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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Created in collaboration with Jack Chung, Shelley Evenson, and Paul Pangaro.

The creative process is not just iterative; it’s also recursive. It plays out “in the large” and “in the small”—in defining the broadest goals and concepts and refining the smallest details. It branches like a tree, and each choice has ramifications, which may not be known in advance. Recursion also suggests a procedure that “calls” or includes itself. Many engineers define the design process as a recursive function:
discover > define > design > develop > deploy

The creative process involves many conversations—about goals and actions to achieve them—conversations with co-creators and colleagues, conversations with oneself. The participants and their language, experience, and values affect the conversations.

See also our How do you design? collection of models.

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Models are ideas about the world—how it might be organized and how it might work. Models describe relationships: parts that make up wholes; structures that bind them; and how parts behave in relation to one another.

Models are ideas about the world— how it might be organized and how it might work.

For example, the sun rises in the east, moves across the sky, and sets in the west. Or the earth orbits the sun. Models support communication and learning. Models help bridge the gap between observing and making, between research communities and design communities [1]. Models are especially important in interaction and service design.

A representation of the Ptolemaic model of the “world system” — a geo-centric view.

A representation of the Copernican model of the “solar system” — a helio-centric view.

Making Sense and Guiding Action

Models help us make sense of things. Stafford Beer wrote, “Now in trying to account for the behavior of a complicated system, the scientist has first to represent it in the formal terms he knows how to manipulate…. The formal representation of the system that he builds is called a model. This model is something different than the diagrams that are drawn.” [2] Alan Kay noted, “Models are our voodoo dolls. We do most of our thinking in models.” [3] Models begin with things or events that we observe. We want to describe or explain what we see. Pieces fit together; patterns emerge; we posit causes and effects. Under this frame, evidence leads to models.

Observations can be a source of new models.

Models are conjectures—hypotheses. They are not formed by deduction or induction but by abduction—inferring the most likely story to explain the evidence. Abduction is the creative heart of science, engineering, and design. Its mechanism remains unknown—though preparation and persistence may aid the process. Models are not the special province of science. We use them all the time. Models help us recognize new situations as similar to others we have encountered. Without a model, recognizing the similarities might be difficult. Models also help us predict likely futures: what actions other actors may take, consequences of those actions, and what actions best respond to threats or most efficiently help us pursue our goals. Armed with our models’ predictions, we act accordingly.

Chris Argyris wrote, “Although people do not [always] behave congruently with their espoused theories [what they say], they do behave congruently with their theories-in-use [their mental models].” [4] Under this frame, models lead to action.

Our models guide our actions.

Learning As Forming and Reforming Models

If we are “present and engaged” (that is, paying attention) and yet we have an accident or make a mistake, the cause may be some defect in our models. That is, our models suggested one outcome, but we have found another. The difference between expectation and outcome creates an opportunity for learning.

Learning involves forming models and reforming them based on feedback. We observe some behavior in our environment; it suggests models, which we use to predict future behavior and guide our actions. Additional observations provide feedback, which helps us revise and refine our models. We learn.

When outcomes do not match our predictions, we have two choices:

1) Reject the data

• Were measurements inaccurate?
• Was the test procedure flawed?
• Was the reporter biased?

2) Accept the data

• Is it relevant to our model?
• Is it a special case? Meaning our model is less useful at the extremes or our model needs refinement or extension,
• Was previous data inaccurate or insufficient? Meaning we need to revise our model.

Under this frame, we modify our models based on the results of our predictions—we subject them to feedback.

We evolve our models as we test their predictions.

Learning involves:

Creating new models

Revising existing models

• Extending a model so that it corresponds to more observations (broadening)
  

For example, Ptolemy introduced cycles within cycles to account for the retrograde motion of Mars.

Refining a model so that it more closely corresponds to observation (deepening)

For example, Kepler found that Brahe’s observations showed that the planets’ follow an elliptical (not circular) path around the sun.

Generalizing models—reframing a model of a specific event as a model of a more general set of phenomena

For example, the shift from the Ptolemaic to Copernican model is an example of a general case that recurs throughout the history of science as one important model gives way to another. Kuhn named this a “paradigm shift.”

Identifying model primitives—finding patterns which recur across many models, often based on fundamental rules of geometry or topology

For example, the earth orbiting the sun is a special case of a more general model of satellites orbiting primary bodies, which describes other cases such as the moon orbiting the earth or suns orbiting the center of a galaxy. A system in which one element revolves around another is a fundamental pattern—a “primitive” or building block of models.

We use models and learn through them, not only as individuals but also as groups. Learning takes place on at least four scales:

1. Individual
   
2. Work-group (or play team), which is composed of individuals
   
3. Organization, which is composed of work-groups
   
4. Culture, which is composed of organizations

Learning—forming and reforming models—begins with individuals. It can expand to work-groups, organizations, and even entire cultures. That is, a model may be highly idiosyncratic, rarely shared with others. Or it may be highly conventional, widely shared by others.

At each scale (individual through culture), three levels of process are at work:

1. Primary—the activity at hand
   understood through models
   

2. Second-order—direct learning (and designing)
   improving primary processes that is, refining models of primary processes
   

3. Third-order—meta-learning (learning about learning)
   improving second-order processes that is, improving models of learning and models of models
   

Passing models from one generation to the next is a responsibility of teachers and managers. Models are what students take away from school and what young people take away from early jobs. Models are what you remember after leaving.

Peter Senge noted that developing and sharing models is fundamental to “learning organizations.” He suggests that a leader’s role is to improve both his or her own mental models and those of the organization—to test and add to the mental models of others [5].

Design is a young profession; design practices that operate as learning organizations are rare. Typically, models remain implicit. Students learn by watching teachers, managers, and colleagues. Universities, professional organizations, and design practitioners have much opportunity to improve the way designers learn—to develop systems for forming and reforming models of design processes.

Limits and Costs

Earlier, I described observation shaping models; but models also shape what we see—what we let ourselves notice. Our models tell us what is important, what counts, what to look for. Peter Senge wrote, “Models [are] so powerful in affecting what we do…because they affect what we see. Two people with different mental models can observe the same event and describe it differently, because they’ve looked at different details.” [5] Under this frame, models also lead to evidence.

Our models affect what we see.

In a similar way, models already shared within an organization may limit its ability to see new evidence, understand changing situations, or act in its own interest. Old models often resist new ones and inhibit learning. That’s why organizations need to expose the fundamental models that guide them and periodically challenge those models.

Creating or revising a model is meta-activity, taking us outside the primary activity in which we were engaged. It requires attention, energy, and time.

But a new or improved model may pay dividends; it may reduce accidents or other unexpected outcomes, or it may make an individual or group more competitive. In this way, forming and reforming models may “pay for itself.”

