1-0 is the same as 0-1 or reduces to 0-0. Output
from a self-regulating system may also be input to
a linear system. If the output of the linear system
is not sensed by the self-regulating system, then
1-0 is no different from 0-0. If the output of the
simple process is measured by the self-regulating
system, then the linear system maybe seen as part
of the self-regulating system.
(with actuators that may or may not work similarly).
An example might be two air conditioners in the
same room. Redundancy is an important strategy
in some cases. Competing systems have competing goals. Imagine an air conditioner and a heater
in the same room. If the air conditioner is set to 75,
and the heater is set to 65—no conflict. But if the air
conditioner is set to 65 and the heater is set to 75,
each will try to defeat the other. This type of interaction is balancing competing systems. While it
may not be efficient, especially in an apartment, it’s
quite important in maintaining the health of social
systems, e.g., political systems or financial systems.
If 1-1 is open loop, that is, if the first system
provides input to the second, but the second does
not provide input to the first, then 1-1 may be
reduced to 0-1.
January + February 2009
The output of a linear
system provides input for
a learning system. If the
learning system also sup-
plies input to the linear sys-
tem, closing the loop, then
the learning system may
gauge the effect of its actions and “learn.”
On the other hand, if the loop is not closed, that
is, if the learning system receives input from the
linear system but cannot act on it, then 0-2 may
be reduced to 0-0.
Today much of computer-human interaction is
characterized by a learning system interacting
with a simple linear process. You (the learning
system) signal your computer (the simple linear
process); it responds; you react. After signaling the
computer enough times, you develop a model of
how it works. You learn the system. But it does not
learn you. We are likely to look back on this form
of interaction as quite limited.
Search services work much the same way.
Google retrieves the answer to a search query, but
it treats your thousandth query just as it treated
your first. It may record your actions, but it has
not learned—it has no goals to modify. (This is
true even with the addition of behavioral data to
modify ranking of results, because there is only
statistical inference and no direct feedback that
asserts whether your goal has been achieved.)
1-2 Managing and Entertaining
The output of a self-regulating system becomes
input for a learning system.
If the output of the learning
system also becomes input
for the self-regulating system,
two cases arise.
The first case is managing automatic systems,
for example, a person setting the heading of an
autopilot—or the speed of a steam engine.
The second variation is a computer running an
application, which seeks to maintain a relationship with its user. Often the application’s goal is
to keep users engaged, for example, increasing
difficulty as player skill increases or introducing surprises as activity falls, provoking renewed
activity. This type of interaction is entertaining—
maintaining the engagement of a learning system.
If 1-2 or 2-1 is open loop, the interaction may be
seen as essentially the same as the open-loop case
of 0-2, which may be reduced to 0-0.
The output of one self-regulating system is input for
another. If the output of the
second system is measured
by the first system (as the
second measures the first),
things are interesting. There
are two cases, reinforcing systems and competing
systems. Reinforcing systems share similar goals
The output of one learning
system becomes input for
another. While there are
many possible cases, two
The simple case is “
it-referenced” interaction. The
first system pokes or directs the second, while the
second does not meaningfully affect the first.