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2. Behavioral Evolution — Transcript

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0:04Stanford University
0:08Often asked question, what's the
0:09difference between bio 150, bio 250, and
0:13is it humbio 160?
0:15No difference. It's exactly the same.
0:18So, like the same requirements, same
0:20units. So, take whichever one makes your
0:23life easiest. Let's see, any other
0:27procedural stuff?
0:29Well, the answers are back from Monday's
0:31questionnaire.
0:33And a variety of interesting answers,
0:36not surprisingly given the size of a
0:38group. Why have you taken this course?
0:41Really want to know about animal
0:43behavior, but willing to deal with
0:44humans.
0:46Because I'm substituting it for bio 43,
0:48which I don't want to take.
0:51My dad used to make me read books about
0:53human behavior and biology as
0:54punishment.
0:56That doesn't make any sense. I know one
0:58of the TAs, so I figure that guarantees
1:00me an A.
1:02Okay, guys, it's in your court.
1:05One I really liked because I want to be
1:07a filmmaker after college. Yay,
1:10interdisciplinary.
1:12What else? My first grade teacher is
1:14making me.
1:16Tom McFadden told me to.
1:20I'm a hyper-oxygenated dilettante.
1:23Um
1:24I wanted to, somewhat correctly pointing
1:26out why have you taken this class? I
1:28haven't taken it yet. A number of people
1:30reporting that in fact, that was the
1:32correct answer. And my favorite, why
1:34have you taken this course? Yes.
1:39Okay.
1:41Relevant background, relevant
1:43background. I'm human, I'm human and I
1:45often behave. I'm human and I have
1:48biology.
1:5019 years of being confused about human
1:51behavior.
1:53Not really, sort of.
1:56Seeing crazy behavior as an RA in an all
1:58frosh dorm.
2:00And I date a biologist.
2:03Let's see. There was also the question
2:05on there of did the thing on the board
2:07look more like an A or a B? And just to
2:09really facilitate that one, I forgot to
2:11put the A and the B up. Um, but that
2:14taps into a cognitive something or other
2:16which maybe I'll get back to it some
2:18point. Telephone numbers, reading them
2:20off, accuracy dramatically tanked as
2:23soon as the three number four number
2:24motif
2:26and when it came back briefly accuracy
2:28came back a little bit. Finally, uh,
2:31let's see. All of you guys conform to a
2:35standard frequent gender difference,
2:38which is everybody was roughly equally
2:40by gender, roughly equally likely to see
2:43dependent as the opposite of
2:44independent. A small minority went for
2:47interdependent.
2:49Um, however, one finding that has come
2:51up over and over is that far more
2:54females are interested in peace than
2:55males. Males are more interested in
2:57justice.
2:59Okay. Have you taken the bio core?
3:01Quote, "No way, Jose."
3:03Uh, somebody pointing out quite
3:05correctly, "Don't settle for peace or
3:08justice." Then of course there was the
3:10person who responded to that question by
3:12writing, "Those words are just symbols.
3:14Need to know assumed meaning."
3:18Okay.
3:20Uh,
3:22there was one questionnaire that was
3:24carefully signed and something
3:26approaching calligraphy. It was so
3:28beautiful and was otherwise blank.
3:30Uh, for years running the course the
3:34subject that most people really want to
3:37hear and most people really don't want
3:38to hear is about the biology of
3:40religiosity. And for 22 years running
3:44now, Stanford students are more
3:45interested in depression than sex.
3:48Okay.
3:49So, we start off. I keep telling
3:52Hennessy about this, but nothing gets
3:54done. Okay, we start off. We start off,
3:58if I can open this.
4:04Which is something you could do if you
4:07have a certain type of training, if
4:09you're some osteologist or whatever
4:12these folks are called. If you are
4:13presented those two skulls and told this
4:17one's a female, this one's a male, you
4:19can begin to figure out stuff like how
4:21heavy, how large the body was of that
4:24individual, what diseases they had, had
4:27they undergone malnutrition, had they
4:29given birth a lot of times, a few times,
4:32were they bipedal, all sorts of stuff
4:35you could figure out from just looking
4:37at these skulls.
4:39What today's lecture and Friday's is
4:41about is the fact that with the right
4:43tools under your belt, you could look at
4:45these two skulls and know that
4:47information. You're a field biologist
4:50and you've discovered this brand new
4:52species and you see that this one nurses
4:55an infant shortly before leaping out of
4:56the tree leaving only the skull and this
4:59one has a penis shortly before leaving
5:01leaping out of the tree and leaving
5:03leaving a skull. So, all you know is
5:05this is an adult female, an adult male.
5:07And if you've got the right tools there,
5:09you can figure out who's more likely to
5:12cheat on the other. Is the female more
5:14likely to mess around or is the male?
5:17How high are the levels of aggression?
5:19Does the female tend to have twins or
5:21one kid at a time? Do females choose
5:24males because they have good parenting
5:26skills or because they're big hunky
5:28guys? What levels of differences in life
5:32expectancy? Do they live the same length
5:34of time? You would be able to tell
5:36whether they have the same life
5:37expectancy or if there's a big
5:39discrepancy between the two, all sorts
5:42of stuff like that. Merely by applying a
5:45certain piece of logic that dominates
5:47all of this. Okay, so you're back
5:50reading those Time-Life nature books
5:52back when, and there was always a style
5:54of thing you would go through, which is
5:56they describe some species doing
5:58something absolutely amazing and
6:00unlikely, and it go like this, "The
6:02giraffe The giraffe has a long neck, and
6:06it obviously has to have a big heart
6:08to pump all that blood up there, and you
6:10lock up a whole bunch of biomechanics
6:12people with, you know, slide rules, and
6:15out they come out with this prediction
6:17as to how big the giraffe heart should
6:19be, and how thick the walls, and you go
6:22and you measure a giraffe heart, and
6:23it's exactly what the equations
6:25predicted, and you say, 'Isn't nature
6:28amazing?' Or you read about some like
6:30desert rodents that drink once every 3
6:33months, and another bunch of folks have
6:35done math and figured out like how many
6:38miles long the renal tubules have to be,
6:41and somebody goes and studies it, and
6:43it's exactly as you expected. Isn't
6:45nature wonderful? No, nature isn't
6:47wonderful. You couldn't have giraffes
6:49unless they had hearts that were that
6:51big. You couldn't have rodents living in
6:53the desert unless they had kidneys that
6:55worked in a certain way. There is an
6:57inevitable logic about how organisms
7:01function, how organisms are built, how
7:03organisms have evolved solving this
7:06problem of optimizing a solution. And
7:09what the next two lectures are about is
7:11you can take the same exact principles
7:14and apply them to thinking about the
7:16evolution of behavior. The same sort of
7:19logic where just as you could sit there
7:21and with logical principles come to the
7:23point of saying, "A giraffe's heart is
7:26going to be this big." You can go
7:27through a different realm of logic built
7:29around evolutionary principles and
7:32figure out all sorts of aspects
7:35of social behavior. And we already know
7:37what's involved in, say, optimizing,
7:40what's the optimal number of whatevers
7:42in your kidney, what's the optimal
7:44behavior strategy or something. All of
7:47us, as soon as we got like some kid
7:49sibling, learned how to do the optimal
7:51strategy in Tic-Tac-Toe, and so that you
7:54could never lose, and it's totally
7:56boring, but that's a case of figuring
7:58out the optimal solution to behavior,
8:01reaching what is called the Nash
8:02equilibrium. And actually, I've no idea
8:05what I just said, but I like making
8:07reference to Nash because it makes me
8:09feel quantitative or something. So, that
8:11is called the
8:13Nash equilibrium. The Nash equilibrium,
8:15and what the entire point here is, the
8:18same sort of process of figuring out
8:20what are the rules of optimizing
8:22Tic-Tac-Toe behavior can be built upon
8:25the principles of evolution to figure
8:27out all sorts of realms of optimized
8:30social behavior.
8:32And broadly, this is a field that's
8:34known as sociobiology,
8:37emerging in the late 1970s, mid- 1970s
8:40or so, and by the late 1980s, giving
8:43birth to another discipline known as
8:45evolutionary psychology, the notion that
8:48you cannot understand behavior, and you
8:51cannot understand internal psychological
8:53states outside the context of evolution
8:57had something to do with sculpting those
8:58behaviors and those psyches.
9:00So, to start off with that basic song
9:03and dance about Darwin, just to make
9:04sure we're up to speed on this. Darwin,
9:07just to get some things out of the way,
9:09Darwin did not discover evolution.
9:11People knew about evolution long before
9:13that. Darwin came up with the notion of
9:16a mechanism for evolution, natural
9:18selection, and in fact, Darwin isn't the
9:21inventor of that. There was another guy,
9:23Alfred Russell Wallace. The two of them,
9:25and for some reason, Wallace has gotten
9:27screwed historically, and Darwin gets
9:29much more attention, but starting off
9:31with a Darwinian view of how evolution
9:34works.
9:35First thing being that there is
9:37evolution. Traits in populations change
9:40over time. Traits can change enough that
9:43in fact you will get speciation. New
9:46species will form. And the logic of
9:48Darwinian evolution is built on just a
9:51few couple of very reasonable steps.
9:55First one is that there are traits that
9:58are heritable.
9:59Traits that could be passed on one
10:01generation to the next. Traits that we
10:04now can translate in our modern parlance
10:06into traits that are genetic. And we
10:09will see soon how that's totally not
10:11correct to have said that, but traits
10:13that are heritable.
10:15The next thing is that there is
10:17variability among those traits. There's
10:20different ways in which this trait can
10:23occur. And
10:25they're all heritable. The next critical
10:27thing, some versions of those traits are
10:31more adaptive than others. Some versions
10:34work better for you. For example,
10:37giraffe who wind up with hearts the size
10:39of like a tomato, that's not an optimal
10:42version. Amid the range of variability,
10:44some will carry with them more fitness,
10:48more adaptiveness than others.
10:50And that translates into another sound
10:53bite that's got to be gotten rid of. All
10:55of this is not about survival of the
10:58most adapted, it's about reproduction of
11:00something we will come to over and over
11:02again. It's about the number of copies
11:04of genes you leave in the next
11:06generation. So you've got to have traits
11:08that are heritable. There's got to be
11:10variability in them.
11:11Some of those traits are more adaptive
11:14than others. Some of those traits make
11:15it more likely that that organism passes
11:18on copies of its genes into the next
11:20generation. And throw those three pieces
11:22together, and what you will get is
11:24evolution in populations, changing
11:27frequencies of traits. And when you
11:29throw in one additional piece, which is
11:32every now and then the possibility to
11:34have a random introduction of a new type
11:37of trait in there, modern parlance, a
11:40mutation. From that, you could begin to
11:43get actual large changes in what a
11:45population looks like. Okay, so these
11:48are the basic building blocks of Darwin,
11:50and it is easy to apply it to giraffes
11:52hearts and kidneys of desert rats and
11:54everything we think about in the world
11:56of physiology, anatomy in the context of
12:00evolution.
12:01So, how do you apply it to behavior? And
12:04the basic notion for folks who've come
12:06from this Darwinian tradition into
12:08thinking about behavior is you do the
12:09exact same thing. There are behaviors
12:12that are heritable,
12:14types, traits, classes of behaviors.
12:17They come with a certain degree of
12:19variation among individuals. Some
12:21versions of them are more adaptive than
12:23others. Over time, the more adaptive
12:26versions will become more commonplace,
12:28and every now and then you can have
12:30mutations that introduce new
12:31variability. Totally logical, absolutely
12:35unassailable, and what we're going to
12:37spend an insane amount of time in this
12:39class on is one simple assumption in
12:42there, which is that certain behaviors
12:44are heritable. That certain behaviors
12:47have genetic components. And as you'll
12:49see, this one is just going to run
12:50through every lecture wrestling with
12:53that issue there. This is a big
12:56incendiary issue there as to how genetic
13:00and that's not the same thing as saying
13:01how genetically determined how genetic
13:04behavior is. Okay, so that's going to be
13:08a issue we come back to again and again.