Sharing models may reduce group costs and thus create value. But the cost of adopting new models can also inhibit their spread. Adoption requires value that clearly outweighs cost.

Agreement and Understanding

Models are closely tied to stories. We explain models by telling stories, and when we tell stories, listeners form models—mental pictures of the actors, how they are related, and how they behave.

Models are explained by stories; stories build models.

Shared models support discussions. They are examples of what Susan Star called “boundary objects,” artifacts that enable discourse at the boundaries between communities of practice [6]. By sharing our models, we may be able to confirm where we agree—and discover where we disagree.

Models provide a basis for shared understanding, agreement, and group action. They also build trust and enable collaboration.

Agreement begins with individual understanding—forming our own models. Through conversation, we begin to understand each other’s models—to form models of the other’s models. We compare our model with the other’s model. Are our models congruent? Do we agree? And then, do we agree that we agree? If so, we have reached “an agreement over and understanding.” We have a basis for trust, collaboration, and action [7].

Shared models are the basis for understanding, agreement, and action.

Models in Design

As designers increasingly focus on systems and communities of systems, we need to improve our modeling skills.

Without modeling, system design is not possible. Often service systems and computer-based applications are partly hidden or invisible, or they stretch across time and space and cannot be seen all at once or from a single vantage point. In such cases, models must stand in for systems during analysis, design, and even operation.

Using models, designers can unify otherwise separate artifacts and actions. Interaction models unify interface widgets. Service models unify customer touch points. Brand models unify messages. Platform models unify individual products.

Drawing has long been an essential skill for designers and the heart of design education. Bill Buxton, Dick Powell, and others assert that “drawing is the essence of design.” [8] Are they correct? Perhaps—if designers focus on objects. But when attention turns to systems, modeling becomes the essence of design. Design education and practice must adapt to this changing reality.

Von Bertalanffy wrote, “The advantages and dangers of models are well known. The advantage is in the fact that this is the way to create a theory—i.e., the model permits deductions from premises, explanation and prediction, with often unexpected results. The danger is oversimplification: to make it conceptually controllable, we have to reduce reality to a conceptual skeleton—the question remaining whether, in doing so, we have not cut out vital parts of the anatomy. The danger of oversimplification is greater, the more multifarious and complex the phenomenon is.” [9]

Keeping in mind the multifarious and complex nature of design—and the attendant dangers—we must bring more rigorous modeling to our work.

Observation may suggest models, but models also frame and filter observations.

Thinking about how to explain our observations may lead us to think of alternatives or related ideas.

The process of representing an idea may change the idea itself.

Sharing a model may also change it.

All these feedback loops, and more, act simultaneously-shaping and reshaping our models.

Questions to Ask When Making Models

For any set of observations (or system), we may imagine many models. And for any (mental) model, we may imagine many representations.

What processes lead to good models?
What processes lead to good representations?

How do we recognize a good model?
How do we recognize a good representation?

All models have a purpose and serve constituents. Models have a point of view; and they advocate it. Models are always political.

Acknowledge the subjectivity of modeling: Considering your constituents. Speak with them to learn their needs and their views of the system (situation).

Directly observe the system; record your observations. If you are modeling a system that does not exist, observe similar systems.

Constituents’ goals and system observations form the criteria against which we judge both model and representation.

Why are we making a model?
What decisions or actions will it support?
Who are the constituents for the model?
What are their goals?
How can the constituents be involved in the modeling process?
How will decisions about the model and representation be made?

Models are not objective. They leave things out. They draw boundaries between what is modeled and what is not; between the system and its environment; and between the elements of the system.

Framing a system—defining it—is editing. What we think of as natural boundaries, inside and outside, are somewhat arbitrary. The people making the model choose what boundaries to draw and where to draw them. That means, they have to agree on the choices.

What should the model attempt to predict?
What is in the system, and what is not?
Who or what are the actors?
What resources do they use?
How do they affect one another?
What level of abstraction or degree of granularity is appropriate?

Enlist others to work with you. Begin with discussion. Use a white-board to record comments. Record the white board in photographs.

Write a working title for the model.

Create quick, low-fidelity sketches. Identify the system’s elements and write the name of each on a Post-It note. At the beginning, don’t worry about having too many elements or the wrong elements. Editing comes later.

Arrange the Post-It notes to describe the system’s structure. Group similar elements. Place elements that often interact near each other. Avoid repeating elements. Label connections.

Review your proto-model to see which model primitives or patterns it includes. Are these appropriate or would others be better? Does the proto-model build on or suggest already established or generalized models?

Revise your proto-model.

Present the proto-model to your constituents; tell them the model’s story. Observe their reactions; ask for feedback; reflect on what was easy or difficult to explain. Record these results; create an “issues” list for debugging the model.

Revise. Increase fidelity and detail as appropriate. (Determining what’s appropriate becomes easier with practice—as your model of modeling grows.)

The quality of models and representations increases with iteration. So: Iterate.

When Judging (Mental) Models,
Consider 4 Primary Criteria:

1) Fit
How does the model fit the evidence?
Is our evidence relevant?
Is it reliable?
Is it sufficiently granular? (depth)
Do we have enough evidence to draw meaningful conclusions? (breadth)
Are the elements of the model necessary and sufficient?
Are the elements of the model “MECE”—mutually exclusive and collectively exhaustive?

2) Least Means
Is there a simpler way to explain the evidence?
Given two models explaining the same evidence, Ockham told us to prefer the simpler.

3) Consistency
Is the model internally consistent?
Is it free from contradiction?

4) Predictive Value
What predictions does the model make?
Are our model’s predictions consistent with later observations?
Do the model’s predictions help us make decisions that might have been more difficult without them?

When Judging Visual Representations,
Consider 5 Primary Criteria:

1) Fit
Is the representation congruent with the model?
Do representation and model have similar structures?
Are all the elements in the model explicit in the representation?

2) Least Means
Could the model be represented in a simpler way?
What can be removed without changing the meaning? (Remove decoration.)
Could conventional symbols or other standard patterns make reading easier?

3) Consistency
Are the means of representation consistent?
(Similar forms should represent similar functions or similar content.
Likewise, similar functions or similar content should be represented by similar forms.)
Are all elements and their connections labeled?

4) Contrast
What about the model should appear to be most important?
Does the most important thing appear most important?
(Not everything is equally important. Important elements of the model should stand out in the representation. One way to achieve contrast is through scale, making more important items larger than less important items.)

5) Hierarchy
How do the elements of the system appear to fit together?
Is the structure of the system clearly visible?
Do we know where to look first?
Can we find a clear path through the model?

The final test of the model (and representation) is with the audience.

Does the audience understand it?
Do they agree with it?
Do they agree that they agree?
Will they act on it?

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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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In the early twentieth century, our understanding of physics changed rapidly; now, our understanding of biology is undergoing a similar rapid change.

