimes work against each other.

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Speaker 2: He's the author of.

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Speaker 1: A book called Moral Tribes, where he argues that our

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Speaker 1: everyday moral sense works beautifully within groups, but can fail

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Speaker 1: spectacularly between groups. And I want to mention that Josh

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Speaker 1: has been working lately on going beyond describing the machinery.

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Speaker 1: He's begun building tools what he calls moral technologies to

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Speaker 1: help societies navigate around our blind spot. So well, here

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Speaker 1: about tools which help people donate in more impactful ways,

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Speaker 1: or online games that measurably reduce political animosity.

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Speaker 2: In other words, how.

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Speaker 1: Can we actually engineer cooperation rather than just hope for it.

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Speaker 1: So today we're going to zoom into the moral mind,

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Speaker 1: where emotions meet reason, where tribes collide, and where ancient

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Speaker 1: circuitry tries to steer a modern world.

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Speaker 2: So let's dive in.

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Speaker 1: So, Josh, when you look at the sense of morality

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Speaker 1: that our brains generate, what is that for? What problem

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Speaker 1: was evolution trying to solve there?

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Speaker 3: So morality is kind of a mystery from an evolutionary

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Speaker 3: point of view, because if you think about evolution in

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Speaker 3: the most straightforward terms, you would think that the greediest,

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Speaker 3: brawniest individuals would be the ones who get the most

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Speaker 3: resources and are able to produce the most offspring. And

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Speaker 3: why would anyone ever be nice to anybody else? And

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Speaker 3: this is something that really bothered Darwin right from from

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Speaker 3: the beginning, and people even said to him, Look, how

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Speaker 3: could you possibly explain any kind of human goodness if

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Speaker 3: nature is red in tooth and claws, as Tennyson famously

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Speaker 3: said about Darwin's theory. And he thought about this and

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Speaker 3: his answer was one that turned out to be very precient.

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Speaker 3: So he recognized that while individuals may benefit from being

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Speaker 3: ruthless and nasty, teams of individuals, groups of individuals can

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Speaker 3: benefit from being more cooperative within the group. Right, if

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Speaker 3: you're a member of a group where you know, if

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Speaker 3: you fall in the river tough luck, then that group

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Speaker 3: may not survive very well, even if the individual who

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Speaker 3: carried on hunting instead of rescuing you does a little

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Speaker 3: bit better. And so the idea that we depend on

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Speaker 3: each other, that teamwork is a powerful weapon for you know,

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Speaker 3: fighting against the elements but also out competing other groups.

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Speaker 3: That idea emerged early on, early on, along with the

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Speaker 3: idea that individuals who are genetically related uh can can

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Speaker 3: benefit their their genes indirectly by by by helping others.

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Speaker 2: So that's the sort of idea.

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Speaker 3: At a strategic biological level, why would anyone ever look

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Speaker 3: out for anybody else? And then on a psychological level,

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Speaker 3: the question is how does this work? And it mostly

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Speaker 3: works at the level of what we might call social emotions.

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Speaker 3: That is, you, you know, if if someone's in trouble,

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Speaker 3: you have a sense of vicarious distress and you're motivated

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Speaker 3: to help them. Or if someone's not being a good

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Speaker 3: cooperative member of the group, you might be angry at

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Speaker 3: them and might want to punish them or let other

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Speaker 3: people know what a jerk that guy is is being.

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Speaker 3: So it kind of operates on on on two levels,

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Speaker 3: uh the level of surviving through cooperation, and I think

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Speaker 3: of morality as a suite of psychological mechanisms that enable

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Speaker 3: us to be more effective cooperators. And then this is

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Speaker 3: implemented largely emotionally, but we can also use our reasoning

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Speaker 3: capacities to figure out how to make our way in

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Speaker 3: the moral and social world. And it's that duality that

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Speaker 3: gives rise to some of the most interesting dilemmas that

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Speaker 3: we've studied.

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Speaker 1: And you've used the analogy of a camera when it

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Speaker 1: comes to that duality, can you unpack that for us?

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Speaker 3: So, at least the old sort of digital SLR camera

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Speaker 3: that I have, you know, have these little automatic settings

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Speaker 3: like portrait mode and landscape mode, and if you want

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Speaker 3: to take a picture of a mountain from a mile away,

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Speaker 3: then you know, you put it in landscape mode and

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Speaker 3: it does everything and configures it in that kind of,

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Speaker 3: you know, familiar situation that the manufacturers of the camera anticipated.

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Speaker 3: But let's say you know you're an artist and you've

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Speaker 3: got your idea about exactly the sort of off kilter

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Speaker 3: shot that you want with the light just so and

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Speaker 3: trying to get a certain weird effect. Then you want

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Speaker 3: to put the camera in manual mode and adjust the

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Speaker 3: f stop and everything yourself to take advantage of your

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Speaker 3: understanding the situation and your understanding of your goals and

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Speaker 3: get exactly the shot that you want. And you can

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Speaker 3: think of intuition and including emotional intuitions as like those

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Speaker 3: automatic settings, where this is a sort of ready made

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Speaker 3: response for this kind of situation, and it can be

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Speaker 3: something that we have acquired biologically speaking, that we automatically

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Speaker 3: dislike certain smells or some people argue that we or

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Speaker 3: other species have an automatic fear of snakes that might

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Speaker 3: be poisonous and things like that. But a lot of

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Speaker 3: it is stuff that we have learned, essentially, habits that

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Speaker 3: we have acquired. But whether it comes from our individual

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Speaker 3: experience or things we've learned culturally, or if it's part

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Speaker 3: of our genetic endowment, it's all in the form of

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Speaker 3: ready made, quick responses to situations that are either familiar

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Speaker 3: in our biological history, our cultural history, or our personal history.

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Speaker 3: And then on the other side, we've got our reasoning abilities,

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Speaker 3: where we can look at the situation and say, Okay,

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Speaker 3: normally I don't like to jump out of buildings, but

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Speaker 3: if the building's on fire, maybe that's something I've got

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Speaker 3: to do in this case.

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Speaker 1: So, with this dual process nature, you've got fast gut reactions,

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Speaker 1: you've got slower, more controlled reasoning. So how does this

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Speaker 1: play out in the domain of morality.

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Speaker 3: You can see this tension between kind of the automatic

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Speaker 3: response and the more detached reason response in moral dilemmas

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Speaker 3: that are sometimes.

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Speaker 2: Called trolley problems.

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Speaker 3: Right, So in the classic pair of cases, you've got

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Speaker 3: a trolley that is headed towards five people, and the

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Speaker 3: only way that you can save them is to hit

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Speaker 3: a switch that will turn the trolley onto another track.

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Speaker 3: But unfortunately there's another person there. And the question is

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Speaker 3: can you hit the switch to avoid having the five

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Speaker 3: get killed? And there most people say yes, that's okay.

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Speaker 3: But from a cognitive science point of view, the most

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Speaker 3: interesting thing is the contrast between that case where you're

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Speaker 3: hitting a switch and turning the trolley away from five

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Speaker 3: but onto one, and the classic footbridge case. So this

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Speaker 3: is where the trolley is again headed towards five people.

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Speaker 3: This time you are on a footbridge over the tracks,

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Speaker 3: and the only way you can say to those five

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Speaker 3: people is to do something that's pretty uncomfortable. There's a

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Speaker 3: guy next to you wearing a big backpack, and you

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Speaker 3: can throw the guy with the big backpack onto the

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Speaker 3: tracks and then he'll be a trolley stopper and stop

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Speaker 3: the trolley from killing the five people. But that person

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Speaker 3: will be killed, and you can't jump yourself because you're

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Speaker 3: not wearing the big backpack, so this wouldn't work. And

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Speaker 3: we're going to suspend disbelief and assume that you have

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Speaker 3: good aim and all of that stuff, and even with

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Speaker 3: all of those somewhat unrealistic assumptions in place, most people

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Speaker 3: say that it's wrong to push the guy off the footbridge,

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Speaker 3: or they at least feel a lot more uncomfortable about it.

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Speaker 3: And so the nice thing about these cases is in

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Speaker 3: some sense they're very similar death by trolley, five lives

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Speaker 3: versus one, and yet we give very different responses to them.

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Speaker 3: And this was the thing that kind of got me

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Speaker 3: into cognitive neuroscience, you know, many years ago, twenty years

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Speaker 3: ago or whatever it was when you and I first

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Speaker 3: met and started looking at this with brain imaging.

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Speaker 1: So give us the punchline of why people are happy

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Speaker 1: to flip the switch in the first case and they

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Speaker 1: are not in the second case, and what your brain

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Speaker 1: imaging studies revealed there.

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Speaker 3: Yeah, so that the short answer seems to be that

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Speaker 3: we have a kind of negative emotional response to the

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Speaker 3: thought of pushing the guy off the footbridge that we

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Speaker 3: don't have in response to hitting the switch in that case.

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Speaker 1: And why and.

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Speaker 3: Then right, And so we can answer that question on

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Speaker 3: sort of two levels. What's going on in the dilemma

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Speaker 3: that makes us feel differently, and then what's going on

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Speaker 3: in our brains that is the basis for having that

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Speaker 3: differential response. So in terms of what's going on on

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Speaker 3: in the dilemma, there are three things that really seem

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Speaker 3: to be driving the effect, although there are other things

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Speaker 3: you could vary as well. But the difference is between

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Speaker 3: the switch case and the footbridge case. And these were

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Speaker 3: nicely identified, and since we're fined by people like my

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Speaker 3: colleague Fiery Cushman.

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Speaker 2: So one is that.

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Speaker 3: Well, actually, one thing that's just in the background is

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Speaker 3: that harm is much more salient when it's active rather

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Speaker 3: than passive, and that's true in both of these cases.

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Speaker 3: The two things that really differentiate these cases are one

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Speaker 3: the harm is more direct when you're pushing the guy

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Speaker 3: off the footbridge, so we call this personal force. This is,

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Speaker 3: you know, if you're pushing with your hands or pushing

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Speaker 3: even with a stick, that feels worse than if you're

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Speaker 3: hitting a switch. Even if you're hitting a switch, that

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Speaker 3: would drop the guy through the trap door.

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Speaker 2: Or something like that.

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Speaker 3: So it's just like the footbridge case, we see a

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Speaker 3: big difference there. That difference interacts with something else which

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Speaker 3: is a bit more subtle. It has a longer philosophic history,

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Speaker 3: and this is the difference between harming somebody purposefully.

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Speaker 2: Or as a side effect.

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Speaker 3: So the idea is that in the switch case, what

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Speaker 3: you're doing is you're turning the trolley away from the

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Speaker 3: five people, and as a side effect, you end up

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Speaker 3: running over the one person.

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Speaker 2: But that one person is not part of your plan.

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Speaker 3: If they were to magically disappear, that would be great,

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Speaker 3: Whereas in the footbridge case, you are using that person

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Speaker 3: as a trolley stopper, right. And it's that combination of

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Speaker 3: harming somebody in this purposeful way, using them as a means,

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Speaker 3: and doing it in this direct personal way. And then

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Speaker 3: in the background, is this the fact that it's active.

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Speaker 3: Those things combine to really give us our sense of

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Speaker 3: like what is a violent action. If you remove any

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Speaker 3: of those three things, it doesn't have that sense of

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Speaker 3: sort of immediate violence, like touching somebody in the face.

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Speaker 3: So that's sort of the trigger in terms of like

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Speaker 3: the features of the dilemma, the differences that make the

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Speaker 3: difference in terms of the situation.

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Speaker 2: Then you say, okay, so.

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Speaker 3: That combination of harming somebody in a way that's active, purposeful.

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Speaker 2: And direct, that gives us the sense of violence.

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Speaker 3: But what's going on in our heads and here I

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Speaker 3: think the best evidence actually comes from cases where people

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Speaker 3: have studied patients with brain damage, similar to the famous

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Speaker 3: case of Phineas Gauge. So if you've taken intro psychology

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Speaker 3: or have heard about it otherwise, you probably know about

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Speaker 3: this case. So this was a railroad form and living

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Speaker 3: in Vermont in the nineteenth century, working on the railroad

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Speaker 3: all the livelong day. And there was a terrible explosion

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Speaker 3: and an iron spike, a tamping iron was blasted out

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Speaker 3: of essentially a cannon, and it went into Phineas Gage's

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Speaker 3: eye socket, through the front of his brain, and out

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Speaker 3: the top of his head. And amazingly he survived once

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Speaker 3: the wound was treated. But when he survived, he didn't

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Speaker 3: survive intact. He his reasoning abilities, what you might loosely

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Speaker 3: call his sort of cognitive abilities, remain intact. He could speak,

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Speaker 3: he could do math problems, he could do basic reasoning.

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Speaker 3: But his personality, his character, his values, and his decision

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Speaker 3: making abilities, those things seem to be compromised, and he

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Speaker 3: ended up going from being this upstanding, you know, a

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Speaker 3: respected railroad officer who people looked or looked looked up to,

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Speaker 3: to a kind of lawless Wanderer, and this was one

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Speaker 3: of the first sort of clear indications that there are

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Speaker 3: distinct systems in the brain for that that that that

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Speaker 3: that handle things like social emotional decision making, things that

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Speaker 3: you kind of have to do by feel, by judgment,

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Speaker 3: rather than following some kind of formula or using some

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Speaker 3: previously acquired skill like your skill for for for for language.

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Speaker 3: After I did those initial brain imaging studies, a group

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Speaker 3: at the University of Iowa, a group in in Italy

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Speaker 3: also did a version of this where they tested patients

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Speaker 3: with damage like Phineas Gage. And these are patients that

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Speaker 3: you know Demasio kind of described as they know the words,

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Speaker 3: but they don't feel the music. They say things like

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Speaker 3: I'm looking at this picture you're showing me of a

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Speaker 3: gory car accident, and I know, before my brain tumor

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Speaker 3: or whatever it was, this used to bother me, but

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Speaker 3: now it just leaves me flat. So they don't have

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Speaker 3: that feeling. They're kind of emotionally cold. And what you

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Speaker 3: find with these people is that they're much more likely

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Speaker 3: to say that it's okay to push the guy off

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Speaker 3: the footbridge, right. And it's not just these two cases.

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Speaker 3: There are a lot of different dilemmas. You know, they

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Speaker 3: don't necessarily involve literal trolleys and things like that, And

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Speaker 3: this effect was huge.

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Speaker 2: You don't need statistics to analyze this.

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Speaker 3: You can just see it's like overwhelmingly, they're much more

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Speaker 3: likely to make those judgments. We've also found or others

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Speaker 3: have found that psychopaths are more likely to say that

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Speaker 3: it's okay to push the guy off the footbridge. And again,

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Speaker 3: the idea is that they can reason, but they don't

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Speaker 3: have that emotional moral sense, that sense of horror or

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Speaker 3: reluctance at directly harming somebody in this violent way.

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Speaker 2: But then something really interesting.

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Speaker 3: This is work that's unpublished, al though I'm pretty confident

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Speaker 3: about it. I had a fantastic undergrad named Shin Sheng

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Speaker 3: who's now long since graduated, who went to Tibet and

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Speaker 3: tested Buddhist monks on the footbridge case. And she tested

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Speaker 3: about fifty of them, and she found that eighty percent

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Speaker 3: of the Buddhist monks approved of pushing the guy off

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Speaker 3: the footbridge.

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Speaker 2: Now you might say why.

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Speaker 3: You know, when we ask people, what do you think

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Speaker 3: Buddhist monks would say about this? And they said, definitely,

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Speaker 3: they are not going to be like, you know, the

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Speaker 3: Phineas gauged patients and the psychopaths and that, so what's

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Speaker 3: that's really weird?

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Speaker 2: Right? And the idea is that you can reach with this.