13:11So, now transitioning into how you would
13:13apply these Darwinian principles. First
13:16thing before starting a caveat. You're
13:19going to wind up in order to think about
13:21all of this most efficiently, hopefully
13:23do some personifying. Personifying as in
13:26you'll sit around and say, "Well, what
13:28would a female chimpanzee want to do at
13:31this point to optimize the number of
13:32copies of her genes in the next
13:33generation? What would this brine shrimp
13:36want to do to deal with this
13:38environmental stressor? What would this,
13:40you know, cherry tree do?"
13:42They're not planning. They're not
13:44conscious. They're not taking classes in
13:45evolutionary biology. "What would this
13:47organism want to do?" is just a
13:50shorthand for something sculpted by the
13:53sort of exigencies of of evolution and
13:56reducing the optimal.
13:58"They want to do this." This is just
14:00going to be a shorthand throughout. Once
14:02you get past the apes, nobody is wanting
14:04to do any of these optimization things.
14:06So, just getting that sort of
14:08terminology out of the way.
14:10Okay, so we start off with what's the
14:12first building block of applying
14:14Darwinian principles to behavior.
14:17Something that is absolutely critical to
14:19emphasize because the first thing we all
14:21need to do is unlearn something we all
14:24learned back when on all those National
14:26Geographic specials and that would
14:28consistently teach us something about
14:30this aspect of evolution and would
14:32always teach it to us wrong.
14:34Here's the scenario. So, you're watching
14:37and there's this wildlife documentary.
14:38It's It's dawn on the savanna and you
14:42see there's a whole bunch of lions on
14:44top of some big old dead thing, some
14:46buffalo or something and they're chewing
14:49away and having a fine time.
14:52So, something happens at that point,
14:55which is they have to deal with how they
14:59divvy up the food.
15:00Or let me give you another example,
15:02another standard sort of endless
15:04vignette that comes up in these films.
15:07Once again now, you're back on the
15:09savanna. It's not dawn this time, but
15:11you are looking at one of the
15:12magnificent things of the natural world,
15:15which is the migration of zebras
15:17throughout East Africa, a herd of 2
15:19million of them migrate around following
15:21a cyclical pattern of rains, so they're
15:24always going where the grass is greener.
15:26So, you've got this wonderful herd of 2
15:27million wildebeest, and there's a
15:29problem, which is there's some great
15:32field right in front of them full of
15:33grass, and bummer, there's a river in
15:36between them and the next field. And
15:38especially a bummer, a river teeming
15:41with crocodiles just ready to grab them.
15:43So, what are the wildebeest going to do?
15:46And in according to the National
15:48Geographic type specials we would get,
15:51out would come a solution. There's all
15:53the wildebeest hemming and hawing in
15:55this agitated state by the end edge of
15:57the river, and suddenly from the back of
16:00the crowd comes this elderly wildebeest
16:03who pushes his way up to the front,
16:05stands on the edge of the river, and
16:06says, "I sacrifice myself for you, mine
16:09kinder." And throws himself into the
16:11river, where immediately the crocs get
16:13busy eating him up, and the other 2
16:15million wildebeest can tiptoe around the
16:17other way across the river, and
16:18everybody's fine. And you're then
16:20saying, "Why'd this guy do this? Why did
16:23this guy fling himself into the river?"
16:25And we would always get the answer at
16:28that point. The answer that is permeated
16:31as like the worst urban myth of
16:33evolution, whatever. Why did he do that?
16:36Because animals behave for the good of
16:38the species.
16:40And this is the notion that has to be
16:42completely trashed right now. Animals
16:45behaving for the good of the species
16:47really came to the forefront a guy in
16:49the early '60s named Wynne Edwards,
16:51hyphenated Wynne Edwards, some
16:53hyphenated Brit zoologist who pushed
16:56most strongly this notion of that
17:00animals behave for the, you know, good
17:02of the species. He is reviled throughout
17:05every textbook, Wynne Edwards and group
17:07selection, that would be the term.
17:09Selection for the good of groups, for
17:11the selection for the good of the
17:12species, Wynne Edward and group
17:14selection. I'm sure the guy did all
17:17sorts of other useful things and anyone
17:19who really is has any depth to them
17:21would find out but all I know is that
17:23the guy is the one who came up with
17:24group selection. Animals behave for the
17:28good of the species. This isn't the case
17:30at all. Animals behave for passing on as
17:34many copies of their genes as possible.
17:37And what we'll see is when you start
17:39looking at the nuances of that,
17:41sometimes it may look like behaving for
17:43the good of the species but it really
17:45isn't the case.
17:47So animals behave in order to maximize
17:51the number of copies of genes they leave
17:52in the next generation. Remember, not
17:54survival of the fittest, reproduction of
17:56the fittest. So first thing you need to
17:58do is go back to that vignette and
18:01saying
18:02"Uh so what's up with the wildebeest
18:03there? And what's up with the elderly
18:05guy who jumps in the river?" And finally
18:08when you look at them long enough
18:10instead of the camera crew showing up
18:11for 3 minutes, when you study this
18:13closely enough, you see something that
18:15wasn't apparent at first, which is this
18:18elderly wildebeest is not fighting his
18:21way through the crowd. This guy is being
18:23pushed from behind. This guy is being
18:25pushed from behind because all the other
18:27ones are saying "Yeah, get the old guy
18:29in the river." Sacrificing himself my
18:32ass. This guy is getting pushed in by
18:34everybody else. He is not sacrificing
18:36himself for the good of the species. He
18:38does not like the idea of this
18:40whatsoever. So he gets pushed in because
18:43the old weak guy none of this group
18:45selection stuff. What came in by the 70s
18:48as a replacement way to think about this
18:50is this notion of animals including us
18:54behaving not for the good of the species
18:55of the group but to maximize number of
18:57copies of genes left in the next
18:59generation. And what do you see is three
19:02ways in which this could occur. Three
19:05building blocks. The first one being
19:07known as individual selection.
19:10The first one built around the notion
19:13that sometimes the behavior of an animal
19:15is meant to optimize the number of
19:17copies of its genes that it leaves in
19:19the next generation by itself
19:22reproducing. The drive to reproduce, the
19:25drive to leave more copies of one's
19:27genes. This was once summarized really
19:30sort of tersely as sometimes a chicken
19:34is an egg's way of making another
19:36chicken.
19:37No, that's backwards. Sometimes
19:41a chicken is an egg's way of making
19:43another egg. Okay, ignore that.
19:45Sometimes what the guy said is sometimes
19:49a chicken is an egg's way of making
19:51another egg. All this behavior stuff and
19:54all this animate sort of social
19:57interaction is just an epiphenomenon to
19:59get more copies of the genes into the
20:02next generation.
20:03Individual selection, a subset of way of
20:06thinking about this is selfish genes.
20:09What behavior is about is maximizing
20:12number of copies of genes in the next
20:13generation and sometimes the best way to
20:16do it, sometimes the way that animals
20:18maximize is to get as many copies by way
20:21of reproducing themselves. It's not
20:23quite equivalent to the selfish gene,
20:25but for our purposes individual
20:28selection.
20:29And this can play out in a number of
20:31realms and bringing in sort of a big
20:33dichotomy in thinking about evolutionary
20:35pressures, Darwin and the theory of
20:37natural selection. What natural
20:40selection is about is processes bringing
20:43about an organism who is more adaptive,
20:46what we just went through. Darwin soon
20:48recognized there was a second realm of
20:50selection which he called sexual
20:52selection. And what that one's about is
20:54this is selecting for traits that have
20:57no value whatsoever in terms of survival
21:00or anything like that, traits that carry
21:02no adaptive value, but for some random
21:05bizarro reason the opposite sex likes
21:08folks who look this way. So, they get to
21:11leave more copies of their genes, and
21:13suddenly, you could have natural
21:14selection bringing about big, sharp
21:17antlers in male moose, and they use that
21:20for fighting off predators or fighting
21:23with another male. That would be natural
21:24selection. Sexual selection might
21:27account for the fact that, like, the
21:29antlers are green paisley patterns all
21:32over for that. And for some reason, that
21:34looks cool to female moose mooses. And
21:37what do you wind up getting as a
21:39mechanism for sexual selection is, as
21:41long as individuals prefer to mate with
21:44individuals with some completely
21:46arbitrary
21:47traits, those traits will also become
21:50more common. So, this dichotomy of
21:53natural selection for traits driven by
21:56traits that really do aid leaving copies
21:59of genes outside the realm of just sheer
22:01sexual preference, sexual selection. And
22:04sometimes, they can go in absolutely
22:06opposite directions. You can get some
22:08species where the female fish prefer
22:12male fish that have very bright
22:14coloration, and that's advantageous then
22:16to have the bright coloration by means
22:18of sexual selection. But, the bright
22:20coloration makes you more likely to get
22:22predated by some other fish, natural
22:24selection pushing against bright
22:26coloration in males. Very often, you've
22:29got the two going against each other,
22:31having to balance.
22:32So, how would that be applied in this
22:34realm of individual selection? This
22:37first building block, sometimes an egg
22:40Damn. Sometimes, a chicken isn't eggs
22:42way of making another egg. Sometimes,
22:44what behavior is about is one individual
22:47trying to maximize the number of copies
22:49of their genes in the next generation, a
22:50natural selection manifestation of it
22:53being you're good at running away from
22:55predators. Selection for speed, for
22:58certain types of muscle metabolism, for
23:00certain sense- systems that will tell
23:02you there's somebody scary around. That
23:05would be the realm of that.
23:07Individual selection, selecting in the
23:09realm of sexual selection to have more
23:11of whatever those traits are that are
23:13attractive. So, this first building
23:15block, it's not group selection, it's
23:17not behaving for the good of the
23:18species, it's behaving to maximize the
23:20number of copies of one's genes in the
23:22next generation, and the most
23:23straightforward way is to behave in a
23:25way to maximize the number of times you
23:28reproduce yourself.
23:30Second building block, which is there's
23:33another way of accomplishing the same
23:36thing that you just did with individual
23:38selection. As follows, one of the things
23:41that could be relied upon in life is
23:42that you are related to your relatives.
23:45And what you get is the more closely
23:48related you are, the more genes you
23:51share in common with them. On a
23:52statistical level, identical twins share
23:55100% of their genes, full siblings 50%,
23:59half siblings 25%. This is exactly
24:02something that's going to be covered in
24:03the catch-up section this week. If
24:05you're not comfortable with this stuff,
24:06this sort of thing will be reviewed in
24:08more detail. Okay, so the closer a
24:10relative is to you, the more copies of
24:13the more genes they share in common with
24:16you.
24:17So, suddenly you've got this issue,
24:19you're an identical twin, and your
24:21identical sibling has the same genes
24:23that you do. Individual selection, you
24:26will be just as successful as passing on
24:29copies of your genes into the next
24:31generation if you forego reproducing to
24:34make it possible for your identical twin
24:37to do so. Because on a level of just
24:39sheer numbers of copies of genes in the
24:42next generation, they are equivalent.
24:45And sometimes you will thus get behavior
24:47which really decreases the reproductive
24:50success of an individual in order to
24:52enhance the success of a relative.
24:56But you've got a constraint there, which
24:58is all of your relatives don't share all
25:01your genes with you. They have differing
25:03degrees of relatedness. And what that
25:06winds up producing is another factor,
25:09another observation, one of the great
25:11like witty geneticists of all time, a
25:13guy named Haldane, who apparently once
25:15in a bar was trying to explain this
25:16principle to somebody, and came up and
25:18said, "I will gladly lay down my life
25:21for two brothers or eight cousins."
25:24And that's the math of the relatedness.