Freeman Dyson wrote, “It is likely that biotechnology will dominate our lives and our economic activities during the second half of the twenty-first century, just as computer technology dominated our lives and our economy during the second half of the twentieth [1].”

Recent breakthroughs in biology are largely about information—understanding how organisms encode it, store, reproduce, transmit, and express it—mapping genomes, editing DNA sequences, mapping cell-signaling pathways.

Changes in our understanding of physics, accompanied by rapid industrialization, led to profound cultural shifts—changes in our view of the world and our place in it. In this context, modernism arose. Similarly, recent changes in our understanding of biology are poised to create new industries and may bring profound cultural shifts—new changes in our view of the world and our place in it.

Already we can see the process beginning. Where once we described computers as mechanical minds, increasingly we describe computer networks with more biological terms—bugs, viruses, attacks, communities, social capital, trust, identity.

How is design changing?

Over the last 30 years, the growing presence of electronic information technology has changed the context and practice of design.

Changes in production tools designers use (software tools, computers, networks, digital displays and printers) have altered the pace of production and the nature of specifications. But production tools have not significantly changed the way designers think about practice. In a sense, graphic designer Paul Rand was correct when he said, “The computer is just another tool, like the pencil [2],” suggesting the computer would not change the fundamental nature of design.

But computer-as-production-tool is only half the story; the other half is computer-plus-network-as-media.

Changes in the media designers use (the internet and related services) have altered what designers make and how their work is distributed and consumed. New media are changing significantly the way designers think about practice. New types of jobs have emerged. For many of us, both what we design and how we design are substantially different than they were a generation ago.

What do electronic media and designing have to do with biology?

Emerging design practice is largely information based—awash in the technologies of information processing and networking. Increasingly design shares with biology a focus on information flow, on networks of actors operating at many levels and exchanging the information needed to balance communities of systems.

Modern design practice arose alongside the industrial revolution. Design has long been tied to manufacturing—to reproduction of objects in editions or “runs.” The cost of planning and preparation (the cost of design) was small compared to the cost of tooling, materials, manufacturing, and distribution. A mistake in design multiplied thousands of times in manufacturing is difficult and expensive to fix.

The realities of manufacturing led to certain practices and in turn to a mindset or even a way of thinking. In the “modern” era, design practice adopted something of the point-of-view or even the philosophy of manufacturing—a mechanical-object ethos.

Now as software and services have become a large part of the economy, manufacturing no longer dominates. The realities of producing software and services are very different than those of manufacturing products.

The cost of software (and “content”) is almost entirely in planning, preparation, and coding (the cost of design). The cost of tooling, materials, manufacturing, and distribution is small in comparison. Delaying a piece of software to “perfect” it invites disaster. Customers have come to expect updates and accept their role as an extension of developers’ QA teams, finding “bugs” that can be fixed in the next “patch.”

Services also have a different nature than hardware products. “Services are activities or events that create an experience through an interaction—a performance co-created at point-of-delivery [3].” Services are largely intangible, as much about process as final product. They are about a series of experiences across a range of related touch-points.

The realties of software and service development lead to certain practices and to a mindset or even a way of thinking. Emerging design practice is adopting something of the point-of-view or even the philosophy of software and service development—an organic-systems ethos.

Models of change

Several critics have commented on facets of the change from technical-object ethos to organic-systems ethos. This article brings together a series of models outlining the change and contrasting each ethos.

The models are presented in the form of an “era analysis.” Two or more eras (e.g., existing-emerging eras or specified time periods) are presented as columns in a matrix with rows representing qualities or dimensions, which may change across each era, characterizing aspects of the era.

The eras are framed as stark dichotomies to characterize the nature of changes. But experience is typically more fluid, lying along a continuum somewhere between extremes.

John Rheinfrank [4] provides a broad summary of the change, which may serve as an introduction and an overview. He begins by describing a change in world-view, similar to the change in ethos described above.

We may expand Rheinfrank’s model, to describe how things come to be and the role of designers and their clients in the process.

A Concern for Users

Austin Henderson and Jed Harris [6] have noted that many computer systems are constrained by a mechanistic world-view. They cite automation projects avoiding errors by drastically reducing options available to users (narrowing language or variety) but in the process crippling communication and organizational flexibility. Henderson and Harris contrast coherent systems to responsive systems. Coherent systems require consistency and predictability; responsive systems support messiness and improvisation. “In a given system, as responsiveness increases, coherence tends to decrease and vice versa—a classic tradeoff. Scaling makes this tradeoff sharper. As systems get larger, they have to work harder to maintain their coherence, and this increasingly makes them unresponsive. Conversely, large systems that allow great local responsiveness (such as the World Wide Web) have difficulty maintaining coherence.”

Henderson [7] pointed out that consistency is an ideology, that other choices are possible, “the core ideology of computer system design is totally permeated with the assumption that computers are rule-following machines, and more generally, that all human activities can and should be described in terms of a consistent set of rules.”

He argues that “feedback loops . . . actually make organizations work, and the constant negotiation that these loops entail . . . computing systems tend to break those loops . . . so people have to bear the brunt of patching them up, and usually have to fight the computer system to do it.” Henderson and Harris propose a new approach, which they describe as “Pliant Computing.”

At the heart of Henderson’s call for “Pliant Computing” is a deep concern for people who use computers. Henderson sees the relationship between designer and audience changing. As Rheinfrank pointed out, the designer is moving from detached expert to collaborator. And the relationship between designer and constituent is moving from expert-patient to what Horst Rittel called, “a symmetry of ignorance (or expertise) [8]” where the views of all constituents are equally valid in defining project goals.

Liz Sanders [9] presents a similar argument with slightly different eras, explicitly introducing the idea of moving beyond human-centered or user-centered design.

Co-development is also a fundamental tenet of open-source software. Eric Raymond [10] wrote, “Treating your users as co-developers is your least-hassle route to rapid code improvement and debugging.” He added, “Even at a higher level of design, it can be very valuable to have lots of co-developers random walking through the design space near your product.” Raymond famously contrasted “cathedrals carefully crafted by individual wizards or small bands of mages working in splendid isolation” to “a great babbling bazaar of differing agendas and approaches.” He suggested traditional “a priori” approaches will be bested by “self-correcting systems of selfish agents.”

The Rise of Service Design

The shift from industrial age to information age mirrors, in part, a shift from manufacturing economy to service economy. In the new economy, as former WiReD editor Kevin Kelley put it, “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 [11].”

Early on, Shelley Evenson saw the importance of service design, and she has led U.S. designers in developing the field. She has provided a framework contrasting traditional business-planning methods with service-design methods. Her framework parallels the larger change in ethos we’ve been discussing.