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Speaker 3: Dual process approach where it's partly about how you feel

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Speaker 3: and partly about how you think you could reach the

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Speaker 3: same conclusion in different ways. So the Phineas gauge people

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Speaker 3: with the emotion related brain damage and the psychopaths who

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Speaker 3: don't have that emotional moral sense, they just don't have

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Speaker 3: the feeling that says no.

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Speaker 2: Don't do that horrible violence. Right.

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Speaker 3: The Buddhist monks said, yeah, I feel that, and I

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Speaker 3: sense that, but I have this more detached and expansive view,

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Speaker 3: and I can see in a case like this, if

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Speaker 3: it is really done with the noble intention of saving

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Speaker 3: more lives, then that can be acceptable. And many of them,

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Speaker 3: I think five different monks cited this sutra, this Buddhist

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Speaker 3: teaching about a ship captain who found himself in this

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Speaker 3: situation where he could kill somebody to prevent a much

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Speaker 3: greater harm, and he did that, and he did it

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Speaker 3: thinking that it was going to be bad karma for him,

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Speaker 3: but in fact he was reborn as a bodhisattva because

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Speaker 3: he had this noble intention.

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Speaker 1: So, given what we know about this moral machinery, are

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Speaker 1: there certain kinds of problems that we are systematically bad at?

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Speaker 2: Well?

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Speaker 3: You know, it's always controversial when it comes to efficult

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Speaker 3: questions of you know what, what's the right answer and

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Speaker 3: what's the wrong answer. But I think there are cases

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Speaker 3: where we're we're bad when it comes to causing harm

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Speaker 3: or or or And for example, when it.

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Speaker 2: Comes to physician assisted suicide.

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Speaker 3: Right, Let's say you have someone who has a terrible

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Speaker 3: terminal illness. They have you know, at most they're going

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Speaker 3: to live in another couple of months, but they're living

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Speaker 3: right now in agonizing pain. Let's say this is someone

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Speaker 3: whose bodies just riddled with cancer and they're just hanging

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Speaker 3: on and you know, despite all the drugs you can

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Speaker 3: give them, their their their their in miserable pain, and

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Speaker 3: they just want to say goodbye and be done for

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Speaker 3: a long time.

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Speaker 2: I don't know if this is still true.

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Speaker 3: The American Medical Association's position on this is no, you know,

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Speaker 3: you can't end life.

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Speaker 2: Life is sacred, et cetera.

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Speaker 3: Right, Whereas in other countries like the Netherlands, for example,

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Speaker 3: there are procedures and protocols and guardrails in place. But

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Speaker 3: if you want to end your life, typically in cases

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Speaker 3: where someone is has as a terminal illness and it

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Speaker 3: is a great deal of pain and distress, you can

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Speaker 3: do that, right, And you can think of this as

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Speaker 3: a case where the greater good is on the side

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Speaker 3: of letting this person and their life if that's what

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Speaker 3: they want. They're experiencing nothing but misery, and everyone around

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Speaker 3: them is just watching them suffer. But there's this sense that,

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Speaker 3: you know, ending this person's life actively is like pushing

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Speaker 3: somebody off of footbridge, and that is just inherently wrong.

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Speaker 1: Our moral instincts evolved for life in small groups. What

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Speaker 1: happens when you take a brain like this and you

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Speaker 1: drop it into the twenty first century?

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Speaker 3: Right, So the way I think about it is as

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Speaker 3: a kind of sequel to this famous.

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Speaker 2: Parable called the traad of the commons.

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Speaker 3: Right. So, the ecologist Garrett Harden had this famous paper

383
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Speaker 3: in nineteen sixty eight, and he was writing about over population,

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Speaker 3: which turned out to be not as big a concern

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Speaker 3: as he thought it was. But he had this very

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Speaker 3: nice story that sort of beautifully illustrates the challenge of

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Speaker 3: life in a group. Right, So he imagined a bunch

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Speaker 3: of herders living on a near a pasture, and each

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Speaker 3: of them has their separate herds, and each of them

390
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Speaker 3: says to themselves, well should I add more animals to

391
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Speaker 3: my herd? And they think, well, they're just grazing on

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Speaker 3: this common pasture, so why not bigger herd, more money

393
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Speaker 3: when I take my animals to market. So they all

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Speaker 3: grow their herds, and they all grow and grow and grow,

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Speaker 3: until at some point there are more animals on the

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Speaker 3: pastor than it can support, No food enough for any

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Speaker 3: of them, as they're scrambling to eat the last few

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Speaker 3: shreds of grass, and they all die. And that's the

399
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Speaker 3: tragedy of the commons. And this is the classic problem

400
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Speaker 3: of me versus us, where if everybody does the thing

401
00:22:58,760 --> 00:23:02,560
Speaker 3: that's in their individuals interest, then everybody ends up being

402
00:23:02,560 --> 00:23:05,959
Speaker 3: worse off or collectively worse off. And this is the

403
00:23:05,960 --> 00:23:09,360
Speaker 3: basic problem that human morality is designed to solve. Right,

404
00:23:09,600 --> 00:23:12,919
Speaker 3: So we have positive emotions and negative emotions that we

405
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Speaker 3: apply to ourselves and that we apply to.

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Speaker 2: Others in order to motivate us to be good hurders.

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Speaker 3: Right, So if you're if if you're a good hurder,

408
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Speaker 3: you have my gratitude. If you are a cheat and

409
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Speaker 3: herder who secretly grows herd, then you know you have

410
00:23:29,320 --> 00:23:32,160
Speaker 3: my anger and perhaps even my disgust. But if you're

411
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Speaker 3: if you if you help me out, then you have

412
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Speaker 3: my sympathy. And if I did something bad, I might

413
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Speaker 3: feel guilty. So that sort of suite of feelings carrots

414
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Speaker 3: and sticks that we can apply to ourselves and apply

415
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Speaker 3: to others that have governs life on the new pasture.

416
00:23:47,400 --> 00:23:52,120
Speaker 3: But in the modern world, we've got many tribes, we've

417
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Speaker 3: got many different groups, and there are different ways that

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00:23:56,480 --> 00:23:59,080
Speaker 3: that that a tribe can get along, right, So you

419
00:23:59,119 --> 00:24:01,600
Speaker 3: could have a more end visualist tribe, let's say, where

420
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Speaker 3: instead of having a common pasture, you just privatize the

421
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Speaker 3: pasture and you divide it up into different plots and

422
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Speaker 3: everybody cooperates by having good fences and respecting other people's property.

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Speaker 2: Rights and things like that.

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Speaker 3: Or you can solve the problem by having everybody just

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Speaker 3: have a common pasture and a common herd, right, and

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Speaker 3: then you don't have to worry about who's growing there

427
00:24:22,760 --> 00:24:25,199
Speaker 3: private herds because there is no private herd. And then

428
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Speaker 3: there are questions about how to organize life more generally.

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Speaker 3: You know, are we going to have collective health insurance

430
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Speaker 3: for our humans and our sheep? Or can you defend

431
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Speaker 3: your sheep with an assault rifle? Or you know, who's

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Speaker 3: allowed to be in charge? Can you be a transgender herder?

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Speaker 3: Or you know, and so on and so forth, right,

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Speaker 3: And in my little sequel to Harden's parable, I imagine

435
00:24:44,960 --> 00:24:47,360
Speaker 3: something like this. You have a bunch of different tribes

436
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Speaker 3: that are living around this forest, and then one hot,

437
00:24:50,800 --> 00:24:53,160
Speaker 3: dry summer, there's a fire and the forest burns down,

438
00:24:53,200 --> 00:24:55,880
Speaker 3: and then the rains come and there's this lovely pasture

439
00:24:55,920 --> 00:24:58,359
Speaker 3: in the middle. And all of the tribes look at

440
00:24:58,400 --> 00:25:01,040
Speaker 3: this new pasture and they say, nice.

441
00:25:00,840 --> 00:25:03,320
Speaker 2: Pasture, and they all move in.

442
00:25:03,800 --> 00:25:05,840
Speaker 3: And the question is, what's going to happen when all

443
00:25:05,880 --> 00:25:08,320
Speaker 3: of those tribes, with their different ways of life, with

444
00:25:08,440 --> 00:25:11,679
Speaker 3: their different religious practices, with their different gender roles, with

445
00:25:11,720 --> 00:25:15,200
Speaker 3: their different ideas about violence and peace, with their different

446
00:25:15,200 --> 00:25:18,360
Speaker 3: ideas about individualism versus collectivism, etc.

447
00:25:19,200 --> 00:25:19,919
Speaker 2: What's it going to be?

448
00:25:20,040 --> 00:25:21,639
Speaker 3: Is it going to be a bloodbath where it's just

449
00:25:21,720 --> 00:25:24,440
Speaker 3: a fight of all against all and the winning tribe

450
00:25:24,440 --> 00:25:28,520
Speaker 3: emerges and imposes their tribal culture on everybody else, or

451
00:25:28,600 --> 00:25:30,880
Speaker 3: is there going to be some kind of new way

452
00:25:30,920 --> 00:25:33,320
Speaker 3: of organizing things that.

453
00:25:35,000 --> 00:25:36,640
Speaker 2: Deals with a modern culture.

454
00:25:36,760 --> 00:25:39,040
Speaker 3: Right, And this is I think exactly what's going on

455
00:25:39,119 --> 00:25:41,679
Speaker 3: in the United States right now and what's going on

456
00:25:42,000 --> 00:25:45,840
Speaker 3: in other countries, where one solution is essentially to say

457
00:25:46,119 --> 00:25:50,600
Speaker 3: blood and soil. This country is for this particular tribe

458
00:25:50,800 --> 00:25:53,480
Speaker 3: that got here first, that has historically been in charge.

459
00:25:53,560 --> 00:25:55,439
Speaker 3: Let's say we are Whites, we are Christians, we have

460
00:25:55,480 --> 00:25:58,080
Speaker 3: European heritage, we have certain ways of doing things and

461
00:25:58,160 --> 00:25:59,800
Speaker 3: other ways that we don't do things, and.

462
00:25:59,760 --> 00:26:01,880
Speaker 2: This is what this country is really about now.

463
00:26:01,880 --> 00:26:04,800
Speaker 3: Of course, in reality that that tribe is not really

464
00:26:04,840 --> 00:26:07,879
Speaker 3: a single tribe it itself is an amalgam of tribes.

465
00:26:07,920 --> 00:26:10,080
Speaker 3: You know, the Germans and the Irish didn't always consider

466
00:26:10,080 --> 00:26:14,800
Speaker 3: themselves the same people. But you know, at least there's

467
00:26:15,040 --> 00:26:20,600
Speaker 3: something closer to a smaller culturally identified US, right. Or

468
00:26:20,720 --> 00:26:24,840
Speaker 3: you can try to have a more modern, pluralistic country

469
00:26:24,880 --> 00:26:27,240
Speaker 3: where you say, all right, there are many different tribes

470
00:26:27,240 --> 00:26:30,080
Speaker 3: with many different cultures, and what we need is something

471
00:26:30,160 --> 00:26:33,119
Speaker 3: like what I call a metamorality. That is where a

472
00:26:33,160 --> 00:26:36,600
Speaker 3: morality is a system that enables a group of otherwise

473
00:26:36,600 --> 00:26:41,800
Speaker 3: selfish individuals to get along as a tribe. A metamorality

474
00:26:41,920 --> 00:26:45,000
Speaker 3: is a moral system that enables a group of otherwise

475
00:26:45,160 --> 00:26:50,480
Speaker 3: tribalistic tribes, where tribalism is essentially selfishness at the group level, right.

476
00:26:51,160 --> 00:26:54,119
Speaker 3: A meta morality is something that enables a group of

477
00:26:54,200 --> 00:26:57,840
Speaker 3: distinct tribes, different cultures, different different people of different backgrounds,

478
00:26:57,840 --> 00:27:00,600
Speaker 3: maybe racist or religions, to.

479
00:27:00,200 --> 00:27:02,320
Speaker 2: Get along together in a modern context.

480
00:27:02,760 --> 00:27:05,320
Speaker 3: And I think that what we're figuring out right now

481
00:27:05,920 --> 00:27:10,680
Speaker 3: in the US, in Europe, in India, in Brazil, in Israel, Gaza,

482
00:27:10,880 --> 00:27:13,439
Speaker 3: is are we going? Is there going to be a

483
00:27:13,480 --> 00:27:16,639
Speaker 3: big us? Are we going to live in a real

484
00:27:16,720 --> 00:27:22,400
Speaker 3: sort of modern modern democracy where power is truly shared

485
00:27:22,560 --> 00:27:26,800
Speaker 3: among groups with different histories and traditionally different moral ideals,

486
00:27:27,119 --> 00:27:30,080
Speaker 3: Or does democracy only work when you have a sort

487
00:27:30,119 --> 00:27:35,000
Speaker 3: of core dominant tribe and guests right as long as

488
00:27:35,000 --> 00:27:35,840
Speaker 3: they're well behaved?

489
00:27:36,000 --> 00:27:36,240
Speaker 2: Right?

490
00:27:37,320 --> 00:27:40,159
Speaker 3: And I think that is the big political question that

491
00:27:40,160 --> 00:27:41,320
Speaker 3: we're that we're facing.

492
00:27:41,600 --> 00:27:44,800
Speaker 1: Give us an example where our gut reaction sort of

493
00:27:44,840 --> 00:27:50,159
Speaker 1: morality is exactly the wrong thing in a modern global context,

494
00:27:50,680 --> 00:27:54,680
Speaker 1: large scale policy decisions, things involving climate or pandemics or

495
00:27:54,760 --> 00:27:55,320
Speaker 1: risk of Ai.

496
00:27:56,160 --> 00:28:00,240
Speaker 3: Yeah, so I would say, you know, cultures can be

497
00:28:00,320 --> 00:28:03,919
Speaker 3: more individualistic or collectivist, and I think you see this

498
00:28:04,040 --> 00:28:05,760
Speaker 3: playing out in a lot of the issues that you mentioned.

499
00:28:05,760 --> 00:28:07,680
Speaker 2: Is ate the case of pandemics.

500
00:28:08,080 --> 00:28:11,040
Speaker 3: There's a real trade off there, right, that that that

501
00:28:11,080 --> 00:28:16,480
Speaker 3: the pandemic restrictions really restricted people's individual freedom, restricted their

502
00:28:16,480 --> 00:28:19,240
Speaker 3: individual economic freedom, their ability to make money. And in

503
00:28:19,280 --> 00:28:23,040
Speaker 3: a country that doesn't have a strong social safety net,

504
00:28:24,080 --> 00:28:26,520
Speaker 3: telling people that they who don't who can't work from home,

505
00:28:26,560 --> 00:28:28,840
Speaker 3: that they're not allowed to work, I mean that's like,

506
00:28:28,960 --> 00:28:31,000
Speaker 3: in some ways, like an economic prison sentence.

507
00:28:31,080 --> 00:28:31,280
Speaker 2: Right.

508
00:28:31,640 --> 00:28:33,600
Speaker 3: But at the same time, there was a real disease

509
00:28:33,640 --> 00:28:36,120
Speaker 3: and we didn't really understand that, and people were dying.

510
00:28:36,240 --> 00:28:38,000
Speaker 2: And you know, and and and and.

511
00:28:37,960 --> 00:28:40,640
Speaker 3: More people interacting with each other would predictably lead to

512
00:28:41,280 --> 00:28:44,560
Speaker 3: more death. And so there was a trade off between

513
00:28:44,640 --> 00:28:47,400
Speaker 3: saying we have a collective problem and we all have

514
00:28:47,440 --> 00:28:51,000
Speaker 3: to make sacrifices to solve it, or saying, well, there

515
00:28:51,000 --> 00:28:53,360
Speaker 3: are trade offs here, and we're going to let individuals

516
00:28:53,720 --> 00:28:57,400
Speaker 3: or or churches or businesses or cities and towns or

517
00:28:57,400 --> 00:29:02,240
Speaker 3: whatever it is make their own decisions about how to

518
00:29:02,320 --> 00:29:06,680
Speaker 3: navigate the trade off between freedom and public health. Similar

519
00:29:06,760 --> 00:29:09,160
Speaker 3: when it comes to climate change, right, I mean, it's partly,

520
00:29:09,280 --> 00:29:11,520
Speaker 3: you know, a debate about the background evidence and whether

521
00:29:11,600 --> 00:29:12,280
Speaker 3: or not it's real.

