Hi folks, this is part 2 of our chapter 1
lecture from the Hubbard text, so we
ended the last one talking about
how markets had a tendency to produce
efficiency and that's certainly true.. and if
there's one thing that you can catch on
to right away it's the economists love
efficiency, pretty much above all else.
Now in terms of how we talk about
efficiency there's two concepts that I'd
like you to grab onto right away. One is
productive efficiency and productive
efficiency is more or less being able to
produce stuff at the lowest possible
cost.  It's probably the thing that
immediately pops to mind when you think
about efficiency.  The new concept for you
is probably allocative efficiency...and
allocative efficiency is basically asking
whether or not the amount of stuff
you're creating makes sense,  so in other
words if you've created an object...was
the benefit of that last object you've
created equal to the cost of creating
that object.. and if that's true then our
golden rule applies MB equals MC.  Now
it's entirely possible that you're in a
situation where you've created objects
that do not have benefits equal to their
costs at the margin, and then of course
this would imply that you were making
stuff society didn't value as much as it
cost society to make.  The reason that
economists like market so much is that
we're going to see that markets - when
they are perfectly competitive - drive us
to create an amount of output that
makes sure that we are following this
golden rule... so markets tend to promote
efficiency in essence because they
foster competition and allow people to
engage in voluntary exchange.  No one has
to do anything a market in a market
unless they want to,  so both parties of
the trade are looking at the trade and
saying yes I want to do that..it makes me
better off. This is a recipe for
efficiency.  Now another thing to think
about here is
is efficiency and altar that we should
sacrifice ourselves upon, because what
we're going to find out is markets tend
to care about very little aside from
this narrow definition of efficiency...so
when you start looking at folks that are
complaining about markets and their
unfairness, oftentimes what they're going
to get at is that they would prefer more
equitable outcomes and those might not
be efficient... so Econ has a tendency to
stray always in the direction of the
efficient outcome.  You're going to find
folks that will tell you I don't really
care if it's efficient
I just find it unpalatable... it's not an
OK outcome it violates some sense of
values that I have or they might just
described it as inequitable outcome.
We'll get into that more in later
chapters but I really think that a lot
of the arguments in economics sort of
fracture along these lines.. right, or I
should say arguments between economists
and folks are critical of market.  Ok so
we've got a pretty good handle on
efficiency let's go ahead and move over
to positive versus normative analysis. 
Econ in recent times, like the last 150
years, has definitely moved in the
direction of positive analysis,  In fact
they've almost developed an allergy to
normative analysis,  and really what's
behind that is they're trying to emulate
the scientific tradition. They want to
create testable hypotheses and what
they're interested in is developing
facts, so they want to talk about what is. 
And the other side of this is...you know,
what ought to be.  In other words, what
should the world look like.  When we start
talking about what should the world look
like or how ought people to behave this
is really normative analysis.. and so if
we want to characterize this simply you
might think about positive analysis as
about facts...
and you could think about normative
analysis essentially as values, value
judgments,  so oftentimes people will talk
about normative analysis as simply
opinions.  I think that you want to make a
distinction here and let me give you a
quick example.  If I tell you that I don't
like the color red let's say.  It pretty
much doesn't imply anything other than
my preference for something.  If I were to
tell you that one shouldn't be cruel to
animal that isn't necessarily a fact....
it's ...it's.. it's clearly a value judgment,
but it does definitely imply that your
behavior should change because of this, 
or I would expect you to conform with
that value judgment.  Now you may not do
that, because we may not share the same
values...but my point is is that when we
start talking about should and ought we're
trying to get people to recognize the
value... and hopefully it's widely enough
shared that you know they can get some
agreement about how to behave. A good example
of this is most people would agree with
"you should not kill people".  In fact a lot
of people would even make the mistake of
considering it a positive statement but
it's not a fact. It's just a value that's
very widely shared..probably something to
do with the value of human life and that
you shouldn't you know ended needlessly.
Okay, that's clearly a normative
statement though, because it is about the
value of human life and how in fact we
would like people to behave ...so that's
really the difference between positive
and normative analysis.  When you turn it
into specific economic sort of inquiries
you might ask what's the optimal bundle
that I should buy to make me most happy.
Microeconomics is going to be very very
good at telling you buy four bananas and
two oranges given your preferences and a
budget constraint.  That's positive
analysis based on our assumptions.
