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  • Stanford University.

  • The often asked question, what's the difference between Bio 150,

  • Bio 250, and-- is it Hum Bio 160?

  • No difference.

  • It's exactly the same.

  • So like the same requirements, same unit.

  • So take whichever one makes your life easiest.

  • Let's see.

  • Any other procedural stuff?

  • Well, the answers are back from Monday's questionnaire.

  • And a variety of interesting answers.

  • Not surprisingly, given the size of a group.

  • Why have you taken this course?

  • Really want to know about animal behavior, but willing to deal

  • with humans.

  • [LAUGHTER]

  • Because I'm substituting it Bio 43, which I don't want to take.

  • My dad used to make me read books about human behavior

  • and biology as punishment.

  • [LAUGHTER]

  • That doesn't make any sense.

  • I know one of the TAs, so I figure

  • that guarantees me an A. OK, guys, that's in your court.

  • One I really liked, because I want

  • to be a filmmaker after college.

  • Yay, interdisciplinary.

  • What else?

  • My first grade teacher is making me.

  • Tom McFadden told me to.

  • I'm a hyper-oxygenated dilettante.

  • I wanted to, somewhat correctly pointing out,

  • why have you taken this class?

  • I haven't taken it yet.

  • A number of people reporting that,

  • in fact, that was the correct answer.

  • And my favorite, why have you taken this course?

  • Yes.

  • [LAUGHTER]

  • OK.

  • Relevant background, relevant background.

  • I'm human, I'm human and I often behave.

  • I'm human and I have biology.

  • 19 years of being confused about human behavior.

  • Not really, sort of.

  • Seeing crazy behavior as an RA in an all frosh dorm.

  • And I date a biologist.

  • Let's see.

  • There was also the question on there of,

  • did the thing on the board look more like an A or a B.

  • And just to really facilitate that one,

  • I forgot to put the A and the B up.

  • But that taps into a cognitive something or other,

  • which maybe I'll get back to at some point.

  • Telephone numbers.

  • Reading them off, accuracy dramatically

  • tanked as soon as the three number,

  • four number motif went down the tubes.

  • And when it came back briefly, accuracy

  • came back a little bit.

  • Finally, let's see.

  • All of you guys conform to a standard frequent gender

  • difference.

  • Which is everybody was roughly equally-- by gender-- roughly

  • equally likely to see dependent as the opposite of independent.

  • A small minority went for interdependent.

  • However, one finding that has come up over and over

  • is that far more females are interested in peace than males,

  • males are more interested in justice.

  • OK, have you taken the bio core.

  • Quote, no way Jose.

  • Somebody pointing out quite correctly,

  • don't settle for peace or justice.

  • Then of course, there was the person

  • who responded to that question by writing those words are just

  • symbols.

  • Need to know assumed meaning.

  • [LAUGHTER]

  • OK.

  • There was one questionnaire that was carefully

  • signed in something approaching calligraphy,

  • it was so beautiful.

  • And was otherwise blank.

  • For years running, the subject that most people really

  • want to hear, and most people really don't want to hear,

  • is about the biology of religiosity.

  • And for 22 years running now, Stanford students

  • are more interested in depression than sex.

  • [LAUGHTER]

  • OK.

  • So we start off.

  • I keep telling Hennessy about this, but nothing gets done.

  • We start off.

  • We start off, if I can open this--

  • which is something you can do if you have

  • a certain type of training.

  • If you're some osteologist, or whatever these folks are

  • called.

  • If you are presented those two skulls

  • and told this one's a female, this one's

  • a male, you can begin to figure out stuff like how heavy,

  • how large the body was of that individual, what

  • diseases they had, had they undergone malnutrition,

  • had they given birth, a lot of times, a few times,

  • were they bipedal.

  • All sorts of stuff you could figure out

  • from just looking at these skulls.

  • What today's lecture, and Friday's, is about

  • is the fact that with the right tools under your belt,

  • you could look at these two skulls

  • and know that information.

  • You are a field biologist, and you've discovered this brand

  • new species.

  • And you see that this one nurses an infant

  • shortly before leaping out of the tree,

  • leaving only the skull.

  • And this one has a penis, shortly

  • before leaping out of the tree and leaving a skull.

  • So all you know is this is an adult female and an adult male.

  • And if you've got the right tools there,

  • you can figure out who's more likely to cheat on the other.

  • Is the female more likely to mess around, or is the male?

  • How high are the levels of aggression?

  • Does the female tend to have twins, or one kid at a time?

  • Do females choose males because they have good parenting

  • skills, or because they're big, hunky guys?

  • What levels of differences in life expectancy?

  • Do they live the same length of time?

  • You would be able to tell whether they have the same life

  • expectancy or if there's a big discrepancy between the two.

  • All sorts of stuff like that, merely

  • by applying a certain piece of logic

  • that dominates all of this.

  • OK, so you're back reading those Time Life nature

  • books back when, and there was always a style of thing

  • you would go through.

  • Which is they'd describe some species

  • doing something absolutely amazing and unlikely,

  • and it goes like this.

  • The giraffe, the giraffe has a long neck,

  • and it obviously has to have a big heart

  • to pump all that blood up there.

  • And you lock up a whole bunch of biomechanics people

  • with slide rules, and out they come out with this prediction

  • as to how big the giraffe heart should be

  • and how thick the walls.

  • And you go and you measure a giraffe heart,

  • and it's exactly what the equations predicted.

  • And you say, isn't nature amazing?

  • Or you read about some desert rodents that drink once

  • every three months, and another bunch of folks

  • have done math and figured out how many miles long

  • the renal tubules have to be.

  • And somebody goes and studies it,

  • and it's exactly as you expect it.

  • Isn't nature wonderful?

  • No, nature isn't wonderful.

  • You couldn't have giraffes unless they

  • had hearts that were that big.

  • You couldn't have rodents living in the desert

  • unless they had kidneys that worked in a certain way.

  • There is an inevitable logic about how organisms function,

  • how organisms are built, how organisms

  • have evolved solving this problem of optimizing

  • the solution.

  • And what the next two lectures are about is,

  • you can take the same exact principles

  • and apply them to thinking about the evolution of behavior.

  • The same sort of logic where, just

  • as you could sit there and, with logical principles,

  • come to the point of saying, a giraffe's heart

  • is going to be this big.

  • You can go through a different realm of logic built

  • around evolutionary principles and figure out all sorts

  • of aspects of social behavior.

  • And we already know what's involved in, say, optimizing.

  • What's the optimal number of whatevers in your kidney.

  • What's the optimal behavior strategy or something.

  • All of us, as soon as we got some kid sibling,

  • learned how to do the optimal strategy in tic-tac-toe.

  • So that you could never lose, and it's totally boring.

  • But that's a case of figuring out the optimal solution

  • to behavior, reaching what is called the Nash equilibrium.

  • And actually, I have no idea what I just said.

  • But I like making reference to Nash,

  • because it makes me feel quantitative or something.

  • So that is called the Nash equilibrium.

  • The Nash equilibrium, and what the entire point here is,

  • the same sort of process of figuring out

  • what are the rules of optimizing tic-tac-toe behavior

  • can be built upon the principles of evolution

  • to figure out all sorts of realms

  • of optimized social behavior.

  • And broadly, this is a field that's known as sociobiology,

  • emerging in the late 1970s-- mid 1970s or so.

  • And by the late 1980s, giving birth

  • to another discipline known as evolutionary psychology.

  • The notion that you cannot understand behavior,

  • and you cannot understand internal psychological states,

  • outside the context of evolution had something to do with

  • sculpting those behaviors and those psyches.

  • So to start off with that, basic song and dance about Darwin.

  • Just to make sure we're up to speed on this.

  • Darwin, just to get some things out of the way.

  • Darwin did not discover evolution.

  • People knew about evolution long before that.

  • Darwin came up with the notion of a mechanism

  • for evolution, natural selection.

  • And in fact, Darwin is the inventor of that.

  • There was another guy, Alfred Russel Wallace,

  • the two of them.

  • And, for some reason, Wallace has gotten screwed historically

  • and Darwin gets much more attention.

  • But starting off with a Darwinian view of how evolution

  • works.

  • First thing being that there is evolution.

  • Traits in populations change over time.

  • Traits can change enough that, in fact, you

  • will get speciation.

  • New species will form.

  • And the logic of Darwinian evolution

  • is built on just a few couple of very reasonable steps.

  • First one is that there are traits that are heritable.

  • Traits that could be passed on one generation to the next.

  • Traits that we now can translate,

  • in our modern parlance, into traits that are genetic.

  • And we will see, soon, how that's totally not correct

  • to have said that.

  • But traits that are heritable.

  • The next thing is that there is variability among those traits.

  • There's different ways in which this trait can occur,

  • and they're all heritable.

  • The next critical thing.

  • Some versions of those traits are more adaptive than others.

  • Some versions work better for you.

  • For example, giraffe who wind up with hearts

  • the size of, like, a tomato, that's not an optimal version.

  • Amid the range of variability, some

  • will carry with them more fitness, more adaptiveness,

  • than others.

  • And that translates into another sound bite

  • that's got to be gotten rid of.

  • All of this is not about survival of the most adapted.

  • It's about reproduction, something we will

  • come to over and over again.

  • It's about the number of copies of genes you

  • leave in the next generation.

  • So you've got to have traits that are heritable.

  • There's got to be variability in them.

  • Some of those traits are more adaptive than others.

  • Some of those traits make it more

  • likely that that organism passes on copies of its genes

  • into the next generation.

  • And throw those three pieces together, and what you will get

  • is evolution in populations.

  • Changing frequencies of traits.

  • And when you throw in one additional piece, which

  • is every now and then the possibility

  • to have a random introduction of a new type of trait

  • in there-- modern parlance, a mutation-- from that,

  • you can begin to get actual large changes in what

  • a population looks like.

  • OK, so these are the basic building blocks of Darwin.

  • And it is easy to apply it to giraffes' hearts

  • and kidneys of desert rats, and everything we think about

  • in the world of physiology, anatomy,

  • in the context of evolution.

  • So how do you apply it to behavior?

  • And the basic notion, for folks who've

  • come from this Darwinian tradition

  • into thinking about behavior, is you do the exact same thing.

  • There are behaviors that are heritable, types, traits,

  • classes of behaviors.

  • They come with a certain degree of variation among individuals.

  • Some versions of them are more adaptive than others.

  • Over time, the more adaptive versions

  • will become more commonplace.