522
00:29:12,440 --> 00:29:13,680
Speaker 2: But I think behind.

523
00:29:13,360 --> 00:29:16,840
Speaker 3: That is a set of different orientations where some people

524
00:29:16,920 --> 00:29:19,680
Speaker 3: are very skeptical of the idea that there is this

525
00:29:19,760 --> 00:29:21,880
Speaker 3: global problem and we all have to change the way

526
00:29:21,880 --> 00:29:25,120
Speaker 3: we live and make sacrifices in order to address it,

527
00:29:26,160 --> 00:29:29,400
Speaker 3: versus people who they're going to set a very high

528
00:29:29,440 --> 00:29:32,000
Speaker 3: bar for the evidence before they give up their individual

529
00:29:32,000 --> 00:29:34,640
Speaker 3: freedom to you know, drive the kind of car they

530
00:29:34,680 --> 00:29:38,760
Speaker 3: want to drive, or pay pay a gasoline tax, or

531
00:29:38,880 --> 00:29:41,600
Speaker 3: vote for politicians who want to you know, change the

532
00:29:41,640 --> 00:29:45,080
Speaker 3: way we get our energy and make electricity prices possibly

533
00:29:45,120 --> 00:29:46,680
Speaker 3: more higher, at least in the short term.

534
00:29:46,720 --> 00:29:49,240
Speaker 1: So, given everything that you've studied about the brain and

535
00:29:49,280 --> 00:29:53,000
Speaker 1: moral decision making, if you were advising a government or

536
00:29:53,000 --> 00:29:57,200
Speaker 1: some international body, what would you advise them about decision making?

537
00:29:58,080 --> 00:30:00,479
Speaker 3: So I think there are sort of two level here, right,

538
00:30:00,480 --> 00:30:03,800
Speaker 3: I mean, Partly, you know, I'm person with my own values,

539
00:30:04,800 --> 00:30:09,160
Speaker 3: and then there's sort of strategy. Whatever your values are. Now,

540
00:30:09,280 --> 00:30:12,720
Speaker 3: my values tend to be for the big us that

541
00:30:12,880 --> 00:30:15,960
Speaker 3: I am. I'm not a big fan of ethnic nationalism,

542
00:30:16,360 --> 00:30:18,600
Speaker 3: and I would like to see us be a more

543
00:30:18,680 --> 00:30:23,040
Speaker 3: effective pluralistic democracy, right, that's my goal. But that's you know,

544
00:30:23,360 --> 00:30:27,600
Speaker 3: if you're a true believer in a tribal way of life,

545
00:30:27,640 --> 00:30:29,840
Speaker 3: you might just say, well, I oppose that, and I'm

546
00:30:29,840 --> 00:30:31,040
Speaker 3: going to fight you every step of the.

547
00:30:30,960 --> 00:30:34,520
Speaker 2: Way whichever you choose.

548
00:30:34,560 --> 00:30:36,680
Speaker 3: But I'm now speaking from the perspective of sort of

549
00:30:36,680 --> 00:30:40,240
Speaker 3: a big tent, big us kind of of person. I

550
00:30:40,280 --> 00:30:45,160
Speaker 3: think the biggest lesson is you have to work. You

551
00:30:45,240 --> 00:30:49,440
Speaker 3: have to meet people where they are. You have to

552
00:30:49,560 --> 00:30:52,720
Speaker 3: understand that people who have different feelings than you do,

553
00:30:52,880 --> 00:30:57,560
Speaker 3: people who have different views about divisive moral issues, they

554
00:30:57,560 --> 00:30:59,920
Speaker 3: don't have to be evil to come to a different

555
00:31:00,080 --> 00:31:03,560
Speaker 3: conclusion from you, partly because they may just have different values,

556
00:31:03,600 --> 00:31:07,840
Speaker 3: and partly because they have made different background assumptions either

557
00:31:07,920 --> 00:31:10,200
Speaker 3: what they've heard from the people they trust about you know,

558
00:31:10,240 --> 00:31:13,240
Speaker 3: particular questions. You know, is climate change reel and things

559
00:31:13,280 --> 00:31:17,000
Speaker 3: like that, or or or or you know that background

560
00:31:17,280 --> 00:31:20,200
Speaker 3: values that come from their their their their upbringing, whether

561
00:31:20,240 --> 00:31:24,560
Speaker 3: it's you know, secular or religious. And so I think

562
00:31:24,680 --> 00:31:29,120
Speaker 3: that you know, people on the left, especially often shoot

563
00:31:29,160 --> 00:31:32,640
Speaker 3: themselves in the foot by being maximalist and by saying

564
00:31:32,800 --> 00:31:36,880
Speaker 3: Anyone who doesn't meet all of these demands right now

565
00:31:37,160 --> 00:31:41,040
Speaker 3: is evil and terrible and wants to you know, doesn't

566
00:31:41,080 --> 00:31:44,120
Speaker 3: care about human rights, doesn't care about the people who's

567
00:31:44,160 --> 00:31:47,160
Speaker 3: whose whose freedoms let's say, are are are are in question,

568
00:31:47,480 --> 00:31:49,600
Speaker 3: and are just bad people who need to be defeated,

569
00:31:49,720 --> 00:31:54,280
Speaker 3: right And I think that that approach it's very good

570
00:31:54,360 --> 00:31:58,560
Speaker 3: for winning votes within your subset, within your wing of

571
00:31:58,600 --> 00:32:02,760
Speaker 3: the Democratic Party, or power in parallel, your right most

572
00:32:02,920 --> 00:32:05,440
Speaker 3: wing of the Republican Party. But then you have a

573
00:32:05,440 --> 00:32:09,280
Speaker 3: hard time bringing the larger US together and speaking to

574
00:32:09,880 --> 00:32:13,959
Speaker 3: you the sixty seventy eighty percent who actually has a

575
00:32:14,000 --> 00:32:17,760
Speaker 3: fair amount of agreement on policy and doesn't want either

576
00:32:17,800 --> 00:32:20,480
Speaker 3: what the extreme right or the extreme left is offering.

577
00:32:20,560 --> 00:32:25,800
Speaker 3: So my general advice is to be pragmatic and strategic

578
00:32:26,360 --> 00:32:31,440
Speaker 3: and be flexible enough to form the kind of coalition

579
00:32:31,480 --> 00:32:36,040
Speaker 3: that can actually move things forward and not insist on

580
00:32:36,160 --> 00:32:37,600
Speaker 3: total moral victory.

581
00:32:37,760 --> 00:32:41,960
Speaker 1: Now, excellent, Okay, So so far we've been talking about

582
00:32:41,960 --> 00:32:45,840
Speaker 1: it's the complexities in the brain that lead to decision

583
00:32:45,880 --> 00:32:48,320
Speaker 1: making in the moral domain. But I want to turn

584
00:32:48,360 --> 00:32:51,520
Speaker 1: out to the fact that you've actually been building things

585
00:32:51,600 --> 00:32:56,120
Speaker 1: to try to steer moral decision making in a better way.

586
00:32:56,320 --> 00:32:59,480
Speaker 1: This is games, platforms interventions. Tell us about that.

587
00:33:00,360 --> 00:33:02,760
Speaker 3: Yeah, so thanks, this is something I'm really excited about,

588
00:33:02,840 --> 00:33:05,040
Speaker 3: and I'm really excited that you're joining the Pods Fight

589
00:33:05,120 --> 00:33:08,920
Speaker 3: Poverty program. I'll say more about that and what our

590
00:33:08,960 --> 00:33:10,720
Speaker 3: goals are, but let me say a little bit about

591
00:33:10,720 --> 00:33:13,680
Speaker 3: the science behind how this got started on my end. So,

592
00:33:14,160 --> 00:33:16,520
Speaker 3: one of the things that's most that are frustrating about

593
00:33:16,560 --> 00:33:19,160
Speaker 3: the trolley dilemas, in a particular one like the footbridge dilemma,

594
00:33:19,440 --> 00:33:22,400
Speaker 3: is that there is no satisfying solution that if you

595
00:33:22,560 --> 00:33:25,720
Speaker 3: say that it's okay to push the guy off the footbridge, yes,

596
00:33:25,840 --> 00:33:28,080
Speaker 3: you know you're saving five lives within the world of

597
00:33:28,080 --> 00:33:30,800
Speaker 3: this scenario, but it's going to feel like a horrible

598
00:33:30,840 --> 00:33:33,520
Speaker 3: act of violence, and you'd say, I wouldn't trust somebody

599
00:33:33,520 --> 00:33:36,600
Speaker 3: who's who would be willing to do that or at

600
00:33:36,640 --> 00:33:37,920
Speaker 3: least feel comfortable doing that.

601
00:33:38,080 --> 00:33:38,280
Speaker 2: Right.

602
00:33:39,680 --> 00:33:41,280
Speaker 3: On the other hand, if you say no, you can't

603
00:33:41,280 --> 00:33:43,160
Speaker 3: push the guy off the footbridge, well, then there are

604
00:33:43,200 --> 00:33:46,360
Speaker 3: five times as many people dead as necessary, and that's

605
00:33:46,360 --> 00:33:49,840
Speaker 3: pretty bad too, right, And I think that as long

606
00:33:49,880 --> 00:33:51,520
Speaker 3: as our brains work the way we work, you can

607
00:33:51,560 --> 00:33:52,920
Speaker 3: have an answer. But it's never going to be a

608
00:33:52,960 --> 00:33:58,000
Speaker 3: completely satisfying answer. In other domains, you actually can find

609
00:33:58,000 --> 00:34:01,920
Speaker 3: a satisfying answer. And in particular, what I have in

610
00:34:01,960 --> 00:34:07,120
Speaker 3: mind is the domain of charitable giving. So this begins

611
00:34:07,120 --> 00:34:10,239
Speaker 3: with a kind of superpower that we have that most

612
00:34:10,280 --> 00:34:10,920
Speaker 3: of us don't.

613
00:34:10,760 --> 00:34:11,520
Speaker 2: Realize that we have.

614
00:34:11,719 --> 00:34:13,520
Speaker 3: For those of us who you know, at the end

615
00:34:13,560 --> 00:34:15,560
Speaker 3: of the year have an extra few hundred dollars or

616
00:34:15,560 --> 00:34:19,400
Speaker 3: a thousand dollars or even more than that, the amount

617
00:34:19,440 --> 00:34:21,319
Speaker 3: of good that we can do is enormous, but it

618
00:34:21,400 --> 00:34:25,920
Speaker 3: requires doing it strategically. When I first sort of learned

619
00:34:25,960 --> 00:34:28,719
Speaker 3: about this, you know, I thought the difference between a

620
00:34:28,760 --> 00:34:31,800
Speaker 3: really effective charity and a charity that's not very effective

621
00:34:31,800 --> 00:34:33,959
Speaker 3: would be something like the difference between someone who's really

622
00:34:34,000 --> 00:34:36,200
Speaker 3: tall and someone who's not so tall. So a really

623
00:34:36,200 --> 00:34:39,800
Speaker 3: tall person might be fifty percent taller than someone's who's

624
00:34:39,800 --> 00:34:42,399
Speaker 3: pretty short. But in fact, the difference between the most

625
00:34:42,400 --> 00:34:45,680
Speaker 3: effective charities and ordinary charities is more like the difference

626
00:34:45,719 --> 00:34:48,760
Speaker 3: between redwood trees and little shrubs. You need one hundred

627
00:34:48,800 --> 00:34:51,040
Speaker 3: times different or close to a thousand times different. So

628
00:34:51,120 --> 00:34:52,840
Speaker 3: let me give you an example.

629
00:34:53,160 --> 00:34:57,600
Speaker 2: There is a.

630
00:34:55,760 --> 00:34:59,799
Speaker 3: Disease called trachoma that is not common in the US,

631
00:34:59,840 --> 00:35:03,160
Speaker 3: but common in other parts of the world, particularly in Africa.

632
00:35:03,400 --> 00:35:05,560
Speaker 3: And this is a disease that infects people's eyes and

633
00:35:05,600 --> 00:35:09,040
Speaker 3: can cause people to go blind, not as common in

634
00:35:09,080 --> 00:35:09,640
Speaker 3: the US.

635
00:35:09,800 --> 00:35:12,240
Speaker 2: In the US, people are blind for other reasons.

636
00:35:12,280 --> 00:35:13,759
Speaker 3: And if you want to help a blind person in

637
00:35:13,760 --> 00:35:15,920
Speaker 3: the US, one thing you could do is support the

638
00:35:15,960 --> 00:35:17,239
Speaker 3: training of a seeing i dog.

639
00:35:17,960 --> 00:35:19,760
Speaker 2: Training a seeing eye dog costs about.

640
00:35:19,520 --> 00:35:23,080
Speaker 3: Fifty thousand dollars, well worth it for the effect that

641
00:35:23,160 --> 00:35:27,920
Speaker 3: it has on someone's life, but fairly expensive as.

642
00:35:29,360 --> 00:35:31,160
Speaker 2: Something you can do to improve some of life goes.

643
00:35:31,920 --> 00:35:35,239
Speaker 3: A surgery that can prevent tracoma in a country in

644
00:35:35,320 --> 00:35:38,560
Speaker 3: Africa can cost less than one hundred dollars, which means

645
00:35:38,640 --> 00:35:41,880
Speaker 3: that you could fund over one hundred, maybe close to

646
00:35:41,920 --> 00:35:48,280
Speaker 3: one thousand trachoma surgeries, preventing hundreds of people from going

647
00:35:48,320 --> 00:35:53,200
Speaker 3: blind in the first place, for the cost of helping

648
00:35:53,200 --> 00:35:55,160
Speaker 3: someone who's already blind in the United States.

649
00:35:55,480 --> 00:35:56,399
Speaker 2: Now, I'm not.

650
00:35:56,520 --> 00:35:59,000
Speaker 3: Saying that we should just forget about people who are

651
00:35:59,040 --> 00:36:01,640
Speaker 3: blind in the United States. It's these people are humans.

652
00:36:01,640 --> 00:36:04,839
Speaker 3: They are part of our community. And you know, I'm

653
00:36:04,840 --> 00:36:07,680
Speaker 3: not saying to hell with them, but I think it

654
00:36:07,680 --> 00:36:11,480
Speaker 3: would be a moral mistake to ignore the enormous sort

655
00:36:11,520 --> 00:36:15,239
Speaker 3: of turbocharge good that we can do by finding the

656
00:36:15,239 --> 00:36:18,640
Speaker 3: most effective ways to help people, typically overseas, not because

657
00:36:18,640 --> 00:36:20,879
Speaker 3: they're far away, but because the money goes so much

658
00:36:20,920 --> 00:36:24,359
Speaker 3: farther and the problems are so much more dire and widespread.