25:27You passing on one copy of your genes to
25:30the next generation is from the sheer
25:33mathematics of just how evolution is
25:35going to play out over the generations
25:37is exactly equivalent as giving up your
25:40life for eight cousins to be able to
25:43each pass on a copy of their genes, cuz
25:45you share 1/8 with each of them, and it
25:47winds up being a whole, or and it's that
25:50math. And out of that, you get something
25:53that makes perfect sense instantly,
25:55which is evolution selects for organisms
25:58cooperating with their relatives,
26:01something along those lines. And thus,
26:03we have the second building block known
26:06as kin selection, inclusive fitness, kin
26:09selection. First building block,
26:12individual selection, passing on copies
26:14of your own genes as a way to maximize
26:16future success. Second version, helping
26:19out relatives. Helping out relatives in
26:22terms of increasing their reproductive
26:25success with this vicious mathematical
26:28logic, which is you know, one identical
26:31twin, two half sibl- two full siblings,
26:34eight cousins, and so on, as a function
26:37of degree of relatedness. And what this
26:39begins to explain is a whole world in
26:42animal behavior of animals being
26:44obsessed with kinship. Animals being
26:47fully aware of who is related to who,
26:51and what sorts of ways animals being
26:53utterly aware of you cooperate with
26:56relatives but as a function of how
26:58closely related they are.
27:00Animals put us in social anthropology
27:03and kinship terms and could you marry
27:05the daughter of your uncle's third wife
27:07or whatever to shame in terms of how
27:09much a lot of social animals deal with
27:12relatedness. So, inclusive fitness, kin
27:16selection. Here would be evidence for
27:18it. Here's one example. Very cool study
27:20done some years back
27:22by a couple Seyfarth and Cheney,
27:24University of Pennsylvania, looking at
27:26vervet monkeys and these were vervet
27:28monkeys out in Tanzania, I believe. And
27:31what they did was a whole bunch of these
27:32vervet monkeys were sitting around and
27:34they, the researchers, had made really
27:37high-quality recor- recordings of
27:39various vocalizations from the monkeys
27:41over time. So, they had the sound of
27:43each animal giving an alarm call, giving
27:45a friendly gesture call, giving a
27:47whatever. And what they would then do is
27:49hide a microphone inside some bushes and
27:52play the sound of one of the infants
27:55from the group giving an alarm call.
27:58So, what does the mother of that infant
28:00do? She instantly gets agitated and
28:03looks over at the bush. That's her
28:05child, all of that. How to know that
28:08everyone else in that vervet group
28:09understands kin selection? What does
28:12everybody else do? They all look at the
28:14mother.
28:16That's whoever's mother. What is she
28:19going to do next? They understand the
28:21relatedness and they understand what the
28:23response will be. All the other vervets
28:26look at the mother at that point. "Whoa,
28:29I'm sure glad that's not my kid giving
28:31an alarm call from the bushes." They
28:32understand kinship.
28:34Another version of that that came out in
28:36these studies. So, you've got two
28:38females, each of whom has a kid, a
28:40daughter, or whatever, and female A and
28:42female B, and one day female A does
28:45something absolutely rotten to female B.
28:49And later that day, the child of female
28:52B is more likely than chance to do
28:55something rotten to the child of female
28:57A.
28:58You're keeping track of not only
29:00revenge, but not revenge on the
29:02individual who did something miserable
29:04to you, but displaced by one degree of
29:07reproduction, keeping track of kinship.
29:10Animals can do this. All sorts of
29:12primate species can do this. And as
29:14we'll see, all sorts of other species
29:17can do this also. There's that caveat
29:19again. All sorts of other species want
29:21to figure out who their cousins and they
29:23don't want to figure out. Evolution has
29:25sculpted an ability to optimize behavior
29:28along lines of relatedness in all sorts
29:31of species.
29:33So, how would natural selection play out
29:35in this realm of kin selection? I will
29:38lay down my life for eight cousins. And
29:41that's just sort of obvious there by
29:43now. How would sexual selection play out
29:46in this realm? I am willing to expend
29:48great amounts of energy to convince
29:50people that my sibling is incredibly
29:53hot.
29:54And with any chance then passing on more
29:57copies of genes. That would be inclusive
29:59fitness kin selection in both cases,
30:02decreasing your own reproductive
30:04potential
30:06by way of being killed by a predator to
30:08save the eight cousins or having to
30:09spend so much time haranguing about your
30:11sibling, doing that in order to increase
30:15the reproductive success of relatives
30:18where you were willing to give up more
30:20energy and potential on your part, the
30:22more closely related the individual is.
30:25So, you throw those two pieces together
30:28and you're suddenly off and running with
30:29explaining a lot of animal behavior.
30:32Individual selection, none of this for
30:34the good of the species, maximizing the
30:36number of copies of your own genes. And
30:39the easiest way, the most
30:41straightforward is you yourself
30:43maximizing reproduction.
30:45Foundation number two to the whole
30:47thing, kin selection. Sometimes the best
30:50way of leaving more copies of genes in
30:51the next generation is using up your own
30:54reproductive potential forgoing to help
30:56relatives as a function of degree of
30:58relatedness.
31:00Okay, that's great. So now the third
31:03piece, the third final building block of
31:05making sense of social behavior in the
31:07context of real contemporary
31:10evolutionary theory, the third block
31:12here, which is you look at animals and
31:16they're not all just competing with
31:18non-relatives and things. Animals like
31:22forego competition at certain points.
31:24Animals would have the potential to be
31:27aggressive to other animals and they
31:28will forego doing so. And there's one
31:31circumstance in which that can happen
31:33where you get what is called a rock,
31:35paper, scissors scenario. You've got
31:38animals A, B, and C.
31:40A has a means of damaging B,
31:43but it costs A. B has a means of
31:45damaging C, but it costs B. C can damage
31:47A, but it costs A. And you get the right
31:49distribution of individuals with one of
31:51those traits in a population and you
31:54will reach a rock, scissors, paper
31:56equilibrium where nobody is doing
31:58anything rotten to each other. Great
32:00example, totally cool example that got
32:03published some years ago by a guy named
32:05Brendan Bohannan who was assistant
32:07professor in the department here at the
32:08time. He was studying something or other
32:10about bacteria showing a rock, paper,
32:13scissors circumstance. You had three
32:16different types, three different
32:18versions of this bacteria in this colony
32:20he had made. The first one could
32:22generate a poison,
32:26but it cost. It had to put the effort
32:28into making that poison and protecting
32:31itself from that poison, all of that.
32:33The second type was vulnerable to the
32:35poison. It happened to have some
32:37transporter on its membrane that took up
32:40the poison and that was bad news, but it
32:42had an advantage, which is the rest of
32:44the time that transporter took up more
32:47food.
32:48The third one The third one The good
32:51thing going for it is that it didn't
32:54have The bad thing was it didn't have a
32:56poison.
32:57The good thing going for it was it
32:59didn't have to spend energy on a poison
33:01and it didn't have that transporter. So,
33:03each one of those has a strength. Each
33:06one of those has a vulnerability.
33:07They're like I don't know Pokemons or
33:09something and you put them all together
33:11there and you get a rock, paper,
33:13scissors scenario where you get
33:16equilibrium where they are not attacking
33:18each other because note, if I am A and I
33:22destroy B,
33:24B's no longer wiping out C, who's the
33:26one who could damage me? It's got to
33:28come to an equilibrium state. So, you
33:31can get the evolution of stalemates like
33:33that and that's quite frequently seen.
33:36And note here, this was the evolution of
33:38stalemates not in chimps, not in
33:41citations, but in bacteria. What we're
33:43going to see is bacterial
33:46behavior to the extent that this is sort
33:48of a metaphor for behavior. Behavior of
33:50all sorts of unlikely species are
33:53subject to these same rules of passing
33:56on copies of your genes. These three
33:58different strains of bacteria are
34:00competing with each other. None of them
34:02are behaving for the good of the species
34:05there of the three of them.
34:07So, rock, paper, scissors is very cool
34:09and you get versions of that in humans
34:11and that's been sort of studied
34:13quantitatively, all of that, but that's
34:15not real cooperation. That's merely
34:17everybody realizing we have to cut back
34:20on the competition. We have to cut back
34:22on the aggression because every time I
34:24damage whoever, I am more vulnerable in
34:26another realm. That's a stalemate.
34:28That's a truce.
34:30But you look at animals and in all sorts
34:32of realms, it's not just rock, paper,
34:34scissors stalemates they're reaching,
34:36they actually cooperate with each other.
34:39And you look close enough and you see
34:41they're not relatives.
34:43They're not relatives, yet you get all
34:45sorts of altruistic behavior and you've
34:48got it under a whole bunch of domains.
34:51Because this brings up the question, why
34:53should you ever be cooperative with
34:55another individual if you're a social
34:56animal? At every possibility, you should
35:00stab them in the back and be selfish.
35:02And the reason why that isn't a good
35:03idea is there's all sorts of
35:05circumstances where many hands make the
35:08task light or whatever that is,
35:10cooperation can have synergistic
35:13benefits. And you see that with species
35:15that are cooperative hunters where they
35:18are not necessarily relatives, they will
35:20chase one chasing an animal while the
35:22other is getting ready to cut a corner
35:24on it, cooperative behavior, and they
35:27increase the likelihood of them getting
35:29a kill.
35:30Another example of this, research by a
35:32guy named Marc Hauser at Harvard looking
35:35at rhesus monkeys. And what he showed
35:37was he would put these monkeys in a
35:39situation where they had access to food.
35:42They had access to food under one
35:44circumstance where they could reach for
35:46it and take it in and share it with
35:50another monkey.
35:52Under the other circumstance, it
35:54required two monkeys to get the food in
35:56there. And what he showed was clear-cut
35:59reciprocity. Monkeys who were sharing
36:02with this guy were more likely to get
36:04shared back with and got more
36:06cooperation when it was a task where two
36:08of them had to work together to get the
36:10food. One alone wasn't enough, many
36:13hands make the task lighter. Under all
36:15sorts of circumstances, cooperation has
36:18a strong evolutionary payoff.
36:21Even among non-relatives.
36:23With a condition.
36:25Which is you're not putting more into it
36:27than you are getting.
36:29That is reciprocal. And this opens up
36:32the third building block of all of this,
36:35which is reciprocal altruism.
36:38Cooperation, altruistic behavior among
36:41non-relatives, but undergoing very
36:44strict sort of constraints of it's got
36:46to be reciprocated with all sorts of
36:48rules like that.
36:50So, what does that look like?
36:52So, you're going to see reciprocal
36:54altruism. When would you see that?
36:56What's the immediate thing? What sort of
36:58species would show systems of reciprocal
37:01cooperation among non-relatives? They
37:04got to be smart animals. They got to be
37:05social. They got to be smart. Why do
37:07they have to be smart? Because they have
37:09to remember this is the guy who like
37:11owes me a favor from last Thursday. They
37:13need to be able to recognize
37:15individuals. They have to be long-lived
37:18enough so that there's a chance of
37:19interacting with that individual again
37:22and establishing this reciprocity. You
37:24would thus predict you would see systems
37:27of reciprocal altruism only in
37:29long-lived social vertebrates.
37:33But you see the exact sorts of things in
37:36bacteria. You see the exact sort of
37:38things in fungi. You see that in all
37:41sorts of other realms. You get social
37:43bacteria, colonizing bacteria, and where
37:46what you might get are two clonal lines
37:48that are together. In other words, two
37:52genetically
37:53two lines, each of which is all the
37:55bacteria have the same genetic makeup.
37:57So, think of it as one individual who's
37:59just kind of dispersed, another one
38:01who's just kind of dispersed, and
38:03they've come together in something
38:05called a fruiting body, which is how
38:07bacteria reproduce or whatever, and
38:10there's two parts to a fruiting body.
38:13There's one which is the stalk which
38:15attaches to something or other, and then
38:18there's the part that actually fruits.
38:20So, you want to be in the fruiting part
38:23because that's the part that actually
38:24reproduces and the stalk is doing all
38:27the work there and what you see is
38:30attempts at cheating.