  • And every now and then, you can have mutations

  • that introduce new variability.

  • Totally logical, absolutely unassailable.

  • And what we're going to spend an insane amount of time

  • in this class on is one simple assumption

  • in there, which is that certain behaviors are heritable.

  • That certain behaviors have genetic components.

  • And as you'll see, this one is just

  • going to run through every lecture wrestling

  • with that issue there.

  • This is a big incendiary issue there as to how genetic--

  • and that's not the same thing as saying

  • how genetically determined-- how genetic behavior is.

  • So that's going to be an issue we come back to again

  • and again.

  • So now, transitioning into how you would apply

  • these Darwinian principles.

  • First thing before starting, a caveat.

  • You're going to wind up, in order to think about all

  • of this most efficiently, hopefully do some personifying.

  • Personifying as in, you'll sit around saying, well,

  • what would a female chimpanzee want

  • to do at this point to optimize the number of copies

  • of her genes in the next generation?

  • What would this brine shrimp want

  • to do to deal with this environmental stressor?

  • What would this cherry tree do?

  • They're not planning.

  • They're not conscious.

  • They're not taking classes in evolutionary biology.

  • What would this organism want to do

  • is just a shorthand for something sculpted

  • by the sort of exigencies of evolution,

  • and reducing the optimal.

  • They want to do this.

  • This is just going to be a short hand throughout.

  • Once you get past the apes, nobody

  • is wanting to do any of these optimization things.

  • So just getting that sort of terminology out of the way.

  • OK, so we start off with what's the first building

  • block of applying Darwinian principles to behavior.

  • Something that is absolutely critical to emphasize,

  • because the first thing we all need to do

  • is unlearn something we all learned back when on all those

  • National Geographic specials and that would consistently

  • teach us something about this aspect of evolution,

  • and would always teach it to us wrong.

  • Here's the scenario.

  • So you're watching, and there's this wildlife documentary.

  • It's dawn on the savanna.

  • And you see, there's a whole bunch lions

  • on top of some big old dead thing.

  • Some buffalo, or something.

  • And they're chewing away and having a fine time.

  • So something happens at that point,

  • which is, they have to deal with how they divvy up the food.

  • Or let me give you another example.

  • Another standard, sort of endless vignette

  • that comes up in these films.

  • Once again, now, you're back on the savanna.

  • It's not dawn this time, but you are

  • looking at one of the magnificent things

  • of the natural world, which is the migration of zebras

  • throughout East Africa.

  • A herd of 2 million of them migrate around, following

  • a cyclical pattern of rains.

  • So they're always going where the grass is greener.

  • So you've got this wonderful herd of 2 million wildebeest,

  • and there's a problem.

  • Which is, there's some great field right in front of them

  • full of grass, and bummer, there's

  • a river in between them and the next field.

  • And especially a bummer, a river teeming with crocodiles

  • just ready to grab them.

  • So what are the wildebeest going to do?

  • And according to the National Geographic type

  • specials we would get, out would come a solution.

  • There's all the wildebeest hemming and hawing

  • in this agitated state by the edge of the river.

  • And suddenly, from the back of the crowd,

  • comes this elderly wildebeest who pushes his way up

  • to the front, stands on the edge of the river

  • and says, I sacrifice myself for you,

  • meine kinder, and throws himself into the river--

  • [LAUGHTER]

  • --where immediately, the crocs get busy eating him up.

  • And the other two million wildebeest

  • could tiptoe around the other way across the river,

  • and everybody is fine.

  • And you're then saying, why'd this guy do this?

  • Why did this guy fling himself into the river?

  • And we would get the answer at that point.

  • The answer that is permeated as, like, the worst urban myth

  • of evolution.

  • Whatever.

  • Why did he do that?

  • Because animals behave for the good of the species.

  • This is the notion that has to be completely trashed right

  • now.

  • Animals behaving for the good of the species

  • really came to the forefront, a guy in the early 60s named

  • Wynne-Edwards.

  • Hyphenated, Wynne-Edwards.

  • Some hyphenated Brit zoologist, who pushed most strongly

  • this notion of that animals behave

  • for the good of the species.

  • He is reviled throughout every textbook, Wynne-Edwards

  • and group selection.

  • That would be the term, selection

  • for the good of groups, for the selection

  • for the good of the species.

  • Wynne-Edwards and group selection.

  • I'm sure the guy did all sorts of other useful things.

  • And anyone who really has any depth to them would find out.

  • But all I know is that the guy is the one who

  • came up with group selection.

  • Animals behave for the good of the species.

  • This isn't the case at all.

  • Animals behave for passing on as many copies of their genes

  • as possible.

  • And what we'll see is, when you start

  • looking at the nuances of that, sometimes it

  • may look like behaving for the good of the species.

  • But it really isn't the case.

  • So animals behave in order to maximize

  • the number of copies of genes they

  • leave in the next generation.

  • Remember, not survival of the fittest,

  • reproduction of the fittest.

  • So first thing you need to do is go back to that vignette

  • and saying, so what's up with the wildebeest there?

  • And what's up with the elderly guy who jumps in the river?

  • And finally, when you look at them long enough

  • instead of the camera crew showing up for three minutes,

  • when you studied this closely enough,

  • you see something that wasn't apparent at first.

  • Which is, this elderly wildebeest

  • is not fighting his way through the crowd.

  • This guy is being pushed from behind.

  • [LAUGHTER]

  • This guy is being pushed from behind,

  • because all the other ones are saying,

  • yeah, get the old guy on the river.

  • Sacrificing himself, my ass.

  • This guy is getting pushed in by everybody else.

  • He is not sacrificing himself for the good of the species.

  • He does not like the idea of this whatsoever.

  • So he gets pushed in because the old, weak guy.

  • None of this group selection stuff.

  • What came in by the '70s as a replacement,

  • a way to think about this, is this notion of animals,

  • including us, behaving not for the good of the species

  • or of the group, but to maximize the number of copies of genes

  • left in the next generation.

  • And what you see is three ways in which this could occur.

  • Three building blocks.

  • The first one being known as individual selection.

  • The first one, built around the notion

  • that sometimes the behavior of an animal

  • is meant to optimize the number of copies of its genes

  • that it leaves in the next generation

  • by itself reproducing.

  • The drive to reproduce, the drive

  • to leave more copies of one's genes.

  • This was once summarized really sort of tersely as,

  • sometimes a chicken is an egg's way of making another chicken.

  • No, that's backwards.

  • Sometimes a chicken is an egg's way of making another egg.

  • OK, ignore that.

  • What the guy said is, sometimes a chicken is an egg's way

  • of making another egg.

  • All this behavior stuff, and all this animate social

  • interaction, is just an epiphenomenon

  • to get more copies of the genes into the next generation.

  • Individual selection, a subset of way of thinking about this

  • is selfish genes.

  • What behavior is about is maximizing

  • the number of copies of genes in the next generation.

  • And sometimes the best way to do it, sometimes

  • the way that animals maximize, is to get as many copies

  • by way of reproducing themselves.

  • It's not quite equivalent to The Selfish Gene,

  • but for our purposes, individual selection.

  • And this can play out in a number of realms.

  • And bringing in sort of a big dichotomy

  • in thinking about evolutionary pressures, Darwin

  • and the theory of natural selection.

  • What natural selection is about is

  • processes bringing about an organism who is more adaptive,

  • what we just went through.

  • Darwin soon recognized there was a second realm of selection,

  • which he called sexual selection.

  • And what that one's about is, this

  • is selecting for traits that have

  • no value whatsoever in terms of survival or anything like that.

  • Traits that carry no adaptive value,

  • but for some random, bizarre reason, the opposite sex

  • likes folks who look this way.

  • So they get to leave more copies of their genes.

  • And suddenly, you could have natural selection bringing

  • about big, sharp antlers in male moose,

  • and they use that for fighting off predators or fighting

  • with a male.

  • That would be natural selection.

  • Sexual selection might account for the fact

  • that the antlers are green, paisley patterns all

  • over for that.

  • And for some reason, that looks cool.

  • The female moose is, and what you wind up

  • getting as a mechanism for sexual selection

  • is, as long as individuals prefer to mate with individuals

  • with some completely arbitrary traits,

  • those traits will also become more common.

  • So this dichotomy of natural selection

  • for traits driven by traits that really do

  • aid leaving copies of genes outside the realm of just

  • sheer sexual preference, sexual selection.

  • And sometimes they can go in absolutely opposite directions.

  • You can get some species where the female fish

  • prefer male fish that have very bright coloration.

  • And that's advantageous, then, to have the bright coloration

  • by means of sexual selection.

  • But the bright coloration makes you

  • more likely to get predated by some other fish.

  • Natural selection pushing against bright coloration

  • in males.

  • Very often, you've got the two going against each other,

  • having to balance.

  • So how would that be applied in this realm

  • of individual selection?

  • This first building block.

  • Sometimes an egg-- damn.

  • Sometimes a chicken is an egg's way of making another egg.

  • Sometimes what behavior is about is one individual trying

  • to maximize the number of copies of their genes

  • in the next generation.

  • A natural selection manifestation of it

  • being, you're good at running away from predators.

  • Selection for speed, for certain types of muscle metabolism,

  • for certain sets of sensory systems that will tell you

  • there's somebody scary around.

  • That would be the realm of that.

  • Individual selection, selecting the realm of sexual selection

  • to have more of whatever those traits that are attractive.

  • So this first building block, it's not group selection.

  • It's not behaving for the good of the species.

  • It's behaving to maximize the number of copies of one's genes

  • in the next generation.

  • And the most straightforward way is

  • to behave in a way to maximize the number of times

  • you reproduce yourself.

  • Second building block, which is, there

  • is another way of accomplishing the same thing that you just

  • did with individual selection, as follows.

  • One of the things that can be relied upon in life

  • is that you are related to your relatives.

  • And what you get is, the more closely related you are,

  • the more genes you share in common with them.

  • On a statistical level, identical twins

  • share 100% of their genes.

  • Full siblings, 50%.

  • Half siblings, 25%.

  • This is exactly something that's going

  • to be covered in the catch up section this week.

  • If you're not comfortable with this stuff, this sort of thing

  • will be reviewed in more detail.

  • OK, so the closer a relative is to you,

  • the more genes they share in common with you.

  • So suddenly, you've got this issue.

  • You're an identical twin and your identical sibling

  • has the same genes that you do.