659
00:36:24,680 --> 00:36:29,000
Speaker 3: So you know, the funding surgeries for tracoma is one example,

660
00:36:29,640 --> 00:36:33,880
Speaker 3: distributing insecticidal malaria nets for about five thousand dollars on average,

661
00:36:33,920 --> 00:36:36,880
Speaker 3: you can save somebody's life. Basically, this is distributing a

662
00:36:36,920 --> 00:36:39,399
Speaker 3: thousand malaria nets at the cost of five dollars each,

663
00:36:40,680 --> 00:36:44,120
Speaker 3: incentivizing mothers to have their children vaccinated. That can save

664
00:36:44,239 --> 00:36:48,800
Speaker 3: about on average, rue life for three thousand dollars. And

665
00:36:48,840 --> 00:36:51,160
Speaker 3: then there are things that have enormous improvements on people's

666
00:36:51,239 --> 00:36:54,240
Speaker 3: quality of life, so deworming treatments. So in other parts

667
00:36:54,239 --> 00:36:57,759
Speaker 3: of the world, people often children are beset by parasitic

668
00:36:57,760 --> 00:37:03,480
Speaker 3: worms that colini people's in digestive tracks very painful and

669
00:37:03,560 --> 00:37:05,640
Speaker 3: makes it hard to go to school and learn. And

670
00:37:05,680 --> 00:37:07,800
Speaker 3: for less than a dollar, you can provide a deworming

671
00:37:07,840 --> 00:37:10,759
Speaker 3: treatment that will rid a child of of of of

672
00:37:11,040 --> 00:37:13,239
Speaker 3: intestinal worms at least for a while until they get

673
00:37:13,280 --> 00:37:16,120
Speaker 3: their next treatment. For one hundred dollars. That's one hundred

674
00:37:16,200 --> 00:37:18,200
Speaker 3: children who are in a better position to go to school.

675
00:37:18,239 --> 00:37:19,799
Speaker 3: And when those kids go to school, of course, they're

676
00:37:19,800 --> 00:37:21,880
Speaker 3: more likely to earn money later in life and have

677
00:37:22,440 --> 00:37:25,080
Speaker 3: long term positive effects. And that's just in the domain

678
00:37:25,120 --> 00:37:28,839
Speaker 3: of of of global poverty and health. I'll mention one

679
00:37:28,840 --> 00:37:32,240
Speaker 3: other charity, which is Give Directly. This is a charity

680
00:37:32,440 --> 00:37:37,759
Speaker 3: that it is not focused on a specific intervention, and

681
00:37:37,800 --> 00:37:41,280
Speaker 3: those interventions often have sort of in randomized control trials,

682
00:37:41,320 --> 00:37:43,400
Speaker 3: the most bang for buck, but something that takes a

683
00:37:43,400 --> 00:37:47,160
Speaker 3: little bit more of an expansive view, this is giving

684
00:37:47,200 --> 00:37:50,239
Speaker 3: people money directly. And the way this happened was giving

685
00:37:50,280 --> 00:37:54,000
Speaker 3: give directly was started by economists who were studying the

686
00:37:54,480 --> 00:37:59,680
Speaker 3: efficacy of different types of health and poverty interventions. And

687
00:37:59,719 --> 00:38:02,560
Speaker 3: they said, well, we're good scientists. We need a control condition,

688
00:38:02,680 --> 00:38:06,560
Speaker 3: like what's standard of care, what's baseline? And they found

689
00:38:06,560 --> 00:38:08,840
Speaker 3: that there wasn't one. There was no sort of standard

690
00:38:08,840 --> 00:38:10,480
Speaker 3: thing to do. So they said, okay, well, let's just

691
00:38:10,520 --> 00:38:12,200
Speaker 3: take as our baseline. What would happen if you took

692
00:38:12,239 --> 00:38:14,799
Speaker 3: the money that you could use for this program and

693
00:38:15,080 --> 00:38:18,000
Speaker 3: just gave it to people directly. And what they found

694
00:38:18,080 --> 00:38:21,719
Speaker 3: was that just giving money people directly had better outcomes

695
00:38:21,960 --> 00:38:24,520
Speaker 3: than most of the things that people were trying to do,

696
00:38:24,920 --> 00:38:27,759
Speaker 3: and so they started this organization called GiveDirectly, which was

697
00:38:27,800 --> 00:38:31,880
Speaker 3: superpowered by the advent of digital banking. So you know,

698
00:38:32,400 --> 00:38:36,080
Speaker 3: in places where there are no telephone polls, right, but

699
00:38:36,200 --> 00:38:37,600
Speaker 3: there are satellites overhead.

700
00:38:38,040 --> 00:38:38,719
Speaker 2: You know, people in.

701
00:38:39,000 --> 00:38:42,200
Speaker 3: A remote poor village in Rwanda, someone there can have

702
00:38:42,239 --> 00:38:44,400
Speaker 3: a cell phone which enables them to do digital banking,

703
00:38:44,400 --> 00:38:47,080
Speaker 3: which opens up a world of economic opportunity. So give

704
00:38:47,120 --> 00:38:50,239
Speaker 3: Directly gives people money directly, and they can spend it

705
00:38:50,280 --> 00:38:54,640
Speaker 3: on immediate necessities, on food, on medicine, and then once

706
00:38:54,680 --> 00:38:58,000
Speaker 3: those basic needs are taken care of, they know what

707
00:38:58,080 --> 00:39:01,400
Speaker 3: to do. They can you know, and infrastructure, fix the

708
00:39:01,440 --> 00:39:03,640
Speaker 3: roof in your house, or they can do things that

709
00:39:03,680 --> 00:39:05,400
Speaker 3: can enable a more long term income.

710
00:39:05,480 --> 00:39:05,640
Speaker 2: Right.

711
00:39:05,719 --> 00:39:07,640
Speaker 3: So if you want to start a business and you

712
00:39:07,760 --> 00:39:10,160
Speaker 3: need a little motorcycle to get around so that you

713
00:39:10,160 --> 00:39:12,200
Speaker 3: can sell your goods, you need to be able to

714
00:39:12,200 --> 00:39:14,799
Speaker 3: make that capital investment. And so you know what I

715
00:39:14,920 --> 00:39:16,279
Speaker 3: like about this, and I think a lot of people

716
00:39:16,440 --> 00:39:19,439
Speaker 3: like about give directly is you know, it's not giving

717
00:39:19,520 --> 00:39:22,160
Speaker 3: someone to fish, and it's not teaching somebody to fish.

718
00:39:22,160 --> 00:39:24,120
Speaker 3: These people already know how to fish, so to speak.

719
00:39:24,320 --> 00:39:27,040
Speaker 3: This is giving somebody the money to buy, you know,

720
00:39:27,200 --> 00:39:30,759
Speaker 3: the fish for today, but also a fishing rod that

721
00:39:30,800 --> 00:39:32,920
Speaker 3: they can use, and they already know how to use,

722
00:39:32,960 --> 00:39:34,879
Speaker 3: and they just need to get over that economic hop.

723
00:39:35,000 --> 00:39:37,080
Speaker 3: So this is an incredible charity and this is one

724
00:39:37,120 --> 00:39:39,880
Speaker 3: that will that we're actively supporting with this program that

725
00:39:39,960 --> 00:39:42,279
Speaker 3: we're doing with podcasts. And I'll say a bit about that.

726
00:39:42,640 --> 00:39:46,759
Speaker 3: But back to the psychology, right, as I said with

727
00:39:46,840 --> 00:39:49,360
Speaker 3: the Footbridge case, you know, you just have this dilemma

728
00:39:49,360 --> 00:39:53,040
Speaker 3: where there's no satisfying solution when it comes to charitable giving.

729
00:39:53,120 --> 00:39:55,520
Speaker 3: There's the default thing that most people do, which is

730
00:39:55,560 --> 00:39:59,279
Speaker 3: to support charities that are personally meaningful to them.

731
00:39:59,360 --> 00:40:01,720
Speaker 2: So you love animals, you support the local animal shelter.

732
00:40:02,040 --> 00:40:04,920
Speaker 3: Your aunt died of breast cancer, so you support a

733
00:40:05,320 --> 00:40:08,279
Speaker 3: charity that does breast cancer research, right, and that is

734
00:40:08,440 --> 00:40:11,279
Speaker 3: a very good and noble and that's like, you know,

735
00:40:11,360 --> 00:40:13,120
Speaker 3: the best of humanity coming out there, right.

736
00:40:13,200 --> 00:40:14,799
Speaker 2: Don't want to say that that is a bad thing.

737
00:40:15,960 --> 00:40:19,600
Speaker 3: However, typically what people feel most connected to is not

738
00:40:19,719 --> 00:40:23,000
Speaker 3: as impactful as the kinds of things that I described,

739
00:40:23,120 --> 00:40:26,440
Speaker 3: like malaria, nets and deworming treatments and trachoma surgeries, and

740
00:40:26,440 --> 00:40:29,799
Speaker 3: and and and giving directly to people in poverty. So

741
00:40:29,880 --> 00:40:33,120
Speaker 3: the conventional sort of thing to do once you're someone

742
00:40:33,120 --> 00:40:35,680
Speaker 3: who's realized we need to be doing a lot more

743
00:40:36,120 --> 00:40:40,719
Speaker 3: super impact stuff is to say to people, hey, instead

744
00:40:40,880 --> 00:40:43,880
Speaker 3: of giving to the local animal shelter or supporting the

745
00:40:43,920 --> 00:40:46,319
Speaker 3: breast cancer research, you really should do this other thing

746
00:40:46,320 --> 00:40:49,440
Speaker 3: that's more impactful. And the problem is that a lot

747
00:40:49,440 --> 00:40:51,600
Speaker 3: of people say, yeah, I get it, but this is

748
00:40:51,640 --> 00:40:53,799
Speaker 3: my aunt, or yeah, I get it, but what I

749
00:40:53,840 --> 00:40:56,880
Speaker 3: really love is animals, right, and and and and you

750
00:40:56,920 --> 00:40:59,040
Speaker 3: know they don't they don't buy it, right. And so

751
00:41:00,360 --> 00:41:04,279
Speaker 3: my then post doc and I Lucius Caviola, who's now

752
00:41:04,760 --> 00:41:09,680
Speaker 3: a professor at Cambridge in the UK, thought is is

753
00:41:09,719 --> 00:41:12,360
Speaker 3: there a third way here? You know, the moral equivalent

754
00:41:12,400 --> 00:41:14,920
Speaker 3: of a third way? And it doesn't exist in the

755
00:41:14,960 --> 00:41:17,920
Speaker 3: trolley problem. And we had a simple idea, which is

756
00:41:17,960 --> 00:41:20,960
Speaker 3: instead of telling people, instead of doing the thing you

757
00:41:21,320 --> 00:41:23,960
Speaker 3: most want to do, do this other thing that's more impactful.

758
00:41:24,200 --> 00:41:25,879
Speaker 2: What if we just said to people, hey, why don't

759
00:41:25,920 --> 00:41:26,520
Speaker 2: you do both?

760
00:41:26,719 --> 00:41:26,879
Speaker 3: Right?

761
00:41:27,320 --> 00:41:30,400
Speaker 2: So we started running these experiments, and in.

762
00:41:30,280 --> 00:41:34,920
Speaker 3: The control condition, we gave people the conventional choice, it'd said, Okay,

763
00:41:35,560 --> 00:41:37,440
Speaker 3: tell us what your favorite charity is, and you give

764
00:41:37,520 --> 00:41:38,480
Speaker 3: us the link, and then you.

765
00:41:38,440 --> 00:41:40,680
Speaker 2: Say, and here's this super effective.

766
00:41:40,200 --> 00:41:42,839
Speaker 3: Deworming charity that where for one hundred dollars you can

767
00:41:42,880 --> 00:41:45,760
Speaker 3: deworm one hundred kids, for ten dollars you can deworm

768
00:41:45,840 --> 00:41:49,520
Speaker 3: ten kids. We're giving you ten dollars, which you want

769
00:41:49,560 --> 00:41:53,320
Speaker 3: to choose, And still, like eighty percent of people chose

770
00:41:53,360 --> 00:41:57,120
Speaker 3: the charity that they identified originally as their personal favorite.

771
00:41:57,400 --> 00:41:59,600
Speaker 3: Some people chose to switch to the one that the

772
00:42:00,000 --> 00:42:02,720
Speaker 3: Spurts focused on impact recommended, but most people didn't.

773
00:42:03,520 --> 00:42:04,640
Speaker 2: That's the control condition.

774
00:42:05,280 --> 00:42:08,720
Speaker 3: In the experimental treatment condition, we give those two options,

775
00:42:08,719 --> 00:42:11,600
Speaker 3: but we give another option, which is to split the difference,

776
00:42:12,080 --> 00:42:13,920
Speaker 3: or instead of doing all the water or all to

777
00:42:13,960 --> 00:42:16,160
Speaker 3: the other, you can do a fifty to fifty split

778
00:42:16,239 --> 00:42:18,440
Speaker 3: between the charity you love and the charity that the

779
00:42:18,440 --> 00:42:21,560
Speaker 3: experts are saying is super effective. And what we found

780
00:42:21,600 --> 00:42:23,520
Speaker 3: was that a little bit over half of the people

781
00:42:24,320 --> 00:42:28,359
Speaker 3: chose to support to do the split right, which meant

782
00:42:28,360 --> 00:42:31,560
Speaker 3: that more money was actually going to the super effective

783
00:42:31,640 --> 00:42:34,640
Speaker 3: charity by giving people the option to split than if

784
00:42:34,680 --> 00:42:37,120
Speaker 3: you force them to make a stark choice. Then we

785
00:42:37,120 --> 00:42:38,840
Speaker 3: did some research to try to figure out, you know,

786
00:42:38,840 --> 00:42:42,520
Speaker 3: what's the underlying psychology here, And what we found was

787
00:42:42,600 --> 00:42:46,399
Speaker 3: a kind of Trolleysque dual process story, which is to say,

788
00:42:47,480 --> 00:42:50,520
Speaker 3: people have sort of two different urges they're trying to satisfy.

789
00:42:50,840 --> 00:42:52,640
Speaker 3: They want to give from the heart. They want to

790
00:42:52,680 --> 00:42:56,279
Speaker 3: give to the charity that they feel personally connected to,

791
00:42:57,280 --> 00:43:01,000
Speaker 3: but they also like the idea of of doing something

792
00:43:01,040 --> 00:43:04,080
Speaker 3: super impactful. It's just not their top priority if they're

793
00:43:04,080 --> 00:43:08,040
Speaker 3: forced to choose. So what we found is that when

794
00:43:08,080 --> 00:43:10,360
Speaker 3: it comes to giving from the heart, it's not about

795
00:43:10,360 --> 00:43:13,720
Speaker 3: how much you give. If you give fifty dollars instead

796
00:43:13,719 --> 00:43:16,200
Speaker 3: of one hundred dollars to the local animal shelter, that

797
00:43:16,239 --> 00:43:19,200
Speaker 3: feels more or less the same if you And then

798
00:43:19,280 --> 00:43:21,319
Speaker 3: that means if you could give fifty there, then you

799
00:43:21,320 --> 00:43:24,120
Speaker 3: could have this other fifty left over to do something

800
00:43:24,120 --> 00:43:28,959
Speaker 3: that's super duper effective, and that scratches a different itch, right,

801
00:43:29,160 --> 00:43:33,040
Speaker 3: and the overall feeling of satisfaction of doing something as

802
00:43:33,080 --> 00:43:35,719
Speaker 3: we say, you know, smart and from the heart at

803
00:43:35,760 --> 00:43:39,400
Speaker 3: the same time, people really like that that that that

804
00:43:39,400 --> 00:43:42,799
Speaker 3: that combo. So then we all, okay, so that's cool.

805
00:43:42,840 --> 00:43:44,640
Speaker 3: We've got that result, we understand why people do that.

806
00:43:44,640 --> 00:43:46,360
Speaker 3: But then we thought, okay, we could publish a paper

807
00:43:46,440 --> 00:43:48,640
Speaker 3: saying hey, everybody, you should split your donations like this,

808
00:43:48,880 --> 00:43:50,719
Speaker 3: and then it would just die in this journal and

809
00:43:50,719 --> 00:43:53,360
Speaker 3: no one would read it, or if just a few researchers.

810
00:43:53,560 --> 00:43:55,040
Speaker 3: So we thought, okay, we need some way to get

811
00:43:55,040 --> 00:43:56,600
Speaker 3: this out there. Well, what if we had to, you know,

812
00:43:56,640 --> 00:43:59,080
Speaker 3: we incentivize people, say well, we'll add money on top

813
00:43:59,680 --> 00:44:02,760
Speaker 3: if you do these split donations, and as you'd expect,

814
00:44:02,760 --> 00:44:05,560
Speaker 3: people like it even better if we're willing to add money.