38:32Attempts at one of these strains trying
38:34to disproportionately wind up in the
38:36fruiting part and what you also see is
38:39the next time around this other strain
38:41will not cooperate with it, will not
38:43form a social colony. So, that's getting
38:46played off at the level of single-cell
38:48organisms forming big social colonies
38:51getting played at that level. Yes, as we
38:53will see reciprocal altruism works most
38:56readily in big, smart, long-lived social
38:59beasts, but it can occur in all sorts of
39:01systems.
39:03So, what it's built around is
39:06reciprocal cooperation and intrinsic in
39:09that is another motivation going on
39:12there, not just to involve that
39:13reciprocal relationship with a
39:14non-relative and many hands and light
39:16tasks and all of that, but also whenever
39:19possible to cheat, to take advantage of
39:22the other individual. And thus, another
39:25key facet of it is to be very good at
39:28detecting when somebody is cheating
39:30against you, to be vigilant about
39:33cheating in what would otherwise be a
39:35stable reciprocal relationship.
39:38And an awful lot of social behavior is
39:42built around animals either trying to
39:44get away with something or spotting
39:46somebody else doing the same. An example
39:49of it, here is a test that's used in
39:51evolutionary psychology where you're
39:53given this very complicated story or
39:55another version of a complicated story
39:58where somebody promises if you do this,
40:00you'll get this reward, but if you do
40:02that, you're going to get this
40:02punishment and like really complex and
40:05in one outcome, the outcome of it is the
40:08person isn't supposed to get rewarded,
40:10but the individual decides to reward
40:12them.
40:13Spontaneous act of kindness. In another
40:16circumstance, the person is the
40:17individual is supposed to get rewarded,
40:19and instead they get punished. A cheater
40:22in that case, and amid these convoluted
40:25stories, people are much better, 75% to
40:2825, are much better at detecting when
40:31cheating has gone on in the story than
40:34when a random act of kindness has gone
40:36on. We are more attuned to picking up
40:39cheating. And remarkably, some very
40:42subtle studies have been done with
40:43chimps showing that chimps have the same
40:45bias. They are much better at picking up
40:48social interactions involving cheating
40:51than ones that involve spontaneous
40:53altruism.
40:55So, you see here this balance between
40:58cooperation and reciprocal and even
41:00among non-relatives, and that's great,
41:02but you should cheat when you can get
41:03away with it, but you should be vigilant
41:05against cheaters. And what of course it
41:07comes down to then is tic-tac-toe and
41:10giraffe hearts and all of that. What is
41:12the optimal strategy in a particular
41:15social species, for a particular
41:17individual, what is the optimal
41:18strategy? When do you cooperate and when
41:22do you cheat? When do you defect on the
41:26cooperative relationship you've had? And
41:28this introduces us to a whole world of
41:31mathematics built around what is called
41:33game theory.
41:35The notion that there are games, formal
41:38games, that have mathematically optimal
41:42strategies or multiple strategies,
41:44multiple equilibriums, and a whole world
41:47of research has been built around them
41:49in terms of when to cooperate and when
41:52to defect.
41:54So, game theory stuff. This was starting
41:57off in a world of like people studying
41:59economics and negotiation and diplomacy
42:02and all of that, and that was a whole
42:04world built around this logic of when do
42:07you cooperate, when do you cheat? And
42:09what came out of there were all sorts of
42:11models of how to optimize behavior in
42:14terms of that, and the building block,
42:16sort of the fruit fly of game theory, is
42:19a game called the prisoner's dilemma.
42:23Prisoner's dilemma, sort of cutting to
42:26sort of getting into the details, two
42:28individuals are
42:30prisoners, and they escape, and they're
42:32both captured, and they're interrogated
42:35separately, and if both of them refuse
42:37to talk, that's great for them. If they
42:39both squeal, they both get punished. If
42:41one of them is able to squeal on the
42:43other one, they get a great reward if
42:44the other one. What do you get formally
42:46are four possible outcomes.
42:49Both individuals cooperate.
42:53Both individuals cheat against each
42:55other.
42:57Individual A cooperates and B cheats.
43:01Individual B cooperates and A cheats.
43:03And what you get in prisoner's dilemma
43:05is a formal payoff for each. What gives
43:08you the greatest payoff? Stab in the
43:10other guy in the back. You cheat and
43:13they cooperate. You have exploited them.
43:15You have taken advantage of them. Isn't
43:17that wonderful? That's the highest
43:19payoff in prisoner dilemma games. Second
43:22highest payoff, you both cooperate.
43:26Third highest payoff, which is beginning
43:29to not count as a payoff, but in a lot
43:30of the games is set up as the start of
43:32punishment, both of you cheat on each
43:34other.
43:36Fourth worst possible payoff is you're
43:39the sucker. You cooperate and the other
43:42individual stabs you in the back.
43:44So, what the prisoner's dilemma game is
43:46set up these circumstances where
43:48individuals will play versions of this
43:51against each other with varying rewards
43:54and that sort of thing and parameters
43:56that we will look at in a lot of detail,
43:58and seeing when is it optimal to
44:00cooperate, when is it optimal to cheat.
44:03When would you do this? So, you've got
44:06examples of this, and this was the
44:08building block. And what anyone would
44:10say looking at this is it's obvious.
44:13What you want to do is in some way
44:15rationally maximize your payoff. This is
44:19this whole world of homo economist, the
44:22notion of humans as being purely
44:24rational decision-makers. And what you
44:26begin to see in this world of game
44:28theory is there is anything but that
44:30going on. And later in the course we're
44:33going to see something very interesting
44:35people playing prisoners dilemma games
44:37inside a brain scanner looking at a part
44:40of the brain that has a lot to do with
44:41pleasure. And what you see is some
44:44individuals activate that part of the
44:46brain when they've successfully stabbed
44:48the other guy in the back. Some
44:50individuals activate it when they have
44:52both cooperated and there's a big gender
44:55difference as to which circumstance. So,
44:58you just guess which one is going on
45:00there. We're going to see a number of
45:02studies like that coming down the line.
45:04So, the question becomes how do you
45:06optimize prisoner dilemma play?
45:09And what emerged at that time was the
45:11notion of all sorts of theoretical
45:13models and stuff. And then in the 1970s
45:16there was an economist at University of
45:18Michigan named Robert Axelrod who
45:21revolutionized the entire field. What he
45:24did was he took some Paleolithic
45:26computer and programmed in how the
45:28prisoners dilemma would be played and he
45:31could program in as if there were two
45:33players and he could program in what
45:36each one's strategy would be.
45:39And what he then did was he wrote to all
45:41of his buddies and all of his
45:43mathematician friends and prize fighters
45:46and theologians and serial murderers and
45:49Nobel Peace Prize winners and in each
45:51case explained what was up and saying
45:53what strategy would you use in a
45:56prisoners dilemma game. And he gets them
45:58all back and he programs all these
46:00different versions and he runs a
46:02round-robin tournament. Every strategy
46:05is paired against every other strategy
46:07at one point or other, and you look at
46:09what the payoff is. You ask, which is
46:11the most optimal strategy?
46:14And out of it, shockingly to everyone,
46:17because this was a computer teaching us
46:20optimizing human behavior, out of it
46:23came one simple strategy that always
46:25outcompeted the others. This is people
46:27sitting there probabilistic ones as to
46:29when to cooperate and lunar cycles as to
46:33what to do. The one that always won is
46:35now called tit-for-tat.
46:38You start off cooperating in the very
46:41first round with the individual. You
46:43cooperate.
46:45If the individual has cooperated with
46:47you in that round, you cooperate in the
46:49next round.
46:51And you cooperate cooperate as long as
46:53the other individual cooperates, but as
46:55soon as there's a round where the
46:56individual cheats against you,
46:59you cheat against them the next time.
47:02If they cheated at you that time also,
47:04you cheat against them the next time. If
47:06they go back to cooperating, you go back
47:08to cooperating the next time.
47:11You have this tit-for-tat strategy in
47:13the absence of somebody stabbing you in
47:15the back, you will always cooperate. And
47:18what they found was run these hundreds
47:20of thousands of versions of these
47:22round-robin tournaments, and tit-for-tat
47:25was the one that was most optimal. To
47:27begin to use a word that is not just
47:29going to be a metaphor, tit-for-tat
47:32always drove the other strategies into
47:34extinction.
47:36And what you wound up seeing is this
47:39optimized strategy. And it was very
47:41clear why tit-for-tat worked so well.
47:44Number one, it was nice. You start off
47:46cooperating. Number two, it retaliates
47:50if you do something crummy to it.
47:52Number three, it is forgiving. If you go
47:55back to cooperating, number four, it's
47:58clear cut in its play. It's not some
48:00probabilistic thing. And what you get
48:03then with tit-for-tat is suppose you're
48:05playing three rounds with another
48:07individual, you both cooperate the first
48:09one, you both cooperate the next one,
48:11you're playing tit-for-tat strategy, so
48:13you cooperate on this one, and they stab
48:15you in the back, and you can't get back
48:16at them because this is the last round.
48:18What you'll see is under lots of
48:20circumstances, tit-for-tat is
48:22disadvantageous,
48:24but what the sound bite is about it is
48:26tit-for-tat may lose the battles, but it
48:29wins all the wars. This pattern of being
48:32nice, but being retaliatory, being
48:34forgiving, and being clear in the rules
48:37drives all the other strategies into
48:39extinction.
48:41Okay, at this point my alarm just went
48:43off, which was to remind me to ask
48:45somebody who is wearing a life vest.
48:49Is somebody wearing a life vest?
48:53Over there. Um, where are you?
48:57She left. Isn't that interesting?
48:59Somebody put me up to having to ask this
49:01person, "Why are you wearing a life
49:03vest?" And apparently the answer she
49:05would give was going to free all sorts
49:07of captives in some like rebel group in
49:09Colombia, and she fled. Okay, what that
49:12does is uh
49:16I don't know what that says about
49:17reciprocal altruism, but what that says
49:19also is after I do a summary, don't make
49:22a move, we will have a 5-minute break.
49:23So, what do we have at this point? We
49:25have the first building block of
49:28optimizing the evolution of behavior,
49:30like optimizing giraffe hearts. First
49:32piece, you don't believe behave for the
49:34good of the species, individual
49:36selection passing on as many copies of
49:38your own genes as possible. Sometime a
49:40chicken is an egg's way of making
49:42another egg, he says triumphantly.
49:44Building block number two, kin
49:46selection, some of the time the best way
49:48to pass on copies of your genes is by
49:50way of helping relatives, kin selection
49:53with the mathematical fierceness of
49:55degree of relatedness driving it. Piece
49:58three, sometimes what's most
50:00advantageous is to cooperate even with
50:02non-relatives, but with the rules of it
50:05has to be reciprocal, and you have to
50:07cheat when possible, you have to be on
50:09guard against cheaters, and as we've
50:10just seen game theory prisoners dilemma
50:13beginning to formalize optimal
50:16strategies for that. Okay, let's take a
50:185-minute break.
50:20But promise you will come back if you go
50:22out and everyone won't wander off.
50:31maximize that behavior in a very
50:33artificial realm, but stay tuned.
50:35Prisoner's dilemma as the building block
50:38of how to do this amid lots of other
50:40types of games that are used, but
50:41prisoner's dilemma is the most basic
50:43one, and that round robin tournament,
50:45that computer simulation, Axelrod asking
50:49all his buddies to tell him what
50:51strategy would you use, run them against
50:52each other, and out comes tit-for-tat.
50:55Tit-for-tat drives all the others into
50:58extinction.
51:00However, there is a vulnerability in
51:02tit-for-tat,
51:03which is
51:17Okay, so
51:19we have the technical way of showing
51:22prisoner's dilemma play, and first round
51:24both individuals are cooperating.
51:27Second round both individuals are
51:28cooperating. Third round, this one
51:31cheats, those are fangs. This one
51:33cheats, and this one cooperates, so the
51:35next round, this one
51:38now cheats, and this one goes back to
51:40cooperating, and we've just gotten
51:42through a scary thing that tit-for-tat
51:45solves, and it's great.
