Much of human behavior is directed toward goals: finding food, selling services, curing cancer, making meaning.

Achieving goals requires action. Action requires effort. Effort requires energy and attention applied over time. Effort overcomes obstacles. Obstacles tax our patience, sap our resolve, and cause us stress.

English (as well as many other languages) includes many metaphors that frame effort as a cost:

• I enjoy spending time with you.
• You’re wasting your energy.
• You’re not paying attention.
• This job is not worth the stress.
• It all takes its toll.

These metaphors suggest an economics of human behavior—a framework for understanding the human cost of living and the trade-offs we make momentby- moment as we choose one course of action over another. This paper begins the development of such a framework for everyday living and suggests how it might be applied to business and design. The authors hope to provide a means for us all to learn to act in better accord with our interests and thereby improve productivity and satisfaction, both individually and in concert with others.

Bio-cost measures human effort

Bio-cost is the energy, attention, and stress that people expend over time to achieve their goals—to get what they want” in Ashby’s sense. [Ashby 1956]

All of life’s activities carry some bio-cost. Most often, we “feel” bio-cost when we meet resistance—when we can’t enter a flow and act simply to get what we want. We experience the drain of bio-cost every day— when we find a stone in our shoe; when traffic slows us; when we struggle to change a channel with a remote control; when the bureaucracy requires we submit another form; when the boss makes contradictory requests; when the stock market sends mixed signals. Bio-cost limits what we can achieve because we may not have the resources to get what we want, or we might spend too much for what we get in return. This is true for individuals, groups, organizations, and species. While we may not be able to quantify bio-cost with precise measures— whether in anticipation of expending it or after the fact—the authors have found considerable utility in construing bio-cost as comprising distinct quantitative components.

Bio-cost is a function of time

All tasks take time to accomplish. The effort required to complete a task can be mapped against time (in basic cases, at least). Graphing against time we see an ebb and flow of effort—e.g., walking to a destination requires relatively constant bio-cost expenditure over time, while flagging down a cab and getting in requires an initial burst of effort followed by a period of relative rest during the ride, as in Figure 1.

Figure 1: Bio-cost of physical effort to travel by taxi (cyan) versus walking (black)

Bio-cost has physical, mental, and emotional components

In the case above, the physical effort can be measured as calories—the greater the effort, the more calories required. There are limits to our physical efforts; when taken to an extreme, we can experience muscle fatigue or exhaustion.

Bio-cost also has a mental component. Mental effort means attention paid to perform a task or even to think about how to perform it. As with physical effort, this use of our brains and all the components of our nervous system that coordinate our thinking and acting also requires effort and also has limits. Some tasks require more concentration than others, so the attention we pay will vary.

Similarly, we reach emotional limits as palpable as physical and mental ones when we get “stressed out” due to factors such as uncertainty and fear.

Bio-cost reveals trade-offs

Because the chemical and hormonal pathways overlay the nervous system, feeling has impact on thinking and vice versa. [von Forester 1973] A second-order awareness of the toll that a task is taking—whether in physical, mental, or emotional terms—may add further stress or alleviate it. This becomes part of a feedback loop that helps us to estimate the bio-cost expenditure required to be successful. When the task is to “save our life”— for example, to undergo invasive surgery to remove a tumor—our stress is increased because the stakes and uncertainties are high. When there are negative consequences for not completing a task by some deadline, such as getting to the airport in time to board a flight, perceived limitations of time can contribute to stress. Even non-time threatened situations raise our stress levels: Will I get fired for that mistake? Will I pass the test? Will she like me?

By reflecting on the bio-cost of specific activities in our daily lives, we can usually make trade-offs among the components—time, energy, attention, and stress as shown in Table I—to minimize the overall cost of getting what we want or need. At any point we may also decide to spend money to lower one or more dimensions of bio-cost. (Here we note without further exploration that this has the side benefit of allowing us to calculate a monetary equivalence for bio-cost, at least in a specific context. For example, avoiding the additional time and physical effort of walking is often worth the $10 monetary cost of a taxi—plus the stress of not knowing whether we can find one in time and whether traffic will cooperate.)

Table 1: Bio-cost components.

Can we replenish our “reserves”?

Clearly, we cannot recover time once spent, but given more time, we may be able to replenish our energy, our ability to concentrate, and our capacity to absorb stress.

After periods of intense activity, we often seek a better “life balance,” that is, we seek to counter-act activities that carry significant bio-cost with those that allow us to restore our physical, mental, and emotional systems. For example, we often say that we “make time” for family and friends, so that we can “recharge our batteries.”

Sleep appears to restore our energy, refresh our brains, and reduce our stress such that we can use our time more efficiently and make better choices. Many other activities also fit this category, such as meditation, the pursuit of sports, crafts, and the arts, or even mastery of a skill.

How do we assess bio-cost trade-offs?

In monetary transactions we commonly consider cost versus gain. This paper argues that the same is true for actions that involve the expenditure of physical, mental, and emotional effort, and that explicit awareness of this affords us the opportunity to reflect on trade-offs and improve the choices we make.

It is important to keep in mind, however, that we can’t always easily calculate the value of reducing bio-cost in monetary terms nor can we translate or commute a given valuation to other circumstances or individuals. Still, we maintain a belief in the gain and a sense of the cost, and we remain capable of generating an opinion as to what we will base our actions on right now. Put another way, we think the view is worth the climb.

In order to characterize a progression of variations of goal setting, taking action, and reaping rewards, the next set of figures start from a single participant and proceed to cover cases of cooperation and collaboration with others.

1 Bio-cost for single participant

Figure 2 draws from Pask’s model of goal/action systems [Pask 1975; Pangaro 2003], reinterpreted such that the “goal” level (L1 in Pask’s original) becomes the gain, while the “means” level (L0 in the original) becomes the cost. Per Pask, the goallevel controls the execution of procedures at the means-level, as indicated by the vertical arrow on right side. Results from execution are returned and compared to the original goal, as indicated by the line on the left with comparator sign.

Figure 2: First canonical form shows goals are achieved via separate means, where the means has a cost and achieving the goal creates a gain.

2 Bio-cost for cooperative participants

The next case involves a distinction between Participant A, who sets the goal, and Participant B, who agrees to perform the actions required to achieve that goal. The components are the same as in Figure 2. However, in Figure 3, there is a clear division (the vertical line) as goal-setting and action-taking are executed by different participants.

Participant B expends the bio-cost to achieve the goal on behalf of Participant A, who compares the result of B’s actions with the goal. We call the interaction cooperation” because there are clear roles and actions for A and for B—they co-operate, that is, they operate together but within agreed boundaries.

Figure 3: Second canonical form shows the allocation of goal-setting to Participant A, and action-taking to Participant B.

3 Bio-cost for collaborative participants

The third case also involves two participants but is more open-ended in that the distribution of roles and actions between participants is not predetermined. Rather, participants A and B collaborate— they “co-labor” or work together—to create and agree on the goals themselves, as well as to agree on who does what to achieve them.