Typically, responsibility for designing individual artifacts rests pretty much with one individual, but systems design almost by definition requires teams of people (often including many specialties of design). The need for teams of designers can be seen easily in the design of software systems and service systems, where many artifacts, touch-points, and sub-systems must be coordinated in a community of cooperating systems. For example, “web-based services” or “integrated systems of hardware, software, and networked applications” require development and management teams with many specialties.

The work of an individual designer on an individual artifact has often been characterized as “hand-craft.” In contrast, Paul Pangaro and I have proposed “service-craft” to describe “the design, management, and ongoing development of service systems.” Design practice in a hand-craft context differs markedly from design practice in a service-craft context. Having assembled a team, care must be taken to negotiate goals, set expectations, define processes, and communicate project status and changes in direction. Care must also be taken to create opportunities for new language to emerge and to create capacity for co-evolution between service and participants.

We also noted, “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 hand-craft). 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 [13].”

Characterizing Services

Robert Lusch [14] wrote about changes in marketing, describing a service-dominant logic in which “value is defined by and co-created with the consumer rather than embedded in output.” The “make-and-sell” strategy of linear value chains gives way to the “sense-and-respond” strategy of self-reinforcing “value cycles.” Lusch described traditional goods-centered dominant logic as focused on “operand resources,” tangible assets with inherent value. He contrasted that logic with emerging service-centered dominant logic focused on “operant resources,” intangible assets, which create value in their use, such as skills, technologies, and knowledge. He also pointed out that service logic is not only compatible with the idea of a learning organization, but it may actually require one.

Nicholas Negroponte has famously contrasted “atoms and bits.” The physical, tangible, here-and-now aspect of products-as-objects makes them relatively easier to evaluate than services. This characteristic is one of the things that make products easier to manage than services. A CEO can pick up a product appearance model and immediately evaluate it, compare it to another, and decide how to proceed. Even a complex product like a car can be evaluated relatively quickly. But services are much harder to evaluate. Services cannot be apprehended all at once; they must be experienced over time. And sometimes service varies from one experience to the next.

Sustainable Design

The mechanical-object–organic-system dichotomy also appears vividly in discussions about ecology. Much of our economy still depends on “consumers” buying products, which we eventually throw “away.” William McDonough and Michael Braungart have pointed out that there is no “away,” that in nature, “waste is food,” They urged us to think in terms of “cradle-to-cradle” cycles of materials use, and they suggested manufacturers lease products and reclaim them for reuse [15]. Theirs is another important perspective on the idea of product-as-service.

Architects, too, have begun to design for disassembly and reconfiguration. Herman-Miller recently published a manifesto on programmable environments, talking about the need for “pliancy” in the built environment and echoing the language The Cathedral and the Bazaar while discussing building design [16].

Sustainable design is emerging as an issue of intense concern for designers, manufacturers, and the public. The same sort of systems thinking required for software and service design is also required for sustainable design. This provides further impetus for changing our approach to design education.

Stuart Walker, Professor of Environmental Design at University of Calgary, has written, “Only by fundamentally changing our approaches to deal with the new circumstances can we hope to develop new models for design and production that are more compatible with sustainable ways of living. Wrestling with existing models and trying to modify them is not an effective strategy.”

Early Parallels

The current shift from a mechanical-object ethos to an organic-systems ethos has been anticipated in earlier shifts.

In the mid-1960s, architects and designers began to focus on “rational” design methods, borrowing from the successes of large military-engineering projects during the war and the years following it. While these methods were effective for military projects with clear objectives, they often proved unsuccessful in the face of social problems with complex and competing objectives. For example, methods suited to building missiles were applied to large-scale construction in urban redevelopment projects, but those methods proved unsuited to addressing the underlying social problems that redevelopment projects sought to cure.

Horst Rittel [8] proposed a second-generation of design-methods, effectively reframing the movement, casting design as conversation about “wicked problems.” His proposal came too late or too early for the design world, which had already moved on to “post-modernism” but had not yet encountered the internet.

Rittel’s work did attract attention in computer science (he was a pioneer in using computers in design planning), where “design rationale” (the process of tracking issues and arguments related to a project) continues as a field of research. More recently, Rittel’s work has attracted attention in business school publications addressing innovation and design management [18][19].

Paul Pangaro and I have also noted that Rittel’s framing of first- and second-generation design methods parallels Heinz von Foerster’s framing of first- and second-order cybernetics. Von Foerster described a shift of focus in cybernetics from mechanism to language and from systems observed (from the outside) to systems-that-observe (observing-systems).

In 1958, von Foerster formed the Biological Computer Laboratory at the University of Illinois Urbana-Champaign. He brought in Ross Ashby as a professor and later Gordon Pask and Humberto Maturana as visiting research professors. The lab focused on problems of self-organizing systems and provided an alternative to the more mechanistic approach of AI followed at MIT by Marvin Minsky and others [22]. In a way, von Foerster anticipated the shift from mechanical-object ethos to organic-systems ethos in computing, design, and perhaps the larger culture.

What do these changes mean for design education?

As design moves into the Age of Biology and shifts from a mechanical-object ethos to an organic-systems ethos, we should reflect on how best to prepare for coming changes in practice. At a recent conference on design education, Meredith Davis described, “the distance between where we are going in the practice of graphic design and longstanding assumptions about design education [23].” (The full text of her talk is included on page 28 of the September issue of Interactions.)

Davis (building on Poggenpahl and Habermas) distinguished between two models of practice, “know how” and “know that,” “design as a craft and design as a discipline.” This distinction parallels the distinction between hand-craft and service-craft Pangaro and I proposed above. Davis asserted “college design curricula, and the pedagogies through which we deliver them, are based almost exclusively on the first model of practice, on know-how, and don’t acknowledge issues that drive emerging practices.”

Davis’ argument and framing are closely related to changes described in this article. Changes Davis advocates are consistent with the spirit of the new ethos and aimed at helping designers grasp the nature of organic-systems work and preparing them for practice in the Age of Biology.

Of course, not all designers welcome the coming change. Form-giving remains a large part of design practice and design education. Will some designers be able to continue to practice primarily as form-givers? That seems likely. But already a schism is developing both in design practice and design education, as individuals and institutions choose to focus on either form-giving or on planning. It remains to be seen if one person, one firm, or one school can bridge the divide and excel at both.

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When businesspeople discuss growth, they often refer to S-curves or “hockey sticks”—diagrams depicting quantity changing over time, typically units sold per month or quarter. Growth begins slowly and gradually increases to an inflection point; from there it accelerates. Eventually, growth begins to slow and tapers off, for instance, as a market saturates or a system stabilizes at a new level.

S-curves describe change over time, often units sold or projected to be sold.