51:47Wonderful. What if though your system is
51:50not 100% perfect? What if there's a
51:53possibility of a mistake being made of
51:57sending the wrong signal? What if
51:59there's the possibility of noise in the
52:02communication system and at some point
52:04an individual who does a cooperative
52:07behavior thanks to a glitch in the
52:09system, it is read as having been
52:11defected defection.
52:14So what happens as a result?
52:16This individual
52:20Forget it. Okay. What happens as a
52:22result? The individual who cooperated
52:26but somehow the message got through is
52:28cheating. They don't know.
52:30Something got lost in the wires between
52:31them in translation. The other
52:33individual was saying, "Whoa, that
52:35individual cheated against me. I'm going
52:37to cheat in the next round." So along
52:39comes the next round and that individual
52:41cheats against them, this one who's
52:43cooperating because they've cooperating
52:45all along. They don't know about this
52:46error and they say, "Whoa, that person
52:49just cheated against me. I'm going to
52:51cheat in the next round." So they cheat
52:53in the next round. This one says, "Whoa,
52:55they just cheated another time." Again
52:57and again and again and what you get is
52:59a seesaw pattern for the rest of time.
53:02You've just wiped out 50% of the
53:04cooperation. And what you've got is tit
53:07for tat strategies are vulnerable to
53:10signal error. That's something that soon
53:12came out in these studies of Axelrod's.
53:15And when I was a kid there was like one
53:17of these like thriller books I remember
53:19reading where there's a glitch in the
53:22system and at the time the mean scary
53:24Soviet Union launched a missile that no,
53:28it was the United States. The United
53:30States by accident launched a missile, a
53:32nuclear weapon where they didn't mean
53:34to. Some cockroach,
53:36you know, chewed through a wire
53:37someplace or other and the missile went
53:39off and wound up being destroying Moscow
53:43and oh my god, we had a cooperative
53:45system of mutually sort of restraint of
53:48aggression all of that and thanks to a
53:50signal error, a cheating signal was
53:52accidentally sent off and how did the
53:54book end? A tit-for-tat response in
53:57order to avoid sort of thermonuclear
53:59wasteland, the Soviet Union was allowed
54:02to destroy New York.
54:04All right, so that shows exactly how you
54:07could then get into a seesawing thing
54:10simply by way of if the system has any
54:13vulnerability to signal error. So it
54:15soon became clear as soon as Axelrod
54:18began to introduce the possibility of
54:20signal errors that tit-for-tat didn't
54:22work as well as another strategy, one
54:25that quickly came to the forefront and
54:28that one
54:32for some strange reason, that's the
54:34way it's shown. That one was called
54:36forgiving tit-for-tat.
54:40What happens with forgiving tit-for-tat?
54:42The usual rule like tit-for-tat, if you
54:44cooperate if they cooperate, you always
54:46cooperate. If they cheat against you,
54:48you punish them in the next round.
54:50Exactly same thing as tit-for-tat, but
54:52oh no, what if there's a signal error in
54:54the system and you've gotten caught in
54:56one of these horrible seesawing things?
54:59What forgiving tit-for-tat does is we'll
55:02have a rule for example that if we
55:04seesaw like this five times in a row, I
55:07will forego cheating the next time and
55:10instead I'll cooperate and that will get
55:12things back on track. I am willing to be
55:16forgiving in one round in order to
55:19reestablish cooperation after the signal
55:22error came in.
55:23And that one as soon as you introduce
55:25the possibility of signal error, that
55:27one outcompetes tit-for-tat.
55:30Because it makes perfect sense. It's a
55:32great way of solving that problem.
55:36So that was terrific. Tit for tat with
55:39the ability to forgive, and what you
55:40would then see is variability. How many
55:43of these do you need to go through
55:45before you forgive? What's the optimal
55:47number of C songs? All of that. So, a
55:49whole world of optimizing how soon you
55:52are forgiving. Nonetheless, the general
55:54theme being forgiving tit for tat
55:56outcompetes tit for tat when you can
55:58have signal error.
55:59But, there was a vulnerability.
56:02There was a vulnerability here to this
56:04one, which is you could be exploited. If
56:07you're playing against, for example, a
56:09tit for tatter, or all sorts of other
56:11strategies where they don't have
56:13forgiving strings of defection, and you
56:17do, what's going to happen is you're
56:19going to keep going back to cooperating,
56:20and they're going to keep stabbing you
56:21in your back.
56:23Forgiving tit for tat is vulnerable to
56:26exploitation playing against individual
56:29players that don't have forgiveness in
56:31them.
56:32So, what soon became apparent was an
56:35even better strategy,
56:37which is
56:45you start off with a tit for tat
56:47strategy, which is you are punitive, you
56:51are retaliatory, amid being forgiving,
56:54clear, nice initially. You are willing
56:56to punish, and you cannot be exploited
56:58in this way.
57:00If and only if you have gone whatever
57:03number of rounds without the other
57:04individual ever cheating on you, if
57:07you've gone long enough without that
57:08happening, you switch over to forgiving
57:11tit for tat. What is that? That's
57:14deciding you trust somebody. You've had
57:16enough interactions with them that you
57:18are willing to trust them. This is the
57:20transition from pure rational optimizing
57:24to switching over, forgiveness coming in
57:28there protects you from signal error,
57:30and of course, now a whole world of how
57:33many rounds do you need to do this
57:34before you switch that as to what the
57:36optimal deal with that is. But again,
57:39this is a way of transitioning to solve
57:41the problem of signal error, but
57:44forgiving too readily and being taken
57:46advantage of.
57:48Soon, another strategy appeared, which
57:51was called Pavlov.
57:53And those of you who know Pavlovian
57:55psychology will see that this in fact
57:58has nothing whatsoever to do with
57:59Pavlovian psychology, and I don't know
58:01why they did that, but they thought it
58:03was kind of cool. But the rule was,
58:05remember, if you stab the other guy in
58:08the back, you get a bunch of points. If
58:10you both cooperate, you get points, not
58:12as many. If you both cheat, you lose
58:16some points. If you're taken advantage
58:18of, you lose a lot of points. So, two
58:20outcomes, you gain, two outcomes, you
58:22lose. In Pavlov, the simple rule is,
58:25when I do something, if I get points, if
58:28I get some degree of reward, I do it
58:31again the next time. If I get rewarded
58:34in either of the first two types of
58:35payoffs, I do the same thing again.
58:39And the other part, of course, is and if
58:41I have if I play my strategy and I lose
58:45one of the two bottom the two bottom
58:47outcomes, I switch to the other strategy
58:49the next time.
58:51And what you see is that can establish
58:54very good tit-for-tat stuff, but if you
58:56sit and spend hours tonight with, you
58:58know, a long roll of toilet paper and
59:00playing out all the rounds of it, you
59:02will see what Pavlov allows you to do is
59:05exploit somebody else who's forgiving.
59:08So, Pavlov goes along just fine with
59:10this, and as long as Pavlov continues,
59:12whenever they switch over to a forgiving
59:15tit-for-tat, Pavlov will outcompete them
59:18because Pavlov exploits.
59:22What then emerged was just zillions of
59:25people studying all sorts of games like
59:27this. And there's other ones, ultimatum
59:29game, there's a trust game where it's
59:32the same notion of business there, which
59:34is you choose to cooperate, you choose
59:36to cheat, what's the optimal outcome?
59:39There are mathematically optimal
59:41outcomes that you can use, and you run
59:43all of it against the computer, and you
59:45get the optimization popping out the
59:47other end. Wonderful. So, there's
59:49Axelrod and his buddies using terms
59:52like, "Oh, this strategy will drive the
59:54other one into extinction." Or, "This
59:57strategy works, but if you program in
59:59that every now and then there could be a
1:00:01glitch, there can be a mutation." This
1:00:04will be They're using all this biology
1:00:06jargon obviously metaphorically, but
1:00:09right around this point, the biologists
1:00:11look at this, who are just beginning to
1:00:13think about the sociobiology stuff,
1:00:16formal patterns of optimizing behavior,
1:00:19and they say, "Whoa, does this apply to
1:00:22the behavior of real organisms?"
1:00:25Cuz at this point, it's just economists
1:00:27and computer types and diplomats
1:00:29learning when to optimize, all that sort
1:00:32of thing. Around the time, there was a
1:00:34paper published somewhat before that.
1:00:37This is a name nobody is going to know,
1:00:39lost in history, a guy named Daniel
1:00:41Ellsberg. Daniel Ellsberg became very
1:00:44famous around 1970 by He was working in
1:00:47the Pentagon, and he stole thousands of
1:00:50pages of secret files there, and gave it
1:00:53to the New York Times showing how
1:00:55utterly corrupt everything that went on
1:00:57behind the scenes was in getting us into
1:00:59Vietnam, major blowout, all of that. He
1:01:03had spent the early part of his career
1:01:05perfectly happily working in the
1:01:06Pentagon for the military as a game
1:01:09theorist. As a game theorist, coming up
1:01:12with optimal patterns, and he wrote one
1:01:14paper called The Optimal Benefits of
1:01:18Perceived Madness.
1:01:20What What times do you want your
1:01:22opponent to think you are absolutely out
1:01:24of your mind and going to do all sorts
1:01:26of crazy stuff and where they wind up
1:01:28cooperating to keep you from doing that,
1:01:31the advantages of madness. What's that?
1:01:33That system's where things like mutually
1:01:35assured destruction doesn't work because
1:01:38you're willing to set it off that the
1:01:40advantages of madness. This whole world
1:01:43of people working on it, mathematicians
1:01:45and war strategist and there's the
1:01:48zoologist now looking at this saying,
1:01:51"Whoa, this is cool. I wonder if animals
1:01:54behave that way." And that's when
1:01:56people, now armed with their insights
1:01:59into prisoners dilemma and tit-for-tat
1:02:01and all this stuff, started to go and
1:02:03study animals out in the wild and see
1:02:06were there any examples where this
1:02:08happened.
1:02:09Yes, in all sorts of interesting realms.
1:02:12First example, vampire bats. Vampire
1:02:16bats, we are all set up to be creeped
1:02:18out by vampire bats, but in actuality,
1:02:21when you see a vampire bat drinking the
1:02:23blood of some cow or something, you are
1:02:25watching a a mommy getting food for her
1:02:28babies because vampire bat mothers are
1:02:31not actually drinking the blood, they're
1:02:33filling up this throat sack thing and
1:02:36they go back to the nest and they
1:02:37disgorge the blood to feed their babies.
1:02:39She's just watching out for her kids. It
1:02:42happens that vampire bats have an
1:02:44interesting system of reciprocal
1:02:46altruism, which is a whole bunch of
1:02:48females will share the same nest, will
1:02:51have all their kids in there mixed in
1:02:53and these are not necessarily related,
1:02:55so we've just left the world of kin
1:02:57selection. They're not necessarily
1:02:59related, but they have reciprocal
1:03:00altruist system. Each female comes in,
1:03:03disgorge disgorges the blood and feeds
1:03:06everybody's babies and they'll all feed
1:03:08each others babies and everything is
1:03:10terrific and they have this blood
1:03:11vampire commune going there and they've
1:03:14reached a in state of stable
1:03:16cooperation. Now, make the bats think
1:03:20that one of the females is cheating on
1:03:22them.
1:03:23Outcomes that female flying off to find
1:03:25some blood, and instead you net her and
1:03:28get a hold of her and take some syringe
1:03:30full of air and pump up the throat sack,
1:03:33so the throat sack is really full and
1:03:35distended, but there's no blood in
1:03:37there. You've just pumped air into there
1:03:38and stick her back into the nest there,
1:03:40and she's just sitting there happily.
1:03:42And the other females are sitting
1:03:43saying, "Look at her. Look at how much
1:03:45blood she's got there. I can't believe
1:03:47it cuz she's not feeding our kids. She's
1:03:50cheating on us." And the next time they
1:03:52go out to feed, the other females don't
1:03:55feed her kids. A tit-for-tat.
