so today we're going to take a step back
and talk
about what complex systems is
emergences and what the feedbacks are
and the reason why we're going to talk
about these is because they're
fundamental in many ways to the types of
problems the agent-based modeling can
solve well and with a great benefit
I should mention before I get too much
into this that there is an entire other
MOOC on complexity Explorer offered by
Melanie Mitchell that's really a great
introduction to complex systems I highly
recommend that if this isn't an
interesting topic for you that you kind
of spend some time with that MOOC and
all the materials that are available for
there I should also mention that this is
my particular take about complex systems
emergence and feedbacks but that this is
a take that I think is fairly well
accepted by others as well so what is
complex systems
well complex systems are a system
proposed of many interacting parts in
which the emergent outcome of the system
is a product the interactions between
those parts and the feedbacks between
that emergent outcome and individual
decisions in complex systems really for
me is defined by these two notions of
emergence and feedbacks so let's talk
about each of those in turn so let's
take an example the traffic model that
you saw already the basic traffic model
with the red car moving along the kind
of stops and starts quite a bit and on
the right I have a picture of an actual
traffic jam ilysm jam is kind of an
illustration of that and what you'll
notice right is that the that the system
here is composed of many interacting
parts you have all the cars there
interacting with each other they're
controlled by the speed of the other
cars but they're also controlled by the
feedback from the emerging outcome of
those parts right so the emergent
outcome of the traffic model is a
traffic jam in many ways which is a set
of stop cars that can't go anywhere
and those stopped cars feedback to
affect the individual decisions of the
cars that are moving in by themselves so
emergence is the idea that the action of
the whole was more than the sum of the
parts and that's from my John Holland's
2014 book on brief introduction to
complexity and let's stay with the
notion of traffic for a while because
this interesting notion where there's a
lot of
emergent phenomenon that occur so let's
- I'm not tell you a little story about
commuting patterns in Washington DC
which may sound kind of boring but it's
kind of actually interesting so in 1975
on the Shirley Memorial freeway in
Washington DC which is a stretch of i-95
/ 395 they instituted what was known as
a high-occupancy vehicle Lane and the
idea behind this Lane is that they
wanted to encourage commuters to ride
share to get more than one person in a
car so that they weren't had all these
cars with just one person this you know
the idea was this would reduce the
number of cars may reduce the number of
carbon dioxide emissions the amount of
carbon dioxide emissions and kind of
overall make the world a better place
and there were a lot of people who are
taking these but there were some
individuals who weren't able to actually
find someone nearby that they knew who's
a friend who they could share a ride
with right and it you know
in the other end they might not be able
to find someone right at the time they
wanted to leave going back to their
suburban neighborhood who would be
willing to take a ride as well or who
was interesting a ride so what developed
was an interesting phenomenon an
emergent process of these kind of policy
interactions and the individual desires
of people to want to move quickly
through the the commuting space was the
notion of slugging and slugging kind of
emerged spontaneously but essentially
what it was was that there started to
develop a series of lines at both in
suburban areas and in downtown DC where
individuals would way to get in the cars
with other individuals who are going to
then use the high frequency vehicle
lanes this would allow them to get much
faster to their destinations and allow
these people to use the HOV lanes and
this was an emergent process it was a
process that came around due to a bunch
of individual decisions and also a
governmental decision to institute high
occupancy vehicle lanes there is also a
bunch of norms that arose
round slogging that we're kind of
interesting that allowed people to kind
of satisfy the safety requirements of
these systems right so for instance
drivers are not allowed to pick up
smugglers and routes or standing outside
of line because that would violate kind
of severe practice facilities a woman is
not to be left on the line alone for her
safety you're not allowed to eat or
smoke or put on makeup in the cars
driver has full control the radio and
climate controls no you're not allowed
to roll down the windows and let's drive
etcetera etcetera there's a bunch of
different rules that kind of
spontaneously emerge and this is a great
example of how emergent processes can
actually come from a variety of
different directions and a variety of
different interactions so emergent
phenomenon don't just come into being
and that's the end of the story what's
interesting about emergent phenomenon in
many ways is that they feed back to then
affect the future decisions of other
individuals in that system
so in this lugging case for instance the
emergence of slug lines might have
shifted people from public transit might
have shifted people from driving by
themselves might have shifted them from
other places to they make decisions
about that they were going to use slug
lines instead this encouraging more slug
lines and encouraging more emergent
phenomena along this space another
example that you can think of is a
market share right so the individual
decision for me to buy a Playstation
versus Xbox is an individual decision on
my own but in fact it then contributes
to the market share of the Xbox versus
the PlayStation and that market share
then contributes to my decision or
future decisions of other people as to
whether to buy a Playstation or an Xbox
all right the same is true of things
like beta versus VHS or the QWERTY
versus the Devore get bored in all these
cases right once we start down a path
where there's an emergent result of a
bunch of individual decisions that
emergent result of a dominant brands or
dominance
standard such as QWERTY in in in Veda
and VHS right then affect my individual
decision as to what kind of products and
what kinds of techniques I'm going to
learn in the future nowadays it makes
very little sense to learn a Dvorak
keyboard because there aren't many
deformed keyboards out there much as it
doesn't make when it made much sense to
buy a beta tape in the 2000 then I need
1990s because there weren't many beta
players are many beta tapes available at
that time so this the emergent patterns
feedback to effect individual decisions
and that in many ways is one of the
second key components of a complex
system so given all these complexities
of complex systems such as emergence and
feedback and the relationships between
individuals and groups how do you begin
to understand complex systems well as
you might expect as a result of these
things complex systems can be very
difficult to predict control and manage
but the goal of agent-based modeling and
complex systems analysis in general for
that matter is often to provide a flight
simulator rather than a perfect
prediction a complex system let's think
about this for a bit so a flight
simulator such as you might use if you
want to train how to fly provides you
with a mock-up of scenarios that you
might see as you're flying a plane or
flying a flying a plane right it doesn't
provide a perfect replication of that
world it doesn't provide a perfect
prediction of exactly what's going to
happen however it does give you some
insight as to how the plane might behave
in those scenarios in much the same way
agent based modeling and simulation in
general often provide you with an
insight into how a social system or an
engineered system our natural system
might behave under a variety of
different circumstances even if it
doesn't provide you with a perfect
prediction of how that system is going
to behave