This is a NetLogo implementation
of the El Farol model

that I described in the previous videos.

This version is a 
slightly modified version

of the one that's in the Models Library

and this one you can download from our
course materials page.

You can see that there's 
sliders here for memory size,

that's how many weeks each agent

can remember the attendance,

the number of strategies each agent has,

and the overcrowding threshold.

So let's do Setup,

and we can see that

there's a hundred people

and here's the El Farol bar.

And when we start the model,
we'll see people deciding to attend,

or not attend, each week.

So let's start out by setting 
the memory size to one.

That is we can only remember 
the previous week,

and the number of strategies to one.

There's only one strategy per agent.

Let's do Setup again,

and then

we'll make the speed a little bit slower,

and Go.

This red line here is the 
overcrowding threshold of 60.

This is the attendance, over time,

and this is the bar attendance.

And this here shows the percent of days

on which the bar is crowded.

So we can see that

if the memory size is only one

and the number of strategies is one,

things are just terrible.

The bar is always overcrowded.

OK, so let's try

increasing the number of strategies.

Let's see if that helps, at all.

Let's increase it to ten,

and do Setup.

We still only remember one week back.

And now,

we get this big oscillation,

where we go from, the bar goes from, 
completely crowded

to completely or almost 
completely uncrowded.

The percent of crowded days is less,

so we have an improvement,

but you'd think that the population

would be able to learn better

what to do.

So let's increase the memory size.

Let's increase it to five.

Do Setup and Go.

And

things are getting a little bit better.

The bar is never completely crowded,

that is a hundred percent of 
the people going there.

The percent of crowded days 
is leveling off

at about 48 to 49 percent.

OK

Well what if we increase the,

leave this at ten,

and increase the memory size to ten,

so remember ten times back.

Let's see if that improves things.

OK

So here we're getting a 
much better result

in terms of the percent of crowded days,

and much closer attendance to

just at the threshold,

which is really what we want.

So somehow

when we have enough strategies

and enough memory

the population as a whole

without communicating,

without, with no communication

among the agents,

can get to a state

where they are very close

to the optimum

where the optimum would be.

Always having a non-overcrowded bar,

but enough people so that it's

right up to the overcrowding threshold

without going over it.

You can see it's getting very close.

The homework assignment will

allow you to experiment 
with this even further.

Let's recap what the El Farol model 
is all about.

It assumes bounded rationality

and limited knowledge,

different from the assumptions 
of traditional economics.

It includes adaptation,

that is, the agents can 
learn from experience,

using induction,

they observe past attendance values.

They use that to decide 
what strategy to use

at each time step.

The learning isn't very 
sophisticated here,

so you can imagine

with even more sophisticated learning,

the population could do even better,

and that's something that

you might want to try

in the homework assignment.

The question was: 
Does self-organized efficiency,

that is the best situation for all,

emerge under these conditions?

Well, we said the best situation for all

would be if the bar was 
never overcrowded,

but the maximum number 
of people, that is 60,

would attend each week.

And we saw that, yes,
to some extent,

with a little bit of error,

it does emerge.

This is a proof of principle

that Brian Arthur proposed to show that

you could get such behavior,

without the very unrealistic assumptions

of traditional economics.

In conclusion, the El Farol model

has demonstrated that self-organized

cooperation and efficiency

are possible without 
the perfect rationality,

complete knowledge and 
deductive reasoning.