In Figure 4, participants A and B converse at two levels: about goals (upper horizontal loop) and about the means to achieve them (lower horizontal loop). They likely also cooperate about means, and use feedback to check whether goals have been achieved (loops that cross from upper to lower level). In an ongoing collaboration, participants may maintain some sense of the trade-offs across time and situations, and they may seek a balance over time.

Figure 4: Second canonical form shows the allocation of goal-setting to Participant A, and action-taking to Participant B. Third canonical form shows that A and B “co-labor” to create goals and share bio-cost to achieve them.

Bio-cost in business and design

Society has benefitted greatly from—one could say society can arise because of—the sharing of bio-cost. As early as the Stone Age, social groups learned how coordinated action could achieve goals that would otherwise have been impossible. A group could successfully hunt a swift and powerful animal for food, whereas a single hunter might have only a slim chance of success and a high risk of injury or death. By sharing such responsibilities, groups could achieve net bio-cost reduction thereby freeing up resources to explore new lands, create new arts and cultures, and develop new means of associating and collaborating.

Since the Renaissance, the corporation has provided one such structure for collaboration. The success of modern corporations is a measure of the huge scale on which they reduce collective bio-cost expenditures. Yet, modern corporations also exact a huge toll in frustration and stress from their employees. In other words, working in a corporation often comes with a high bio-cost. For example, on a mundane level the noise and interruptions of cubicle life” can make focused attention difficult. On a more critical level, uncertainty about goals and criteria can lead to rework; uncertainty about roles and responsibilities can lead to unproductive conflict; and uncertainty about continued employment can lead to fear. Such bio-costs are an extraordinary and persistent waste of “human resources.”

Transforming a corporation from a current state of high bio-cost to a more efficient state requires a complex system that learns as it goes—and the bio-cost of learning, even for those who thrive on it, is very high. [Geoghegan and Pangaro 2003] This appears to be one reason why corporations often fail to find new paths to success when markets change. [Dubberly, Esmonde, Geoghegan & Pangaro 2002]

On the other hand, “strong teamwork” means that there is mutual trust (itself a huge bio-cost reducer) as well as clarity of direction, role and proper action (all proxies for low uncertainty and hence low bio-cost situations). At best, the beliefs and goals of the individuals in a corporation are highly aligned.

In addition to applying the framework of bio-cost to organizational design, we can also apply it to product and service design. Minimizing or at least reducing a user’s bio-cost can be an important design goal. Even though the precision of bio-cost measures is limited, a focus on bio-cost permits a deep conversation during the design process. Instead of seeking to make products “simple” or intuitive”—laudable goals but not very specific— designers can use the dimensions of bio-cost to participate in a more directed design process where trade-offs are made explicit and clear.

Why bio-cost is important

We see an opportunity for organizations to create value by focusing on bio-cost. First, bio-cost provides a framework for improving productivity; by getting better at understanding bio-cost, we can get better at reducing it. In addition, bio-cost provides a framework for innovation; identifying bio-cost is identifying inefficiency, identifying an unmet user need, identifying an opportunity for new products and services.

In summary, it is our conviction that reducing biocost leads to:

• greater efficiency in achieving goals, which leads to…
• greater capacity or resources in the system, which allows the cultivation of…
• greater variety, which means…
• greater ability to generate higher-level plans for reducing bio-cost even further, resulting in…
• even lower bio-cost—a positive feedback loop and a virtuous cycle.

Reducing bio-cost creates value. It expands the space in which additional choices may be generated and evaluated. It can be an ethical motivation in the design process and lead to a more humane world. We believe that a bio-cost economy underlies all exchanges of value, and it always will, because it involves the management of the least fungible and most valuable aspect of life: how we spend our time.

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Interaction describes a range of processes. A previous “On Modeling” article presented models of interaction based on the internal capacity of the systems doing the interacting [1]. At one extreme, there are simple reactive systems, such as a door that opens when you step on a mat or a search engine that returns results when you submit a query At the other extreme is conversation. Conversation is a progression of exchanges among participants. Each participant is a “learning system,” that is, a system that changes internally as a consequence of experience. This highly complex type of interaction is also quite powerful, for conversation is the means by which existing knowledge is conveyed and new knowledge is generated.

We talk all the time, but we’re usually not aware of when conversation works, when it doesn’t, and how to improve it. Few of us have robust models of conversation. This article addresses the questions: What is conversation? How can conversation be improved? And, if conversation is important, why don’t we consider conversation explicitly when we design for interaction? This article hopes to move practice in that direction. If, as this forum has often argued, models can improve design, we further ask, what models of conversation are useful for interaction design?

We begin by contrasting “conversation” with “communication” in a specific sense. We then offer a pragmatic but not exhaustive model of the process of conversing and explore how it is useful for design.

What Isn’t Conversation?

Claude Shannon developed a rigorous model of a transmission channel used to convey messages between an information source and a destination. While his context was analog telephones with wires highly susceptible to noise, Shannon produced a model that applies to a wide range of situations.

Shannon’s Model of Communication:
A message flows from an information source through a transmitter that encodes a signal. The communication channel, shown as the tiny square box subject to noise, conveys the signal to a receiver, which decodes the signal into a message that is delivered to a destination.

In Shannon’s model an information source selects a message from a known set of possible messages, for example, a dot or a dash, a letter of the alphabet, or a word or phrase from a list. Human communication often relies on context to limit the expected set of messages. If you receive a call from a friend (the source) arriving by train, you expect to hear “I’m getting on the train,” or “I’m on the train,” or “the train is late,” and so on—messages that are drawn from a set of possibilities known to both of you. The channel is effective if it enables you (the destination) to select which of the possible messages is currently being transmitted. (Voice communication is more than sufficient for this, and Shannon’s interest was highly encoded transmission. But this simplified example draws useful distinctions for the discussion that follows.)

Communication in the sense of distinguishing among possible messages known in advance is important for much of our daily life. It allows us to synchronize a wide range of actions with others. But it has limits. Shannon’s model captures a fundamental limit of nearly all human-to-computer interaction: Our input gestures can only activate an existing interface command (select a message) from the preprogrammed set. While we can automate sequences of existing commands, we can’t ask for something novel. If our software application does anything novel, we file a bug report!

In Shannon’s model, how can we say something novel to one another? The answer is, we can’t. It’s not designed for that. We need the capacity for new messages to be generated and the resultant understanding confirmed or denied. We call interaction with these capacities “conversation.” Only in conversation can we learn new concepts, share and evolve knowledge, and confirm agreement. To describe how this works, we draw on the cybernetic models of conversation theory and Gordon Pask, because they are based on a deep study of human-to-human and human-to-machine interaction and because of their prescriptive power [2].

What Is the Process of Conversation?

Conversation at its simplest takes place when participants perform these tasks:

1) Open a channel.
When participant A sends an initial message, the possibility for conversation opens. For conversation to follow, the message must establish common ground; it must be comprehensible to participant B.

Conversation for Agreement:
As a result of conversation, participants agree on their understanding of a concept in that they share a similar model, and they believe that they agree.