815
00:44:05,600 --> 00:44:07,240
Speaker 3: In fact, we found it was like a seventy percent

816
00:44:07,239 --> 00:44:09,320
Speaker 3: boost if we said that we would add add money

817
00:44:09,320 --> 00:44:11,320
Speaker 3: on top. So that's great. But then, of course the

818
00:44:11,400 --> 00:44:13,960
Speaker 3: question is where does that money come from? And then

819
00:44:13,960 --> 00:44:16,360
Speaker 3: what we said was, well, what if we asked people

820
00:44:16,440 --> 00:44:20,240
Speaker 3: who were agreed to split between a personal favorite charity

821
00:44:20,560 --> 00:44:22,680
Speaker 3: and this charity that they just learned about, like the

822
00:44:22,719 --> 00:44:25,160
Speaker 3: deworming charity, said, what if instead of giving the the

823
00:44:25,200 --> 00:44:28,280
Speaker 3: deworming charity, you put that fifty percent in a fund

824
00:44:28,600 --> 00:44:31,400
Speaker 3: that will add money on top for the next people,

825
00:44:31,440 --> 00:44:34,200
Speaker 3: so kind of pay it forward program to keep this going.

826
00:44:34,480 --> 00:44:37,160
Speaker 3: And we found that not everybody but enough people were

827
00:44:37,160 --> 00:44:39,640
Speaker 3: willing to do that such that the money they would

828
00:44:39,680 --> 00:44:43,359
Speaker 3: put into that fund more than enough to cover the

829
00:44:43,400 --> 00:44:45,600
Speaker 3: matching donations for the people who said, now I'll just

830
00:44:45,640 --> 00:44:48,239
Speaker 3: take the matching funds. So we thought, my gosh, this

831
00:44:48,280 --> 00:44:51,560
Speaker 3: could be like a self sustaining virtuous circle where you

832
00:44:51,600 --> 00:44:53,759
Speaker 3: have some people who put money into the matching fund

833
00:44:54,320 --> 00:44:57,120
Speaker 3: and some people who are incentivized by the money in

834
00:44:57,160 --> 00:44:58,800
Speaker 3: the matching fund, and.

835
00:45:00,200 --> 00:45:00,880
Speaker 2: Thing just works.

836
00:45:01,160 --> 00:45:05,319
Speaker 3: So Lucius and his Techi friends, most most notably the

837
00:45:05,360 --> 00:45:11,560
Speaker 3: amazing web designer Fabio kun Uh, created Giving Multiplier, which

838
00:45:11,600 --> 00:45:14,520
Speaker 3: is our our our our web platform which does this.

839
00:45:14,760 --> 00:45:17,560
Speaker 3: And if if if you go to giving Multiplier, like

840
00:45:17,600 --> 00:45:20,319
Speaker 3: what you'd see is you know this description of how

841
00:45:20,320 --> 00:45:20,640
Speaker 3: it works.

842
00:45:20,680 --> 00:45:22,919
Speaker 2: This is giving Multiplier dot dot dot org.

843
00:45:24,120 --> 00:45:27,319
Speaker 3: You'll see a place where you can find your your

844
00:45:27,360 --> 00:45:30,040
Speaker 3: favorite charity. So any charity registered as a five oh

845
00:45:30,040 --> 00:45:32,600
Speaker 3: one C three in the US you enter that. In

846
00:45:32,760 --> 00:45:35,120
Speaker 3: the second thing is a list of the super effective

847
00:45:35,200 --> 00:45:37,040
Speaker 3: charities that we support. So I've named a bunch of

848
00:45:37,080 --> 00:45:41,200
Speaker 3: them UH already so give directly and UH and and

849
00:45:41,239 --> 00:45:44,520
Speaker 3: the Against Malaria Foundation and the Malaria Consortium, and then

850
00:45:44,560 --> 00:45:48,880
Speaker 3: other ones related to climate change and animal welfare, but

851
00:45:48,960 --> 00:45:52,440
Speaker 3: ones that that that that have a super outsize impact.

852
00:45:52,600 --> 00:45:54,560
Speaker 3: You pick one of those, and then we have this

853
00:45:54,640 --> 00:45:58,080
Speaker 3: cool slider thing where you decide how you want to

854
00:45:58,120 --> 00:46:01,000
Speaker 3: allocate your money between the two charity and the more

855
00:46:01,160 --> 00:46:05,240
Speaker 3: you allocate to the super effective charities from our list,

856
00:46:05,760 --> 00:46:08,399
Speaker 3: the more money we add on top. But you could

857
00:46:08,400 --> 00:46:10,719
Speaker 3: still give a majority to your personal favorite charity and

858
00:46:10,800 --> 00:46:13,479
Speaker 3: we'll still add something on top to both.

859
00:46:13,640 --> 00:46:15,320
Speaker 1: Let me get one thing straight, which is the money

860
00:46:15,320 --> 00:46:16,880
Speaker 1: that I put in. I put one hundred dollars into

861
00:46:16,880 --> 00:46:19,560
Speaker 1: the matching fund that's going to charities. I just don't

862
00:46:19,600 --> 00:46:20,560
Speaker 1: know which ones.

863
00:46:20,480 --> 00:46:23,839
Speaker 3: That's That's right, it's charities that will be chosen by

864
00:46:23,880 --> 00:46:25,320
Speaker 3: other people, future people.

865
00:46:25,719 --> 00:46:29,239
Speaker 1: Excellent, Okay, got it. So tell us about POD's Fight Poverty.

866
00:46:29,760 --> 00:46:34,120
Speaker 3: So we now having over thirty podcasts who are signed

867
00:46:34,160 --> 00:46:37,720
Speaker 3: on and our goal is to raise a million dollars

868
00:46:38,120 --> 00:46:42,960
Speaker 3: and to We're aiming for three villages in the Bikar

869
00:46:43,200 --> 00:46:45,680
Speaker 3: region of Northern Rwanda where.

870
00:46:45,480 --> 00:46:48,920
Speaker 2: People have very little. You know, people are very poor.

871
00:46:49,040 --> 00:46:51,840
Speaker 3: And have are kind of stuck in an economic rut

872
00:46:51,880 --> 00:46:54,080
Speaker 3: where they don't have the resources they need to to

873
00:46:54,840 --> 00:46:57,160
Speaker 3: get out of it. And our goal is to lift

874
00:46:57,760 --> 00:47:01,399
Speaker 3: seven hundred families out of poverty, giving a little over

875
00:47:01,400 --> 00:47:04,120
Speaker 3: one thousand dollars to each family, which can be life

876
00:47:04,200 --> 00:47:05,120
Speaker 3: changing for a family.

877
00:47:05,760 --> 00:47:06,120
Speaker 2: There.

878
00:47:06,320 --> 00:47:11,080
Speaker 3: Listeners are encouraged to to to go to GiveDirectly dot

879
00:47:11,200 --> 00:47:18,359
Speaker 3: org slash cosmos for for your listeners and giving Multiplier

880
00:47:18,680 --> 00:47:22,600
Speaker 3: is adding fifty percent match while our while our supplies last.

881
00:47:22,600 --> 00:47:24,600
Speaker 3: So we're we're committed to putting up a half a

882
00:47:24,600 --> 00:47:27,600
Speaker 3: million dollars for this campaign, So anything your listeners give

883
00:47:28,760 --> 00:47:33,360
Speaker 3: giving Multiplier will be matching at fifty percent. And the

884
00:47:33,440 --> 00:47:36,120
Speaker 3: results with with give Directly are amazing. I mean, there

885
00:47:36,120 --> 00:47:40,400
Speaker 3: have been twenty five randomized control trials, so gold standard

886
00:47:40,480 --> 00:47:45,240
Speaker 3: experiments with give directly specifically, and they find that that

887
00:47:45,239 --> 00:47:48,200
Speaker 3: that these donations cut infant mortality rates in half.

888
00:47:48,880 --> 00:47:49,920
Speaker 2: And not only.

889
00:47:49,840 --> 00:47:51,960
Speaker 3: Does it help the people who get the money, it

890
00:47:52,080 --> 00:47:55,120
Speaker 3: boosts the local economy by a factor of two point five.

891
00:47:55,320 --> 00:47:56,960
Speaker 2: Right, So this is getting back to this.

892
00:47:57,040 --> 00:47:59,879
Speaker 3: You know a lot of people worry about anti pop

893
00:48:00,040 --> 00:48:03,840
Speaker 3: pretty mechanisms, especially in poor countries. You say, yeah, this

894
00:48:04,000 --> 00:48:06,200
Speaker 3: is just pouring money down the hole where there's some

895
00:48:06,200 --> 00:48:09,680
Speaker 3: temporary relief, but it doesn't really go anywhere. I don't

896
00:48:09,719 --> 00:48:12,680
Speaker 3: want to under sell temporary relief if you're starving or

897
00:48:12,680 --> 00:48:14,839
Speaker 3: if your child is you know, in danger of dying

898
00:48:14,840 --> 00:48:15,839
Speaker 3: of malaria or whatever.

899
00:48:15,880 --> 00:48:16,160
Speaker 2: It is.

900
00:48:16,960 --> 00:48:20,040
Speaker 3: Temporary relief matters, right, but you also want to think

901
00:48:20,040 --> 00:48:21,839
Speaker 3: about the long term. And part of what's great about

902
00:48:21,840 --> 00:48:24,960
Speaker 3: gift directly is that it gives people the power, the agency,

903
00:48:25,040 --> 00:48:28,000
Speaker 3: the flexibility to take the money that they that they're

904
00:48:28,040 --> 00:48:32,200
Speaker 3: getting that they can use beyond you know, immediate survival,

905
00:48:32,640 --> 00:48:35,600
Speaker 3: to build things that that can help them survive. And

906
00:48:35,960 --> 00:48:38,800
Speaker 3: we see this in the growth of these local economies

907
00:48:38,800 --> 00:48:42,959
Speaker 3: as a result of this. So that's what we're trying

908
00:48:43,000 --> 00:48:44,680
Speaker 3: to do. And thank thank you David for being part

909
00:48:44,719 --> 00:48:44,880
Speaker 3: of this.

910
00:48:45,440 --> 00:48:48,640
Speaker 1: And are you or someone else doing follow up studies

911
00:48:48,640 --> 00:48:51,040
Speaker 1: to see what happens with this village over the next

912
00:48:51,040 --> 00:48:51,760
Speaker 1: five ten years.

913
00:48:52,160 --> 00:48:52,399
Speaker 2: Yeah?

914
00:48:52,440 --> 00:48:55,600
Speaker 3: Absolutely, So give give directly. You know, they track the

915
00:48:55,920 --> 00:48:58,520
Speaker 3: effects of every every campaign they run, all of the

916
00:48:58,560 --> 00:48:59,480
Speaker 3: money that they spend.

917
00:49:14,440 --> 00:49:16,960
Speaker 1: Okay, so you know, by the way, Josh, you and

918
00:49:16,960 --> 00:49:19,719
Speaker 1: I have known each other for our whole careers and neuroscience,

919
00:49:19,719 --> 00:49:23,000
Speaker 1: and it's so wonderful to see you taking all the

920
00:49:23,000 --> 00:49:26,880
Speaker 1: stuff you know about decision making and morality and build

921
00:49:27,400 --> 00:49:30,960
Speaker 1: moral technologies. So the last thing I wanted to ask

922
00:49:31,000 --> 00:49:33,640
Speaker 1: you about is you had a Nature paper earlier this

923
00:49:33,719 --> 00:49:37,720
Speaker 1: year about a game that people could play to cross

924
00:49:37,760 --> 00:49:40,240
Speaker 1: to bridge political divides. Tell us about tango.

925
00:49:40,960 --> 00:49:42,560
Speaker 3: Yeah, so let me say a little bit about how

926
00:49:42,600 --> 00:49:44,040
Speaker 3: I think all of this stuff fits together.

927
00:49:44,280 --> 00:49:44,440
Speaker 2: Right.

928
00:49:45,640 --> 00:49:47,920
Speaker 3: You know, I was one side of conference recently and

929
00:49:48,000 --> 00:49:50,440
Speaker 3: I had to have like my little name tag, and

930
00:49:50,440 --> 00:49:52,360
Speaker 3: at this particular conference, you had to put like a

931
00:49:52,400 --> 00:49:54,200
Speaker 3: one line thing on, like what's your deal?

932
00:49:54,320 --> 00:49:55,160
Speaker 2: Like what are you about?

933
00:49:55,440 --> 00:49:57,319
Speaker 3: And I never had to do that before, And I

934
00:49:57,360 --> 00:49:59,800
Speaker 3: thought back to, you know, what are my heroes Peter Singer,

935
00:50:00,040 --> 00:50:03,080
Speaker 3: philosopher and his notion of the expanding circle, the idea

936
00:50:03,120 --> 00:50:07,600
Speaker 3: that over time humans have gone from you know, not

937
00:50:07,719 --> 00:50:11,040
Speaker 3: just caring about their family and their immediate relationships, but

938
00:50:11,239 --> 00:50:14,680
Speaker 3: you know, the circle of moral concern has grown from

939
00:50:15,080 --> 00:50:18,200
Speaker 3: the village to the tribe, to the chiefdom to the

940
00:50:18,280 --> 00:50:21,160
Speaker 3: nation and and and beyond nations. And what I sort

941
00:50:21,160 --> 00:50:22,880
Speaker 3: of put on that little name tag, which I now

942
00:50:22,920 --> 00:50:26,400
Speaker 3: think of is my tagline, is is expanding the circle

943
00:50:26,560 --> 00:50:30,680
Speaker 3: of of of of altruism and cooperation and as we're

944
00:50:30,680 --> 00:50:34,000
Speaker 3: trying to do and with the trolley stuff, you know,

945
00:50:34,120 --> 00:50:37,400
Speaker 3: in this weird way, it wasn't weird to me, but

946
00:50:37,440 --> 00:50:39,160
Speaker 3: it's it's sort of maybe not obvious.

947
00:50:39,560 --> 00:50:40,759
Speaker 2: That was the goal there as well.

948
00:50:41,080 --> 00:50:44,239
Speaker 3: I thought the way to move forward is we need

949
00:50:44,280 --> 00:50:50,360
Speaker 3: a better moral philosophy. And there's I'm a consequentialist utilitarian,

950
00:50:50,360 --> 00:50:52,040
Speaker 3: although I don't like the U word. I prefer to

951
00:50:52,080 --> 00:50:55,080
Speaker 3: call myself a deep pragmatist. But there are these objections

952
00:50:55,400 --> 00:50:57,200
Speaker 3: to that kind of view, like is it okay to

953
00:50:57,280 --> 00:50:59,160
Speaker 3: tell one person to say five people you know in

954
00:50:59,200 --> 00:50:59,960
Speaker 3: the footbridge case?

955
00:51:00,040 --> 00:51:00,719
Speaker 2: And isn't that wrong?