1:03:58And what you saw here is an exact
1:04:00example of introducing signal error,
1:04:02signal error in this case being some
1:04:04grad student pumping up the throat of
1:04:06some vampire bat and showing that
1:04:10they're using a version of a tit-for-tat
1:04:12strategy. Totally amazing. People were
1:04:15blown away by this. Another example,
1:04:19fish. Stickleback fish, who in the world
1:04:22of animals, you know, bats are probably
1:04:24not some of the brightest folks around,
1:04:25but I don't think sticklebacks are
1:04:27within light-years of them, but
1:04:29stickleback fish can do a tit-for-tat
1:04:31strategy. Here's what you do. You have a
1:04:34stickleback fish in your in your fish
1:04:37tank, and you make the fish you make him
1:04:40believe that he's being attacked by
1:04:42another fish.
1:04:44What do you do? You put a mirror up
1:04:45against the edge of the tank there. So,
1:04:48within a very short time, I told you
1:04:49they were not that smart, so within a
1:04:51very short time, he's lunging forward at
1:04:54this mirrored thing and maintaining his
1:04:56territory against this guy and barely
1:04:58holding on, and that other guy is just
1:05:00he doesn't get tired. Thank God I don't
1:05:03get tired. And they're just going at it
1:05:05and now make him think he has a
1:05:08cooperative partner.
1:05:10Put in a second mirror that's
1:05:12perpendicular here. In other words, he
1:05:15sees his reflection there. And every
1:05:17time he moves forward, he sees that one
1:05:20moving forward, and which is fortunate
1:05:22because he's also seeing another fish
1:05:24coming from that way, and he's sitting
1:05:26there saying, "This is great. I don't
1:05:27know who this guy is, but wow, what a
1:05:29team we are. Doubles. This is great.
1:05:31He's in there in the thick. It's funny
1:05:33how those two guys are so synchronized.
1:05:34But wow, we're holding them off and
1:05:37we're doing it." Now, make him think his
1:05:39cooperating partner is in fact cheating
1:05:42on him.
1:05:43Take the mirror and angle it back a
1:05:45little bit, so the reflection is set
1:05:47back some, and what he now sees is the
1:05:51fish moving forward, but not all the way
1:05:53up to the wall there. The fish is
1:05:55hanging back there. The fish is
1:05:56cheating. And the stickleback is sitting
1:05:59there saying in effect, "That son of a
1:06:01I can't believe he's doing that
1:06:03to me. We've worked together for years.
1:06:04I can't believe he's doing it. Oh, he's
1:06:06pretending to go forward, but I see he's
1:06:08not really doing that. Fortunately, that
1:06:10guy isn't coming forward anymore either.
1:06:12Phew. But I can't believe that guy's
1:06:14cheating." And the next time you set up
1:06:16this scenario, the next time with the
1:06:18chance, the stickleback doesn't attack
1:06:21its own reflection there. It is
1:06:23tit-for-tatting against this guy.
1:06:26So, here we've managed to set up one of
1:06:28these deals within one fish and carrying
1:06:30it out forever. One fish ultimately with
1:06:32some very blistered lips. Tit-for-tat
1:06:35once again. Another example. This is the
1:06:38most bizarre one I can imagine and leads
1:06:41to all sorts of subjects that are going
1:06:43to come many lectures from now, but
1:06:44there are fish species that will change
1:06:47sex.
1:06:48And they do it under all sorts of
1:06:50strategic circumstances that suddenly
1:06:52begin to fit into this realm of what
1:06:54we've been learning about. And you've
1:06:56got one of these things called black
1:06:58hamlet fish, and they can change gender.
1:07:02So, you'll have a pair of them who hang
1:07:04out with each other of opposite genders,
1:07:06and they take turns. They flip back and
1:07:08forth for a while. This one's female,
1:07:10and for a while this one's female, and
1:07:11they go back and forth. And that's
1:07:13great, but there's an inequity there,
1:07:15which is that the price of reproduction
1:07:17is greater for the female than for the
1:07:19male.
1:07:21As is the case in so many species, the
1:07:23female is doing all that egg and oviduct
1:07:26and progesterone stuff or whatever it
1:07:28is, and the male's just got to come up
1:07:29with some sperm there. Doing
1:07:31reproduction as a cooperating pair,
1:07:34they're not relatives, reciprocal
1:07:36altruism, maximizing each of their
1:07:38reproductions. Whoever's the female in
1:07:40any given round is the one who's paying
1:07:43more.
1:07:44And what you see are reciprocal
1:07:46relationships there of the fish using
1:07:49tit-for-tat. If you get one fish that
1:07:52begins to cheat and winds up being a
1:07:54male too much of the time, the other
1:07:56fish stops cooperating with them. Again,
1:07:59tit-for-tat stuff. So, people were just
1:08:02blown out of the water at this point,
1:08:04seeing, "Whoa, forget rational human
1:08:07economic thinking and all of that. You
1:08:09go out in the wild and bats and
1:08:11stickleback fish and gender-switching
1:08:14fish and all of that, they're following
1:08:16some of the exact same strategies. Isn't
1:08:18nature amazing?" No, nature isn't an
1:08:21amazing. It's the exact same logic as
1:08:23saying a giraffe has to have a heart
1:08:26that's strong enough to pump blood to
1:08:27the top of the head of a giraffe, or
1:08:29else there wouldn't be giraffe. And when
1:08:31you look at this realm, it's applying
1:08:33the same notion, the same sort of wind
1:08:36tunnel of selective optimization for
1:08:39behavior in this case, when to cheat,
1:08:41when to cooperate, sculpt something that
1:08:44is as optimized as a giraffe's heart
1:08:47being the right size.
1:08:48So, this made perfect sense. Wonderful.
1:08:52But then people began to look a little
1:08:53bit closer and began to see sort of the
1:08:56very distressing real-world beginning to
1:08:58creep in there, which were exceptions.
1:09:01First exception, this was done by a guy
1:09:04named Craig Packer at University of
1:09:05Minnesota looking at lions in East
1:09:08Africa and what you get is typically
1:09:11prides are a whole bunch of relatives,
1:09:13usually females, sisters, nieces, all of
1:09:16that. But you will sometimes get prides
1:09:17that are not of close relatives.
1:09:19Nonetheless, they will get, you know,
1:09:21reciprocal altruistic things going on.
1:09:24Lions in this case, having the same
1:09:26trick as was done on those vervet
1:09:28monkeys, researcher putting inside the
1:09:30bush there a speaker and playing the
1:09:33sound of like 400 menacing lions all at
1:09:36once. And you know, what you're supposed
1:09:39to do is freak out at that point and all
1:09:42of you need to very carefully approach
1:09:44and see what's going on in that bush.
1:09:46So what would happen in a reciprocal
1:09:48system and everybody who does this does
1:09:51this or if one time one of them cheats
1:09:53on you, you push that one forward the
1:09:54next time or some such thing. That's
1:09:56what you would expect, but what he would
1:09:58begin to notice is in a bunch of these
1:10:00groups, there'd be one scaredy cat lion,
1:10:04one who habitually stayed behind the
1:10:07others and who wasn't punished for it.
1:10:10So this produced this first puzzle that,
1:10:12oh, sometimes animals aren't optimizing
1:10:15tit for tat. Sometimes animals haven't
1:10:16read Robert Axelrod's landmark 1972
1:10:19paper, that sort of thing. And what you
1:10:21suddenly have is the real world.
1:10:24What could be possible explanations? One
1:10:27thing being
1:10:28maybe they're not really paying
1:10:30attention. Maybe they're not quite that
1:10:32smart. Wait, bacteria are doing versions
1:10:34of tit for tat. What else could be going
1:10:36on? Oh, lions interact in other realms.
1:10:39Maybe this individual is doing very
1:10:42reciprocal stuff, forgiving overly
1:10:45altruistic stuff in some other realm of
1:10:47behavior. Maybe this lion is eats less
1:10:51of the meat and backs off earlier or
1:10:53something like that. Maybe there's
1:10:55another game going on simultaneously.
1:10:59And this introducing the real world in
1:11:02which it is not just two individuals
1:11:04sitting there playing prisoners dilemma
1:11:07and optimizing, you suddenly begin to
1:11:09get real world complexities coming in
1:11:11there. And by the time we get to the
1:11:13lectures way down the line on aggression
1:11:15and cooperation, what you will see is
1:11:17things get really complicated when you
1:11:20have individuals playing games
1:11:21simultaneously, the rules that you apply
1:11:24to one psychologically begin to dribble
1:11:26into the other one, all sorts of things
1:11:29like that. It will get very complicated.
1:11:31So, a first hint there that in fact
1:11:34everything doesn't work perfectly along
1:11:35those lines. Here's another version.
1:11:38Here's one of the truly weird species
1:11:40out there, something called the naked
1:11:43mole rat.
1:11:44If you ever have nothing to do and
1:11:46you've got Google image up there, go
1:11:49like spend the evening looking up
1:11:51close-up pictures of naked mole rats.
1:11:53These are like the weirdest things out
1:11:55there. They're the closest things among
1:11:57the mammals to social insects in terms
1:12:00of how their colonies work. They're
1:12:02totally bizarre, all of that, but they
1:12:04live in these big cooperative colonies
1:12:07that are predominantly underground in
1:12:09Africa and they were discovered, I
1:12:11think, only in the 1970s or so. And for
1:12:13a while when zoologists got together, if
1:12:16you were a naked mole rat person, you
1:12:18were just the coolest around and
1:12:19everybody else would feel intimidated
1:12:21cuz you were working on the best species
1:12:23out there and you would see these big
1:12:25cooperative colonies soon shown to not
1:12:28necessarily be a relatives and
1:12:31reciprocity, all sorts of rules, but
1:12:33people soon began to recognize there
1:12:36would be one or two animals in each
1:12:38colony that weren't doing any work.
1:12:40Work digging out tunnels,
1:12:44bookkeeping, I don't know what naked
1:12:45mole rats do in terms of work, but there
1:12:47would be a few individuals who would
1:12:49just be sitting around and they were
1:12:50these big old naked mole rats. They were
1:12:53much bigger than the other ones, and
1:12:55they were scarfing up food left and
1:12:56right. There goes Robert Axelrod down
1:12:59the drain. There goes all that
1:13:00optimization, because no one would be
1:13:02punishing these guys. What's the deal?
1:13:05And it took enough watching these
1:13:07animals long enough to see this notion
1:13:09of, "Oh, there's another game going on
1:13:12in which they play a more important
1:13:14role, and it is sort of dribbling
1:13:17across." When the rainy season comes,
1:13:20these big naked mole rats go up and turn
1:13:23around, and they plug the entry to the
1:13:25tunnels there.
1:13:27That's what they do. And suddenly, these
1:13:30guys who have been sitting around doing
1:13:31no work whatsoever all year and eating
1:13:33tons of stuff, they suddenly have to now
1:13:36stick their rear ends out for the
1:13:37coyotes to be around or whatever it is
1:13:39that predates them. What we have is role
1:13:42diversification. Real animals, real
1:13:45organisms, are not just playing one
1:13:48formal prisoner's dilemma game against
1:13:50each other at the same time.
1:13:52And by the time we again get to the
1:13:54later lectures on aggression,
1:13:56cooperation, all of that, we will not
1:13:57only see that things get much more
1:13:59complicated when you're playing
1:14:01simultaneous games, when you're playing
1:14:03a game against one individual while
1:14:05you're playing against another one, and
1:14:06then against triangular circumstances,
1:14:09how play differs if you know how many
1:14:12rounds you are playing against the
1:14:13individual versus if you have no idea,
1:14:16how play differs if when you were about
1:14:18to play against someone, you get to find
1:14:21out what their behavior has been in the
1:14:23previous trials with other individuals.