2) Commit to engage.
Participant B must pay attention to the message and then commit to engaging with A. Such a commitment may amount to nothing more than continuing to pay attention. For conversation to persist, the commitment must be symmetrical, and either side may break off for any reason, at any time. Put another way, each participant must see value in continuing the conversation, which offsets the personal cost of being engaged: what we call the “bio-cost,” or the energy, time, attention, and stress required [3].

3) Construct meaning.
Conversation enables us to construct (or reconstruct) meaning, including meaning that is new to the destination. Conversation theory has a highly detailed model that we must leave to other descriptions though it is useful even in this skeletal form [4].

Messages are composed with topics or distinctions that are already shared, on the basis of prior conversation or shared contexts, such as common language and social norms. Participant A uses the message channel to convey what these topics are and how they are distinct from one another (descriptive dynamics), along with a kind of “glue” that explains just how these topics interact to make up the new concept (prescriptive dynamics). Participant B “takes all this in” and “puts it all together” to reproduce A’s meaning (or something close enough).

This can occur because, first, the descriptive and prescriptive dynamics come together to express an inherent coherence for the concept—they fit together like gears in a watch and only in a limited way or ways. Second, the human nervous system has evolved especially to make sense of the messages that arrive [5]. This “meaning making” (the taking all this in and putting it all together) is a mini AHA moment, every time we “get” what someone is saying [6].

4) Evolve.
Participant A or B (or both) are different after the interaction. Either or both hold new beliefs, make decisions, or develop new relationships, with others, with circumstances or objects, or with themselves.

Here we define an “effective conversation” as an interaction in which the changes brought about by conversation have lasting value to the participants.

5) Converge on agreement.
Participant B may wish to confirm understanding of A’s concept. To do so, B must create and transmit a different formulation of the topic(s) under discussion, one that captures his model of the concept. On receipt, participant A attempts to make sense of B’s formulation and compares it with her original intention. This may lead to further exchanges. When both A and B judge that the concepts match sufficiently, they have reached “an agreement over an understanding.” Such agreement may involve a fact about the world or merely shared belief. Sometimes participants agree on the qualities of a song, or that they like each other enough to continue talking.

6) Act or Transact.
Sometimes one or more of the participants agrees to perform an action as a result of, and beyond, the conversation that has taken place. For example, they may agree to play a game together or enter into a relationship. Or they may agree to an exchange, as when money is traded for a product or service.

Thus we have a simplified description of conversation. All of us experience breakdowns in conversations; it is near miraculous that we understand each other at all. But if you comprehend this, the process of conversation is working right now.

What Does Conversation Offer?

Conversation enables participants to:

1) Learn.
We learn a great deal via conversation, including conversations with ourselves. We learn highly valuable life lessons, for example, ways to avoid being run over by a bus. At an opposite extreme, what we learn might seem simple: Our partner prefers drinking noncarbonated, room-temperature water; registering a credit card on a website saves time when buying airline tickets. Trivial as these examples may seem, learning basic things may save time later, freeing our future attention for other, less trivial, things. This is a valuable benefit of interactions that have memory and that evolve into relationships.

Conversation to Learn:
Conversation is a means to convey concepts and to confirm agreement. When a conversation changes one of the participants, we say the participant has “learned.”

2) Coordinate.
We spend a great deal of time with others not merely synchronizing (“You’ve arrived, so let’s start!”), but also coordinating our actions in ways that are mutually beneficial. Anytime we negotiate one favor for another, we use conversation to reach an agreement to transact:

“I’ll pick up the laundry if you stop for groceries, OK?”

“No, you have to take the car in for servicing.”

“I can do both, but you’ll have to cook if you want to eat on time.”

“That still works for me.”

“OK, good.”

In practice, society is a complex market of coordination based in conversation. Money is often used in the transaction, but not always. Subsets of the population agree to perform some actions (grow food, manufacture products, educate children, enforce the law) paid for by others who are free to do what they do, for (hopefully) mutual benefit.

Individuals and society become more efficient by coordinating work. This frees resources for other activities—including the design of more efficient products and services, in a recursive and generative process—which supported the Industrial Revolution. Conversation is the primary mechanism for complex human social coordination. It is a highly effective form of bio-cost reduction and therefore an engine of society.

3) Collaborate.
Coordination of action assumes relatively clear goals, but many times social interaction involves the negotiation of goals. (Horst Rittel believed this to be a fundamental challenge of design [6].) We may want to eat together, but one of us prefers Italian food, while the other doesn’t want to spend too much or listen to opera while eating. Or, we need to redesign our Web service but have conflicting demands for features, quality of experience, and development time. Or we would like to see a more equitable healthcare system. Conversation is a requisite for agreeing on goals, as well as for agreeing upon, and coordinating, our actions.

Conversation to Coordinate:
Participant B agrees to trade an action for payment from participant A. B performs the action and confirms that his action has created the correct result. A confirms her goal is achieved and compensates B as agreed. Compensation may be monetary, return of favor, barter, etc.

What Are the Limits To a Conversation?

When designing for conversation, it is critical to consider what cannot happen. What can’t be talked about can’t be learned, conveyed, agreed on, or transacted. Conversations may be limited in two fundamental ways:

1) Conversational infrastructure.
We are frustrated when we can’t open a channel for conversation or when the channel is full of noise (experienced by every U.S. mobile phone user). Or we’re frustrated when we can’t use the available interface functions to get what we want. So, when software is the connection between participants, we ought to ask, “How well does the infrastructure support the conversational connection?”

2) Conversational participants.
Inherent in the capacities for a given conversation are the individual limits of its participants. Individuals contribute both what they know in depth and breadth and their style of interaction. Given a specific group of participants, conversations may go nowhere—they have no value; they create no lasting change in the participants. Other conversations create their own energy and go places—they are generative, have momentum, and lead to new and unexpected knowledge. We prize the individuals with whom we achieve this. “When assembling a design team we ought to ask, What expertise and what collaborative style(s) do we need? What variety is required to succeed?”

Conversation to Collaborate:
Agreeing on goals and coordinating actions to achieve them

Types of Participants

A human participant in conversation is usually a single person, although Pask suggests additional possibilities [7].

Conversations may take place between groups. For example, different political parties, religious groups, or nations interact with each other—they send messages, commit to engage (or not), evolve each other’s beliefs, and sometimes lead to transactions such as trade or war.

Similarly, we often have internal conversations—conversations with ourselves. I explore alternative perspectives, exchange points of view, come to a stable viewpoint about a belief or action (or, when I can’t, remain conflicted)—all inside my own mind.

We generate new ideas by combining old topics in new ways. This is important to interaction design because we spend so much time in front of screens talking to ourselves. Interaction design is as much about connecting humans across the murky “Internet cloud” (fostering community and conversation) as connecting an individual with his or her own capacity to explore what is possible and generate new possibilities (supporting internal conversations).

Why Does Conversation Matter?

Conversation matters to any community of interest (including our community of a single mind), but nowhere is the value of conversation more clear than in commerce, because commerce cannot flourish, or even exist, without conversation.

Requirements for conversation

Establish environment and mindset—context
Use shared language
Engage in mutually beneficial, peer-to-peer exchange
Confirm shared mental models
Engage in a transaction—execute cooperative actions

Marketplace example from user perspective

What’s new in mobile phones?
How is this like a Blackberry?
Can I use this in Europe?
What will that cost?
Yes, this product suits me.
I accept your price and terms; here is my payment.