S-curves may look backward (tracking progress, adoption, or consumption) or forward (projecting growth). In the early days of Google, cofounder Sergey Brin began tracking search queries per day and maintaining a graph, which grew to fill the wall of a stairwell in the original Google building.

Nicholas Felton has a wonderful graph showing the rates of adoption of various home appliances and electronic devices [1]. Each curve is a successive wave of technology: flatter curves for early technologies and steeper curves for more recent ones, suggesting adoption rates have increased.

For startup companies, the dream is to catch a technology or market just as the “hockey stick” turns from blade to handle and ride the curve all the way up—or at least to a successful IPO.

S-curves describe change over time, often units sold or projected to be sold.

What do S-curves have to do with design?

S-curves can also represent learning. Through study and practice, learning increases over time, describing a learning curve. Learning curves may represent a designer acquiring knowledge and skills, starting slowly, picking up speed, and leveling off as the designer reaches proficiency. Likewise, learning curves may represent an organization growing or maturing.

Design comprises many domains (e.g., design of environments, objects, and messages); in turn, each domain comprises many skills (e.g., typography, grid-system development, and data visualization). The skills required to practice effectively within even one design domain can change, particularly as means of production or communication change (e.g., as personal computers became pervasive in the workplace) or as the context of practice changes (e.g., as the Internet became a channel for providing services). From time to time, new design domains also emerge (e.g., interaction design and service design).

Each curve represents a wave of knowledge and skill acquisition in the competition to develop new products.

In a time of change, individual designers can ensure that they remain competitive by regularly reviewing the skills needed to practice in their chosen domains and by regularly assessing their proficiency in required skills. For example, have CSS and Flash programming become required skills for people beginning to practice interaction design? What about experience with the Processing programming language or with the Arduino platform?

Likewise, organizations can ensure that they remain competitive by regularly reviewing the design domains that affect their ability to develop new products. For example, are new skills needed to stay competitive in existing design domains? Have new design domains begun to emerge, demanding new skills? Is there a risk of falling behind? Are there opportunities to step ahead of competitors—to differentiate the organization and its products?

Over the past 30 years, design practice has changed enormously, and so has the process of developing new products. In many cases the very nature of new products has changed.

Four successive waves of knowledge and skill acquisition are changing the nature of competition in product development, particularly in the consumer-electronics segment. Much of the ability to differentiate products has moved from engineering and manufacturing to design and its various disciplines. Two waves of change are well established or mature (manufacturing quality and product-design quality); a third is unevenly advanced (interaction-design quality); and a fourth is just emerging (service-design quality).

Each curve represents a wave of knowledge and skill acquisition in the competition to develop new products.

Curve 1: Manufacturing Quality

The foundation skill for product development is managing manufacturing quality. At one time “Made in USA” suggested that a product was better than something made in other countries. Japan worked hard to improve quality and went from laggard to leader; eventually, U.S. companies responded to the Japanese competition. In the 1980s total quality management (TQM) was the mantra. In the 1990s Six Sigma methods became pervasive. Today statistical process control can reduce defects to almost zero, ensuring the quality of manufacturing output. These skills are now understood throughout the world. Leading Korean and Taiwanese firms compete at world levels with European, Japanese, and U.S. firms. As manufacturing booms in mainland China, the Chinese too are learning quality management. India, Brazil, and Eastern Europe are also improving manufacturing quality.

What this means is that competing on the basis of quality (of having a better-made product) is increasingly difficult. In a sense quality has become a commodity. In practice much of manufacturing is outsourced to plants in Taipei and Shanghai. While quality management remains crucial, the field of competition has moved on—to product design and beyond—to ensuring that what is manufactured is right for its audience (another view of “quality management”).

Curve 2: Product-Design Quality

Close behind managing manufacturing quality is product design, another foundation skill. Apple and Sony have long traditions of great product design. Samsung, long a fast follower of Sony, embarked on a 10-year effort to develop world-class product-design capabilities. In the past few years, Chinese companies have begun to show signs of “getting” design. China is awash in foreign design consultants. And it has more than 600 design schools of its own. The AIGA (a leading U.S. design-professional organization) has even opened an office in Beijing. And some Chinese outsource manufacturing firms are beginning to offer design services to attract business. Like manufacturing quality, product-design quality has become a necessary part of competing at a world-class level, but great product design is not sufficient. Competition has again moved on—to interaction design and beyond.

Curve 3: Interaction-Design Quality

William Gibson said, “The future is already here. It’s just not evenly distributed yet [2].” The same is true for interaction design. Some firms, such as Apple and Starbucks, “get” interaction design in a deep way. Other firms are just waking up to it. The discipline is less than 25 years old and far from mature.

Interaction design has emerged as a new field of competition, a way for organizations to differentiate their products. Today this competition has become intense in the mobile phone market. Motorola’s troubles and Nokia’s long-term success are due, at least in part, to the quality of their products’ interaction design. Motorola’s Razr was a hit largely because of its slim metal profile—because of its product design—but the Razr interface was often described as difficult (or worse). Last summer Apple’s iPhone set a new standard for mobile devices, largely on the strength of its interaction design. Of course the iPhone’s product design is good, as is its packaging design and advertising design. And the manufacturing quality appears to be good too. But its interaction design sets the iPhone apart.

Google has entered Android, a new mobile platform, into the competition. By the time this article is printed, devices running Android will be available, and its effect on interaction design—and on competition for developing new mobile products—should be discernable.

Curve 4: Service-Design Quality

While competition increases in the field of interaction design, a new domain is already emerging: service design—the integration of hardware, software, and networks to deliver services.

Apple is becoming a leader in service design, shifting the field of competition and creating a new system of differentiation before rivals have even noticed. Other consumer-electronics firms envy Apple’s success with iPod, almost to the point of obsession. Differentiating an MP3 player on the basis of manufacturing quality is almost impossible, but many companies have worked hard to improve the quality of product design for their MP3 players.

The New York Times reported that Samsung has hired one of the original members of the iPod interface team, Paul Mercer, to work on the interface of one of its MP3 players, the Z5. David Pogue wrote in the Times, “The result is the easiest-to-navigate software since the iPod [3].” He adds, “Samsung has even improved on the iPod’s design in several important ways.” Pogue’s conclusion? “The Z5, then, will not cause any discernible dip in iPod’s market share.”

To understand the success of iPod, compare all four measures of design quality, not just product-design quality. Apple and its competitors are roughly even on manufacturing quality and product design, but Apple has a considerable lead in both interaction design and service design.

While Pogue says the Z5 “deserves to be a hit for Samsung,” he also notes that he was unable to connect with the Rhapsody store and Windows Media Player. In short, the service failed—that is, the pieces did not work as an integrated system. That may be because they were conceived and developed individually, not together. The Z5’s service design was poorly coordinated, especially when compared with iPod’s service design.