1:14:25In other words, if somebody shows up
1:14:27with a reputation, we'll see this is a
1:14:29much more complicated world of playing
1:14:32out these games, a much more realistic
1:14:34one.
1:14:35So, we begin to see a first pass at all
1:14:37this optimization stuff and how great
1:14:39that all is. One final interesting
1:14:42addition to this game theory world of
1:14:44thinking about behavior like that, which
1:14:47came from a guy named James Holland, who
1:14:48apparently
1:14:50might have a different first name, but
1:14:52Holland apparently has an interesting
1:14:54piece in history. He's the first first
1:14:56person to ever get a PhD in computer
1:14:58sciences, which I think was in the late
1:15:00'50s, University of Michigan.
1:15:02Apparently, there are realms of
1:15:04computer programmers who worship this
1:15:06guy, and he, like a lot of other folks
1:15:09in that business, got interested in this
1:15:10game theory evolution of optimal
1:15:13strategies, and he designed ways of
1:15:16running all of this, and he introduced a
1:15:17new ripple, which is the possibility of
1:15:20a strategy suddenly changing. The
1:15:23possibility of a mutation. And what he
1:15:26could then study was mutations, how
1:15:28often they were adaptive, how often they
1:15:31spread throughout the strategy there of
1:15:34individuals playing, how often they
1:15:36drove the other strategies into
1:15:38extinction, versus ones that were
1:15:40quickly driven to extinction themselves.
1:15:42More cases where we are getting these
1:15:44systems where maybe they're not just
1:15:46metaphorically using terms from biology,
1:15:49maybe they are exactly modeling the same
1:15:52thing, and we will see more and more
1:15:53evidence for that.
1:15:55Okay, so reciprocal altruism. How would
1:15:58that play out in the world of natural
1:16:00selection? Natural selection,
1:16:02cooperative hunting. And there's lots of
1:16:05species that have cooperative hunting,
1:16:07wild dogs, jackals, some other species
1:16:09as well. That's clearly that's like the
1:16:12definition of cooperative hunting, of
1:16:13cooperative reciprocal altruism if
1:16:16they're not relatives. How would sexual
1:16:18selection play out in the realm of
1:16:20reciprocal altruism? A little bit less
1:16:22obvious there. That would be if you and
1:16:24some non-relative spent an insane amount
1:16:26of energy and time making sure you both
1:16:29look really good before going to the
1:16:31prom.
1:16:32That would be sexual selection working
1:16:34on reciprocal altruistic system. So,
1:16:36what we have now are our three building
1:16:39blocks. This whole trashing of it's not
1:16:42survival of the fittest, it's not
1:16:44behaving for the good of the species,
1:16:46it's not behaving for the good of the
1:16:48group, but instead these three building
1:16:49blocks, the ways to optimize as many
1:16:52copies of your genes in the next
1:16:54generation as possible. Way number one,
1:16:57individual selection, a version of
1:17:00selfish genes, sometimes a chicken is an
1:17:02egg's way of making another egg,
1:17:04behavior is just the way of getting
1:17:06copies of genes into the next
1:17:07generation.
1:17:09Piece number two, inclusive fitness, kin
1:17:11selection, that whole business that
1:17:13sometimes the best way of passing on
1:17:14copies is to help relatives do it, and
1:17:17it's as a function of how related they
1:17:19are, the whole world of cooperation more
1:17:21among related organisms than unrelated
1:17:24ones. And as we will see way down the
1:17:27line, what is very challenging in
1:17:29different species is how do you figure
1:17:31out who you're related to. And humans do
1:17:34it in a very unique way that sets them
1:17:36up for being exploited in all sorts of
1:17:39circumstances that begin to explain why
1:17:42culture after culture people are really
1:17:45not nice to thems, and it flows along
1:17:48those lines. This is something we will
1:17:49get to in a lot of detail. So, degree of
1:17:52relatedness, a lecture coming, how do
1:17:54you tell who you're related to, but that
1:17:56second piece, kin selection. Third
1:17:58piece, reciprocal altruism, you scratch
1:18:01my back and I'll scratch your back, and
1:18:03whenever possible, you want to instead
1:18:05scratch your back, and they want to make
1:18:07sure you're not scratching your back or
1:18:09whatever cheating counts as, but trying
1:18:11to cheat, being vigilant against it,
1:18:13formal games where you can optimize it,
1:18:16very complicated, and can you believe
1:18:18it, you go out into the real world and
1:18:20you find examples of precisely that,
1:18:23optimization with tit for tat, isn't
1:18:24nature wonderful? It's got to work that
1:18:26way, and then you begin to see how the
1:18:28real world is more complicated, multiple
1:18:31roles, naked mole rats stuck in
1:18:33plumbing, things of that sort.
1:18:36Okay, so these are the principles, and
1:18:38what people of the school of
1:18:39evolutionary thought would say armed
1:18:42with these sorts of principles, you
1:18:44could now look at all sorts of
1:18:45interesting domains of animal behavior
1:18:48and understand what the behavior is
1:18:50going to be like by using these. Okay,
1:18:53we start with the first example. Here we
1:18:55return to these guys
1:18:57and we have one species here and knowing
1:19:01this guy had a penis and this one
1:19:02nursed, we've got an adult male and
1:19:04adult female. What is it that you can
1:19:06conclude in this species? Males are a
1:19:10lot bigger than females.
1:19:12So let's state it here as there's a big
1:19:14ratio of males to females. Meanwhile, in
1:19:17the next county you've discovered
1:19:18another species where somebody's got a
1:19:20penis and somebody else is nursing and
1:19:22their skulls are the exact same size.
1:19:24Oh, here's the species where there's no
1:19:26difference in body size between males
1:19:29and females.
1:19:30Okay, so let's begin to see just using
1:19:32the principles we've got in hand
1:19:34already, what sort of stuff we can
1:19:36predict.
1:19:38Starting. Okay, which of those species
1:19:40in one case you have males being a lot
1:19:43bigger than females. In one case you've
1:19:46got males being the same size as
1:19:47females. In which of those species, the
1:19:50first one like this or the same size
1:19:52ones, in which ones would you expect to
1:19:54see more male aggression?
1:19:58First one. Okay, how come?
1:20:02Their bodies are built for it, which
1:20:03begins to tell you something. Their
1:20:05bodies are built for it maybe because
1:20:07females have been selecting for that,
1:20:09you will see higher levels of aggression
1:20:12in species like this
1:20:14where there's a big body size difference
1:20:16and
1:20:17much less of it in these guys. Next, you
1:20:20now ask how much variability is there in
1:20:24male reproductive success. In one of
1:20:26these species
1:20:28all the males have one or two kids over
1:20:31their lifetime. In another species, 95%
1:20:34of the reproducing is carried out by 5%
1:20:37of the males, a huge variability skew in
1:20:40male reproductive success. Which species
1:20:43do you get the every male has a couple
1:20:46of kids and that's about it and all
1:20:48equally so. Which one?
1:20:50Second one. How come?
1:20:56Cuz these guys are being selected for
1:20:59aggression. If they're fighting, there
1:21:01is going to have to be something they're
1:21:03fighting for, differential reproductive
1:21:05access. Okay, so you see more
1:21:08variability
1:21:10in species that look like this.
1:21:13Next, females come into the equation.
1:21:16What do females want? What do females
1:21:18want in the species on the left versus
1:21:20the one on the right? The one on the
1:21:22right,
1:21:23you know, again, skull's the same size,
1:21:25same body size. On the left, what does
1:21:27the female want?
1:21:32What sort of male is the female
1:21:34interested in?
1:21:35Big. Exactly. That's exactly the driving
1:21:38force on this. How come? Because she's
1:21:42not going to get anything else out of
1:21:43this guy. This guy is just going to like
1:21:45the present is going to be some sperm.
1:21:47It might as well be some good sperm,
1:21:49some genetically well-endowed sperm that
1:21:51makes her a big, healthy offspring,
1:21:53increasing the odds of her passing on
1:21:56copies of her genes in the next
1:21:57generation.
1:21:58What about in this species?
1:22:01What's females looking for?
1:22:06Okay.
1:22:08Good. Hold on to that for a second and
1:22:10let's jump ahead a few lines.
1:22:13One of the species, males have never
1:22:16been known to do the slightest
1:22:18affiliative thing with infants. They
1:22:20just get irritated and harass them and
1:22:22all of that. And the other, you have
1:22:24soccer dads who are doing as much
1:22:27raising of the kids as the females are.
1:22:30In which species do you get lots of male
1:22:32parental behavior?
1:22:35The one on the right. Okay. So, lots of
1:22:37male parental behavior here.
1:22:42So, somebody just gave the answer here,
1:22:44female choice. What would you see in
1:22:46this species? You want big muscular
1:22:49guys. You want whatever is selling that
1:22:51season for what counts as a hot male
1:22:54because you want your offspring to have
1:22:56those traits. And somebody else called
1:22:58out here, what do females want in this
1:23:00category? And what was it you said?
1:23:03Good personality.
1:23:04Good personality. Yes.
1:23:07Able to express emotions.
1:23:11That, too.
1:23:12Okay, somebody else shouted out
1:23:14something that gets at the broader, more
1:23:17globally Oprah version. Okay, somebody
1:23:19shouted out
1:23:20paternal parental behavior.
1:23:23You want a male who's going to be
1:23:25competent at raising your children. What
1:23:28is it that you want really most deeply?
1:23:31You want to get the male who is the most
1:23:33like a female you can get a hold of. You
1:23:35don't want some big old stupid guy with
1:23:38a lot of muscle and canines who's
1:23:40wasting energy on stuff like that that
1:23:42he could be using instead on, you know,
1:23:44reading Goodnight Moon or some such
1:23:46thing. What you want instead is
1:23:49somebody who's as close to a female as
1:23:51you can get to without getting this
1:23:53lactation stuff. Males are chosen who
1:23:56are the same size as females.
1:23:59So, the term given here is
1:24:02you know, choosing for paternal
1:24:04behavior, parental behavior. Parental,
1:24:06let's just put that in there. And that
1:24:08begins to explain the top line, species
1:24:11in which there's a lot of sexual
1:24:13dimorphism, morphism shapes of things,
1:24:16sexual dimorphism big difference in body
1:24:18size as as function of gender. And in
1:24:22these sorts of species where you get
1:24:23male parental behavior, not much
1:24:25variability male reproductive success,
1:24:27low levels of aggression, and what
1:24:29females want is a competent male, these
1:24:31are ones where you see low degrees of
1:24:33sexual dimorphism.
1:24:35So, how's a female going to figure out
1:24:37that this guy is going to be a competent
1:24:38parent? You know, once again, we just
1:24:40figured out if he looks kind of like
1:24:42you, because that suggests he hasn't
1:24:44wasted health and metabolism on stupid
1:24:47pointless muscles when there's more
1:24:48important things in life for making sure
1:24:50your kids have good values. What else
1:24:52would the female want to know when she's
1:24:53first considering mating with the male?
1:24:58Is he a nice guy? Is he sensitive? Does
1:25:00he express his feelings? Is he competent
1:25:02at being a parent? What do you want the
1:25:05individual to do? Prove to you that he
1:25:08can provide for the kids.
1:25:10And suddenly you have a world of bird
1:25:13male birds courting the females by
1:25:15bringing them worms, bringing them
1:25:17evidence that they are able to
1:25:19successfully forage, they are able to
1:25:21get food. Female choice is built around
1:25:24appearance and behavioral competence at
1:25:27being able to be a successful parent in
1:25:29order to pass on as many copies of genes
1:25:31to the next generation as possible.
1:25:33Okay, how about lifespan?
1:25:35In which species is there a big
1:25:37difference in life expectancy as a
1:25:39function of gender?
1:25:42First one. Here are you choosing for
1:25:44males to be as close to females as
1:25:46possible unless the physiology. Here you
1:25:48got these guys who are like using huge
1:25:51amounts of energy to build up all this
1:25:53muscle, which takes a lot more work to
1:25:55keep in calories, and you're more
1:25:57vulnerable in famines. You've got these
1:26:00males with high testosterone, which does
1:26:02bad stuff to your circulatory system.