But many products and services, on the Web and off, connect individuals for broader reasons. Social networks such as Facebook and LinkedIn match two ends of a channel for mutual benefit, whether or not money changes hands. Sometimes what occurs is a sharing of interests, ideas, or even intimacy. But in all these cases, conversation is required.

Summarizing, conversation is infrastructure for commerce because:

• Long-term success means ongoing commerce.
• Ongoing commerce needs ongoing trust.
• Ongoing trust is built via ongoing relationships.
• Ongoing relationships are built via agreeing on goals and actions.
• Agreeing on goals and actions is possible only through effective conversation. So, effective conversation is essential to commerce.

What Can Designers Do?

If conversation is important to “users,” we should explicitly model conversation as we design. Here are four broad proposals:

View every user (persona) as a participant in a conversation, and every scenario as a conversation to define or achieve one or more goals. Use models of conversation to make design decisions, such as:

1. What channel is being opened to begin the conversation? Is the interruption reasonable in how and when it intrudes? What is the bio-cost of the intrusion relative to its benefit? Are there better ways to interrupt?

2. Is the first message clear? Does it offer something to the recipient?

3. Once accepted, does the ongoing exchange convey the potential benefits in continuing the engagement? Is there learning or delight? Is curiosity or interest stimulated? At what bio-cost? How can it be improved?

4. Is meaning easily understood; that is, do the messages speak to the participants’ context, needs, interests, values, and in their language? How difficult is it for users to “put together”? How can messages be made more efficient or clear or entertaining, as appropriate?

5. How can users convey intention and meaning to the software? Are those means sufficiently expressive or easy or delightful? Where do they fall short?

6. Do participants evolve during the interaction? Aside from entertainment or delight, do they acquire something useful, learn a new point of view, or gain new knowledge? (This applies to human participants as well as software, which may evolve a model of the user for the sake of having more effective or more efficient conversations in the future.)

7. Do both sides agree? Can the participants agree to disagree?

8. Can sharing or exchange or transaction continue beyond this conversation, whether in the form of commerce or barter or simply agreeing to continue the conversation at a later time? In other words, has the conversation begun or continued a relationship?

Invest in a better understanding of conversation:

1. Review past projects and recast them as conversations: How could design outcomes be improved?

2. Look at new technologies or techniques in terms of conversation: Do they help generate more effective conversations?

3. When developing new projects, do models of conversation help in choosing technologies or techniques?

4. Can we design for conversations that directly improve trust, and therefore create stronger communities or greater lifetime customer value?

Investigate trends, tools, and technologies that will change online conversations in the next five years:

1. Personal journeys: How do physical age and technology exposure change predilections for media, modes of collaboration, and personal values?

2. Social computing: How will conversational technology transform individuals and organizations?

3. Portable and secure identity tools: How do OpenID and equivalents create secure and controllable online identities? How do they build trust? What can’t they do?

4. Cloud computing: How can we deliver the same experience everywhere, at lower cost?

5. Sensors: How does a seamless “network of objects,” when capable of conversational interaction, better extend our capacity for learning, coordinating, and collaborating?

Invest in design of conversations via prototyping:

1. For stakeholders: Build trust and value for employees, shareholders, clients, partners, competitors, and communities of interest.

2. Inside the organization: Instill coevolution as the process for understanding the market, defining and delivering the offering, and increasing customer satisfaction and shareholder value.

3. Across organizational and cultural boundaries: Explore a “marketplace of ideas.”

The Impact

Imagine a design movement that takes conversation seriously. Could it create a revolution?

The Industrial Revolution harnessed physical machines to extend and enhance our muscles. The Information Revolution harnessed virtual machines to extend and enhance our nervous systems. A “Conversation Revolution” would harness the existing infrastructure of physical machines and virtual machines to create a mesh out of “networks of objects” and networks of individuals and organizations. Such a mesh would enhance coordination and collaboration and create wealth by introducing new efficiencies. It would also expand opportunities to generate new knowledge.

Imagine a search engine designed for effective conversation, with all the knowledge on the Web participating. We would no longer be focused on “search,” nor would we be using an “engine.” What should it be called? Who will build it first?

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When we discuss computer-human interaction and design for interaction, do we agree on the meaning of the term “interaction”? Has the subject been fully explored? Is the definition settled?

A Design-Theory View

Meredith Davis has argued that interaction is not the special province of computers alone. She points out that printed books invite interaction and that designers consider how readers will interact with books. She cites Massimo Vignelli’s work on the National Audubon Society Field Guide to North American Birds as an example of particularly thoughtful design for interaction [1].

Richard Buchanan shares Davis’s broad view of interaction. Buchanan contrasts earlier design frames (a focus on form and, more recently, a focus on meaning and context) with a relatively new design frame (a focus on interaction) [2]. Interaction is a way of framing the relationship between people and objects designed for them—and thus a way of framing the activity of design. All man-made objects offer the possibility for interaction, and all design activities can be viewed as design for interaction. The same is true not only of objects but also of spaces, messages, and systems. Interaction is a key aspect of function, and function is a key aspect of design.

Davis and Buchanan expand the way we look at design and suggest that artifact-human interaction be a criterion for evaluating the results of all design work. Their point of view raises the question: Is interaction with a static object different from interaction with a dynamic system?

An HCI View

Canonical models of computer-human interaction are based on an archetypal structure—the feedback loop. Information flows from a system (perhaps a computer or a car) through a person and back through the system again. The person has a goal; she acts to achieve it in an environment (provides input to the system); she measures the effect of her action on the environment (interprets output from the system—feedback) and then compares result with goal. The comparison (yielding difference or congruence) directs her next action, beginning the cycle again. This is a simple self-correcting system—more technically, a first-order cybernetic system.

In 1964 the HfG Ulm published a model of interaction depicting an information loop running from system through human and back through the system [3].

Man-Machine System

Gulf of Execution and Evaluation

Don Norman has proposed a “gulf model” of interaction. A “gulf of execution” and a “gulf of evaluation” separate a user and a physical system. The user turns intention to action via an input device connected to the physical system. The physical system presents signals, which the user interprets and evaluates—presumably in relation to intention [4].

Norman has also proposed a “seven stages of action” model, a variation and elaboration on the gulf model [5]. Norman points out that “behavior can be bottom up, in which an event in the world triggers the cycle, or top-down, in which a thought establishes a goal and triggers the cycle. If you don’t say it, people tend to think all behavior starts with a goal. It doesn’t—it can be a response to the environment. (It is also recursive: goals and actions trigger subgoals and sub-actions) [6].”

Seven Stages of Action

Interaction

Like Norman’s models, Bill Verplank’s wonderful “How do you…feel-know-do?” model of interaction is also a classic feedback loop. Feeling and doing bridge the gap between user and system [7].

Representing interaction between a person and a dynamic system as a simple feedback loop is a good first approximation. It forefronts the role of information looping through both person and system [8]. Perhaps more important, it asks us to consider the user’s goal, placing the goal in the context of information theory—thus anchoring our intuition of the value of Alan Cooper’s persona-goal-scenario design method [9].