When we start getting into normative
stuff we might start asking who really
deserves output within society and how
should we portion out the products of
all of our labors. This is clearly
straying over into some of those
questions about distribution and it's
most definitely going to be a normative
thing, so hopefully that gives you a
little bit of a lens to look through,
but those are definitely two
concepts that you'll see in other areas
and you should know.  Okay so our last
major point out of the six that we
talked about at the beginning of this
lecture is about models... creating them,
using them... really it's about what we
should expect from them, and one of the
things that can be most frustrating to
students, particularly with
microeconomics, is that we're building a
model that is basically rooted in a
rational choice framework. We're making
an assumption that everyone is going to
rationally use the resources around them
to produce the best outcomes for them...
right happiness or profit.  A lot of folks
immediately start saying I'm not sure I
see rationality everywhere around me
that sort of seems like a bad assumption
and so if your model is built on
rationality then maybe I should just
ignore it.
I think that the way to start attacking
this problem with micro... and getting a
good sense of what's reasonable to
expect is understanding the role of
abstraction.  Why is it that we're going
to come up with these generalities or
these assumptions that are not always
going to be perfectly true... but we pretty
much act like they are.  The real issue
here is that it's an absolute necessity
to do this.  We might start building an
elaborate theory about me and my
preferences... and I like hockey and I
prefer to fly fish... and you know I really
like wool clothing and sweaters I don't
like shorts much...
and we just get some sense about you
know chase,  and what chase is into, that's
not particularly useful when we start
thinking about the entire planet. If
we're trying to come up with a general
theory about how human beings make
choices we really shouldn't focus on the
particular concrete things about me.  We
should start thinking about essentially
the least common denominator, or the
abstract generalities that describe how
human beings probably try to do things. 
So you'll hear people say that's fine in
theory, but what about in the real world
and this is usually a complaint that the
model is getting a little too abstract...
and you may have heard people say this
before to you after you've explained
some new thing you've learned and they
immediately start coming up with counter
examples or ways in which they're you
know your model doesn't perfectly
describe things.   I think that you need to
start becoming okay with imperfection, 
and this leads us into our next point...
models are essentially metaphors
they're stories.. they are not intended to
be the real world.  If in fact the real
world was easily accessible,  we wouldn't
have tried to create theory to explain
it.  Theories are necessarily imperfect
and abstract because they help us get
started on telling a story,  a story that
out there in the real world has a few
too many moving parts and is too
complicated to actually be understood
unless we start paring it down,  and
looking for those you know those rules
and assumptions that we can help to
start building an explanation around,  so I
want you to think about models
essentially as a metaphor and you could
even use a real simple example, a silver
dollar is the moon.   So you got a metaphor
there and you could think about it in
terms of a silver dollar is literally
the moon
and of course that's not the real world...
it's not accurate.. and you would say that
this is a bad model... but if no one had
ever seen the moon before and you told
them that it was a silver dollar and a
silver dollar did mean something to them...
they might start out with the idea that
it's roughly roundish,  it's shiny,
perhaps it's made of metal,  you basically
would get started on an explanation and
of course it's not good enough you're
going to want to add more details to it
later and you're going to get into you
know the fact that the moon is actually
roughly a sphere and it's you know not
actually silver it's a bunch of
different rock but the Sun shines on it
and reflects and so it looks light
coloured.  You could get into all this
detail later, but if you started out with
all that complexity and someone really
didn't know what the moon was you
probably drowned them almost at the
outset.  Your conversation would just be
too much, so I want you to get
comfortable with the idea that we're
going to come up with some assumptions
and they're not going to be perfect, but
what we do want to do is start getting
some explanatory power out of them and
we want to generate stories and
explanations that are useful... and ideally
we're going to be able to take those
explanations out and we're going to be
able to test them.  Do they help us
explain actual behavior that we're
seeing out in the world.  It's not going
to do it perfectly all the time, but if
it gets us going in the right
direction we can always add more detail
to it later.  Okay, so essentially I want
you to think about a model as being
abstract and that's a good thing, because
it makes the real world something you
can deal with.. and that abstraction
allows you to start telling stories and
hopefully start making predictions about
what's going to happen.  Now if it fails
then we need to examine our assumptions
and revise, so there you go.  There's a
little primer on models and one thing
that I would caution you and the sort of
ties back into our first complaint that
we talked about up here.  Models are not
reality do not make the mistake of
assuming that the model is reality
Theorists who continually say "but the
model says" even in the face of the real
world behaving differently have got a
problem... right, so you're always going to
to expect to be
testing and refining these things over time
 