To understand the success of iPod, compare all four measures of design quality, not just product-design quality. Apple and its competitors are roughly even on manufacturing quality and product design, but Apple has a considerable lead in both interaction design and service design.

Service design is the next important field of competition. Apple already has considerable experience, having built the iTunes Store and tightly integrated it with iTunes and iPod. Amazon’s new Kindle electronic book comes with Internet access built-in, thanks to a relationship with Sprint, so users can access Amazon from pretty much anywhere and buy a new book whenever they want—another example of well-thought-through service design.

Increasingly, organizations will compete on the quality of service design, with customers assuming (and demanding) high levels of manufacturing quality, product-design quality, and interaction-design quality.

Each of these learning curves represents a wave of knowledge and skills to be traversed by both individuals and the organizations that employ them. Each represents a new field of competition, a new strategy for differentiation. A new wave does not replace the one that came before; each new wave adds to those already here. Each wave sets a new standard for performance, “raising the bar” or “upping the ante,” in the metaphors of business. While professional focus changes, earlier skills are still necessary; they become “table stakes” as the game shifts and competition moves to a new field.

For many consumer-electronics makers, the changes represented by the learning curves for design can be difficult to negotiate. Successful firms typically have hardware engineering cultures. The quantitative basis of Six Sigma methods is relatively comfortable for engineers. Product design is less comfortable, but good exemplars have been around long enough for the discipline to get real traction. Also, the models that result from product-design exercises are tangible, easy to see, and can be evaluated quickly. Interaction design, however, is less tangible, and interfaces require considerable time to evaluate. Also, software engineers often take a back seat to hardware engineers within hardware companies. Thus, interaction-design quality has only recently emerged as a goal for many firms, and proficiency remains low for most. Now a new field of competition, service design, is emerging. To suddenly find a need to think in terms of systems of products and networks of services, as well as interaction design, is doubly daunting to hardware companies. They may recognize the need on a rational level—and may even fund development efforts—but the move to service design represents a large cultural shift, one that may ultimately require a new generation of managers.

Difficult as it is, some hardware companies have begun the transition to services. IBM has spun off its PC division and is concentrating on services. And last year Dutch consumer-electronics giant Philips spun off its chip division and purchased two health-care-services businesses. Just recently, HP announced plans to acquire EDS in order to bolster its services offering and more effectively compete with IBM. It’s also interesting to see software companies—Internet companies, really—like Google (fourth-largest maker of servers in the U.S. and Android mobile-platform author) and Amazon (maker of the Kindle electronic book reader) beginning to develop hardware platforms for delivering their services.

These changes suggest a need for both individual designers and the organizations that employ them to reassess what design skills are important in order to remain competitive. Organizations (and individuals too) must judge where to “play,” where to focus their energy. And they must gauge where they are on the learning curves of established design disciplines, while keeping watch for the emergence of new disciplines.

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In this article, we contrast the “sales cycle” and related models with the “experience cycle” model. The sales cycle model is a traditional tool in business. The sales cycle frames the producer-customer relationship from the producer’s point of view and aims to funnel potential customers to a transaction. 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 experience cycle frames the producer-customer relationship from the customer’s point of view and aims to move well beyond a single transaction to establish a relationship between producer and customer and foster an on-going conversation.

We acknowledge the sales cycle model has value. And designers need to be familiar with it. But when the sales cycle comes up as a topic of discussion in a client engagement, designers should also think of the experience cycle as an alternative frame—and should introduce it into the discussion. We believe the experience cycle is a more useful model not only for designers but also for marketing and sales people, because it is more likely to lead to an experience of lasting value for customers, and thus greater long-term value for producers.

Sales Cycle

The “sales cycle” is a model commonly used in business. It often frames the basic structure of marketing and sales activities, providing a practical template for planning.

The sales cycle describes the series of steps leading to a sale (or purchase), including awareness, consideration, and selection. The goal is to push customers to buy—advertising to increase familiarity, informing to build knowledge, offering incentives to close a deal.

The sales cycle also refers to the time required to complete the sales process. The length of the sales cycle varies depending on the cost, complexity, and context of use of the product being sold. For example, a hospital information system might have a three-year sales cycle; a new game console might have a sales cycle lasting a few days or weeks.

The sales cycle does not have a single, canonical form. Many variations appear in the literature, and in practice people often tailor the model adding or subtracting steps to fit their own situations. A common characteristic of sales cycle models is the funnel shape, a visual analogy to a process that begins with a large pool of candidates, narrows to a group of interested prospects, and narrows again to those who purchase. The funnel model is useful in managing a “sales pipeline.” Defining a series of steps in the sales process creates opportunities for setting goals, tracking performance, and analyzing effectiveness, which makes forecasting more reliable and enables improvement of the process.

Sales Cycle – Expanded

This model updates the sales cycle, framing stages in the process as goals the seller has for customer thinking and adding actions the seller may take to achieve those goals and measures of their effectiveness. This model also adds a stage for customer feedback, important for product improvement and innovation.

Related to the sales cycle model are models of decision-making and technology-adoption. Rogers 6. articulates a five-step innovation-decision process:

• Knowledge
• Persuasion
• Decision
• Implementation
• Confirmation

Kotler and Armstrong 4. articulate another variation on the decision process:

• Problem recognition: Perceiving a need
• Information search: Seeking value
• Alternative evaluation: Assessing value
• Purchase decision: Buying value
• Post-purchase behavior: Value in consumption or use

Defining the first step as problem recognition may imply the “problem” has an objective existence, independent of the customer—and the producer. Framing the decision process as problem-solving suggests the customer is a “rational actor.” The danger is that people often act more on emotion than by rationally calculating self-interest. And their definitions of problems depend on their point of view and are often formed in conversations with others—including producers. Indeed part of the innovation process is reframing an existing situation to create consensus around a new definition of a problem.

>Models of decision-making as problem-solving echo models of the design process as problem-solving which were common in discussions of first-generation design methods. In proposing a second generation of design methods, Horst Rittel 5. articulated the limitations of design as problem-solving and offered as an alternative a view of design as conversation.

Bitner 1. articulates a six-step self-service technology adoption process:

• Awareness
• Investigation
• Evaluation
• Trial
• Repeated use
• Commitment

Bitner suggests “trial” is the most important stage because it is influenced by customer readiness or the expectations that they bring to the interaction—can they do “it” (ability), do they know what to do (clarity), and do they see benefit in doing it (motivation). These ideas are consistent with the concept of transparency in interaction design. Of course, producers (and designers) have goals for their customers’ experience. But all they can do is provide artifacts and services that create opportunities for experience. We should be cautious about proposing to “design experience.” Ultimately, construction of experience remains with the customer. You own your experience. No one else can construct your experience for you. In John Dewey’s words, “a beholder must create his own experience.” 3.

So: What is the customers’ view of their experience?