In the feedback-loop model of interaction, a person is closely coupled with a dynamic system. The nature of the system is unspecified. (The nature of the human is unspecified, too!) The feedback-loop model of interaction raises three questions: What is the nature of the dynamic system? What is the nature of the human? Do different types of dynamic systems enable different types of interaction?

A Systems-Theory View

The discussion that gave rise to this article began when Usman Haque observed that “designers often use the word ‘interactive’ to describe systems that simply react to input,” for example, describing a set of Web pages connected by hyperlinks as “interactive multimedia.” Haque argues that the process of clicking on a link to summon a new webpage is not “interaction”; it is “reaction.” The client-server system behind the link reacts automatically to input, just as a supermarket door opens automatically as you step on the mat in front of it.

Haque argued that “in ‘reaction’ the transfer function (which couples input to output) is fixed; in ‘interaction’ the transfer function is dynamic, i.e., in ‘interaction’ the precise way that ‘input affects output’ can itself change; moreover in some categories of ‘interaction’ that which is classed as ‘input’ or ‘output’ can also change, even for a continuous system [10].”

For example, James Watt’s fly-ball governor regulates the flow of steam to a piston turning a wheel. The wheel moves a pulley that drives the fly-ball governor. As the wheel turns faster, the governor uses a mechanical linkage to narrow the aperture of the steam-valve; with less steam the piston fills less quickly, turning the wheel less quickly. As the wheel slows, the governor expands the valve aperture, increasing steam and thus increasing the speed of the wheel. The piston provides input to the wheel, but the governor translates the output of the wheel into input for the piston. This is a self-regulating system, maintaining the speed of the wheel—a classic feedback loop.

Of course, the steam engine does not operate entirely on its own. 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 the steam valve. In Haque’s terminology, the transfer function is changed.

Our model of the steam engine has the same underlying structure as the classic model of interaction described earlier! Both are closed information loops, self-regulating systems, first-order cybernetic systems. While the feedback loop is a useful first approximation of human computer interaction, its similarity to a steam engine may give us pause.

The computer-human interaction loop differs from the steam-engine-governor interaction loop in two major ways. First, the role of the person: The person is inside the computer-human interaction loop, while the person is outside the steam-engine-governor interaction loop. Second, the nature of the system: The computer is not characterized in our model of computer-human interaction. All we know is that the computer acts on input and provides output. But we have characterized the steam engine in some detail as a self-regulating system. Suppose we characterize the computer with the same level of detail as the steam engine? Suppose we also characterize the person?

Types of Systems

So far, we have distinguished between static and dynamic systems—those that cannot act and thus have little or no meaningful effect on their environment (a chair, for example) and those that can and do act, thus changing their relationship to the environment.

Within dynamic systems, we have distinguished between those that only react and those that interact—linear (open-loop) and closed-loop systems.

Some closed-loop systems have a novel property—they can be self-regulating. But not all closed-loop systems are self-regulating. The natural cycle of water is a loop. Rain falls from the atmosphere and is absorbed into the ground or runs into the sea. Water on the ground or in the sea evaporates into the atmosphere. But nowhere within the cycle is there a goal.

Types of Systems

A self-regulating system has a goal. The goal defines a relationship between the system and its environment, which the system seeks to attain and maintain. This relationship is what the system regulates, what it seeks to keep constant in the face of external forces. A simple self-regulating system (one with only a single loop) cannot adjust its own goal; its goal can be adjusted only by something outside the system. Such single-loop systems are called “first order.”

Learning systems nest a first self-regulating system inside a second self-regulating system. The second system measures the effect of the first system on the environment and adjusts the first system’s goal according to how well its own second-order goal is being met. The second system sets the goal of the first, based on external action. We may call this learning—modification of goals based on the effect of actions. Learning systems are also called second-order systems.

Some learning systems nest multiple self-regulating systems at the first level. In pursuing its own goal, the second-order system may choose which first-order systems to activate. As the second-order system pursues its goal and tests options, it learns how its actions affect the environment. “Learning” means knowing which first-order systems can counter which disturbances by remembering those that succeeded in the past.

Water Cycle

Linear system

Self-regulating system

Learning system

A second-order system may in turn be nested within another self-regulating system. This process may continue for additional levels. For convenience, the term “second-order system” sometimes refers to any higher-order system, regardless of the number of levels, because from the perspective of the higher system, the lower systems are treated as if they were simply first-order systems. However, Douglas Englebart and John Rheinfrank have suggested that learning, at least within organizations, may require three levels of feedback:

• basic processes, which are regulated by first-order loops
• processes for improving the regulation of basic processes
• processes for identifying and sharing processes for improving the regulation of basic processes

Of course, division of dynamic systems into three types is arbitrary. We might make finer distinctions. Artist-researcher Douglas Edric Stanley has referred to a “moral compass” or scale for interactivity “Reactive > Automatic > Interactive > Instrument > Platform” [11].

Cornock and Edmonds have proposed five distinctions:
(a) Static system
(b) Dynamic-passive system
(c) Dynamic-interactive system
(d) Dynamic-interactive system (varying)
(e) Matrix [12]

Kenneth Boulding distinguishes nine types of systems [13].

Levels of systems

System Combinations

One way to characterize types of interactions is by looking at ways in which systems can be coupled together to interact. For example, we might characterize interaction between a person and a steam engine as a learning system coupled to a self-regulating system. How should we characterize computer-human interaction? A person is certainly a learning system, but what is a computer? Is it a simple linear process? A self-regulating system? Or could it perhaps also be a learning system?

Working out all the interactions implied by combining the many types of systems in Boulding’s model is beyond the scope of this paper. But we might work out the combinations afforded by a more modest list of dynamic systems: linear systems (0 order), self-regulating systems (first order), and learning systems (second order). They can be combined in six pairs: 0-0, 0-1, 0-2, 1-1, 1-2, 2-2.

0-0 Reacting
The output of one linear system provides input for another, e.g., a sensor signals a motor, which opens a supermarket door. Action causes reaction. The first system pushes the second. The second system has no choice in its response. In a sense, the two linear systems function as one.

This type of interaction is limited. We might call it pushing, poking, signaling, transferring, or reacting. Gordon Pask called this “it-referenced” interaction, because the controlling system treats the other like an “it”—the system receiving the poke cannot prevent the poke in the first place [15].

A special case of 0-0 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.

0-1 Regulating
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. In this case, the linear system may be seen as part of the self-regulating system—a sort of dial. (Later we will discuss the system that turns the dial. See 1-2 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.

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
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 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.)

1-1 Balancing
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. 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.

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.

2-2 Conversing
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.

More interesting is the case of what Pask calls “I/you-referenced” interaction: Not only does the second system take in the output of the first, but the first also takes in the output of the second. Each has the choice to respond to the other or not. Significantly, here the input relationships are not strict “controls.” This type of interaction is a like a peer-to-peer conversation in which each system signals the other, perhaps asking questions or making commands (in hope, but without certainty, of response), but there is room for choice on the respondent’s part. 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. We might even say they are capable of design—of agreeing on goals and means of achieving them. This type of interaction is conversing (or conversation). It builds on understanding to reach agreement and take action [16].