  • Individual selection, you will be just as successful

  • as passing on copies of your genes into the next generation

  • if you forgo reproducing to make it

  • possible for your identical twin to do so.

  • Because on the level of just sheer numbers

  • of copies of genes in the next generation,

  • they are equivalent.

  • And sometimes, you will thus get behavior which really decreases

  • the reproductive success of an individual

  • in order to enhance the success of a relative.

  • But you've got a constraint there,

  • which is, all of your relatives don't

  • share all your genes with you.

  • They have differing degrees of relatedness.

  • And what that winds up producing is another factor,

  • another observation.

  • One of the great, witty geneticists of all time, a guy

  • named Haldane who, apparently, once in a bar

  • was trying to explain this principle to somebody

  • and came up and said, I will gladly

  • lay down my life for two brothers or eight cousins.

  • And that's the math of the relatedness.

  • You passing on one copy of your genes

  • to the next generation is, from the sheer mathematics of just

  • how evolution is going to play out over the generations,

  • is exactly equivalent as giving up your life for eight cousins

  • to be able to each pass on a copy of their genes.

  • Because you share 1/8 with each of them,

  • and it winds up being a whole [INAUDIBLE].

  • And it's that math.

  • And out of that, you get something that

  • makes perfect sense instantly.

  • Which is, evolution selects for organisms cooperating

  • with their relatives.

  • Something along those lines.

  • And thus we have this second building block

  • known as kin selection.

  • Inclusive fitness.

  • Kin selection.

  • First building block, individual selection, passing on copies

  • of your own genes as a way to maximize future success.

  • Second version, helping out relatives.

  • Helping out relatives in terms of increasing

  • their reproductive success with this vicious mathematical

  • logic, which is one identical twin

  • to have two full siblings, eight cousins, and so on,

  • as a function of degree of relatedness.

  • And what this begins to explain is a whole world

  • in animal behavior of animals being obsessed with kinship.

  • Animals being fully aware of who is related to who in what

  • sorts of ways.

  • Animals being utterly aware of you

  • cooperate with relatives, but as a function of how

  • closely related they are.

  • Animals put us in Social Anthropology, in kinship terms,

  • and could you marry the daughter of your uncle's third wife

  • or whatever, to shame in terms of how

  • much a lot of social animals deal with relatedness.

  • So inclusive fitness, kin selection.

  • Here would be evidence for it.

  • Here's one example.

  • Very cool study done some years back by a couple, Seyfarth

  • and Cheney, University of Pennsylvania,

  • looking at vervet monkeys.

  • And these were vervet monkeys out in Tanzania, I believe.

  • What they did was, a whole bunch of these vervet monkeys

  • were sitting around.

  • And they, the researchers, had made really high quality

  • recording recordings of various vocalizations

  • from the monkeys over time.

  • So they had the sound of each animal giving an alarm call,

  • giving a friendly gesture call, giving a whatever.

  • And what they would then do is hide a microphone

  • inside some bushes and play the sound of one of the infants

  • from the group giving an alarm call.

  • So what does the mother of that infant do?

  • She instantly gets agitated and looks over at the bush.

  • That's her child, all of that.

  • How to know that everyone else in that vervet group

  • understands kin selection, what does everybody else do?

  • They all look at the mother.

  • That's whoever's mother, what is she going to do next?

  • They understand the relatedness, and they understand

  • what the response will be.

  • All the other vervets look at the mother at that point.

  • Whoa, I'm sure glad that's not my kid giving an alarm

  • call from the bushes.

  • They understand kinship.

  • Another version of that came out in these studies.

  • So you've got two females, each of whom has a kid, a daughter,

  • whatever.

  • And female A and female B. And one day, female A

  • does something absolutely rotten to female B.

  • And later that day, the child of female B

  • is more likely than chance to do something rotten

  • to the child of female A. They're

  • keeping track of not only revenge,

  • but not revenge on the individual who

  • did something miserable to you, but displaced by one

  • degree of reproduction.

  • Keeping track of kinship.

  • Animals can do this.

  • All sorts of primate species can do this.

  • And as we'll see, all sorts of other species can do this also.

  • There is that caveat again.

  • All sorts of other species want to figure out

  • who their cousins-- they don't want to figure out.

  • Evolution has sculpted an ability

  • to optimize behavior along lines of relatedness

  • in all sorts of species.

  • So how would natural selection play out

  • in this realm of kin selection, I will lay down

  • my life for eight cousins.

  • And that's just sort of obvious there by now.

  • How would sexual selection play out in this realm.

  • I am willing to expend great amounts of energy

  • to convince people that my sibling is incredibly hot.

  • And with any chance, then passing

  • on more copies of genes.

  • That would be inclusive fitness, kin selection in both cases.

  • Decreasing your own reproductive potential

  • by way of being killed by a predator to save the 8 cousins,

  • or having to spend so much time haranguing about your sibling.

  • Doings that, in order to increase

  • the reproductive success of relatives,

  • where you were willing to give up more energy

  • and potential on your part, the more closely related

  • the individual is.

  • So you throw those two pieces together,

  • and you're suddenly off and running

  • with explaining a lot of animal behavior.

  • Individual selection, none of this

  • for the good of the species.

  • Maximizing the number of copies of your own genes.

  • And the easiest way, the most straightforward,

  • is you yourself maximizing reproduction.

  • Foundation number two the whole thing, kin selection.

  • Sometimes the best way of leaving more copies of genes

  • in the next generation is using up

  • your own reproductive potential foregoing

  • to help relatives as a function of degree of relatedness.

  • OK, that's great.

  • So now the third piece, the third final building block

  • of making sense of social behavior in the context

  • of real contemporary evolutionary theory,

  • the third block here.

  • Which is, you look at animals and they're not all just

  • competing with non-relatives.

  • Animals forego competition at certain points.

  • Animals would have the potential to be

  • aggressive to other animals, and they will forego doing so.

  • And there's one circumstance in which that can happen,

  • where you get what is called a rock-paper-scissors scenario.

  • You've got animals A, B, and C. A has a means of damaging B,

  • but it costs A. B has a means of damaging C,

  • but it costs B. C can damage A, but it

  • costs A. You get the right distribution of individuals

  • with one of those traits in a population,

  • and you will reach a rock-scissors-paper equilibrium

  • where nobody's doing anything rotten to each other.

  • Great example, totally cool example that got

  • published some years ago by a guy named Brendan Bohannan, who

  • was assistant professor in the department here at the time.

  • He was studying something or other about bacteria

  • showing a rock-paper-scissors circumstance.

  • You had three different types, three different versions,

  • of this bacteria in this colony he had made.

  • The first one could generate a poison, but it cost.

  • It had to put the effort into making that poison

  • and protecting itself from that poison, all of that.

  • The second type was vulnerable to the poison.

  • It happened to have some transporter on its membrane

  • that took up the poison, and that was bad news.

  • But it had an advantage, which is the rest of the time,

  • that transporter took up more food.

  • The third one, the good thing going

  • for it is that it didn't have-- the bad thing

  • was, it didn't have poison.

  • A good thing going for it was it didn't

  • have to spend energy on a poison,

  • and it didn't have that transporter.

  • So each one of those has a strength, each one of those

  • has a vulnerability.

  • They're like, I don't know, Pokemon or something.

  • And you put them all together there,

  • and you get a rock-paper-scissors scenario

  • where you get equilibrium, where they are not

  • attacking each other.

  • Because note, if I am A and I destroy B,

  • B's no longer wiping out C, who's

  • the one who could damage me.

  • It's got to come to an equilibrium state.

  • So you can get the evolution of stalemates like that,

  • and that's quite frequently seen.

  • And note here, this was the evolution

  • of stalemates not in chimps, not in cetaceans, but in bacteria.

  • What we're going to see is bacterial behavior,

  • to the extent that this is sort of a metaphor for behavior.

  • Behavior of all sorts of unlikely species

  • are subject to these same rules of passing

  • on copies of your genes.

  • These three different strains of bacteria

  • are competing with each other.

  • None of them are behaving for the good of the species there

  • of the three of them.

  • So rock-paper-scissors is very cool,

  • and you get versions of that in humans.

  • That's been sort of studied quantitatively, all of that.

  • But that's not real cooperation.

  • That's merely everybody realizing

  • we have to cut back on the competition.

  • We have to cut back on the aggression.

  • Because every time I damage whoever,

  • I am more vulnerable in another realm.

  • That's a stalemate.

  • That's a truce.

  • But you look at animals, and in all sorts of realms,

  • it's not just rock-paper-scissors stalemates

  • they're reaching.

  • They actually cooperate with each other.

  • And you look close enough, and you see they're not relatives.

  • They're not relatives, yet you get

  • all sorts of altruistic behavior,

  • and you've got it under a whole bunch of domains.

  • Because this brings up the question,

  • why should you ever be cooperative

  • with another individual if you are a social animal.

  • At every possibility, you should stab them in the back

  • and be selfish.

  • And the reason why that isn't a good idea

  • is, there's all sorts of circumstances where

  • many hands make the task light.

  • Or whatever that is, cooperation can have synergistic benefits.

  • And you see that with species that

  • are cooperative hunters, where they are not necessarily

  • relatives.

  • They will chase one, chasing an animal

  • while the other is getting ready to cut a corner on it.

  • Cooperative behavior, and they increase the likelihood

  • of them getting a kill.

  • Another example of this.

  • Research by a guy named Mark Hauser

  • at Harvard looking at rhesus monkeys.

  • And what he showed was, he would put these monkeys

  • in a situation where they had access to food.

  • They had access to food under one circumstance,

  • where they could reach for it and take it in and share it

  • with another monkey.

  • Under the other circumstance, it required two monkeys

  • to get the food in there.

  • And what he showed was clear cut reciprocity.

  • Monkeys who were sharing with this guy

  • were more likely to get shared back with

  • and got more cooperation when it was a task where two of them

  • had to work together to get the food.

  • One alone wasn't enough.

  • Many hands make the task lighter under all sorts

  • of circumstances.

  • Cooperation has a strong evolutionary payoff,

  • even among non-relatives, with a condition.

  • Which is, you're not putting more into it

  • than you are getting.

  • That is reciprocal.

  • And ' opens up the third building block of all of this,

  • which is reciprocal altruism.

  • Cooperation, altruistic behavior among non-relatives,

  • but undergoing very strict constraints of,

  • it's gotta be reciprocated with all sorts of rules like that.

  • So what does that look like.

  • You're going to see reciprocal altruism,

  • when would you see that.