956
00:51:00,760 --> 00:51:03,440
Speaker 3: And I wanted to understand the psychology so that I

957
00:51:03,440 --> 00:51:06,920
Speaker 3: could say, look, this philosophy makes sense, but we have

958
00:51:07,040 --> 00:51:10,640
Speaker 3: these over generalizations of certain moral instincts that block us

959
00:51:10,680 --> 00:51:13,320
Speaker 3: from there. So it was a kind of philosophical approach

960
00:51:13,360 --> 00:51:16,279
Speaker 3: to expanding the circle. As I've gotten older, I thought,

961
00:51:16,320 --> 00:51:18,319
Speaker 3: you know, instead of like kind of trying to fly

962
00:51:18,520 --> 00:51:20,799
Speaker 3: up into the clouds, do some philosophy and then come

963
00:51:20,840 --> 00:51:22,520
Speaker 3: back down to earth, I'm just going to drive along

964
00:51:22,520 --> 00:51:25,759
Speaker 3: the ground, take you know, what we think we know

965
00:51:26,600 --> 00:51:30,000
Speaker 3: about human nature and pack that up and and see

966
00:51:30,000 --> 00:51:34,600
Speaker 3: what we can do with it. So giving multiplier, I see,

967
00:51:34,680 --> 00:51:38,840
Speaker 3: is about expanding the circle largely from nation to world

968
00:51:39,480 --> 00:51:42,680
Speaker 3: that we are supporting charities where we in the affluent

969
00:51:42,760 --> 00:51:45,960
Speaker 3: West primarily can do an enormous amount of good for

970
00:51:46,040 --> 00:51:50,879
Speaker 3: other human beings who happen to not be our co nationals, right,

971
00:51:51,280 --> 00:51:54,879
Speaker 3: and then from also from species, the human world beat

972
00:51:54,960 --> 00:52:00,120
Speaker 3: beyond that is one of the charities we support is

973
00:52:00,360 --> 00:52:04,239
Speaker 3: the Humane League, which you know, there are billions of

974
00:52:04,280 --> 00:52:07,279
Speaker 3: animals that suffer in factory farms every year, billions, Like

975
00:52:07,719 --> 00:52:09,920
Speaker 3: you know, it's hard to get your head around this,

976
00:52:10,080 --> 00:52:12,239
Speaker 3: right if aliens were visiting Earth and saying, like, what's

977
00:52:12,280 --> 00:52:15,160
Speaker 3: the greatest moral tragedy here? Depending on what you believe

978
00:52:15,160 --> 00:52:19,120
Speaker 3: about animal consciousness, you might think that it's actually factory farming.

979
00:52:19,800 --> 00:52:24,600
Speaker 3: That's a whole other story. But giving multiplier supports charities

980
00:52:24,640 --> 00:52:28,720
Speaker 3: that are looking to end tortuous, miserable factory farming, either

981
00:52:28,800 --> 00:52:33,240
Speaker 3: through policy or through developing meat alternatives. And so that's

982
00:52:33,400 --> 00:52:36,080
Speaker 3: going from nation to world and from world to other species,

983
00:52:36,160 --> 00:52:40,520
Speaker 3: human world, other species. Tango goes back to our earlier

984
00:52:40,560 --> 00:52:44,040
Speaker 3: discussion about the tragedy of the commons and the tragedy

985
00:52:44,080 --> 00:52:48,640
Speaker 3: of common sense morality and going from a tribal us

986
00:52:48,920 --> 00:52:53,440
Speaker 3: to a larger, multi tribal us. So when you know,

987
00:52:53,480 --> 00:52:56,200
Speaker 3: when I finished my my and published my book Moral Tribes,

988
00:52:56,200 --> 00:52:58,080
Speaker 3: which he mentioned. You know, I was happy with the

989
00:52:58,080 --> 00:53:00,319
Speaker 3: book in a lot of ways, but I also felt

990
00:53:00,320 --> 00:53:02,919
Speaker 3: like it was kind of an unfulfilled promise in some ways.

991
00:53:02,960 --> 00:53:04,480
Speaker 3: I mean, you look at a book like that, the

992
00:53:04,480 --> 00:53:07,720
Speaker 3: titles Moral Tribes callon Emotion, Reason, and the Gap between

993
00:53:07,800 --> 00:53:09,920
Speaker 3: Us and Them, you might think that that book was

994
00:53:09,960 --> 00:53:14,359
Speaker 3: going to give you like practical tools to solve tribalism.

995
00:53:14,920 --> 00:53:17,160
Speaker 2: And I think it falls short in that way.

996
00:53:17,239 --> 00:53:19,680
Speaker 3: It really gives you sort of a kind of guiding

997
00:53:20,200 --> 00:53:26,160
Speaker 3: general philosophy and some psychological under self knowledge that could

998
00:53:26,160 --> 00:53:28,760
Speaker 3: help you get to that philosophy, but it's not really

999
00:53:28,960 --> 00:53:32,959
Speaker 3: immediately applicable tools. And so after that kind of era,

1000
00:53:34,320 --> 00:53:36,160
Speaker 3: I said, all right, I want to try to try

1001
00:53:36,200 --> 00:53:38,480
Speaker 3: to fulfill that promise. So I thought, okay, well, what

1002
00:53:38,520 --> 00:53:41,439
Speaker 3: does it take to solve tribalism? What does it take

1003
00:53:41,480 --> 00:53:45,600
Speaker 3: to bring groups with distinct identities and with some animosity

1004
00:53:45,760 --> 00:53:49,440
Speaker 3: towards each other together? And I thought, okay, well, I'm smart.

1005
00:53:49,440 --> 00:53:51,840
Speaker 3: Maybe I'll have some big new theory about how to

1006
00:53:51,880 --> 00:53:55,759
Speaker 3: do this. And I looked at the existing research and

1007
00:53:55,760 --> 00:53:59,680
Speaker 3: what I kind of concluded was that actually, we've got old.

1008
00:53:59,440 --> 00:54:02,680
Speaker 2: Ideas that are pretty good, like really good. Right.

1009
00:54:02,960 --> 00:54:06,960
Speaker 3: So on the biological front, everything points to the idea

1010
00:54:07,040 --> 00:54:11,440
Speaker 3: that mutually beneficial cooperation is the key. Mutually beneficial cooperation

1011
00:54:11,880 --> 00:54:15,440
Speaker 3: is the story of life, starting with primordial soup, and

1012
00:54:15,560 --> 00:54:19,799
Speaker 3: basically molecules come together to form cells because cells can

1013
00:54:19,920 --> 00:54:23,240
Speaker 3: can survive and reproduce in ways that loan molecules can't.

1014
00:54:23,360 --> 00:54:27,239
Speaker 3: And cells form more complicated eu caryotic cells, which form colonies,

1015
00:54:27,360 --> 00:54:30,560
Speaker 3: which form organisms, which form societies.

1016
00:54:30,200 --> 00:54:33,480
Speaker 2: And all the way out up to tribes and nations

1017
00:54:33,520 --> 00:54:35,040
Speaker 2: and occasionally United nations.

1018
00:54:35,400 --> 00:54:41,239
Speaker 3: Every living system is built and sustained on mutually beneficial cooperation.

1019
00:54:41,400 --> 00:54:45,239
Speaker 3: It's parts coming together for teamwork because they can accomplish

1020
00:54:45,280 --> 00:54:47,800
Speaker 3: things that they can't accomplish on their own.

1021
00:54:48,160 --> 00:54:50,439
Speaker 2: But there's competition at every level, right.

1022
00:54:50,600 --> 00:54:53,879
Speaker 3: Organisms are competing to survive, societies are competing with each

1023
00:54:53,920 --> 00:54:54,239
Speaker 3: other for.

1024
00:54:55,760 --> 00:54:58,839
Speaker 2: Resources, and so the.

1025
00:54:58,880 --> 00:55:02,319
Speaker 3: Challenge is can we cooperate at the highest level, right,

1026
00:55:02,560 --> 00:55:05,960
Speaker 3: whether that's tribes within a country or countries in the world, right,

1027
00:55:06,840 --> 00:55:10,640
Speaker 3: And that may not come so naturally, right, so we

1028
00:55:10,640 --> 00:55:13,759
Speaker 3: need tools for that. That's the biological perspective. On the

1029
00:55:13,800 --> 00:55:16,040
Speaker 3: social science side, it's much the same story. You go

1030
00:55:16,160 --> 00:55:19,400
Speaker 3: back to ideas from the fifties, like Gordon Allport's famous

1031
00:55:19,520 --> 00:55:22,640
Speaker 3: contact theory. Basically, what he argued is that the way

1032
00:55:22,680 --> 00:55:25,840
Speaker 3: you bring groups that are intentioned together is well, you

1033
00:55:25,920 --> 00:55:27,640
Speaker 3: need to have them come to some kind of contact,

1034
00:55:27,719 --> 00:55:30,360
Speaker 3: and it has to be under the right kinds of conditions, essentially,

1035
00:55:30,360 --> 00:55:33,120
Speaker 3: conditions that are conducive to cooperation.

1036
00:55:33,680 --> 00:55:36,240
Speaker 2: And you know, Alport wrote.

1037
00:55:36,080 --> 00:55:37,799
Speaker 3: This all out of the fifties, but really this is

1038
00:55:37,880 --> 00:55:41,040
Speaker 3: very intuitive. I mean, people have surely recognized for centuries

1039
00:55:41,080 --> 00:55:43,160
Speaker 3: that you put people on the same team, and they're

1040
00:55:43,200 --> 00:55:45,279
Speaker 3: more likely to get along, right, if there's really a

1041
00:55:45,320 --> 00:55:46,319
Speaker 3: shared purpose there.

1042
00:55:46,400 --> 00:55:49,520
Speaker 2: Right, So if.

1043
00:55:49,440 --> 00:55:53,040
Speaker 3: We've known this for decades, if not centuries, then why

1044
00:55:53,080 --> 00:55:56,360
Speaker 3: have we not solved our human tribal problem?

1045
00:55:56,520 --> 00:55:56,719
Speaker 2: Right?

1046
00:55:57,080 --> 00:55:59,879
Speaker 3: And I think the answer is twofold. The optimistic part

1047
00:55:59,920 --> 00:56:02,960
Speaker 3: is to some extent we already have. I mean, as

1048
00:56:02,960 --> 00:56:06,799
Speaker 3: my colleague Stephen Pinker has documented with a lot of

1049
00:56:06,840 --> 00:56:08,200
Speaker 3: resistance to people who don't like.

1050
00:56:08,160 --> 00:56:11,240
Speaker 2: This conclusion, but it's very well supported, is that.

1051
00:56:11,200 --> 00:56:15,920
Speaker 3: Humans have over millennia and centuries and decades become overall

1052
00:56:15,960 --> 00:56:17,480
Speaker 3: more peaceful and less milor right.

1053
00:56:18,000 --> 00:56:19,520
Speaker 2: And although there's been a.

1054
00:56:21,000 --> 00:56:25,279
Speaker 3: Reversal in recent years unfortunately, but nothing close to sort

1055
00:56:25,320 --> 00:56:28,320
Speaker 3: of at the level of the overall arc of our history.

1056
00:56:28,600 --> 00:56:31,960
Speaker 3: And really every modern city is a testament to the

1057
00:56:32,040 --> 00:56:37,000
Speaker 3: idea that people with different cultures, different races, different ethnicities

1058
00:56:37,040 --> 00:56:40,759
Speaker 3: and religions can view each other more as fellow citizens

1059
00:56:40,760 --> 00:56:44,440
Speaker 3: and cooperation partners than as enemies to be distrusted.

1060
00:56:44,560 --> 00:56:46,400
Speaker 2: Right, So these.

1061
00:56:46,280 --> 00:56:48,640
Speaker 3: Can grow organically, right, And we see this in other

1062
00:56:48,680 --> 00:56:52,719
Speaker 3: contexts where there's an immediate need. During World War Two,

1063
00:56:53,200 --> 00:56:55,600
Speaker 3: there was you know, we needed soldiers in the United States,

1064
00:56:55,600 --> 00:56:59,760
Speaker 3: and there was a push to have racially integrated units

1065
00:56:59,760 --> 00:57:02,200
Speaker 3: in the military. But some people thought this would never work.

1066
00:57:02,239 --> 00:57:04,919
Speaker 3: You could never have white people and were then called

1067
00:57:05,000 --> 00:57:08,400
Speaker 3: negroes fighting with each other against the common enemy. And

1068
00:57:08,400 --> 00:57:09,960
Speaker 3: some people said, well, we've got to try, and we

1069
00:57:10,000 --> 00:57:12,680
Speaker 3: think this could work. And it worked beautifully, right, And

1070
00:57:12,719 --> 00:57:15,120
Speaker 3: the US military was far ahead of the rest of

1071
00:57:15,120 --> 00:57:19,720
Speaker 3: civilian life in the US in terms of racial integration. Likewise,

1072
00:57:19,760 --> 00:57:22,000
Speaker 3: in sports, you know, just when there's a warn tobe

1073
00:57:22,080 --> 00:57:23,760
Speaker 3: one or a game to be won, you want the

1074
00:57:23,760 --> 00:57:26,320
Speaker 3: best players playing together and working together. And that's a

1075
00:57:26,400 --> 00:57:28,600
Speaker 3: kind of circumscribed context where that can to work. So

1076
00:57:29,080 --> 00:57:32,880
Speaker 3: part of the answer is in the right when circumstances

1077
00:57:33,280 --> 00:57:39,320
Speaker 3: demand it, you can get cooperation across tense lines of division.

1078
00:57:39,920 --> 00:57:44,200
Speaker 3: The challenge is how do you engineer it where the

1079
00:57:44,240 --> 00:57:46,880
Speaker 3: ball doesn't seem to be rolling in the right direction right,

1080
00:57:47,360 --> 00:57:50,360
Speaker 3: and where we have these divisions, in these levels of

1081
00:57:50,400 --> 00:57:54,480
Speaker 3: distrust and disrespect, how do we engineer that deliberately? So

1082
00:57:54,800 --> 00:57:57,480
Speaker 3: my thinking was, well, we need a way to get

1083
00:57:57,480 --> 00:58:00,840
Speaker 3: people from opposite sides of whatever divide the same team.

1084
00:58:01,000 --> 00:58:03,880
Speaker 3: So you know, this can be Republicans and Democrats in

1085
00:58:03,920 --> 00:58:07,880
Speaker 3: the US, or Jews and Arab slash Palestinians in Israel, Gaza,

1086
00:58:08,120 --> 00:58:12,000
Speaker 3: or Hindus and Muslims in India. You got to get

1087
00:58:12,000 --> 00:58:17,240
Speaker 3: people on the same team. Okay, how do you do that? Well,

1088
00:58:17,280 --> 00:58:19,160
Speaker 3: you need to put people on the same team. It

1089
00:58:19,200 --> 00:58:20,959
Speaker 3: needs to be scalable and it needs to be fun.

1090
00:58:21,520 --> 00:58:25,280
Speaker 3: My lab's answer to that was a cooperative quiz game

1091
00:58:25,800 --> 00:58:28,520
Speaker 3: which was originally developed with my amazing grad student Van

1092
00:58:28,560 --> 00:58:32,439
Speaker 3: d Philippus, and now those work is being led by

1093
00:58:32,560 --> 00:58:33,920
Speaker 3: the amazing Lucas Woodley.

1094
00:58:35,360 --> 00:58:37,840
Speaker 2: So this game is now called Tango.

1095
00:58:38,840 --> 00:58:42,080
Speaker 3: And the way it works is you sign on to

1096
00:58:42,160 --> 00:58:45,360
Speaker 3: the game and you answer a few questions about yourself

1097
00:58:45,680 --> 00:58:48,280
Speaker 3: and in the typical version of this. In the experiments

1098
00:58:48,280 --> 00:58:50,080
Speaker 3: I've done, you might say I'm a Republican or I'm

1099
00:58:50,080 --> 00:58:52,240
Speaker 3: a Democrat, or I'm a liberal arm a conservative. You

1100
00:58:52,280 --> 00:58:54,440
Speaker 3: also answer kind of fun, you know, get to know

1101
00:58:54,520 --> 00:58:56,919
Speaker 3: you questions. You know, what's your favorite superpower that you'd

1102
00:58:56,960 --> 00:59:00,520
Speaker 3: like to have, and things like that, and you you

1103
00:59:00,560 --> 00:59:02,240
Speaker 3: answer those questions that you get paired up with your

1104
00:59:02,240 --> 00:59:05,600
Speaker 3: partner and you have a little get to know you

1105
00:59:05,720 --> 00:59:07,960
Speaker 3: chat and you say, oh see, we both would like

1106
00:59:08,000 --> 00:59:10,600
Speaker 3: to have the power of flying or invisibility or whatever

1107
00:59:10,600 --> 00:59:12,600
Speaker 3: it is. And you can see you know what the

1108
00:59:12,600 --> 00:59:15,480
Speaker 3: person's politics is. And then you get into the game.

