Interactions - January/February 2009

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.

0-2 Learning

(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.

1-1 Balancing
2-2 Conversing

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