  • What's the immediate thing, what sort of species

  • would show systems of reciprocal cooperation

  • among non-relatives.

  • They've got to be smart animals.

  • They've got to be social.

  • They've got to be smart.

  • Why do they have to be smart?

  • Because they have to remember, this

  • is the guy who owes me a favor from last Thursday.

  • They need to be able to recognize individuals.

  • They have to be long lived enough so that there's

  • a chance of interacting with that individual

  • again and establishing this reciprocity.

  • You would thus predict you would see

  • systems of reciprocal altruism only in long

  • lived social vertebrates.

  • But you see the exact sorts of things in bacteria.

  • You see the exact sort of things in fungi.

  • You see that in all sorts of other realms.

  • You get social bacteria, colonizing bacteria.

  • And where what you might get are two clonal lines

  • that are together.

  • In other words, two genetically-- two lines,

  • each of which is, all the bacteria

  • have the same genetic makeup.

  • So think of it as one individual who's just kind of dispersed.

  • Another one who's just kind of dispersed.

  • And they've come together in something

  • called a fruiting body, which is how bacteria reproduce

  • or whatever.

  • And there's two parts to a fruiting body.

  • There's one which is the stalk, which

  • attaches to something or other.

  • And then there is the part that actually fruits.

  • So you want to be in the fruiting part,

  • because that's the part that actually reproduces,

  • and the stalk is doing all the work there.

  • And what you see is attempts at cheating.

  • Attempts at one of these strains trying

  • to disproportionately wind up in the fruiting part,

  • and what you also see is, the next time

  • around, this other strain will not cooperate with it.

  • Will not form a social colony.

  • So that's getting played off at the level of single cell

  • organisms forming big social colonies.

  • Getting played at that level.

  • Yes, as we will see, reciprocal altruism

  • works most readily in big, smart, long

  • lived social beasts.

  • But it can occur in all sorts of systems.

  • What it's built around is reciprocal cooperation.

  • And intrinsic in that it is another motivation going

  • on there.

  • Not just to involve the reciprocal relationship

  • with a non-relative, and many hands, and light tasks,

  • and all of that.

  • But also, whenever possible, to cheat.

  • To take advantage of the other individual.

  • And thus, another key facet of it

  • is to be very good at detecting when somebody

  • is cheating against you.

  • To be vigilant about cheating in what

  • would otherwise be a stable, reciprocal relationship.

  • And an awful lot of social behavior

  • is built around animals either trying

  • to get away with something or spotting somebody else

  • doing the same.

  • An example of it.

  • There is a test that's used in evolutionary psychology

  • where you are given this very complicated story,

  • or another version of a complicated story, where

  • somebody promises if you do this, you'll get this reward.

  • But if you do that, you're going to get this punishment.

  • And really complex.

  • And one outcome, the outcome of it

  • is, the person isn't supposed to get rewarded.

  • But the individual decides to reward them.

  • Spontaneous act of kindness.

  • In another circumstance, the person

  • is the individual who is supposed to get rewarded,

  • and instead, they get punished.

  • A cheater in that case.

  • And amid these convoluted stories,

  • people are much better-- 75% to 25%--

  • are much better at detecting when cheating has gone

  • on in the story than when a random act of kindness has gone

  • on.

  • We are more attuned to picking up cheating.

  • And remarkably, some very subtle studies

  • have been done with chimps showing

  • that chimps have the same bias.

  • They are much better at picking up

  • social interactions involving cheating than ones that

  • involve spontaneous altruism.

  • So you see here, this balance between cooperation,

  • reciprocal, even among non-relatives.

  • And that's great, but you should cheat

  • when you can get away with it.

  • But you should be vigilant against cheaters.

  • And what, of course, it comes down to then

  • is tic-tac-toe and giraffe hearts and all of that.

  • What is the optimal strategy in a particular social species

  • for a particular individual.

  • What is the optimal strategy.

  • When do you cooperate and when do you cheat.

  • When do you defect on the cooperative relationship

  • you've had.

  • And this introduces us to a whole world

  • of mathematics built around what is called game theory.

  • The notion that there are games, formal games, that

  • have mathematically optimal strategies,

  • or multiple strategies, multi-equilibrium.

  • And a whole world of research has

  • been built around them in terms of when to cooperate

  • and when to defect.

  • So game theory stuff.

  • This was starting off in a world of people

  • studying economics, and negotiation, and diplomacy,

  • and all of that.

  • And that was a whole world built around this logic

  • of when do you cooperate, when do you cheat.

  • And what came out of there were all sorts

  • of models of how to optimize behavior in terms of that.

  • And the building block, sort of the fruit fly of game theory,

  • is a game called the prisoner's dilemma.

  • Prisoner's dilemma, sort of cutting to-- sort of getting

  • rid of the details.

  • Two individuals are prisoners, and they escape,

  • and they're both captured.

  • And they're interrogated separately.

  • And both of them refuse to talk, that's great for them.

  • If they both squeal, they both get punished.

  • If one of them is able to squeal on the other one,

  • they get a great reward.

  • If the other one-- what you get formally

  • are four possible outcomes.

  • Both individuals cooperate, both individuals

  • cheat against each other, individual A cooperates and B

  • cheats, individual B cooperates and A cheats.

  • And what you get in prisoner's dilemma

  • is a formal payoff for each.

  • What gives you the greatest payoff, stabbing

  • the other guy in the back.

  • You cheat and they cooperate.

  • You have exploited them, you have taken advantage of them,

  • isn't that wonderful.

  • That's the highest payoff in prisoner dilemma games.

  • Second highest payoff, you both cooperate.

  • Third highest payoff-- which is beginning

  • to not count as a payoff, but in a lot of the games, this set up

  • is the start of punishment-- both of you

  • cheat on each other.

  • Fourth worst possible payoff is you're the sucker.

  • You cooperate, and the other individual

  • stabs you in the back.

  • So what the prisoner's dilemma game

  • is set up these circumstances where individuals will play

  • versions of this against each other with varying rewards

  • and that sort of thing, and parameters that we will

  • look at in a lot of detail.

  • And seeing when is it optimal to cooperate,

  • when is it optimal to cheat.

  • When would you do this.

  • So you've got examples of this, and this

  • was the building block.

  • And what anyone would say looking at this is,

  • it's obvious.

  • What you want to do is, in some way,

  • rationally maximize your payoff.

  • This whole world of Homo economists,

  • the notion of humans as being purely rational decision

  • makers.

  • And what you begin to see in this world of game theory is,

  • there is anything but that going on.

  • Later in the course, we're going to see

  • something very interesting.

  • People playing prisoner's dilemma games inside a brain

  • scanner, looking at a part of the brain that

  • has a lot to do with pleasure.

  • And what you see is, some individuals

  • activate that part of the brain when they have successfully

  • stabbed the other guy in the back.

  • Some individuals activate it when they have both cooperated.

  • And there's a big gender difference

  • as to which circumstance.

  • [LAUGHTER]

  • So you just guess which one is going on there.

  • We're going to see a number of studies

  • like that coming down the line.

  • So the question becomes, how do you

  • optimize prisoner dilemma play?

  • And what emerged at that time was

  • the notion of all sorts of theoretical models and stuff.

  • And then in the 1970s, there was an economist

  • at University of Michigan named Robert Axelrod who

  • revolutionized the entire field.

  • What he did was he took some paleolithic computer

  • and programmed in how the prisoner's dilemma would

  • be played.

  • And he could program in as if there were two players.

  • And he could program in what each one's strategy would be.

  • And what he then did was, he wrote to all of his buddies

  • and all of his mathematician friends and prize fighters

  • and theologians and serial murderers and Nobel Peace Prize

  • winners, and in each case, explained

  • what was up and saying, what strategy would you use

  • in a prisoner's dilemma game?

  • And he gets them all back, and he programs

  • all these different versions.

  • And he runs a round robin tournament.

  • Every strategy is paired against every other strategy

  • at one point or other.

  • And you look at what the payoff is.

  • You ask, which is the most optimal strategy.

  • And out of it, shockingly to everyone--

  • because this was a computer teaching us optimizing

  • human behavior-- out of it came one simple strategy that

  • always out-competed the others.

  • This is people sitting there, probabilistic ones

  • as to when to cooperate, and lunar cycles as to what to do.

  • The one that always won is now called tit for tat.

  • You start off cooperating in the very first round

  • with the individual.

  • You cooperate.

  • If the individual has cooperated with you in that round,

  • you cooperate in the next round.

  • And you cooperate, cooperate, as long

  • as the other individual cooperates.

  • But as soon as there is a round where the individual cheats

  • against you, you cheat against them the next time.

  • If they cheated at you that time also,

  • you cheat against them the next time.

  • If they go back to cooperating, you

  • go back to cooperating the next time.

  • You have this tit for tat strategy.

  • In the absence of somebody stabbing you in the back,

  • you will always cooperate.

  • And what they found was, run these hundreds

  • of thousands of versions of these round robin tournaments,

  • and tit for tat was the one that was most optimal,

  • to begin to use a word that is not just

  • going to be a metaphor.

  • Tit for tat always drove the other strategies

  • into extinction.

  • And what you wound up seeing is this optimized strategy.

  • And it was very clear why tit for tat worked so well.

  • Number one, it was nice.

  • You start off cooperating.

  • Number two, it retaliates if you do something crummy to it.

  • Number three, it is forgiving.

  • If you go back to cooperating.

  • Number four, it's clear cut in its play.

  • It's not some probabilistic thing.

  • What you get, then, with tit for tat is,

  • suppose you're playing three rounds with another individual.

  • You both cooperate the first one,

  • you both cooperate the next one.

  • You're playing tit for tat strategy,

  • so you cooperate on this one.

  • And they stab you in the back, and you can't get back at them,

  • because this is the last round.

  • What you'll see is, under lots of circumstances,

  • tit for tat is disadvantageous.

  • But what the soundbite is about it is, tit for tat

  • may lose the battles, but it wins all the wars.

  • This pattern of being nice, but being retaliatory, being

  • forgiving, and being clear in the rules,

  • drives all the other strategies into extinction.

  • OK, at this point my alarm just went off,

  • which was to remind me to ask somebody

  • who is wearing a life vest-- is somebody wearing a life vest?

  • [INAUDIBLE]

  • Over there.

  • Where are you?

  • She just left.

  • She left.

  • Isn't that interesting?

  • Somebody put me up to having to ask this person,

  • why are you wearing a life vest?