1109
00:59:15,840 --> 00:59:18,000
Speaker 3: And in the most interesting case, you're playing the game

1110
00:59:18,040 --> 00:59:21,160
Speaker 3: with someone who is, let's say in the US politically

1111
00:59:21,240 --> 00:59:23,960
Speaker 3: different from you. You know, they're a Republican or a Democrat,

1112
00:59:23,960 --> 00:59:26,439
Speaker 3: and you're on the opposite side, right, And the game

1113
00:59:26,520 --> 00:59:29,400
Speaker 3: starts out with questions that are designed to have a

1114
00:59:29,480 --> 00:59:32,760
Speaker 3: kind of complementarity, but not anything that's likely to be

1115
00:59:32,800 --> 00:59:37,160
Speaker 3: divisive or controversial. So, for example, Republicans are more likely

1116
00:59:37,200 --> 00:59:40,240
Speaker 3: to be able to answer questions about the show Duck Dynasty,

1117
00:59:40,360 --> 00:59:43,360
Speaker 3: and that this is not stereotypes like this is validated

1118
00:59:43,360 --> 00:59:47,120
Speaker 3: with data, whereas Democrats are more likely to know about

1119
00:59:47,800 --> 00:59:51,280
Speaker 3: stranger things or the queen's gambit. Right, So you have

1120
00:59:51,360 --> 00:59:53,520
Speaker 3: questions where one side's likely to be able to help

1121
00:59:53,560 --> 00:59:55,880
Speaker 3: the other side and vice versa, and that gets people

1122
00:59:55,960 --> 00:59:58,440
Speaker 3: into the game. Yeah you're a Democrat, but that's okay.

1123
00:59:58,560 --> 01:00:01,240
Speaker 3: Yeah you're a Republican, but that's okay. We're winning, we're

1124
01:00:01,280 --> 01:00:04,120
Speaker 3: high fiving, we're playing together. Everything's great. Then you get

1125
01:00:04,200 --> 01:00:08,440
Speaker 3: questions that are about more divisive issues but still grounded

1126
01:00:08,440 --> 01:00:11,200
Speaker 3: in facts. So if you ask what percentage of gun

1127
01:00:11,240 --> 01:00:16,200
Speaker 3: debts in the US involve assault style weapons? If you ask,

1128
01:00:16,400 --> 01:00:18,360
Speaker 3: you know, liberals, they're likely to say, I don't know,

1129
01:00:18,440 --> 01:00:22,440
Speaker 3: thirty percent, fifty percent. If you ask people who are conservative,

1130
01:00:22,480 --> 01:00:25,240
Speaker 3: they're more likely to say no, assault weapons or which

1131
01:00:25,280 --> 01:00:27,720
Speaker 3: they wouldn't even call assault weapons, are like, you know,

1132
01:00:27,720 --> 01:00:32,160
Speaker 3: two percent, it's more mostly handguns. Right, So that's a

1133
01:00:32,240 --> 01:00:35,480
Speaker 3: case where the conservative Republicans are more likely to be correct.

1134
01:00:35,560 --> 01:00:38,080
Speaker 3: But then if you ask a question about, you know,

1135
01:00:38,120 --> 01:00:43,640
Speaker 3: how who commits more crimes per capita, immigrants or native

1136
01:00:43,640 --> 01:00:47,360
Speaker 3: born Americans, Republicans think are more likely to think that

1137
01:00:48,000 --> 01:00:51,080
Speaker 3: immigrant crime is sky high, when in fact, immigrants commit

1138
01:00:51,120 --> 01:00:52,160
Speaker 3: relatively few crimes.

1139
01:00:53,080 --> 01:00:54,720
Speaker 2: That's the case where the liberal is more likely to

1140
01:00:54,760 --> 01:00:55,120
Speaker 2: be right.

1141
01:00:55,640 --> 01:00:57,520
Speaker 3: But at this point in the game, you know where

1142
01:00:57,520 --> 01:01:00,520
Speaker 3: you might think, oh, man talking about immigration or guns,

1143
01:01:01,000 --> 01:01:03,640
Speaker 3: one of them, it would be a disaster. But by

1144
01:01:03,640 --> 01:01:06,440
Speaker 3: then you're into the game, you're playing your teammates, you've

1145
01:01:06,440 --> 01:01:08,520
Speaker 3: worked well together, you've already gotten to know each other

1146
01:01:08,560 --> 01:01:11,720
Speaker 3: as respectful, decent people. And people just play the game

1147
01:01:11,720 --> 01:01:13,440
Speaker 3: and they try to win those points and some you know,

1148
01:01:13,600 --> 01:01:15,840
Speaker 3: and everyone gets to be surprised and everyone gets to

1149
01:01:15,880 --> 01:01:18,200
Speaker 3: be right, and you have this cooperative experience and at

1150
01:01:18,200 --> 01:01:21,080
Speaker 3: the end, people like say these tiery goodbyes, Man, it

1151
01:01:21,120 --> 01:01:22,440
Speaker 3: was so much fun playing with you. I hope it

1152
01:01:22,440 --> 01:01:24,320
Speaker 3: could meet in real life and things like that, and

1153
01:01:24,360 --> 01:01:27,200
Speaker 3: it's great. What we want to know is what is

1154
01:01:27,200 --> 01:01:30,520
Speaker 3: the effect of having this cooperative experience. So we've now

1155
01:01:30,560 --> 01:01:33,520
Speaker 3: done a series of randomized control trials with online with

1156
01:01:33,600 --> 01:01:37,880
Speaker 3: Republicans and Democrats, and what we found exceeded our expectations.

1157
01:01:38,120 --> 01:01:41,880
Speaker 3: We find it playing tango with someone who's politically different

1158
01:01:41,880 --> 01:01:45,840
Speaker 3: from you for less than an hour has positive effects

1159
01:01:46,040 --> 01:01:50,240
Speaker 3: that last at least four months. So we ask people

1160
01:01:50,560 --> 01:01:52,760
Speaker 3: how warm or cold do you feel towards the other

1161
01:01:52,840 --> 01:01:55,120
Speaker 3: party on a scale of zero to one hundred. How

1162
01:01:55,120 --> 01:01:57,560
Speaker 3: would you divide one hundred dollars between a random Republican

1163
01:01:57,600 --> 01:02:01,640
Speaker 3: and a random Democrat? Do your respect Republicans or Democrats?

1164
01:02:01,680 --> 01:02:05,680
Speaker 3: Do you trust Republicans or Democrats? And what we found

1165
01:02:06,800 --> 01:02:09,720
Speaker 3: is that when people play with someone like this, to

1166
01:02:09,760 --> 01:02:12,640
Speaker 3: take sort of the best known measure, which is that

1167
01:02:12,720 --> 01:02:16,880
Speaker 3: feeling thermometer, people play the game, and immediately after the game,

1168
01:02:16,920 --> 01:02:20,480
Speaker 3: we see like, on average, a nine point increase in

1169
01:02:20,640 --> 01:02:22,640
Speaker 3: warmth towards the other party, where you can think of

1170
01:02:22,640 --> 01:02:25,920
Speaker 3: it as a decrease in cold And you know, we say,

1171
01:02:25,920 --> 01:02:28,840
Speaker 3: well nine points, what does that mean. That's the equivalent

1172
01:02:28,840 --> 01:02:34,360
Speaker 3: of rolling back about fifteen years of increased polarization in

1173
01:02:34,400 --> 01:02:36,760
Speaker 3: the United States. Now that's the immediate effect. Of course,

1174
01:02:36,840 --> 01:02:40,840
Speaker 3: you know, it's not magic. It doesn't last. But when

1175
01:02:40,880 --> 01:02:43,680
Speaker 3: we when we go back to people four months later,

1176
01:02:44,040 --> 01:02:47,880
Speaker 3: we still see an effect of you know, that's the

1177
01:02:47,880 --> 01:02:51,960
Speaker 3: equivalent of like five years of depolarization. And the cool

1178
01:02:52,040 --> 01:02:54,880
Speaker 3: thing about the game is that you can play it

1179
01:02:54,960 --> 01:02:57,479
Speaker 3: more than once. It's like Jeopardy, you could play every night, right,

1180
01:02:57,600 --> 01:02:59,560
Speaker 3: And we also find that people really like it. So

1181
01:02:59,800 --> 01:03:02,440
Speaker 3: our our median enjoyment rating was ten out of ten.

1182
01:03:02,480 --> 01:03:06,120
Speaker 3: Now these are research participants who were not expecting to

1183
01:03:06,120 --> 01:03:08,120
Speaker 3: have a lot of fun, so you know, it's not

1184
01:03:08,160 --> 01:03:10,680
Speaker 3: surprising that they really enjoyed it.

1185
01:03:11,720 --> 01:03:12,600
Speaker 2: But we.

1186
01:03:14,120 --> 01:03:18,280
Speaker 3: See these long lasting positive effects you with this scalable

1187
01:03:18,320 --> 01:03:21,160
Speaker 3: tool and in a way that people really enjoy. So

1188
01:03:21,240 --> 01:03:23,320
Speaker 3: that's the that's the main points of the paper that

1189
01:03:23,320 --> 01:03:25,880
Speaker 3: we published in Nature Human Behavior this summer.

1190
01:03:26,440 --> 01:03:28,360
Speaker 2: In the last year or two, we've been doing is

1191
01:03:28,400 --> 01:03:30,000
Speaker 2: working on getting this out in the world.

1192
01:03:31,120 --> 01:03:34,840
Speaker 3: We are building an online tool where people can play,

1193
01:03:34,840 --> 01:03:37,160
Speaker 3: but where that's not there yet, and I can talk

1194
01:03:37,200 --> 01:03:39,200
Speaker 3: about kind of what we're doing and what we need

1195
01:03:39,200 --> 01:03:41,920
Speaker 3: for that. But our most immediate traction has been in

1196
01:03:42,000 --> 01:03:45,160
Speaker 3: higher education. And you know, historically when it comes to

1197
01:03:45,480 --> 01:03:48,880
Speaker 3: psychology research, like testing college students is what you do

1198
01:03:49,000 --> 01:03:51,640
Speaker 3: on the cheap, you know, just a convenience sample. But now,

1199
01:03:51,720 --> 01:03:55,360
Speaker 3: as you know, especially at place like Harvard, higher ed

1200
01:03:55,400 --> 01:03:57,680
Speaker 3: is sort of ground zero for a lot of our

1201
01:03:57,760 --> 01:04:02,360
Speaker 3: cultural divisions, right, so we have been working with schools

1202
01:04:02,400 --> 01:04:04,880
Speaker 3: to deploy this, you know, not as research participants, but

1203
01:04:05,280 --> 01:04:06,720
Speaker 3: to students living their lives.

1204
01:04:06,800 --> 01:04:09,280
Speaker 2: And our biggest events so.

1205
01:04:09,240 --> 01:04:15,080
Speaker 3: Far have been at orientation for Harvard and Cornell and

1206
01:04:15,120 --> 01:04:19,160
Speaker 3: Penn State, at Harvard this year, we did the entire

1207
01:04:19,240 --> 01:04:23,320
Speaker 3: incoming class of twenty twenty nine, so over a thousand students,

1208
01:04:23,960 --> 01:04:26,960
Speaker 3: and we're using some different questions. It's not so much

1209
01:04:26,960 --> 01:04:29,320
Speaker 3: about your political party as like more like are you

1210
01:04:29,400 --> 01:04:34,000
Speaker 3: politically liberal or conservative? And we're also asking questions about

1211
01:04:34,000 --> 01:04:38,160
Speaker 3: things that are more divisive within a campus like Harvard,

1212
01:04:38,280 --> 01:04:41,920
Speaker 3: so like Israel, Gaza, sorts of things in addition to

1213
01:04:42,880 --> 01:04:45,640
Speaker 3: things like guns and immigration and stuff like that. And

1214
01:04:45,720 --> 01:04:48,320
Speaker 3: what we found is that playing the game for about

1215
01:04:48,360 --> 01:04:54,200
Speaker 3: twenty minutes had significant positive effects on acknowledgement that the

1216
01:04:54,240 --> 01:04:57,800
Speaker 3: other side can make valid points interesting, getting to know

1217
01:04:57,960 --> 01:05:02,240
Speaker 3: people who are different from you, comfort voicing controversial opinions

1218
01:05:02,280 --> 01:05:05,479
Speaker 3: on campus. My favorite pair of results for Harvard because

1219
01:05:05,480 --> 01:05:08,160
Speaker 3: I think this speaks to a challenge that we are

1220
01:05:08,280 --> 01:05:12,360
Speaker 3: really working to address. When when liberal students at Harvard

1221
01:05:12,400 --> 01:05:17,280
Speaker 3: at orientation in August played with a conservative student, they

1222
01:05:17,280 --> 01:05:21,640
Speaker 3: were seven points less negative towards conservatives, so that's also

1223
01:05:21,960 --> 01:05:24,720
Speaker 3: a pretty big effect. And then when conservatives played with

1224
01:05:24,760 --> 01:05:29,160
Speaker 3: a liberal student, they felt five points more open towards

1225
01:05:29,200 --> 01:05:32,959
Speaker 3: expressing controversial views on campus, So.

1226
01:05:32,880 --> 01:05:34,160
Speaker 2: This is opening people up.

1227
01:05:34,640 --> 01:05:38,360
Speaker 3: We also sort of allowed people to take two behavioral steps.

1228
01:05:38,800 --> 01:05:40,400
Speaker 3: So we asked people at the end, he said, Hey,

1229
01:05:40,440 --> 01:05:42,400
Speaker 3: you played with your partner anonymously, do you want to

1230
01:05:42,440 --> 01:05:43,040
Speaker 3: meet your partner?

1231
01:05:43,080 --> 01:05:44,560
Speaker 2: If so, leave your contact info.

1232
01:05:45,080 --> 01:05:47,840
Speaker 3: And we found that eighty percent of students gave their

1233
01:05:47,840 --> 01:05:53,120
Speaker 3: contact info. So you've got people shifting attitudes in making

1234
01:05:53,160 --> 01:05:59,880
Speaker 3: campuses more open, more more hospitable to both liberals and conservatives,

1235
01:06:00,080 --> 01:06:01,960
Speaker 3: and people taking steps in the real world. And then

1236
01:06:02,000 --> 01:06:03,320
Speaker 3: the most fun part of all of this is that

1237
01:06:03,360 --> 01:06:05,800
Speaker 3: the winning teams went to Fenway Park for a Red

1238
01:06:05,880 --> 01:06:08,200
Speaker 3: Sox game, and so people went with their partners, whether

1239
01:06:08,280 --> 01:06:11,520
Speaker 3: they were similar or or politically different.

1240
01:06:12,000 --> 01:06:15,680
Speaker 2: So we are, you know, rolling this out.

1241
01:06:15,840 --> 01:06:18,880
Speaker 3: And I think of this as kind of the opposite

1242
01:06:18,880 --> 01:06:21,600
Speaker 3: of divisive online content. I mean, you think of what

1243
01:06:21,680 --> 01:06:25,360
Speaker 3: internet trolls and operatives have managed to do by creating

1244
01:06:25,680 --> 01:06:29,440
Speaker 3: by spreading ill will and just trust sort of at scale,

1245
01:06:29,600 --> 01:06:33,520
Speaker 3: using very engaging content like fact checking. Can't fight that.

1246
01:06:34,400 --> 01:06:37,640
Speaker 3: The opposite of that is not, you know, an earnest

1247
01:06:37,680 --> 01:06:40,360
Speaker 3: fact check, although we need to do that. It's something

1248
01:06:40,360 --> 01:06:42,800
Speaker 3: to compete with it in a positive way, something that

1249
01:06:42,840 --> 01:06:45,720
Speaker 3: people find engaging, that millions of people can do, and

1250
01:06:45,760 --> 01:06:50,160
Speaker 3: that trust spreads respect and trust at scale.