There are still more cases. Two are especially interesting and perhaps not covered in the list above, though Boulding surely implies them:

• learning systems organized into teams
• networks of learning systems organized into communities or markets

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:

• reacting to another system
• regulating a simple process
• learning how actions affect the environment
• balancing competing systems
• managing automatic systems
• entertaining (maintaining the engagement of a learning system)
• conversing

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.

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Abstract
Argues design practice has moved from hand-craft to service-craft and that service-craft exemplifies a growing focus on systems within design practice. Proposes cybernetics as a source for practical frameworks that enable understanding of dynamic systems, including specific interactions, larger systems of service, and the activity of design itself. Shows development of first- and second-generation design methods parallels development of first- and second-generation cybernetics, particularly in placing design within the political realm and viewing definition of systems as constructed. Proposes cybernetics as a component of a broad design education.

Key Words
Cybernetics, design, design methods, hand-craft, interaction design, politics, rhetoric, service, service-craft, service design, system.

A History of Connections Between Cybernetics and Design

The influence of cybernetics on design thinking goes back 50 years.[1] Yet today, cybernetics remains almost unknown among practicing designers and unmentioned in design education or discussions of design theory.

Designers’ early interest in cybernetics accompanied cybernetics’ brief time in the spotlight of popular culture. First-generation thinking on cybernetics influenced first-generation thinking on design methods.[2] And second-generation design methods[3] has many parallels in second-order cybernetics.[4]

Both cybernetics and the design-methods movement failed to sustain wide interest. One reason is that initially both had limited practical application; in some sense, they were ahead of their times and the prevailing technologies. That may be changing. Particularly in the world of design, cybernetics is newly relevant.

Ross Ashby lists as the “peculiar virtues of cybernetics” its treatment of behavior and complexity.[5] Both topics increasingly concern designers, especially those designing “soft” products, those engaged in interface design, interaction design, experience design, or service design. In these areas, where designers are concerned with “ways of behaving”—with what a thing does as much as what it is or how it looks—here, cybernetics can help designers.

Designs Shift to Service-Craft

Over the last century, the arc of development of design practice[6] has been from objects, to systems, to communities of systems. Design practice has moved from a focus on hand-craft and form, through an increased focus on meaning and structure, to an increasing focus on interaction and services—what we call “service-craft.”

Service-craft includes the design, management, and ongoing development of service systems, the connected touch-points of service delivery. Touch-points are where participants interact with service providers or machines, either in person or through communications networks. For an airline, its website, check-in kiosk, flight attendants, and seats are some of many touch-points. Shelly Evenson describes service as “the experience participants have as they move through a series of touch-points.”[7]

For some people, service still connotes menial tasks, e.g., washing dishes. Yet service systems are at the cutting edge of consumer electronics, e.g., Apple’s iPod-iTunes-Store system or Nintendo’s Wii-online service. Service systems are also the very definition of e-business, e.g., Amazon, eBay, or Google.

Kevin Kelly, former editor of Wired magazine put it very well, “. . . commercial products are best treated as though they were services. It’s not what you sell a customer, it’s what you do for them. It’s not what something is, it’s what it is connected to, what it does. Flows become more important than resources. Behavior counts.”[8]

Today, the leading edge of design practice increasingly involves teams of people (often including many specialties of design) collaborating on the development of connected systems, teams of people collaborating in service-craft.

The difference between traditional and emerging design practice may be characterized as:

Hand-Craft

• Subject Things
• Participant(s) Individual
• Thinking Intuitive
• Language Idiosyncratic
• Process Implicit
• Work Concrete
• Construction Direct

Service-Craft

• Subject Behaviors
• Participant(s) Team
• Thinking Reasoned
• Language Shared
• Process Explicit
• Work Abstracted
• Construction Mediated

Of course, hand-craft has not gone away, nor is service-craft divorced from hand-craft. Hand-craft plays a role in service-craft (just as in developing software applications, coding remains a form of handcraft). While service-craft focuses on behavior, it supports behavior with artifacts. While service- craft requires teams, teams rely on individuals. Service-craft does not replace hand-craft; rather service-craft extends or builds another layer upon hand-craft.

The Need for New Language in Design

Service-craft is emerging within the context of larger changes. The shift to a knowledge-service economy and the rise of information-communication technology are changing the way we live. The interplay of the two fuels the growth of both, which continues to accelerate. Another revolution is in the making, as sensors proliferate, e.g., Apple’s iPhone will include at least 5 sensors: camera, microphone, proximity sensor, motion sensor, and touch sensor (and maybe GPS, too). And more things have internet addresses, e.g., soon, you may be able to find your car keys by using Google.

These changes create opportunity for new products, new businesses, and new types of human activity. They create opportunity for new areas of design practice, but the approach to design that is efficient for a craftsman making individual objects does not scale for teams developing service systems. To take advantage of the opportunities now opening up, designers must develop new tools, new methods, and new language.

Service-craft requires new language—language that is not a part of hand-craft. The need for new language in service-craft stems from at least three sources.

First, service-craft takes place in teams. Each team member wants to know what to expect and what others expect in return. Communication is key. Process, goals, and measures must be made explicit to everyone on the team. Designers need new language to talk to each other about complex projects. Hand-craft has no such language.

Second, services are largely immaterial. Birgit Mager notes that services are manufactured at point of delivery.[9] Their essence is more about relationships than entities. In a sense, services are alive. Feedback and dialog (conversation) take on special importance. Designers need new language to help them discuss behavior. Hand-craft has no such language.

Third, systems often reveal only a few facets at one time. Understanding a whole system can be difficult. In service-craft, the final object of design cannot be viewed directly or in total. It must always be viewed in part, often only by proxy or through mediation. Trying to understand the community of systems that make up an online service such as Amazon is difficult, because we have nowhere to stand, which affords a complete view. Looking at Amazon through a web-browser is like looking at Versailles through a keyhole in a gate in the wall around the garden; you have a sense of a few parts but cannot easily grasp the complete structure. And for most visitors at least, the complex plumbing that powers the fountains remains almost invisible. Making matters more difficult, electronic systems change frequently. Designers need new language to represent dynamic systems. Hand-craft has no such language.

A language for thinking about living systems is becoming essential for the practice of design, at least in the world of service-craft.

Learning a new language increases our repertoire. New words may enable us to think about new ideas. More words may enable us to make finer distinctions. Our thinking and communicating become more precise—we become more efficient. We can work at deeper levels and take on more complex tasks—we become more effective. Our view of our work and ourselves takes on greater coherence—we become more integrated.

Cybernetics as a Source for New Language in Design

Forty years ago, in Notes on the Synthesis of Form, Christopher Alexander described the growing role of modeling in design practice.[10] In the last 10 years, much of design practice has come to rely on modeling. Designers have begun to develop language for discussing behavior—ways of understanding dynamic systems and visualizing patterns of information flows through systems.