  • And apparently the answer she would give

  • was going to free all sorts of captives in some rebel group

  • in Colombia.

  • And she fled.

  • OK, what that does is--

  • [LAUGHTER]

  • I don't know what that says about reciprocal altruism.

  • But what that says also is, after I do a summary,

  • don't make a move.

  • We will have a five minute break.

  • So what do we have at this point,

  • we have the first building block of optimizing

  • the evolution of behavior, like optimizing giraffe hearts.

  • First piece, you don't behave for the good of the species.

  • Individual selection, passing on as many copies

  • of your own genes as possible.

  • Sometimes a chicken is an egg's way of making another egg,

  • he says triumphantly.

  • Building block number two, kin selection.

  • Some of the time, the best way to pass on copies of your genes

  • is by way of helping relatives.

  • Kin selection, with the mathematical fierceness

  • of degree of relatedness driving it.

  • Piece three, sometimes what's most advantageous

  • is to cooperate, even with non-relatives,

  • but with the rules of it has to be reciprocal

  • and you have to cheat when possible.

  • You have to be on guard against cheaters.

  • And as we've just seen, game theory, prisoner's dilemma,

  • beginning to formalize optimal strategies for that.

  • OK, let's take a five minute break.

  • But promise you will come back if you go out,

  • and everyone won't wander off.

  • Altruism [INAUDIBLE] game theory as being a form or way

  • to maximize that behavior in a very artificial realm,

  • but stay tuned.

  • Prisoner's dilemma as the building block

  • of how to do this amid lots of other types

  • of games that are used.

  • But prisoner's dilemma is the most basic one.

  • And that round robin tournament, that computer simulation,

  • Axelrod asking all his buddies to tell him

  • what strategy would you use, run them against each other,

  • and out comes tit for tat.

  • Tit for tat drives all the others into extinction.

  • However, there is a vulnerability

  • in tit for tat, which is-- OK, so.

  • We have the technical way of showing prisoner's dilemma

  • play.

  • And first round, both individuals are cooperating.

  • Second round, both individuals are cooperating.

  • Third round, this one cheats-- those are fangs.

  • This one cheats and this one cooperates.

  • So the next round, this one now cheats and this one

  • goes back to cooperating, and we've just

  • gotten through a scary thing that tit for tat solves,

  • and it's great.

  • Wonderful.

  • What if, though, your system is not 100% perfect.

  • What if there's a the possibility

  • of a mistake being made, of sending the wrong signal.

  • What if there's the possibility of noise in the communication

  • system.

  • And at some point, an individual who

  • does a cooperative behavior, thanks to a glitch

  • in the system, it is read as having been defection.

  • So what happens as a result?

  • This individual-- forget it.

  • OK, what happens as a result. The individual who cooperated,

  • but somehow the message got through as cheating,

  • they don't know.

  • Something got lost in the wires between them in translation.

  • The other individual was saying whoa,

  • that individual cheated against me.

  • I'm going to cheat in the next round.

  • So along comes the next round, and that individual

  • cheats against them.

  • This one who's cooperating, because they've

  • been cooperating all along.

  • They don't know about this error.

  • And they say whoa, that person just cheated against me.

  • I'm going to cheat in the next round.

  • So they cheat in the next round.

  • This one says whoa, they just cheated another time,

  • again and again and again.

  • And what you get is a seesaw pattern for the rest of time.

  • You've just wiped out 50% of the cooperation.

  • And what you've got is tit for tat strategies

  • are vulnerable to signal error.

  • That's something that soon came out in these studies

  • of Axelrod's.

  • When I was a kid, there was like one of these thriller

  • books I remember reading where there's a glitch in the system.

  • And at the time, the mean scary Soviet Union

  • launched a missile that-- no, it was the United States.

  • The United States, by accident, launched

  • a missile, a nuclear weapon, where they didn't mean to.

  • Some cockroach chewed through a wire some place or other.

  • And the missile went off, and wound up destroying Moscow.

  • And oh my god, we had a cooperative system

  • of mutually restraint of aggression, all of that.

  • And thanks to a signal error, a cheating signal

  • was accidentally sent off.

  • And how did the book end?

  • A tit for tat response.

  • In order to avoid thermonuclear wasteland,

  • the Soviet Union was allowed to destroy New York.

  • All right, so that shows exactly how

  • you could then get into a see-sawing thing,

  • simply by way of if the system has any vulnerability

  • to signal error.

  • So it soon became clear, as soon as Axelrod

  • began to introduce the possibility of signal errors,

  • that tit for tat didn't work as well as another strategy, one

  • that quickly came to the forefront.

  • And that one-- for some strange reason,

  • that's the way it's shown.

  • That one was called forgiving tit for tat.

  • What happens with forgiving tit for tat?

  • The usual rule, like tit for tat, if you cooperate,

  • if they cooperate, you always cooperate.

  • If they cheat against you, you punish them in the next round.

  • Exactly the same thing as tit for tat,

  • but oh no, what if there's a signal error in the system

  • and you've gotten caught in one of these horrible seesawing

  • things.

  • What forgiving tit for tat does is,

  • we'll have a rule, for example, that if we

  • see saw like this five times in a row,

  • I will forego cheating the next time.

  • And instead, I'll cooperate.

  • And that will get things back on track.

  • I am willing to be forgiving in one round

  • in order to re-establish cooperation

  • after the signal error came in.

  • And that one-- as soon as you introduce

  • the possibility of signal error, that one out-competes

  • tit for tat.

  • Because it makes perfect sense.

  • It's a great way of solving that problem.

  • So that was terrific.

  • Tit for tat with the ability to forgive,

  • and what you would then see is variability, how many of these

  • do you need to go through before you forgive,

  • what's the optimal number of see-sawings, all of that.

  • So a whole world of optimizing how soon you're forgiving.

  • Nonetheless, the general theme being forgiving tit

  • for tat out-competes tit for tat when you can have signal error.

  • But there is a vulnerability.

  • There is a vulnerability here to this one, which

  • is, you could be exploited.

  • If you're playing against, for example, a tit for tatter,

  • or all sorts of other strategies, where

  • they don't have forgiving strings of defection

  • and you do, what's going to happen

  • is you're going to keep going back to cooperating,

  • they're going to keep stabbing you in your back.

  • Forgiving tit for tat is vulnerable to exploitation

  • playing against individual players that

  • don't have forgiveness in them.

  • So what soon became apparent was an even better strategy, which

  • is you start off with a tit for tat strategy.

  • Which is, you are punitive, you are

  • retaliatory amid being forgiving, clear and nice

  • initially.

  • You are willing to punish, and you cannot be exploited in this

  • way.

  • If and only if you have gone whatever number of rounds

  • without the other individual ever cheating on you, if you've

  • gone long enough without that happening,

  • you switch over to forgiving tit for tat.

  • What is that?

  • That's deciding you trust somebody.

  • You've had enough interactions with them

  • that you are willing to trust them.

  • This is the transition from pure rational optimizing

  • to switching over, forgiveness coming in there protects you

  • from signal error.

  • And of course, now, a whole world of how many rounds do you

  • need to do this before you switch that as to what

  • the optimal deal with that is.

  • But again, this is a way of transitioning

  • to solve the problem of signal error,

  • but forgiving too readily and being taken advantage of.

  • Soon, another strategy appeared, which was called Pavlov.

  • And those of you who know Pavlovian psychology

  • will see that this, in fact, has nothing whatsoever to do

  • with Pavlovian psychology, and I don't know why they did that.

  • But they thought it was kind of cool.

  • But the rule was remember, if you

  • stab the other guy in the back, you get a bunch of points.

  • If you both cooperate, you get points, not as many.

  • If you both cheat, you lose some points.

  • If you're taken advantage of, you lose a lot of points.

  • So two outcomes you gain, two outcomes you lose.

  • In Pavlov, the simple rule is when I do something,

  • if I get points, if I get some degree of reward,

  • I do it again the next time.

  • If I get rewarded in either of the first two types of payoffs,

  • I do the same thing again.

  • And the other part, of course, is, if I play my strategy

  • and I lose one of the two bottom outcomes,

  • I switch to the other strategy the next time.

  • And what you see is that can establish very good tit

  • for tat stuff.

  • But if you sit and spend hours tonight

  • with a long roll of toilet paper and playing

  • out all the rounds of it, you will

  • see what Pavlov allows you to do is exploit

  • somebody else who is forgiving.

  • So Pavlov goes along just fine with this.

  • And as long as Pavlov continues, whenever they switch over

  • to a forgiving tit for tat, Pavlov

  • will out-compete them, because Pavlov exploits.

  • What then emerged was just zillions of people studying

  • all sorts of games like this.

  • There's other ones, ultimatum game, there's a trust game.

  • It's the same notion of business there,

  • which is you choose to cooperate, you choose to cheat,

  • what's the optimal outcome.

  • There are mathematically optimal outcomes that you can use,

  • and you run all of it against the computer,

  • and you get the optimization popping out the other end.

  • Wonderful.

  • So there's Axelrod and his buddies using terms

  • like oh, this strategy will drive

  • the other one into extinction.

  • Or this strategy works, but if you program in

  • that every now and then there could be a glitch,

  • there can be a mutation, this will be-- they're

  • using all this biology jargon, obviously metaphorically.

  • But right around this point, the biologists

  • look at this, who are just beginning to think

  • about the social biology stuff.

  • Formal patterns of optimizing behavior.

  • And they say whoa, does this apply to the behavior

  • of real organisms?

  • Because at this point, it's just economists and computer types

  • and diplomats learning when to optimize,

  • all that sort of thing.

  • Around the time there was a paper published,

  • somewhat before that.

  • This is a name nobody is going to know,

  • lost in history, a guy named Daniel Ellsberg.

  • Daniel Ellsberg became very famous around 1970,

  • by he was working in the Pentagon

  • and he stole thousands of pages of secret files

  • there, and gave it to the New York Times

  • showing how utterly corrupt everything that

  • went on behind the scenes was in getting us into Vietnam.

  • Major blowout, all of that.

  • He had spent the early part of his career perfectly happily

  • working in the Pentagon for the military as a game theorist.

  • As a game theorist coming up with optimal patterns.

  • And he wrote one paper called "The Optimal

  • Benefits of Perceived Madness".

  • What times do you want your opponent

  • to think you are absolutely out of your mind

  • and going to do all sorts of crazy stuff,

  • and where they wind up cooperating

  • to keep you from doing that.

