so in this unit we're going to be
discussing how to extend an Age based
model you might uh see that you want to
take a closer look at that someone else
has already created but before we get
there I want to talk about uh four
characteristics of Age based modeling
that uh we put at the beginning in the
textbook these are at the beginning of
the chapter 3 which is this unit which
is what this unitor responds to and they
kind of are things to look for in these
age of based models cuz I think they'll
help you understand other age of based
models and how uh the how they might
have interesting patterns uh so you know
the first characteristic is simple rules
can generate complex phenomenon right
and we've already seen this a number of
times including in the last unit when we
talked about the heroes and cowards
rules and how simple they are and how
just kind of reversing the order of one
of them changes the game completely uh
and we'll see that again in this uh unit
the second is that Randomness at the
level of the individual can result in
nearly deterministic population Behavior
Uh population level Behavior right uh
and again you know we saw this in some
of the models that we've looked at
before like the flocking model where the
birds are just randomly moving and yet
the pattern and the behavior of the
flock itself you know not it's hard to
call it deterministic but you know the
the existence of a flock is fairly
deterministic right um complex patterns
can self organized without any central
leader number three and of course this
is some ways related to the previous one
right uh but you know again thinking
about the flocking model right the
patterns there um don't have there is no
bird leader that causes them to occur
and finally different models emphasize
different aspects of the world uh and
we'll see this we'll see models that
kind of look at the same problem but
from different angles and in fact in
this uh particular unit uh the problem
we're going to be exploring is the L
forall Bar problem which I'll EXP
explain it a little bit and one of the
things we might want to do with it is uh
tweak it so that we can look at
different aspects of it um so those are
you know four uh characteristics of Age
based models that I want you to kind of
keep an eye out for as we move through
this course so let's talk specifically
about what we're going to uh discuss
most during all of unit 3 uh which is
the L forall Bar problem so uh this is
actually a problem that was first posed
uh by Brian Arthur um and
and while he was at the Santa Fe
Institute and here's the basic idea um
Brian Arthur is an Irishman he likes
Irish music uh and you know he proposed
an idealized version of this problem
which is that imagine that there are 100
people in Santa Fe who like Irish music
now on Thursday an Irish band plays at a
bar called the L for all the lantern in
English um that is very popular it's uh
WR by a bunch of the artists on Canyon
Road I've been there several times
myself
um however more than 60 people are at
the bar on that Thursday night then the
bar becomes too crowded and no one
really has any fun right uh and just for
the sake of this problem let's imagine
that the newspaper publishes the
attendance each week right so on the
basis of the Past end weeks how would an
individual decide whether or not to go
to the bar right and that was the basic
problem that Brian Arthur posed and the
the idea is like how do people make that
choice how do they decide whether or not
they're going to attended event um and
he kind of gave it some additional
nuances right to make it a little bit
easier to understand but that's the
basic problem right and uh when Arthur
originally um exposed it or explored it
he um postulated the problem and
proposed a solution right he said let's
imagine that each individual has a bag
of strategies to predict attendance so
for instance one of the strategies might
be that take last week's attendance and
multiply times two and I'll use that for
as a prediction of next week right or
maybe I'll take the attendance two weeks
ago I'll subtract off last week maybe uh
maybe I'll just assume that I'll take
the mean of the last five weeks right
and so each individual has this bag of
strategies that they use as predictors
and then each agent determines which
strategy would have worked the best had
they used it in the last 5 weeks um and
so then they use that strategy to
determine whether or not they're going
to attend this week right um and then
they decide whether or not to go to the
bar we realize the outcome of all those
decisions and then we see what the
population level is at the bar right uh
and you know what he showed and what was
interesting about what we showed and
what we'll explore is that even with
this strategy uh which doesn't seem very
rational right the set of strategies or
very um not not exactly what we call
deductive thinking about the problem
right the the population at the bar
converge to about on average 60
individuals right uh which is the
optimal amount of attendance right
because you want the bar to be as well
re re used as Resource as possible right
and so you know what Brian Arthur uh
argued for and in fact the original
paper is called bounded rationality and
inductive reasoning was that most humans
do not act perfectly rational instead
they act in a boundedly rational way um
and most humans do not reason
deductively from first principles
instead they reason inductively from
past experience right so the question he
kind of posed that the heart of the AL
for all bar problem was if you assume
the agents humans participating in this
way act this way right what are the
results right what what comes out of
this um and you know what he found as
kind of a
um potential for a against kind of more
of the traditional Neo Economist views
of the world that said that you know you
can assume people just act rationally
you can assume they have infinite
computational time to think about their
problems all these things at the time uh
was the in fact you still get a fairly
good usage of this resource of the bar
right um so yeah so that's the basic bar
problem um and in the next lecture we're
going to talk a little bit about the net
logo version The implementation
of the alal bar problem that we'll be
working with