We might call it pushing, poking, signaling, transferring, or reacting. A special case of has the output of the second or third or more systems fed back as input to the first system. Such a loop might form a self-regulating system. The output of a linear system provides input for a self-regulating system. Input may be characterized as a disturbance, goal, or energy. Input as "disturbance" is the main case. The linear system disturbs the relation the self-regulating system was set up to maintain with its environment.
The self-regulating system acts to counter disturbances. In the case of the steam engine, a disturbance might be increased resistance to turning the wheel, as when a train goes up a hill. Input as "goal" occurs less often.
A linear system sets the goal of a self-regulating system. Later we will discuss the system that turns the dial. See below. Input as "energy" is another case, mentioned for completeness, though a different type than the previous two. A linear system fuels the processes at work in the self-regulating system; for example, electric current provides energy for a heater. Here, too, the linear system may be seen as part of the self-regulating system.
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 is no different from 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.
The output of a linear system provides input for a learning system. If the learning system also supplies input to the linear system, 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 may be reduced to 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.
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.
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 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. 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. If 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 may be reduced to 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 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.
If or is open loop, the interaction may be seen as essentially the same as the open-loop case of , which may be reduced to The output of one learning system becomes input for another. While there are many possible cases, two stand out. The simple case is "it-referenced" interaction. The first system pokes or directs the second, while the second does not meaningfully affect the first. Each has the choice to respond to the other. Significantly, here the input relationships are not strict "controls.
Furthermore, the systems learn from each other, not just by discovering which actions can maintain their goals under specific circumstances as with a standalone second-order system but by exchanging information of common interest.
They may coordinate goals and actions. This type of interaction is conversing or conversation. It builds on understanding to reach agreement and take action [ 15 ]. There are still more cases.
Two are especially interesting and perhaps not covered in the list above, though Boulding surely implies them:. The coordination of goals and actions across groups of people is politics. It may also have parallels in biological systems.
As we learn more about both political and biological systems, we may be able to apply that knowledge to designing interaction with software and computers. Having outlined the types of systems and the ways they may interact, we see how varied interaction can be:. We may also see that common notions of interaction, those we use every day in describing user experience and design activities, may be inadequate.
Pressing a button or turning a lever are often described as basic interactions. Yet reacting to input is not the same as learning, conversing, collaborating, or designing. Even feedback loops, the basis for most models of interaction, may result in rigid and limited forms of interaction.
By looking beyond common notions of interactions for a more rigorous definition, we increase the possibilities open to design. And by replacing simple feedback with conversation as our primary model of interaction, we may open the world to new, richer forms of computing. Davis, M. Buchanan, R. Maldonado, T. HfG Ulm, Ulm, Norman, D. The Design of Everyday Things. New York: Basic Books, What is the nature of the human? Do different types of dynamic systems enable different Of course, the steam engine does not operate types of interaction?
It receives its goal from outside; a person sets the speed of the wheel by adjusting the length of the linkage connecting the fly-ball governor to A Systems-Theory View the steam valve. In Haque's terminology, the transfer function is changed. Both are closed information loops, to input, for example, describing a set of Web pages self-regulating systems, first- order cybernetic systems. While the feedback loop is a useful first approximation 3 What is interaction? First, the role of the person:The person is to keep constant in the face of external forces.
Second, the nature of the system:The by something outside the system. Such single-loop computer is not characterized in our model of systems are called first order.. All we know is that the Learning systems nest a first self-regulating system computer acts on input and provides output. But we inside a second self-regulating second have characterized the steam engine in some detail as a system measures the effect of the first system on the self-regulating system.
Suppose we characterize the environment and adjusts the first system's goal computer with the same level of detail as the steam according to how well its own second-order goal is engine? Suppose we also characterize the person?
We may call this learning. Some learning systems nest multiple self- So far, we have distinguished between static and regulating systems at the first level. In pursuing its own dynamic systems those that cannot act and thus have goal, the second-order system may choose which little or no meaningful effect on their environment a first-order systems to activate. As the second-order chair, for example and those that can and do act, thus system pursues its goal and tests options, it learns how changing their relationship to the environment.
Learning means Within dynamic systems, we have distinguished knowing which first-order systems can counter which between those that only react and those that interact disturbances by remembering those that succeeded in linear open-loop and closed-loop systems.
Some closed-loop systems have a novel property A second-order system may in turn be nested within they can be self-regulating. But not all closed-loop another self-regulating process may systems are natural cycle of water is continue for additional levels.
For convenience, the term a loop. Rain falls from the atmosphere and is absorbed second-order system sometimes refers to any higher- into the ground or runs into the sea. The experience cycle is a new tool, synthesizing and giving form to a broader, more holistic approach being taken by growing numbers of designers, brand experts, and marketers.
The analysis-synthesis bridge model Written for Interactions magazine by Hugh Dubberly, Shelley Evenson, and Rick Robinson — 1 March The simplest way to describe the design process is to divide it into two phases: analysis and synthesis. Or preparation and inspiration. How do designers move from analysis to synthesis? From problem to solution? From current situation to preferred future? From research to concept? From constituent needs to proposed response? From context to form?
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