1251
01:06:49,800 --> 01:06:51,200
Speaker 2: Not that everybody has to agree.

1252
01:06:51,200 --> 01:06:54,120
Speaker 3: We're not trying to change people's minds about issues, but

1253
01:06:54,200 --> 01:06:58,800
Speaker 3: getting to the point where people can disagree respectively and constructively.

1254
01:06:59,160 --> 01:07:00,920
Speaker 3: So our goal over the next year is to get

1255
01:07:00,920 --> 01:07:03,000
Speaker 3: this out there and have you know, thousands, if we

1256
01:07:03,040 --> 01:07:07,040
Speaker 3: can millions of people have the positive experience that people

1257
01:07:07,160 --> 01:07:10,040
Speaker 3: on college campuses and in our experiments have already had.

1258
01:07:10,400 --> 01:07:14,120
Speaker 1: Good for you, were you surprised that this works anonymously

1259
01:07:14,200 --> 01:07:17,320
Speaker 1: because the contact hypothesis suggests that you need to get

1260
01:07:17,360 --> 01:07:19,920
Speaker 1: people together in person to have conversation.

1261
01:07:20,760 --> 01:07:22,800
Speaker 2: Yeah, that's it's a very astute point.

1262
01:07:23,400 --> 01:07:25,560
Speaker 3: That was one of the things we weren't sure about, right,

1263
01:07:25,640 --> 01:07:27,400
Speaker 3: And you know, one thing we wondered is do we

1264
01:07:27,480 --> 01:07:29,400
Speaker 3: have to do this like on zoom where people give

1265
01:07:29,440 --> 01:07:31,760
Speaker 3: their names and you can see their faces. And one

1266
01:07:31,760 --> 01:07:34,320
Speaker 3: of the cool things about this was that you didn't

1267
01:07:34,360 --> 01:07:37,880
Speaker 3: have to have that conventional name in a face contact

1268
01:07:38,280 --> 01:07:40,200
Speaker 3: to get this to work and to have the positive

1269
01:07:40,240 --> 01:07:44,880
Speaker 3: experience generalize to other people. It's possible that things will

1270
01:07:44,920 --> 01:07:49,040
Speaker 3: work even better if we have those kinds of more direct,

1271
01:07:49,040 --> 01:07:51,919
Speaker 3: face to face with a name kind of contact. It's

1272
01:07:51,960 --> 01:07:56,880
Speaker 3: also possible that there's something stealth effective about having it

1273
01:07:56,920 --> 01:08:01,520
Speaker 3: be anonymous because it makes people feel more safe, right

1274
01:08:01,920 --> 01:08:04,400
Speaker 3: that that that you can kind of ease into it

1275
01:08:04,720 --> 01:08:06,360
Speaker 3: where you know, they don't know who I am, they

1276
01:08:06,360 --> 01:08:07,480
Speaker 3: don't know my name, they don't.

1277
01:08:07,280 --> 01:08:08,040
Speaker 2: Know what I look like.

1278
01:08:08,200 --> 01:08:10,760
Speaker 3: No one's taking a screenshot of me and putting it

1279
01:08:10,800 --> 01:08:14,439
Speaker 3: online and say who is this person? Right, So there's

1280
01:08:14,480 --> 01:08:16,680
Speaker 3: a kind of safety that comes with the anonymity, and

1281
01:08:16,680 --> 01:08:21,760
Speaker 3: then they can move from the anonymous context too in

1282
01:08:21,800 --> 01:08:24,200
Speaker 3: person like the students did at Harvard in the dining hall,

1283
01:08:24,600 --> 01:08:26,680
Speaker 3: or if we're when we're building this out online, we

1284
01:08:26,720 --> 01:08:29,160
Speaker 3: might people give give people the option after they've already

1285
01:08:29,160 --> 01:08:33,320
Speaker 3: had a friendly anonymous interaction to say, Okay, here's my

1286
01:08:33,920 --> 01:08:37,040
Speaker 3: here's my social media handle for this platform or whatever

1287
01:08:37,120 --> 01:08:39,080
Speaker 3: that we're both on, and you know, people can get

1288
01:08:39,080 --> 01:08:43,719
Speaker 3: to know each other, you know, online and in something

1289
01:08:43,760 --> 01:08:46,960
Speaker 3: that's more real life than than than an anonymous game.

1290
01:08:47,120 --> 01:08:49,439
Speaker 3: So these are great questions that we want to experiment

1291
01:08:49,479 --> 01:08:50,920
Speaker 3: with when we when we have the chance.

1292
01:08:51,360 --> 01:08:55,320
Speaker 1: Great one last question, if everyone on earth understood moral

1293
01:08:55,360 --> 01:09:00,559
Speaker 1: psychology as you do, what is one belief or habits

1294
01:09:00,600 --> 01:09:04,480
Speaker 1: that you might hope would change individually and institutionally.

1295
01:09:04,960 --> 01:09:07,320
Speaker 3: I think that at the psychological level, like at the

1296
01:09:07,400 --> 01:09:11,720
Speaker 3: level of managing one's own mind, people would have a

1297
01:09:11,760 --> 01:09:16,320
Speaker 3: little distance between their first thought, which may or be right.

1298
01:09:16,240 --> 01:09:19,760
Speaker 2: Or may maybe not, and what they actually act on.

1299
01:09:20,200 --> 01:09:23,240
Speaker 3: And you know this is there's a great bumper sticker,

1300
01:09:23,320 --> 01:09:25,880
Speaker 3: don't believe everything you think, which I think sort of

1301
01:09:25,920 --> 01:09:30,160
Speaker 3: beautifully captures this logic that people need to sort of

1302
01:09:30,200 --> 01:09:32,439
Speaker 3: recognize that what you feel or your first thought is

1303
01:09:32,479 --> 01:09:35,880
Speaker 3: not necessarily the right thing to do. And then I

1304
01:09:35,920 --> 01:09:41,040
Speaker 3: think we need to have a kind of openness where

1305
01:09:41,040 --> 01:09:44,840
Speaker 3: we recognize that whatever our differences, we have so much

1306
01:09:44,840 --> 01:09:45,360
Speaker 3: in common.

1307
01:09:45,760 --> 01:09:49,599
Speaker 2: We all, you know, we all want to be happy.

1308
01:09:49,640 --> 01:09:52,240
Speaker 3: We don't want to suffer. We care about our families,

1309
01:09:52,320 --> 01:09:55,040
Speaker 3: we care about our friends. We want to live in

1310
01:09:55,040 --> 01:09:57,320
Speaker 3: a world that's almost all of us. Want to live

1311
01:09:57,320 --> 01:10:01,000
Speaker 3: in a world that's peaceful rather than the violent. We

1312
01:10:01,040 --> 01:10:04,800
Speaker 3: want to live in that larger US cooperative world at

1313
01:10:04,880 --> 01:10:10,000
Speaker 3: least where we're not harming each other. Right, But the

1314
01:10:10,160 --> 01:10:15,280
Speaker 3: terms of that cooperation are what's challenging. And what I

1315
01:10:15,320 --> 01:10:17,320
Speaker 3: would hope to see is when people can sort of

1316
01:10:17,360 --> 01:10:20,759
Speaker 3: let go of the grip of their prejudices and first judgments,

1317
01:10:20,920 --> 01:10:22,840
Speaker 3: that people would be willing to take a step and

1318
01:10:22,880 --> 01:10:25,000
Speaker 3: act on their curiosity and be able to say, okay,

1319
01:10:25,600 --> 01:10:27,479
Speaker 3: can I get to know people who are differently, not

1320
01:10:27,479 --> 01:10:29,320
Speaker 3: making any promises that I'm going to agree with them

1321
01:10:29,360 --> 01:10:31,080
Speaker 3: or want to be their best friend or go into

1322
01:10:31,080 --> 01:10:35,200
Speaker 3: business together, but at least try to understand. And if

1323
01:10:35,240 --> 01:10:38,120
Speaker 3: I think we took those first two steps of liberating

1324
01:10:38,120 --> 01:10:45,880
Speaker 3: ourselves from our intuitions and then liberating ourselves from our isolation,

1325
01:10:46,120 --> 01:10:49,800
Speaker 3: only learning about other people from what bad actors on

1326
01:10:49,880 --> 01:10:53,479
Speaker 3: social media have to say and encountering people more directly,

1327
01:10:55,120 --> 01:10:58,479
Speaker 3: those things would make us see the humanity in each

1328
01:10:58,520 --> 01:11:02,400
Speaker 3: other and we be I think that we would be

1329
01:11:02,520 --> 01:11:05,479
Speaker 3: able to solve our biggest problems.

1330
01:11:10,000 --> 01:11:12,600
Speaker 1: That was my interview with Josh Green. I still have

1331
01:11:12,640 --> 01:11:14,519
Speaker 1: a bunch to say, but I want to remind you,

1332
01:11:14,640 --> 01:11:19,720
Speaker 1: if you're able, go to GiveDirectly dot org slash cosmos.

1333
01:11:20,040 --> 01:11:21,679
Speaker 1: I have that link in the show notes as well.

1334
01:11:21,960 --> 01:11:25,840
Speaker 1: Give directly dot org slash cosmos. Donate whatever you can.

1335
01:11:26,000 --> 01:11:29,320
Speaker 1: All the money goes directly to people in deep need

1336
01:11:29,400 --> 01:11:33,280
Speaker 1: in Rwanda. No contribution is too small. So let's come

1337
01:11:33,320 --> 01:11:36,839
Speaker 1: back to this idea of Josh's that our moral brains

1338
01:11:37,120 --> 01:11:41,000
Speaker 1: are beautifully designed for a world that no longer exists.

1339
01:11:41,080 --> 01:11:45,759
Speaker 1: They're exquisitely tuned for small circles, for family, for friends,

1340
01:11:45,760 --> 01:11:49,000
Speaker 1: for the people whose faces we can see and whose

1341
01:11:49,040 --> 01:11:53,400
Speaker 1: pain we can imagine. Our brains give us loyalty and

1342
01:11:53,520 --> 01:11:57,439
Speaker 1: indignation and gratitude and guilt. This is why we'll run

1343
01:11:57,479 --> 01:11:59,759
Speaker 1: into the street to pull a stranger out of traffic,

1344
01:11:59,800 --> 01:12:02,960
Speaker 1: and also why we can feel more outrage about one

1345
01:12:03,439 --> 01:12:07,880
Speaker 1: vivid case than about a million small tragedies. But the

1346
01:12:08,120 --> 01:12:13,559
Speaker 1: challenges that define our modern life, from pandemics to global poverty,

1347
01:12:13,600 --> 01:12:17,240
Speaker 1: to AI alignment to the future of democracies, these aren't

1348
01:12:17,520 --> 01:12:21,559
Speaker 1: small circle problems. In many cases, they are statistical, They

1349
01:12:21,560 --> 01:12:26,479
Speaker 1: are long term. They're geographically scattered. No one is knocking

1350
01:12:26,520 --> 01:12:29,599
Speaker 1: on our door asking for help. There's no crying baby,

1351
01:12:29,640 --> 01:12:33,920
Speaker 1: there's no burning building across the street. And so our

1352
01:12:34,439 --> 01:12:37,920
Speaker 1: moral camera, as Joshua might put it, is just aiming

1353
01:12:37,960 --> 01:12:41,880
Speaker 1: at the wrong scale. This is a design mismatch. We're

1354
01:12:41,960 --> 01:12:46,200
Speaker 1: running stone age moral software on a planetary scale system.

1355
01:12:46,880 --> 01:12:50,360
Speaker 1: So that leads us with two tasks. At the individual level.

1356
01:12:50,960 --> 01:12:55,080
Speaker 1: The task is to become more bilingual in our own minds,

1357
01:12:55,160 --> 01:12:59,920
Speaker 1: to recognize our emotional reactions, to recognize what they're good at,

1358
01:13:00,520 --> 01:13:03,800
Speaker 1: but also to notice when they're steering us wrong, to

1359
01:13:03,920 --> 01:13:07,400
Speaker 1: be willing when the stakes are big and abstract, to

1360
01:13:07,560 --> 01:13:12,360
Speaker 1: flip into manual mode, to ask, okay, what actually helps

1361
01:13:12,400 --> 01:13:15,679
Speaker 1: the most people? What am I missing because it doesn't

1362
01:13:16,000 --> 01:13:20,479
Speaker 1: feel emotionally salient. At the societal level, the task is

1363
01:13:20,479 --> 01:13:25,920
Speaker 1: to build better scaffolding around these imperfect brains. That means

1364
01:13:26,000 --> 01:13:29,720
Speaker 1: institutions and norms and technologies, and even little bits of

1365
01:13:30,240 --> 01:13:34,200
Speaker 1: choice architecture like the kinds of moral technologies that Josh

1366
01:13:34,280 --> 01:13:39,439
Speaker 1: is working on, building things that nudge our caring in

1367
01:13:39,560 --> 01:13:43,479
Speaker 1: directions that our emotions wouldn't find on their own. We

1368
01:13:43,560 --> 01:13:48,280
Speaker 1: need systems to help our local, tribal instincts add up

1369
01:13:48,320 --> 01:13:53,519
Speaker 1: to something globally wise. That doesn't mean turning ourselves into

1370
01:13:53,560 --> 01:13:57,320
Speaker 1: cold calculators. Our emotions are part of what we're ultimately

1371
01:13:57,360 --> 01:14:02,240
Speaker 1: trying to protect. Love and loyal and solidarity. These are

1372
01:14:02,240 --> 01:14:05,400
Speaker 1: the best parts of being human. So the idea is

1373
01:14:05,439 --> 01:14:09,639
Speaker 1: to use reason and evidence not to erase those feelings,

1374
01:14:09,640 --> 01:14:13,160
Speaker 1: but to aim them. So the question to carry forward

1375
01:14:13,200 --> 01:14:17,400
Speaker 1: from today's episode is how can each of us expand

1376
01:14:17,560 --> 01:14:20,400
Speaker 1: the circle of the people that we care for and

1377
01:14:20,439 --> 01:14:24,400
Speaker 1: feel responsible for without losing the warmth of the.

1378
01:14:24,400 --> 01:14:26,320
Speaker 2: Small circle we evolved for.

1379
01:14:26,600 --> 01:14:29,599
Speaker 1: That's a simple question that sits at the intersection of

1380
01:14:29,640 --> 01:14:35,280
Speaker 1: our psychology, our politics, and fundamentally the future of our species.

1381
01:14:35,920 --> 01:14:40,080
Speaker 1: Once again, if you can go to GiveDirectly dot org

1382
01:14:40,160 --> 01:14:43,799
Speaker 1: slash cosmos. This is one way to turn your moral

1383
01:14:43,840 --> 01:14:47,680
Speaker 1: intuitions into action and take everything we're learning about the

1384
01:14:47,720 --> 01:14:51,760
Speaker 1: brain and use it to optimize how we go about

1385
01:14:51,880 --> 01:14:59,639
Speaker 1: trying to heal the world. Go to eagleman dot com

1386
01:15:00,080 --> 01:15:03,960
Speaker 1: podcasts for more information and to find further reading. Join

1387
01:15:04,000 --> 01:15:06,720
Speaker 1: the weekly discussions on my substack and check out and

1388
01:15:06,760 --> 01:15:10,200
Speaker 1: subscribe to Inner Cosmos on YouTube for videos of each

1389
01:15:10,240 --> 01:15:14,000
Speaker 1: episode and to leave comments. Until next time, may your

1390
01:15:14,160 --> 01:15:18,080
Speaker 1: moral instincts be kind and generous. I'm David Eagleman, and

1391
01:15:18,120 --> 01:15:24,519
Speaker 1: this is Inner Cosmos.