Today, most models found in design practice are highly specific to the situation at hand. Designers rarely view the situation they are modeling as an example of a larger class and thus rarely draw on broader frameworks as a basis for their modeling. To be sure, designers have developed some conventions for modeling, e.g., site maps, application flow diagrams, and service blueprints. But for the most part, conventions for modeling are still not widely shared or well-defined within design practice.

In design discourse, most frameworks have been “cherry-picked” from the social sciences and semiotics. For the most part, designers have not established a firm foundation or organized systems for their modeling. Richard Buchanan’s formulation of design within frameworks of rhetoric—“design as rhetoric”—is a notable exception.[11]

The authors propose cybernetics as another rich source of frameworks for design practice, similar to the social sciences, semiotics, and rhetoric. We also propose cybernetics as a language—a self-reinforcing system, a system of systems or framework of frameworks for enriching design thinking.

Cybernetic Frameworks for Modeling What We Design

Much of design practice comes down to two models: a model of the current situation and a model of the preferred situation. Alexander points out the need to abstract the essence of the existing situation from the complexity of its concrete manifestation. Abstracting the situation makes it easier for us to consider meaningful changes, to find alternatives we might prefer. Alexander also underscores the need to make models visible, to provide representations for ourselves and others to analyze and discuss.[12]

Cybernetics offers conceptual frameworks for understanding and improving the things we design.

At the heart of cybernetics is a series of frameworks for describing dynamic systems. Individually these frameworks provide useful models for anyone seeking to understand, manage, or build dynamic systems. Together, these frameworks offer much of the new language design needs as it moves from hand-craft to service-craft.

In our teaching and in our practice, we have found seven cybernetic frameworks to be especially useful.

1. First-Order Cybernetic System

A first-order cybernetic system detects and corrects error; it compares a current state to a desired state, acts to achieve the desired state, and measures progress toward the goal. A thermostat-heater system serves as a canonical example of a first-order cybernetic system, maintaining temperature at a set-point.

A first-order cybernetic system provides a framework for describing simple interaction. It introduces and defines feedback. It frames interaction as information flowing in a continuous loop through a system and its environment. It frames control in terms of a system maintaining a relationship with its environment. It forms a coherence in which goal, activity, measure, and disturbance each implies the others.

This framework is useful for designers thinking about interfaces. It provides a template for modeling basic human interaction with tools, machines, and computers. It also provides a template for modeling machine-to-machine interaction or the interaction of processes running on computer networks.

2. Requisite Variety

Ross Ashby’s definition of requisite variety provides a framework for describing the limits of a system—the conditions under which it survives and those under which it fails. For example, humans have variety sufficient to regulate their body temperature within a fairly narrow range; if we get too cold or too hot, we will die quickly.

This framework is useful because it forces designers to be specific when describing systems. It suggests crisp definition of range, resolution, and frequency for measures related to goals, actuators, and sensors. The framework also enables discussion of the validity of goals. What is the range of disturbances we should design the system to withstand? Is the cost of additional variety in the system warranted by the probability of additional variety in disturbances?

3. Second-Order Cybernetic System

A second-order cybernetic system nests one first-order cybernetic system within another. The outer or higher-level system controls the inner or lower-level system. The action of the controlling system sets the goal of the controlled system. Addition of more levels (or “orders”) repeats the nesting process.

A second-order cybernetic system provides a framework for describing the more complex interactions of nested systems. This framework provides a more sophisticated model of human-device interactions. A person with a goal acts to set that goal for a self-regulating device such as a cruise-control system or a thermostat.

This framework is also useful for modeling complex control systems such as a GPS-guided automatic steering system. It is also useful for modeling ecologies or organizational or social control systems such as the relationship between insurer, disease management organization, and patient. This framework also provides a way of modeling the hierarchy of goals often at play in discussions of “user motivation,” which take place during design of software and service systems.

4. Conversation, Collaboration, and Learning (Participatory System)

Gordon Pask defined a conversation as interaction between two second-order systems.[13] This framework distinguishes between discussions about goals and discussions about methods, and it provides a basis for modeling their mutual coordination—or what Humberto Maturana called “the consensual coordination of consensual coordination of action.” It also distinguishes between it-directed (control) and other-directed (communication). Pask also used the framework in discussions of collaboration and learning. Michael Geoghegan wryly observed, “The mouse teaches the cat. . . Of course, . . . the cat also teaches the mouse.”[14]

This framework is useful for modeling the larger service systems in which many of the products of interaction design are situated. It provides a basis for beginning to model communities, exchanges, and markets, and interactions such as negotiation, cooperation, and collaboration.

The conversation framework suggests a sort of ideal: two second-order systems collaborating. Comparing this model of human-human interaction with typical human-computer interaction suggests many opportunities for improvement. Today, the typical framework for human-computer interaction might best be described as a second-order system (a person) interacting with a first-order system (a device). Designing second-order software systems to understand user goals and aid goal formation suggests a new way for people to work with computers.[15]

5. Bio-cost

The notion of bio-cost grows out of conversations between the authors and Michael Geoghegan. We define bio-cost as the effort a system expends to achieve a goal.[16]

This framework is useful for evaluating and comparing existing and proposed interaction methods. It may be possible to measure bio-cost and thus make notions of “ease-of-use” more concrete. We speculate that the bio-cost framework may be useful in developing key-performance indicator (KPI) systems for evaluating software usability and service quality.

6. Autopoiesis

Francisco Varela, Humberto Maturana, and Ricardo Uribe introduced the idea of autopoiesis or “self-making” to describe processes by which a system maintains itself and achieves autonomy.[17]

This framework is useful for discussing organizations and communities—how they form and how they maintain themselves. It holds promise for organizational designers. [The authors are aware of the disagreement as to whether the original, rigorous biological meaning holds for social organizations; we find the concept a unique and powerful metaphor for application to design in any case.]

7. Evolution

Geoghegan points out that “all evolution is co-evolution.”[18] A population changes in response to changes (disturbances) in its environment. In turn, the new population, behaving in new ways, may provoke changes in its environment. Of course, the idea of evolution by natural selection (or natural destruction) precedes the origin of cybernetics as a science, but framing evolution in cybernetic terms expands the scope and value of the earlier frameworks; for example, requisite variety can be seen as a mechanism of evolution and mutations as changes in variety. In addition, framing evolution in cybernetic terms strengthens the set of cybernetic frameworks, giving the whole a sort of completeness.

This framework is useful for discussing the evolution of services and businesses—and the process of innovation. Already, a few leading design thinkers such as John Rheinfrank and Austin Henderson [19] have begun to discuss designing for emergent behavior and designing for evolution. Still new is the idea that the product of design practice is not fixed, but rather something that will evolve as others use it and themselves design with it. We believe this idea will grow in importance and become a major trend in design. If that happens, frameworks for modeling evolution will be useful.

These seven frameworks are useful in a variety of ways, for example: analyzing existing systems; comparing systems which may at first appear very different; discerning and organizing patterns of interaction; and evaluating the way a proposed design fits its context. These frameworks apply at several scales: simple interaction between human and device; interaction among component sub-systems; interactions among people through devices or services; interactions between people and businesses (C2B) and between businesses (B2B); and interactions within markets.