1:26:05You've got males who, thanks to all this
1:26:06aggression, are getting more injuries,
1:26:09more likely to In species in which you
1:26:11have a lot of sexual dimorphism in body
1:26:14size, you You a lot of sexual dimorphism
1:26:16in lifespan.
1:26:18And then, you look at these guys and
1:26:20it's basically no difference by gender.
1:26:23Moving on, considering primates that are
1:26:26of one of these two patterns, in which
1:26:28one do you always want to give birth to
1:26:31twins? In which one do you never want to
1:26:33give birth to twins? Who gives birth to
1:26:34twins?
1:26:36The one on the right, of course. How
1:26:38come? Because you got two parents on the
1:26:40scene. You were not a single mother and
1:26:42you were a single mother rhesus monkey
1:26:44or something and you give birth to twins
1:26:46and you do not have the remotest chance
1:26:49of enough energy, enough calories on
1:26:51board to get both of them to survive. A
1:26:54twin that is born in a species like this
1:26:57has the same rate that it occurs in
1:26:59humans, about a 1% rate, and it is
1:27:01almost inevitable that one of them does
1:27:03not survive. Meanwhile, there's a whole
1:27:05world of primate species with this
1:27:07profile where the females always twin.
1:27:13Finally, you are the female and you are
1:27:16con- contemplating bailing out on your
1:27:18kids and disappearing because there's
1:27:20some really hot guy over there who you
1:27:23want to mate with and you are trying to
1:27:25figure out the strategy. So, you are
1:27:28going to leave and abandon your kids. In
1:27:30which species do you see that behavior?
1:27:34The one on the right. The one on the
1:27:35right because you bail out and the male
1:27:37is there taking care of them. You bail
1:27:39out in here and you've lost your
1:27:41investment in copies of your genes for
1:27:43the next generation. You see female
1:27:45cuckoldry, this great Victorian term.
1:27:47You see females cheating on the fathers
1:27:50in this species, but not in species like
1:27:53this because the father is long gone in,
1:27:56you know, three other counties there
1:27:57according somebody else and doesn't
1:28:00matter, you're not going to get any help
1:28:01from him. In primate species of this
1:28:03profile, you always see twinning.
1:28:06And they both survive. And what studies
1:28:09have shown in these species, and we'll
1:28:10get to them shortly, is after birth, in
1:28:13fact, the males are expending more
1:28:15calories taking care of the offspring
1:28:17than the females go bail out on him and
1:28:20go find some other hot guy, which in
1:28:22your species counts as some guy who
1:28:24looks even more like you than he does in
1:28:26terms of what you want out of the
1:28:28individual. So, that So, what have we
1:28:30done here? We've just gone through
1:28:31applying these principles in this
1:28:33logical way and everybody from the very
1:28:36first step was getting the right
1:28:37outcome. And go and these are exactly
1:28:40the profiles you find in certain
1:28:42species. Among social mammals, these
1:28:45would be referred to as a tournament
1:28:47species. A tournament species, whereas
1:28:50the one on the right is referred to as a
1:28:52pair-bonding, a monogamous species,
1:28:55because in this one males and females
1:28:58stay together because they both have
1:29:00equivalent investment in taking care of
1:29:02the kids, all of that. What you have
1:29:04here is this contrast between tournament
1:29:06species and pair-bonding species.
1:29:10Tournament species, these are all the
1:29:12species where you get males with big,
1:29:15bright plumage. These are peacocks.
1:29:18These are all those bird and fish
1:29:20species where the males are all brightly
1:29:21colored. What are the females choosing
1:29:24for? Peacock feathers does not make for
1:29:26a good peacock mother. Peacock feathers
1:29:29are signs of being healthy enough that
1:29:32you can waste lots of energy on these
1:29:33big, stupid, pointless feathers. That's
1:29:35a sign of health. That's a sign of all
1:29:38I'm getting from this peacock is genes.
1:29:41I might as well go for good ones. That's
1:29:43the world of peacocks. That's the world
1:29:44of chickens with pecking orders,
1:29:46dominating like that, lots of
1:29:48aggression. That's the world of primates
1:29:50where, as in savanna baboons, the male
1:29:53is twice as big as the female,
1:29:55tournament species. Where a lot of
1:29:58passing on of genes is decided by
1:30:01male-male aggression in the context of
1:30:03tournaments, producing massive amounts
1:30:06of variability
1:30:07in reproductive success where males are
1:30:10being selected for being good like this.
1:30:12So, they sure being selected for having
1:30:14big bodies, which winds up meaning a
1:30:16shortened lifespan for a bunch of
1:30:17reasons. Females are choosing for that.
1:30:19These are guys who are not using their
1:30:21energy on parental behavior. Thus, you
1:30:23do not want to have twins if you were a
1:30:24female baboon, and you do not want to
1:30:27bail out on the kids cuz nobody else is
1:30:28going to take care of them. Go and look
1:30:31at a new primate species and see this
1:30:33much of a difference in skull size, and
1:30:36you've just be able to derive everything
1:30:38else about its social behavior.
1:30:40Meanwhile, these guys on the right, pair
1:30:42bonding species, these are found among
1:30:44South American monkeys, marmosets,
1:30:47tamarins. You put up a picture of them,
1:30:49which I will do if I ever master
1:30:51PowerPoint in some subsequent lecture.
1:30:53You put up a picture of a marmoset pair,
1:30:55and you can't tell who's the male and
1:30:57the female. This is not the world of
1:30:59mandrill baboons with males with big
1:31:01bright bizarre coloration on the face
1:31:04and with antlers when the females don't
1:31:06and that whole world of sexual
1:31:08dimorphism. You can't tell which one is
1:31:10the male and which one is the female
1:31:12marmoset by looking at them. You can't
1:31:14tell by seeing how long they live. You
1:31:16can't tell by how much they're taking
1:31:17care of the kids. You can't tell in
1:31:19terms of their reproductive variability.
1:31:22That's a whole different world of
1:31:23selection. All of the South American
1:31:26tamarins and marmosets, the females
1:31:28always twin. They have a higher rate of
1:31:31cuckoldry of abandoning the kids. The
1:31:33males take as much care if not more of
1:31:35the kids than the female does. Very low
1:31:37levels of aggression, same body size,
1:31:39same lifespan. All the males have low
1:31:41degree of variability. How come? Because
1:31:44if you're some marmoset male, you don't
1:31:46want to get 47 marmoset females pregnant
1:31:49because you are going to have to take
1:31:50care of all the kids because as we will
1:31:53see way down the line in lectures on
1:31:55parental behavior, the wiring there is
1:31:57such is bonding with the offspring and
1:32:00taking care of them. No wonder among
1:32:02species like these, you have very low
1:32:04variability. All the males reproduce
1:32:07once or twice. This is the world of 5%
1:32:09of the guys accounting for 95% of the
1:32:11matings.
1:32:12This is totally remarkable because
1:32:15again, that starting point, you start
1:32:17off here and you look at these and oh,
1:32:19you can tell if they were bipedal and
1:32:21were they diseased and malnourished
1:32:22simply by applying these principles of
1:32:25individual selection, reciprocity, all
1:32:28of that. One factor you see a new
1:32:31primate species and you see one nursing
1:32:33and one with a penis and they're the
1:32:35same size or they're as different in the
1:32:37size and you already know all about
1:32:39their social system. Very consistent
1:32:42across birds, across
1:32:44fish, across primates, across all of
1:32:47those, this dichotomy between tournament
1:32:49species and pair-bonding species. As we
1:32:52will see way down the line, among some
1:32:55species, types of voles, rodents that
1:32:57are famous in Hallmark cards for their
1:33:00pair-bonding, for their monogamy, as
1:33:02we'll see, they're not quite as
1:33:03monogamous as you would think, but
1:33:05nonetheless, a general structure like
1:33:07this.
1:33:09So, one asks, expectedly, where do
1:33:12humans fit in on this one?
1:33:14Where do humans fit? And the answer is
1:33:17complicatedly.
1:33:19Are we a tournament species? Are we a
1:33:21pair-bonding species? What's up with
1:33:23that? What we will see is we're kind of
1:33:26in between. When you look at the degree
1:33:29of sexual dimorphism, we are not like
1:33:32baboons, but we're sure not like
1:33:33marmosets, we're somewhere in the
1:33:35middle. Variability is somewhere in the
1:33:37middle there.
1:33:39I'm not going near that one. Lifespan,
1:33:42the dimorphism and lifespan tends to be
1:33:44in between
1:33:46and parental behavior and likelihood of
1:33:48all of those, you look at a number of
1:33:50measures and by next lecture, we'll be
1:33:52looking at some genetics of what
1:33:55monogamous species and tournament
1:33:57species look like and we're right in the
1:33:59middle.
1:34:00In other words, that explains like 90%
1:34:03of literature because we're not a
1:34:06classic tournament species and we're not
1:34:08a classic pair bonding one. We are
1:34:10terribly confused in the middle there
1:34:12and everything about anthropology
1:34:15supports that. Most people on the planet
1:34:19right now are in a form of monogamous
1:34:22relationships in a culture that allows
1:34:25that demands monogamy.
1:34:27An awful lot of people who are in
1:34:29monogamous relationships in such
1:34:30cultures aren't really in monogamous
1:34:32relationships. Traditionally, most
1:34:35cultures on this planet allowed
1:34:37polygamy.
1:34:38Nonetheless, in most of those polygamous
1:34:41cultures, the majority of individuals
1:34:43were pair bonded and monogamous. You get
1:34:46two different versions of polygamy in
1:34:47different social systems of humans. One
1:34:49is economic polygamy,
1:34:52which is you're basically sitting around
1:34:54and the wealthiest guy in the village is
1:34:57the one who can have the largest number
1:34:59of wives. An enormous skew in
1:35:01reproductive success that's driven by
1:35:03economics. The other type is
1:35:05demographic. You have a culture where,
1:35:08for example, you have a warrior class.
1:35:10Guys spend 10 years as warriors warriors
1:35:13warriors New York City accent as
1:35:16warriors. They don't worry. There's no
1:35:18anxiety, but they eventually worry about
1:35:19getting a wife because by the time
1:35:21they're done being a warrior, they're
1:35:22like 25 and they marry someone who's 13,
1:35:26which is what you see in a lot of
1:35:27traditional cultures that follow that
1:35:29pattern. And at that point, you got a
1:35:31problem, which is an awful lot of those
1:35:33guys have been killed over the course of
1:35:3510 years of being involved in high
1:35:38levels of aggression and 10 more years
1:35:40of life expectancy to catch up with you.
1:35:42There's a shortage of males. So, you see
1:35:45polygamy there driven by demographics
1:35:47and you see polygamy driven by economics
1:35:50and other types of society. So, most
1:35:52cultures on this planet allow
1:35:55traditionally before the missionaries
1:35:57got them, most cultures on this planet
1:35:59allow polygamy. Nonetheless, within most
1:36:02polygamous cultures, the majority of
1:36:04people are not polygamous. We have one
1:36:07really confused, screwed-up species here
1:36:10because we are halfway in between in all
1:36:13sorts of these measures. Okay. So, what
1:36:16do we have next, which we will pick up
1:36:18on on Friday? What we've just started
1:36:20with here is the first case of using all
1:36:22these principles, individual selection,
1:36:24kin selection, reciprocal altruism to
1:36:26understand all sorts of aspects of
1:36:28behavior. We will then move on to seeing
1:36:31how they explain other aspects of animal
1:36:33behavior, some ones which, if you are
1:36:36behaving for the good of the species
1:36:37circa 1960, there's no explanation at
1:36:41all because you're doing things like
1:36:43killing other members of your species.
1:36:45And then finally, we will see how this
1:36:47applies to humans and some of the
1:36:50witheringly appropriate

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