  • The advantages of madness, what's that.

  • That's systems where things like mutually assured destruction

  • doesn't work, because you are willing to set it off.

  • The advantages of madness.

  • This whole world of people working on it, mathematicians

  • and war strategists.

  • And there's the zoologists now looking at this saying whoa,

  • this is cool.

  • I wonder if animals behave that way.

  • And that's when people, now armed with their insights

  • into prisoner's dilemma and tit for tat,

  • all this stuff, started to go and study animals out

  • in the wild and see, were there any examples where

  • this happened.

  • Yes.

  • In all sorts of interesting realms.

  • First example, vampire bats.

  • Vampire bats, we are all set up to be creeped out

  • by vampire bats.

  • But in actuality, when you see a vampire bat drinking

  • the blood of some cow or something,

  • you are watching a mommy getting food for her babies.

  • Because vampire bat mothers are not actually

  • drinking the blood.

  • They're filling up this throat sack thing,

  • and they go back to the nest and they disgorge

  • the blood to feed their babies.

  • She's just watching out for her kids.

  • It happens that vampire bats have an interesting system

  • of reciprocal altruism, which is a whole bunch of females

  • will share the same nest.

  • Will have all their kids in there mixed in.

  • And these are not necessarily related,

  • so we've just left the world of kin selection.

  • They're not necessarily related, but they have

  • reciprocal altruists system.

  • Each female comes in, disgorges the blood,

  • and feeds everybody's babies.

  • And they all feed each other's babies,

  • and everything is terrific.

  • And they have this blood vampire commune going there.

  • And they've reached a nice state of stable cooperation.

  • Now, make the bats think that one of the females

  • is cheating on them.

  • Out comes that female flying off to find some blood, and instead

  • you net her and get a hold of her,

  • and take some syringe full of air

  • and pump up the throat sack so the throat

  • sack is really full and distended,

  • but there's no blood in there.

  • You've just pumped air into there.

  • And stick her back into the nest there.

  • And she's just sitting there happily,

  • and the other females are sitting

  • saying look at her, look at how much blood she's got there.

  • I can't believe it, because she's not feeding our kids.

  • She's cheating on us.

  • And the next time they go out to feed,

  • the other females don't feed her kids.

  • A tit for tat.

  • What you saw here is an exact example

  • of introducing signal error.

  • Signal error, in this case, being some grad student

  • pumping up the throat of some vampire bat

  • and showing that they're using a version of a tit for tat

  • strategy.

  • Totally amazing.

  • People were blown away by this.

  • Another example, fish.

  • Stickleback fish who, in the world of animals-- you know,

  • bats are probably not some of the brightest folks around.

  • But I don't think sticklebacks are within light years of them.

  • But stickleback fish can do a tit for tat strategy.

  • Here's what you do.

  • You have a stickleback fish in your fish tank,

  • and you make the fish believe that he's

  • being attacked by another fish.

  • What do you do?

  • You put a mirror up against the edge of the tank there.

  • So within a very short time-- I told you

  • they were not that smart.

  • So within a very short time, he's

  • lunging forward at this mirrored thing

  • and maintaining his territory against this guy

  • and barely holding on.

  • And that other guy is just-- he doesn't get tired.

  • Thank god I don't get tired.

  • And they're just going at it.

  • And now make him think he has a cooperative partner.

  • Put in a second mirror that's perpendicular here.

  • In other words, he sees his reflection there.

  • And every time he moves forward, the

  • sees that one moving forward, which is fortunate

  • because he's also seeing another fish coming from that way.

  • And he's sitting there saying, this is great.

  • I don't know who this guy is, but wow, what a team we are.

  • [LAUGHTER]

  • Doubles, this is great.

  • He's in there and the thing is, it's funny how those two

  • guys are so synchronized.

  • But whoa, we're holding them off and we're doing it.

  • Now make him think his cooperating partner is,

  • in fact, cheating on him.

  • Take the mirror and angle it back a little

  • bit so the reflection is set back some.

  • And what he now sees is the fish moving forward,

  • but not all the way up to the wall there.

  • The fish is hanging back there.

  • The fish is cheating.

  • And this stickleback is sitting there saying, in effect,

  • that son of a bitch.

  • I can't believe he's doing that to me.

  • We've worked together for years.

  • I can't believe he's-- oh he's pretending to go forward.

  • But I see he's not really doing that.

  • Fortunately, that guy isn't coming forward anymore, either.

  • Phew.

  • But I can't believe the guy is cheating.

  • And the next time you set up this scenario, the next time

  • there's a chance the stickleback doesn't attack

  • its own reflection there.

  • It is tit for tatting against this guy.

  • So here we've managed to set up one of these deals

  • within one fish and carrying it out forever.

  • One fish, ultimately with some very blistered lips.

  • Tit for tat, once again.

  • Another example.

  • This is the most bizarre one I can imagine, and leads

  • to all sorts of subjects that are going

  • to come many lectures from now.

  • But there are fish species that will change sex.

  • And they do it under all sorts of strategic circumstances

  • that suddenly begin to fit into this realm of what

  • we've been learning about.

  • And you've got one of these things called black hamlet

  • fish.

  • And they can change gender.

  • So you'll have a pair of them who

  • hang out with each other of opposite genders,

  • and they take turns.

  • They flip back and forth.

  • For a while, this one's female, and for a while,

  • this one's female.

  • And they go back and forth, and that's great.

  • But there's an inequity there, which

  • is that the price of reproduction

  • is greater for the female than for the male.

  • As is the case in so many species,

  • the female doing all that egg and ovaduct

  • and progesterone stuff, or whatever it is.

  • And the male's just got to come up with some sperm there.

  • Doing to reproduction as a cooperating pair,

  • they're not relatives.

  • Reciprocal altruism, maximizing each of their reproductions.

  • Whoever's the female in any given round

  • is the one who's paying more.

  • What you see are reciprocal relationships there

  • of the fish using tit for tat.

  • If you get one fish that begins to cheat and winds up

  • being a male too much of the time,

  • the other fish stops cooperating with them.

  • Again, tit for tat stuff.

  • So people were just blown out of the water at this point,

  • seeing whoa, forget rational human economic thinking,

  • all of that.

  • You go out into the wild, and bats and stickleback fish

  • and gender switching fish and all of that,

  • they're following some of the exact same strategies.

  • Isn't nature amazing.

  • No, nature isn't amazing.

  • It's the exact same logic as saying

  • a giraffe has to have a heart that's

  • strong enough to pump blood to the top

  • of the head of a giraffe.

  • Or else there wouldn't be a giraffe.

  • And when you look at this realm, it's applying the same notion.

  • This same sort of wind tunnel of selective optimization

  • for behavior-- in this case, when to cheat,

  • when to cooperate-- sculpts something

  • that is as optimized as a giraffe's heart

  • being the right size.

  • So this made perfect sense.

  • Wonderful.

  • But then people began to look a little bit closer,

  • and began to see the very distressing real world

  • beginning to creep in there.

  • Which were exceptions.

  • First exception.

  • This was done by a guy named Craig Packer, University

  • of Minnesota, looking at lions in East Africa.

  • What you get is, typically, prides

  • are a whole bunch of relatives, usually female, sisters,

  • nieces, all of that.

  • But you will sometimes get prides that

  • are not of close relatives.

  • Nonetheless, they will get reciprocal altruistic things

  • going on.

  • Lions, in this case, having the same trick as

  • was done on those vervet monkeys.

  • Researcher putting inside the bush there a speaker,

  • and playing the sound of like 400 menacing lions all at once.

  • What you're supposed to do is freak out at that point.

  • And all of you need to very carefully approach and see

  • what's going on in that bush.

  • So what would happen in a reciprocal system,

  • and everybody does this.

  • Or if one time, one of them cheats on you,

  • you push that one forward the next time.

  • Or some such thing.

  • That's what you would expect.

  • But what he would begin to notice

  • is, in a bunch of these groups, there'd be one scaredy cat

  • lion, one who habitually stayed behind the others

  • and who wasn't punished for it.

  • So this produced this first puzzle

  • that oh, sometimes animals aren't optimizing tit for tat.

  • Sometimes animals haven't read Robert Axelrod's landmark 1972

  • paper, that sort of thing.

  • And what you suddenly have is the real world.

  • What could be possible explanations?

  • One thing being, maybe they're not really paying attention.

  • Maybe they're not quite that smart.

  • Wait, bacteria are doing versions of tit for tat.

  • What else could be going on?

  • Oh, lions interact in other realms.

  • Maybe this individual is doing very reciprocal stuff,

  • forgiving overly altruistic stuff in some other realm

  • of behavior.

  • Maybe this lion eats less of the meat

  • and backs off earlier, or something like that.

  • Maybe there's another game going on simultaneously.

  • And this is introducing the real world

  • in which it is not just two individuals

  • sitting there playing prisoner's dilemma and optimizing.

  • You suddenly begin to get real world complexities coming

  • in there.

  • And by the time we get to the lectures,

  • way down the line, on aggression and cooperation,

  • what you'll see is things get really complicated

  • when you have individuals playing games simultaneously.

  • The rules that you apply to one psychologically

  • begin to dribble into the other one.

  • All sorts of things like that.

  • It will get very complicated.

  • So a first hint there that, in fact, everything

  • doesn't work perfectly along those lines.

  • Here's another version.

  • Here's one of the truly weird species out there,

  • something called the naked mole rat.

  • If you ever have nothing to do and you've

  • got Google Image up there, go spend the evening looking up

  • close up pictures of naked mole rats.

  • These are the weirdest things out there.

  • They are the closest things among the mammals

  • to social insects, in terms of how their colonies work.

  • They're totally bizarre, all of that.

  • But they live in these big, cooperative colonies

  • that are predominately underground in Africa.

  • And they were discovered, I think, only in the 1970s or so.

  • And for a while when zoologists got together,

  • if you were a naked mole rat person,

  • you were just the coolest around.

  • And everybody else would feel intimidated,

  • because you were working on the best species out there.

  • And you would see these big cooperative colonies,

  • soon shown to not necessarily be of relatives.

  • And reciprocity and all those sorts of rules.

  • But people soon began to recognize

  • there would be one or two animals in each colony that

  • weren't doing any work.

  • Work digging out tunnels, bookkeeping,

  • I don't know what naked mole rats do in terms of work.

  • But there would be a few individuals who

  • would just be sitting around.

  • And they were these big old naked mole rats.