These frameworks also provide a way to look forward in design and suggest the kinds of research from which design practice—and development of software applications and services—may benefit. Of particular interest for design research are systems that model user’s goals, systems that help users model and clarify their own goals, systems that facilitate participation, self-organizing systems, and systems that evolve.

Cybernetic Frameworks for Modeling How We Design

The previous section described the application of cybernetic frameworks to design practice. It emphasized using the frameworks to model existing situations and imagine preferred situations. It focused on using cybernetic frameworks to model what we design. This section focuses on using cybernetic frameworks to model how we design—to model the design process itself. Another way to approach this subject is to think of designing the design process; that is, adapting the design process to its context. Here again, cybernetic frameworks may be useful.

Cybernetics offers conceptual frameworks for understanding and improving design processes and thus their outcomes.

The seven frameworks we described in the previous section can also model the design process:

1. First-Order Cybernetic System

Design is a cybernetic process. It relies on a simple feedback loop: think, make, test (in Walter Shewhart’s words, “plan, do, check.”)[20] It requires iteration through the loop. It seeks to improve things, to converge on a goal, by creating prototypes of increasing fidelity.

In Herbert Simon’s words, “Design is devising courses of action aimed at turning existing situations into preferred ones.”[21] Alan Cooper has called this process “goal directed.”[22] When we design, we try to achieve goals, often by imaging the goals of people we hope will use our products.

A model of design as a feedback process applies equally well to design in the traditional hand-craft mode or in the new service-craft mode. In both cases, the designer relies on feedback. What differs is the nature of their prototypes and the degree to which they articulate their goals separately from their product.

2. Requisite Variety

Design teams, product development teams, or whole companies (as well as individual designers) have variety; that is, they have a set of skills and experience which they may bring to a project. We can evaluate the fitness of a team or even individuals for a task in terms of the variety they bring. Does the team have the variety required to be successful in this task? Of course, to answer the question, we must understand the goals of the task and possible disturbances.

3. Second-Order Cybernetic System

Douglas Engelbart has described a process he calls “bootstrapping”, which involves three nested cybernetic systems. Level 1 is “a basic process.” Level 2 is ‘a process for improving “basic processes.”’ And level 3 is a process for improving ‘the process of improving “basic processes.”’

Here’s an example. John’s team is responsible for producing a new widget—a level 1 process. John begins holding weekly meetings (Friday afternoon beer busts) at which his team discusses problems—a level 2 process. Implementing ideas from their meetings lowers the widget defect rate. Management asks John to share his improvement and decides to mandate Friday afternoon beer busts for the entire company—a level 3 process.

John Rheinfrank pointed out the need for three-level systems in creating sustained quality management and building true learning organizations.[23]

4. Conversation

Design is conversation, between designer and client, between designer and user, between the designer and himself or herself. Design involves the consensual coordination of goals and methods.

Framing design in terms of conversation has broad implications, challenging the designer’s role as expert and casting him instead as facilitator. [More about this idea later.]

5. Bio-cost

Robert Pirsig has written eloquently about “gumption traps,” ways in which people loose the energy necessary to sustain quality work.[24] A gumption trap is a source of bio-cost in the design process.

6. Autopoiesis

One of the great challenges facing the design profession is how it can create sustained learning about design practice. In recent years, several universities have begun to grant Ph.D.s in design, but design research is still young and relatively unformed. The feedback systems necessary to sustain it are not yet in place. Designers need a self-sustaining, learning system whose components make and re-make itself: the curricula must contain ‘the practice’ while also capturing processes that learn while also sustaining those that already exist. Inherent in the seven cybernetic frameworks are mechanisms to make such activities explicit for the design community and for the institutions (schools, consulting studios, and corporate design offices) that support it.

7. Evolution

Designers also lack tools for evolving their tools and processes. Progress is slow; innovation is infrequent. Globalization may put pressure on the current environment and force more rapid change.

A Constructivist View—Design as Politics

The design process is more than a feedback loop, more than a bootstrapping process, more even than a “simple” conversation. An approach to design that considers second-order cybernetics must root design firmly in politics. It views design as co-construction, as agreeing not just on solutions but also on problems. It recognizes what Horst Rittel called “the symmetry of ignorance” between designer and client.[25] It views design as facilitation—as managing a conversations about issues.

For Rittel the main thing in design was managing the myriad issues involved in defining what a team is designing. His view led to early work in creating issues-based information systems (IBIS), which provided a foundation for more recent research in design rationale, which is still an on-going area of inquiry.[26]

Heinz von Foerster pointed out the limitations of defining systems in objective terms. Von Foerster asked, “What is the role of the observer?”[27]

Horst Rittel pointed out the limitations of defining design in objective terms. Designers often describe their work as problem solving, but Rittel asked, “Whose problem is it?” He showed that the framing of the problem is a key part of the process. He posited agreement on definition of the problem as a political question. And he noted that some (“wicked-hard”) problems defy agreement, for example, in modern times, bringing peace to Palestine or creating universal health care.[28]

Rittel also noted that if design is political, then argumentation is a key design skill. Here is a design theorist with a background in physics and operations research, influenced by cybernetics, concluding design is not objective but instead political and thus rooted in rhetoric. He comes to the same conclusion as Richard Buchanan, who has a background in the humanities. This link is extraordinary.

A Call for Curriculum Change

Our culture is undergoing a change as profound as the industrial revolution, which gave birth to the design profession. The ongoing shift to a knowledge-service economy and the continuing growth of information-communication technology will profoundly change the practice of design.

Design educators need to respond to these changes.

Cybernetics can help designers make sense of the complex new world they face. Cybernetics can inform design on at least three levels: 1) modeling interaction—human-human, human-machine, or machine-machine, 2) modeling the larger service systems in which much interaction takes place, and 3) modeling the design process itself.

As the founders of cybernetics and the design methods movement pass away,[29] the risk increases that much of what they learned could be lost to future generations. That would be a tragedy.

We urge design educators to radically alter the current approach to design education and to adopt a systems view incorporating in their teaching the language of cybernetics—and rhetoric.

1. First-Order Cybernetic System

2. Requisite Variety
A regulator achieves a goal (preserves an essential variable) against a set of disturbances. To succeed, variety in the regulator must be equal to or greater than the variety of disturbances threatening the system. If this is so, we say the system has requisite variety.

3. Second-Order Cybernetic System
An automatic feedback system (first-order) is controlled by another automatic feedback system (second-order). The first system is ‘nested’ inside the second.

4. Collaboration and Learning (Participatory System)

5. Bio-cost
Bio-cost describes the effort (in energy, attention, time, stress) expended by an organism to reach a goal. Value = Bio-gain – Bio-cost

6. Autopoiesis
Maturana writes, “ . . . living beings are characterized in that, literally, they are continually self-producing.” They contain a set of dynamic transformations that maintain themselves and their boundary. The two arise together, not in sequence.

7. Evolution (in Terms of Requisite Variety)

Download PDF

We collaborated with Michael Geoghegan, Paul Pangaro, and Peter Esmonde to produce a booklet for Sun about leadership and language.

Below are a few interior spreads. You can also download a PDF of the entire booklet.