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Download "2. Behavioral Evolution"

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Table of contents
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Table of contents

8:01
Nash Equilibrium
8:34
Sociobiology
14:11
The First Building Block of Applying Darwinian Principles to Behavior
15:15
Migration of Zebras throughout East Africa
19:08
Individual Selection
20:51
Sexual Selection
29:07
Keeping Track of Kinship
31:34
Rock-Paper-Scissors Scenario
33:44
Bacterial Behavior
36:35
Reciprocal Altruism
41:33
Game Theory
43:44
Prisoner's Dilemma Game
45:19
Robert Axelrod
50:34
Prisoner's Dilemma
1:00:40
Daniel Ellsberg
1:02:13
Vampire Bats
1:04:17
Fish Stickleback Fish
1:06:44
Fish Species That Will Change Sexes
1:06:57
Black Hamlet Fish
1:11:42
Naked Mole Rat
1:13:41
Role Diversification
1:17:09
Two Inclusive Fitness Kin Selection
1:25:33
Lifespan
1:27:44
Female Cuckoldry
1:29:05
Tournament Species
1:30:41
Pair Bonding Species
1:33:13
Where Do Humans Fit
1:34:49
Economic Polygamy
Video tags
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Video tags

human life
evolution
science
nature
nash equilibrium
social
society
culture
evolutionary psychology
trait
darwin
natural selection
speciation
variability
genetic
heritable
reproduction
kinship
fitness
animal
reciprocal altruism
cooper
Subtitles
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Subtitles

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

Description:

(March 31, 2010) Stanford professor Robert Sapolsky lectures on the biology of behavioral evolution and thoroughly discusses examples such as The Prisoner's Dilemma. Stanford University https://www.stanford.edu/ Stanford Department of Biology https://biology.stanford.edu/ Stanford University Channel on YouTube https://www.youtube.com/stanford

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