Customers interact with producers through “touch- points,” clusters of elements combined into artifacts that foster product or service experiences. These touch-point experiences form a larger arc or path: the customer journey. The series of customer experiences aggregate to form an impression of the product or service in its context—developing an idea of what it does, what it means, and what its worth—what the customer thinks of the brand. Indeed, the impression (the sum of the experiences) is the brand. 7.

Ideally, the experiences build a strong relationship between customer and producer. John Rheinfrank, Shelley Evenson, and others developed a model of the ideal “experience cycle” as they worked on a usability design strategy for Xerox in the 1980s. They were searching for a way to describe a copier in its broader context—in its ecology—so that they could design the product to fit its context. The initial model had seven steps, but over the years the team refined it to five.

Experience Cycle

The experience cycle model describes the steps people go through in building a relationship with a product or service:

• connecting (first impression)
• becoming oriented (understanding what’s possible)
• interacting with the product (direct experience)
• extending perception or skill and use (mastery)
• telling others (teaching or spreading activation)

Explicit in the experience cycle is the process by which customers become advocates and introduce others to the product, beginning the cycle anew. This frame suggests a shift in focus from “the sale” as a point event or “trial” as a single interaction to nurturing a series of relationships in a continuous cycle that yields increasing returns.

The experience cycle model suggests attributes for an ideal experience—criteria for evaluating experience or even key performance indicators (KPI)—which designers can address. A good product or service experience is:

• compelling (it captures the user’s imagination)
• orienting (it helps users navigate the product and the world)
• embedded (it becomes a part of users’ lives)
• generative (it unfolds, growing as users’ skills increase)
• reverberating (it delights so much that users tell other people about it)

In Csikszentmihalyi’s concept of “flow,” people are completely involved in an activity for its own sake. In peak flow experiences, people are engaged in discovery, transported to a new reality. 2. Though in most experiences we cannot expect people to “become so involved that nothing else matters,” addressing the facets of experience can make flow easier to achieve.

The experience cycle also helps designers reflect upon another important design consideration—what expectations people bring to the experience. At each stage, resources for experience must account for or consciously disregard a customer’s expectations for the stage and design accordingly. The experience cycle plays out at multiple scales. It plays out “in-the-large,” across the life of the relationship between a customer and a product. It also plays out “in-the-small,” across the experience a customer has with each touch point. For example, a good magazine ad connects immediately with readers, presents a clear structure, draws readers in, extends their knowledge, and delights them so much that they show it to other people. A good product package, a good interface, a good support service, and other well-executed touch points enable a similar cycle of experience. These interactions build on one another and further cement the producer-customer relationship.

The experience cycle model suggests experience has a fractal quality—that experience has a self- similar structure at different scales. The model suggests recursion—each stage stands for itself but can also “call” the whole model. The recursion process can continue down to a fi ne scale as designers work out the ways an experience ramifies. (Design also has a self-similar structure at different scales; employs recursion; and ramifies.) Thus the experience cycle model is useful to designers both in early stages of a project when working out the broad outlines of a product or service and also throughout the process as successive iterations add increasingly finer levels of detail.

See an Apple Case Study of the experience cycle.

Experience cycle “in the large” and “in the small” Integrated experience across multiple scales: Apple as a case study
The fractal nature of the experience cycle

In the large
Multiple touch-points across the life of a product

In the middle
Multiple touch-points in the store experience

In the small
Multiple touch-points within the in-store purchase process

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The simplest way to describe the design process is to divide it into two phases: analysis and synthesis. Or preparation and inspiration. But those descriptions miss a crucial element—the connection between the two, the active move from one state to another, the transition or transformation that is at the heart of designing. 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?

How do designers bridge the gap?

The bridge model illustrates one way of thinking about the path from analysis to synthesis—the way in which the use of models to frame research results acts as a basis for framing possible futures. It says something more than “then the other thing happens.” It shows how designers and researchers move up through a level of analysis in order to move forward through time to the next desired state. And models act as the vehicle for that move.

The bridge model here is organized as a two-by-two matrix. The left column represents analysis (the problem, current situation, research, constituent needs, context). The right column represents synthesis (the solution, preferred future, concept, proposed response, form). The bottom row represents the concrete world we inhabit or could inhabit. The top row represents abstractions, models of what is or what could be, which we imagine and share with others.

Analysis-Synthesis Bridge Model

Ideally, the design process begins in the lower-left quadrant with observation and investigation—an inventory (or description) of the current situation. As the process moves forward, it moves to the upper-left quadrant. We make sense of research by analysis, filtering data we collect to highlight points we decide are important or using tools we’re comfortable with to sort, prioritize, and order. We frame the current situation, but move out of the strictly concrete. We define the problem. We interpret. Analysis begins as thoughtful reflection on the present and continues as conversation with the possible. Crucial for progress is documenting and visualizing our analysis, making it possible for us to come back to it, making it possible to imagine alternatives, making it possible ultimately to discuss and agree with others on our framing and definition. We might write down a list of findings or a statement defining the problem. Better still is writing a story. A story describes actors and actions; it suggests relationships, which we may represent in visual form. A story of what happens suggests a model of what is—an interpretation of our research. The process of coming to a shared representation externalizes individual thinking and helps build trust across disciplines and stakeholders.

Having agreed on a model of what is (framed the current situation, defined the problem) then the other side of the coin (the preferred future, the solution) is implied. An interpretation provides “a description of the everyday in such a way as to see how it might be different, better, or new [1].” We can devise stories about what could happen. We can model alternatives in relation to our first model. In doing so, we’ve moved to the upper-right quadrant, to the use and development of models of what could be. It is in the realm of abstraction—by thinking with models—that we bridge the gap between analysis and synthesis. These models are hypotheses, speculations, imagined alternatives to the concrete we started with, but they are still abstract themselves. It is easy to “play” with models at this point, to test and explore. But design requires that the work return to the concrete, that we make things real, realize our models as prototypes or even finished form. This is the lower-right quadrant. Of course, results improve with iteration. Submitting the new prototype to testing, further observation and investigation, continuing around the quadrants, we learn and refine our work. The bridge model has several antecedents and variations.

Robinson Model

The bridge model grew out of personal discussions over the past few years. Rick Robinson has written about “the space in between” research and concept. He has described anthropologist Clifford Geertz’s essay, “Deep Play: Notes on the Balinese Cockfight,” as an example of abstracting a model from research, and one that parallels strongly the moves that other forms of research and design make in moving from description through interpretation to application. “[The construct of] Deep Play becomes a lens through which Geertz can show what’s important about the Balinese cockfight, and his colleagues can understand important underlying factors in something like fan riots at soccer matches [1].”

Beer Model

Writing about the relationship of science to management, Stafford Beer presented a more elaborate model of the move from cases to consensus, from particular to general. He points out that several levels of models are involved [2].