  • They were much bigger than the other ones,

  • and they were scarfing up food left and right.

  • There goes Robert Axelrod down the drain.

  • There goes all that optimization,

  • because no one would be punishing these guys.

  • What's the deal?

  • And it took enough watching these animals long enough

  • to see this notion of oh, there's

  • another game going on in which they

  • play a more important role.

  • And it is sort of dribbling across.

  • When the rainy season comes, these big naked mole rats

  • go up and turn around and they plug the entry to the tunnels

  • then.

  • [LAUGHTER]

  • That's what they do.

  • And suddenly, these guys who have

  • been sitting around doing no work whatsoever all year

  • and eating tons of stuff, they suddenly have to now stick

  • their rear ends out for the coyotes to be around

  • or whatever it is that predates them.

  • What we have is role diversification.

  • Real animals, real organisms, are not just

  • playing one formal prisoner's dilemma game against each other

  • at the same time.

  • And by the time we, again, get to the later lectures

  • on aggression, cooperation, all of that,

  • we will not only see that things get much more complicated when

  • you're playing simultaneous games,

  • when you're playing a game against one individual

  • while you're playing against another one,

  • and then against triangular circumstances.

  • How play differs if you know how many rounds

  • you are playing against the individual versus

  • if you have no idea.

  • How play differs if, when you are

  • about to play against someone, you

  • get to find out what their behavior has

  • been in the previous trials with other individuals.

  • In other words, if somebody shows up with a reputation,

  • we'll see this is a much more complicated world

  • of playing out these games.

  • A much more realistic one.

  • So we begin to see a first pass at all this optimization stuff,

  • and how great that all is.

  • One final interesting addition to this game theory world

  • of thinking about behavior like that, which came from a guy

  • named James Holland, who apparently-- might

  • have a different first name.

  • But Holland, apparently, as an interesting piece in history,

  • he's the person first person to ever get

  • a PhD in Computer Sciences.

  • Which I think was in the late 50s, University of Michigan.

  • Apparently, there are realms of computer programmers

  • who worship this guy.

  • And he, like a lot of other folks in that business,

  • got interested in this game theory evolution

  • of optimal strategies.

  • And he designed ways of running all of this.

  • And he introduced a new ripple, which

  • is the possibility of a strategy suddenly changing.

  • The possibility of a mutation.

  • What he could then study was mutations,

  • how often they were adaptive, how often they

  • spread throughout the strategy there, of individuals playing.

  • How often they drove the other strategies

  • into extinction versus ones that were quickly

  • driven to extinction themselves.

  • More cases where we are getting these systems

  • where maybe they're not just metaphorically using terms

  • from biology.

  • Maybe they are exactly modeling the same thing.

  • And we will see more and more evidence for that.

  • OK, so reciprocal altruism.

  • How would that play out in the world of natural selection.

  • Natural selection, cooperative hunting.

  • And there's lots of species that have cooperative hunting.

  • Wild dogs, jackals, some other species as well.

  • Clearly, that's like the definition

  • of cooperative hunting, of reciprocal altruism,

  • if they're not relatives.

  • How would sexual selection play out

  • in the realm of reciprocal altruism?

  • A little bit less obvious there.

  • That would be if you and some non-relative

  • spent an insane amount of energy and time

  • making sure you both look really good before going to the prom.

  • That would be sexual selection working on reciprocal altruism

  • system.

  • So what we have now are three building blocks.

  • This whole trashing of it's not survival of the fittest.

  • It's not behaving for the good of the species.

  • It's not behaving for the good of the group.

  • But instead, these three building

  • blocks, the ways to optimize as many copies of your genes

  • in the next generation as possible.

  • Way number one, individual selection,

  • a version of selfish genes.

  • Sometimes a chicken is an egg's way of making another egg.

  • Behavior is just a way of getting copies of genes

  • into the next generation.

  • Piece number two, inclusive fitness kin selection.

  • That whole business, that sometimes the best

  • way of passing on copies is to help relatives do it.

  • And it's a function of how related they are.

  • The whole world of cooperation more among related organisms

  • than unrelated ones.

  • And as we will see way down the line, what

  • is very challenging in different species

  • is, how do you figure out who you are related to?

  • And humans do it in a very unique way that sets them up

  • for being exploited in all sorts of circumstances that

  • begin to explain why culture after culture, people

  • are really not nice to thems, and it flows along those lines.

  • This is something we will get to in a lot of detail.

  • So degree of relatedness, a lecture coming.

  • How do you tell who you're related to.

  • But that second piece, kin selection.

  • Third piece, reciprocal altruism.

  • You scratch my back and I'll scratch your back.

  • And whenever possible, you want to instead scratch your back,

  • and they want to make sure you're

  • not scratching your back.

  • Or whatever cheating counts as.

  • But trying to cheat, being vigilant against it,

  • formal games where you can optimize it, very complicated.

  • And can you believe it, you go out into the real world,

  • and you find examples of precisely that.

  • Optimization with tit for tat, isn't nature wonderful.

  • It's gotta work that way.

  • Then you begin to see how the real world is more complicated.

  • Multiple roles, naked mole rats stuck in plumbing,

  • things of that sort.

  • These are the principles.

  • And what people of this school of evolutionary thought

  • would say, armed with these sorts of principles,

  • you could now look at all sorts of interesting domains

  • of animal behavior and understand

  • what the behavior is going to be like by using these.

  • OK, we start with the first example.

  • Here we return to these guys.

  • And we have one species here, and knowing

  • this guy had a penis and this one nursed,

  • we've got an adult male and an adult female.

  • What is it that you can conclude?

  • In this species, males are a lot bigger than females.

  • Let's state it here as there's a big ratio of males to females.

  • Meanwhile in the next county, you've

  • discovered another species where somebody's got a penis

  • and somebody else is nursing.

  • And their skulls are the exact same size.

  • Oh, here's a species where there's

  • no difference in body size between males and females.

  • Let's begin to see, just using the principles

  • we've got in hand already, what sort of stuff we can predict.

  • Starting, which of those species-- in one case,

  • you have males being a lot bigger than females.

  • In one case, you've got males being the same size as females.

  • In which of those species, the first one like this,

  • or the same size ones, which ones

  • would you expect to see more male aggression?

  • First one.

  • First one.

  • OK, how come?

  • Their bodies are built for it.

  • Their bodies are built for it.

  • Which begins to tell you something,

  • their bodies are built for it, maybe

  • because females have been selecting for that.

  • You will see higher levels of aggression

  • in species like this, where there's a big body size

  • difference, and much less of it in these guys.

  • Next, you now ask how much variability is there

  • in male reproductive success.

  • In one of these species, all the males

  • have one or two kids over their lifetime.

  • In another species, 95% of the reproducing

  • is carried out by 5% of the males.

  • A huge variability skew in male reproductive success.

  • Which species do you get the every male has

  • a couple of kids, and that's about it, and all equally so?

  • Which one?

  • [INAUDIBLE]

  • Second one.

  • How come?

  • Because these guys are being selected for aggression.

  • If they're fighting, there's going

  • to have to be something they're fighting for.

  • Deferential reproductive access.

  • OK, so you see more variability in species that look like this.

  • Next, females come into the equation.

  • What do females want?

  • What do females want in the species on the left

  • versus the one on the right?

  • The one on the right, again, skull's

  • the same size, same body size.

  • On the left, what does the female want?

  • [INAUDIBLE]

  • What sort of male is the female interested in?

  • [INAUDIBLE]

  • Big.

  • Exactly.

  • That's exactly the driving force on this.

  • How come?

  • Because she's not going to get anything else out of this guy.

  • This guy is just going to, like-- the present

  • is going to be some sperm.

  • It might as well be some good sperm, some genetically

  • well-endowed sperm that makes her a big healthy offspring,

  • increasing the odds of her passing on copies of her genes

  • in the next generation.

  • What about in this species?

  • What's females looking for?

  • [INAUDIBLE]

  • OK, good.

  • Hold on to that for a second, and let's

  • jump ahead a few lines.

  • One of the species, males have never

  • been known to do the slightest affiliative thing with infants.

  • They just get irritated and harass them and all of that.

  • In the other, you have soccer dads

  • who are doing as much raising of the kids as the females are.

  • In which species do you get lots of male parental behavior?

  • Smaller.

  • The one on the right.

  • OK.

  • So lots of male parental behavior here.

  • Somebody just gave the answer here, female choice.

  • What would you see in this species?

  • You want big, muscular guys.

  • You want whatever is selling that season for what

  • counts as a hot male, because you want your offspring

  • to have those traits.

  • And somebody else called out here,

  • what do females want in this category?

  • And what was it you said?

  • Good personality.

  • [LAUGHTER]

  • Good personality.

  • Yes.

  • Able to express emotions.

  • [LAUGHTER]

  • That, too.

  • OK, somebody else shouted out something

  • that gets at the broader, more globally Oprah version.

  • OK, somebody shouted out--

  • [INAUDIBLE]

  • --parental behavior.

  • You want a male who is going to be competent at raising

  • your children.

  • What is it that you want, really most deeply?

  • You want to get the male who is the most like a female you

  • can get a hold of.

  • You don't want some big old stupid guy

  • with a lot of muscle and canines who's wasting energy on stuff

  • like that he could be using instead on reading Goodnight

  • Moon or some such thing.

  • What you want instead is somebody

  • who's as close to a female as you can get to without getting

  • this lactation stuff.

  • Males are chosen who are the same size as females.

  • So the term given here is choosing for paternal behavior,

  • parental behavior.

  • Parental, let's just put that in there.

  • And that begins to explain the top line, species

  • in which there's a lot of sexual dimorphism.

  • Morphism, shapes of things.

  • Sexual dimorphism, big difference

  • in body size as a function of gender.

  • And in these sorts of species where

  • you get male parental behavior, not

  • much variability in male reproductive success,

  • low levels of aggression, and what

  • females want is a competent male.

  • These are ones where you see low degrees of sexual dimorphism.

  • So how's a female going to figure out

  • that this guy is going to be a competent parent?

  • Once again, we just figured out, if he looks kind of like you.

  • Because that suggests he hasn't wasted health and metabolism

  • on stupid, pointless muscles when there's

  • more important things in life for making sure

  • your kids have good values.

  • What else would the female want to know when she's first

  • considering mating with a male?

  • Is he a nice guy, is he sensitive,

  • does he express his feelings.

  • Is he competent at being a parent.

  • What do you want the individual to do?

