Definitions
Donella A Meadows
has defined a feedback loop in her book ”Thinking in Systems” as
”A closed chain of causal connections from a stock through a set of
decisions or rules or physical laws or actions that are depended on
the level of the stock, and back again through a flow to change the
stock.” A little simpler definition can be ”A path by which some
of the output of a circuit, a system, or a device is returned to the
input.”
Balancing feedback loops
These loops are
goal or balance-seeking. They try to keep stocks stable and within a
certain range. They also resist the change in systems. If stock level
gets too high, balancing feedback loop will try to get it lower. If
stock level is too low it tries to get it higher. For example, you
have a room in which you want to keep a temperature within a certain
range. You have a balancing feedback loop that keeps the temperature
within the range you want. There are always some losses of heat in
the room, when the temperature outside is colder. Your balancing loop
adjusts the heating depending on how much colder the temperature is
outside. When the temperature outside is much warmer than in the
room, the heat outside comes in. Then your balancing loop will cool
the air inside.
These balancing
loops are not always working properly. The information from them can
come too late, be unclear or incomplete or hard to understand. The
action they cause can be delayed or ineffective, etc.
Stock-maintaining balancing loops must have their goals set for
compensating the draining or inflowing processes that change the
level of the stock. All these loops have their own breaking point.
This is the point where the pull of other loops is stronger than the
pull of the balancing loop. When your heating system is not having
enough heating power compared to the leaks outside, your room
temperature gets too cold.
Reinforcing feedback loops
These loops are
amplifying or reinforcing. They can cause virtuous or vicious effects
of healthy growth or complete destruction. It creates a bigger or
smaller inflow to a stock than is already there. Reinforcing loops
enhance the direction of the change in a stock or a system. When a
system element can reproduce itself or grows as a constant fraction
of itself, reinforcing loops are found in the system. Reinforcing
loops produce an exponential growth. Growth gets faster all the time.
For example, the bigger the interest in your bank account, the more
money you will get into your account every year, because you get the
interest for the interest too. These loops may not change the system
until the path of least resistance is overcome. For example, the
sales of the new product may not start growing faster until a certain
amount of product is sold.
Feedback loops
are mostly linked together
Single loops are
seldom at work in systems. Mostly, there are complex patterns of
interconnecting loops. A single stock can have many balancing and
reinforcing loops pulling in many directions. A single
interconnecting flow can be attached to many different stocks. This
flow can increase the ouflow of one stock and at the same time
increase the inflows of many stocks. These feedback loops create
a system behavior which is hard to predict. When you change the
functioning or a goal of one loop, you may create changes in many
others. In other words, one change can produce combinations of
changes instead of one.
Lets keep things
simple and concentrate on one stock system like population with both
a balancing and a reinforcing feed back loops. This is one of the
most common and important system structures. Lets start by
identifying the most important inflows and outflows of the
population. The most important inflow is people born into the
population. The most important outflow is people dying. This kind of
system changes when the relative strength of the loops change. In a
normal situation, more people are born than dying. This means that
the net effect of these loops is increasing population. It also means
that the net effect is self-reinforcing growth in population.
When there is a
sudden humanitarian catastrophe or a civil war in some population,
the change reverses into diminishing population and the balancing
loop dominates the behavior of the system. When these changes in relative
strengths of feedback loops happen, the behavior of the
system changes. A stock governed by linked balancing and reinforcing
loops grows exponentially if the reinforcing loop dominates the
balancing one. Stock declines exponentially if the balancing loop is
dominant.
Other things to know about feedback
loops
You have to
remember that the delivery of information through a feedback loop
will only affect future behavior. Feedback loop cannot deliver signal
fast enough to have an effect in present. There are three typical
delays in the real world. First, a perception delay. For example, a
shopowner doesn´t react to any small changes in sales. She normally
reacts, when the sales have changed for a longer time period, like
five days. Second, there is a response delay. A shopowner makes up
some part of any shortage with each new order. Third, there is a
delivery delay. A subcontractor delivers the goods with delay because
he has to process the order and deliver it. Changing the length of
the delay will likely cause a large change in the system. When the
delay is too short, the system behavior will likely oscillate.
Physical, growing
systems are going to encounter limits. Those limits are balancing
feedback loops. When the limits are achieved, these loops start
dominating the systems by either strengthening the outflows or
weakening the inflows. These limits are temporary or permanent.
Eventually, the system will adjust to the limit or the limit will
adjust to the system. There has to be at least one reinforcing loop
delivering th growth and at least one balancing loop limiting the
growth in the physical, exponentially growing systems. One
interesting and important thing about feedback loops is that systems
with similar feedback structures produce similar behaviors.
Physically different parts do not really change behaviors.
Donella H Meadows, Thinking in Systems
John H. Miller Crude Look at the Whole
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