  • Prove to you that he can provide for the kids.

  • And suddenly you have a world of male birds courting the females

  • by bringing them worms.

  • Bringing them evidence that they are

  • able to successfully forage, they are able to get food.

  • Female choice is built around appearance

  • and behavioral competence at being

  • able to be a successful parent in order

  • to pass on as many copies of genes

  • to the next generation as possible.

  • OK, how about life span.

  • In which species is there a big difference in life expectancy

  • as a function of gender?

  • First one.

  • First one.

  • Here you're choosing for males to be

  • as close to females as possible, and thus the physiology.

  • Here you've got these guys who are

  • using huge amounts of energy to build up

  • all this muscle, which takes a lot more work

  • to keep in calories.

  • And you're more vulnerable in famines.

  • You've got these males with high testosterone,

  • which does bad stuff to your circulatory system.

  • You've got males who, thanks to all this aggression,

  • are getting more injuries, more likely.

  • In species in which you have a lot of sexual dimorphism

  • in body size, you get a lot of sexual dimorphism in life span.

  • Then you look at these guys, and it's basically

  • no difference by gender.

  • Moving on.

  • Considering primates that are one

  • of these two patterns, in which one do you always

  • want to give birth to twins, in which one do you never

  • want to give birth to twins?

  • Who gives birth to twins?

  • [INAUDIBLE]

  • The one of the right, of course.

  • How come?

  • Because you've got two parents on the scene.

  • You are not a single mother.

  • And you are a single mother rhesus monkey or something,

  • and you give birth to twins, and you do not

  • have the remotest chance of enough energy,

  • enough calories on board, to get both of them to survive.

  • A twin that is born in a species like this

  • has the same rate that it occurs in humans, about a 1% rate.

  • And it is almost inevitable that one of them does not survive.

  • Meanwhile, there's a whole world of primate species

  • with this profile where the females always twin.

  • Finally, you are the female and you

  • are contemplating bailing out on your kids

  • and disappearing, because there's

  • some really hot guy over there who you want to mate with.

  • And you are trying to figure out this strategy.

  • So you are going to leave and abandon your kids.

  • In which species do you see that behavior?

  • The one on the right.

  • The one on the right, because you bail out and the male

  • is there taking care of them.

  • You bail out in here, and you've lost your investment and copies

  • your genes for the next generation.

  • You see female cuckoldry, this great Victorian term.

  • You see females cheating on the fathers in this species,

  • but not in species like this.

  • Because the father is long gone and three other counties there,

  • courting somebody else.

  • And it doesn't matter, you're not

  • going to get any help from him.

  • In primate species of this profile,

  • you always see twinning.

  • And they both survive.

  • And what studies have shown in these species,

  • and we'll get to them shortly, is

  • after birth, in fact, the males are expending more calories

  • taking care of the offspring, then

  • the females go bail out on him and go find some other hot guy.

  • Which, in your species, counts as some guy

  • who looks even more like you than he does in terms of what

  • you want out of the individual.

  • So that.

  • So what have we done here?

  • We've just gone through applying these principles

  • in this logical way, and everybody

  • from the very first step was getting the right outcome.

  • And go, and these are exactly the profiles

  • you find in certain species.

  • Among social mammals, these would be referred to

  • as a tournament species.

  • A tournament species, whereas the one on the right

  • is referred to as a pair bonding, a monogamous species.

  • Because in this one, males and females

  • stay together, because they both have equivalent investment

  • in taking care of the kids.

  • All of that.

  • What you have here is this contrast

  • between tournament species and pair bonding species.

  • Tournament species, these are all the species

  • where you get males with big, bright plumage.

  • These are peacocks, these are all those birds

  • and fish species where the males are all brightly colored.

  • What are the females choosing for?

  • Peacock feathers does not make for a good peacock mother.

  • Peacock feathers are signs of being healthy enough

  • that you can waste lots of energy

  • on these big stupid pointless feathers.

  • That's a sign of health.

  • That's a sign of all I'm getting from this peacock is genes,

  • I might as well go for good ones.

  • That's the world of peacocks, that's

  • the world of chickens with pecking orders,

  • dominating like that, lots of aggression.

  • That's the world of primates where, as in savanna baboons,

  • the male is twice as big as the female.

  • Tournament species, where a lot of passing on of genes

  • is decided by male-male aggression in the context

  • of tournaments producing massive amounts of variability

  • in reproductive success.

  • Where males are being selected for being good at this,

  • so they sure are being selected for having big bodies, which

  • winds up meaning a shortened life

  • span for a bunch of reasons.

  • Females are choosing for that.

  • These are guys who are not using their energy

  • on parental behavior, thus you do not

  • want to have twins if you are a female baboon,

  • and you do not want to bail out on the kids

  • because nobody else is going to take care of them.

  • Go and look at a new primate species,

  • and see this much of a difference in skull size,

  • and you'd just be able to derive everything

  • else about its social behavior.

  • Meanwhile, these guys on the right, pair bonding species.

  • These are found among South American monkeys, marmosets,

  • tamarins.

  • You put up a picture of them, which

  • I will do if I ever master PowerPoint

  • in some subsequent lecture-- you put up

  • a picture of a marmoset pair, and you can't tell

  • who's the male and the female.

  • This is not the world of the mandrill baboons,

  • with males with big, bright, bizarre coloration on the face,

  • and with antlers when the females don't,

  • and that whole world of sexual dimorphism.

  • You can't tell which one is the male

  • and which one is the female marmoset by looking at them.

  • You can't tell by seeing how long they live.

  • You can't tell by how much they're

  • taking care of the kids.

  • You can't tell in terms of their reproductive variability.

  • That's a whole different world of selection.

  • All of the South American tamarins and marmosets,

  • the females always twin.

  • They have a higher rate of cuckoldry,

  • of abandoning the kids.

  • The males take as much care, if not more,

  • of the kids than the female does.

  • Very low levels of aggression.

  • Same body size, same lifespan.

  • All the males have low degree of variability.

  • How come?

  • Because if you're some marmoset male,

  • you don't want to get 47 marmoset females pregnant.

  • Because you are going to have to take care of all the kids.

  • Because as we will see way down the line in lectures

  • on parental behavior, the wiring there is such

  • is bonding with the offspring and taking care of them.

  • No wonder among species like these,

  • you have very low variability.

  • All the males reproduce once or twice.

  • This is the world of 5% of the guys accounting

  • for 95% of the matings.

  • This is totally remarkable because again,

  • that starting point.

  • You start off here, and you look at these,

  • and oh, you can tell if they were bipedal and were

  • they diseased or malnourished, simply by applying

  • these principles of individual selection, reciprocity,

  • all of that.

  • One factoid, you see a new primate species,

  • and you see one nursing and one with a penis,

  • and they're the same size or there's difference in the size,

  • and you already know all about their social system.

  • Very consistent across birds, across fish, across primates.

  • Of course, all of those, this dichotomy

  • between tournament species and pair bonding species.

  • As we will see way down the line,

  • among some species, types of voles, rodents,

  • that are famous in Hallmark cards for their pair bonding,

  • for their monogamy.

  • As we'll see, they're not quite as monogamous

  • as you would think.

  • But nonetheless, a general structure like this.

  • So, one asks expectedly, where do humans fit in on this one?

  • Where do humans fit?

  • And the answer is, complicatedly.

  • Are we a tournament species, are we a pair bonding species.

  • What's up with that?

  • What we will see is we're kind of in between.

  • When you look at the degree of sexual dimorphism,

  • we are not like baboons, but we're sure not like marmosets.

  • We're somewhere in the middle.

  • Variability is somewhere in the middle there.

  • I'm not going near that one.

  • Life span, the dimorphism in lifespan

  • tends to be in between.

  • Parental behavior and likelihood-- all of those,

  • you look at a number of measures.

  • And by next lecture, we'll be looking

  • at some genetics of what a monogamous species

  • and tournament species look like.

  • And we're right in the middle.

  • In other words, that explains like 90% of literature.

  • Because we're not a classic tournament species and we're

  • not a classic pair bonding one.

  • We are terribly confused in the middle there.

  • And everything about anthropology supports that.

  • Most people on the planet right now

  • are in a form of monogamous relationships

  • in a culture that demands monogamy.

  • An awful lot of people who are in monogamous relationships

  • in such cultures aren't really in monogamous relationships.

  • Traditionally, most cultures on this planet allowed polygamy.

  • Nonetheless, in most of those polygamous cultures,

  • the majority of individuals were pair bonded and monogamous.

  • You get two different versions of polygamy

  • in different social systems of humans.

  • One is economic polygamy, which is you're basically

  • sitting around, and the wealthiest guy in the village

  • is the one who can have the largest number of wives.

  • An enormous skew in reproductive success

  • that's driven by economics.

  • The other type is demographic.

  • You have a culture where, for example, you

  • have a warrior class.

  • Guys spend 10 years as warriors-- worriers, warriors,

  • New York City accent.

  • As warriors, they don't worry.

  • There's no anxiety.

  • But they eventually worry about getting a wife,

  • because by the time they're done being a warrior,

  • they're like 25.

  • And they marry someone who's 13, which

  • is what you see in a lot of traditional cultures

  • that follow that pattern.

  • And at that point, you've got a problem,

  • which is an awful lot of those guys have been killed

  • over the course of 10 years of being involved

  • in high levels of aggression and 10 more years of life

  • expectancy to catch up with you.

  • There's is a shortage of males.

  • So you see polygamy there driven by demographics,

  • and you see polygamy driven by economics

  • in other types of society.

  • So most cultures on this planet allow-- traditionally,

  • before the missionaries got them--

  • most cultures on this planet allow polygamy.

  • Nonetheless, within most polygamous cultures,

  • the majority of people are not polygamous.

  • We have one really confused, screwed up species here.

  • Because we are halfway in between in all sorts of these

  • measures.

  • OK, so what do we have next, which

  • we will pick up on Friday.

  • What we've just started with here

  • is the first case of using all these principles,

  • individual selection, kin selection, reciprocal altruism,

  • to understand all sorts of aspects of behavior.

  • We will then move on to seeing how

  • they explain other aspects of animal behavior, some ones

  • which, if you are behaving for the good of the species circa

  • 1960, there's no explanation at all, because you're

  • doing things like killing other members of your species.

  • And then finally, we will see how

  • this applies to humans and some of the witheringly

  • appropriate--

  • For more, please visit us at stanford.edu.

Stanford University.

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