Alexander Model

At the beginning of his career, Christopher Alexander described a six-part model. It differs from the bridge model in two important respects. First, Alexander explicitly separates the mental picture (model) from a formal picture of the mental picture (a representation of the model). Second, his notion of a model (at that time at least) was highly mathematical [3].

Kumar Model

Vijay Kumar has proposed a model of the innovation process.[4] He frames it as a two-by- two matrix, moving from research, to “Framing Insights,” “Exploring Concepts,” and “Making Plans.” He notes, “’Framing Insights’ are primarily about descriptive modeling, creating abstract mental pictures about the patterns that we recognize about reality. ‘Exploring Concepts’ and ‘Making Plans’ are about prescriptive modeling.” Where the bridge model forefronts the role of models, Kumar’s model forefronts steps that make use of modeling. He recently published a wonderful poster that maps the steps in the “innovation process” to a series of methods.

Kaiser-IDEO Model

During the process of writing this article, interactions co-editor Richard Anderson pointed out this model of the innovation process. Christi Zuber reports that Kaiser Permanente’s Innovation Center (working with IDEO) developed this model in 2004 as part of an innovation toolkit created for use inside Kaiser. This model is similar to Kumar’s model, but the Kaiser model emphasizes storytelling and brainstorming as key methods.

Suri-IDEO Model

Responding to questions about the origin of the Kaiser/IDEO model, Jane Fulton Suri supplied this recent model of the process of moving from synthesis to strategy. It shares the same basic structure as the Robinson model; though synthesis (depicted as the right column in other models) is here depicted as the left column. The framing of models as a link between patterns and principles is a useful addition [5].

While practitioners and educators increasingly make use of models, few forefront the role of modeling in public summaries of their work processes. Glossing over modeling can limit design to the world of form-making and misses an opportunity to push toward interaction and experience. We see modeling becoming an integral part of practice, especially in designing software, services, and other complex systems.

The bridge model makes explicit the role of modeling in the design process. Explicit modeling is useful in at least two ways. First, it accelerates the design process by encouraging team members to understand and agree on the elements of a system and how those elements interact with each other and their environment. Second, by making the elements and their interactions visible, it reduces the likelihood of overlooking differences in point of view, which might otherwise eventually derail a project.

Explicit modeling also helps scale the design process. It enables designers to develop larger and more complex systems and makes the process of working with larger and more complex organizations easier. Discussing the role of modeling in design also invites comparison and interaction with other disciplines that use models. Ideally, practitioners that use models may, over time, be able to see patterns across their models that will advance the practice of design.

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For the last few years, innovation has been a big topic in conversation about business management. A small industry fuels the conversation with articles, books, and conferences.

Designers, too, are involved. Prominent product design firms offer workshops and other services promising innovation. Leading design schools promote “design thinking” as a path to innovation.

But despite all the conversation, there is little consensus on what innovation is and how to get it.

The current conversation about innovation is similar to an earlier conversation about quality. As recently as the late 1980s, quality was something businesses actively sought but had trouble defining. Today, statistical process control, TQM, Kaizen, and Six-Sigma management are common tools in businesses around the world.

As businesses have become good at managing quality, quality has become a sort of commodity—“table stakes,” necessary but not sufficient to ensure success. When everyone offers quality, quality no longer stands out. Businesses must look elsewhere for differentiation. The next arena for competition has become innovation.

The question becomes: Can innovation be “tamed” as quality was?

A key step in taming quality was Walter Shewhart and Edward Deming proposing a process model. (Shewhart, 1939) Their quality cycle is now widely taught and has become an important part of the quality canon. But innovation has no corresponding model.

Can we reach consensus on such a model for innovation?

One step may be to propose models for discussion.

Last year, Lance Carlson, President of the Alberta College of Art and Design (ACAD), initiated a project (through ACAD’s Institute for the Creative Process) to create a “concept map” of innovation. The Institute worked with ACAD faculty, Dubberly Design Office, Paul Pangaro, and Nathan Felde to develop a series of models and published one as a poster.

This article describes the published model and illustrates the process of developing it.

Concept maps

This model of innovation takes the form of a concept map. “A concept map is a schematic device for representing a set of concept meanings embedded in a framework of propositions.” (Novak and Gowan, 1984) In a concept map, nodes and links form a web of meaning, a semantic mesh. Nodes are nouns. Links are verbs. A noun-verb-noun sequence forms a proposition, a sentence. Concept maps are similar to entity-relationship diagrams and entailment meshes, though less constrained and less rigorous.

This concept map uses text direction and arrows to indicate reading direction. Type size indicates importance and hierarchy. Colored backgrounds join related terms.

Creating concept maps involves trade-offs. Adding terms provides detail and may help clarify, but more terms mean more links, increasing the reader’s effort.

Concept maps differ from traditional texts by making links explicit, by creating multiple pathways. People often ask, “Where should I start reading?” You can start anywhere. Concept maps have no real starting point; they are webs. Still, like any model, concept maps benefit from explanation. They can be explained by telling a story. Conversely, telling a story paints a picture, creates a model in the mind of the listener.

PDCA quality cycle
In 1939, mathematician Walter Shewhart published Statistical Method from the Viewpoint of Quality Control, in which he introduced the PDCA quality cycle. Edward Deming worked with Shewhart at Bell Laboratories and later popularized the quality cycle, especially in Japan.

Model-story cycle
Explaining a model involves telling a story, navigating a path through the model. Similarly, telling a story builds a model of actors and their relationships in the mind of the listener.

Reading the map

The map is built on the idea that innovation is about the evolution of paradigms. In contrast to innovation processes, quality processes typically work within existing paradigms. Quality is largely about improving efficiency, whereas innovation is largely about improving effectiveness. Improving quality is decreasing defects. Defects can be measured, progress monitored, quality managed.

Business Week design editor Bruce Nussbaum asserts, “You can’t Six Sigma your way to high-impact innovation.” (Nussbaum, 2005) Though some six-sigma advocates disagree, Nussbaum points out a fundamental difference between managing quality and managing innovation. Innovation is not getting better at playing the same game; it’s changing the rules and changing the game. Innovation is not working harder; it’s working smarter.

Chris Conley suggests a slightly different frame. He contrasts innovation with operations. He observes, “Most businesses organize for operation, not innovation.” Organizations by their nature are conservative. They maintain a way of doing business, a way of living, a way of using language. They conserve convention.

Vertical axis: The innovation cycle

The map situates innovation between two conventions. An innovation replaces an earlier convention and in time becomes a new convention. It is a cycle—a process in which insight inspires change and creates value.

We rarely recognize innovation while it’s happening. Instead, innovation is often a label applied after the fact, when the results are clear and the new convention has become established. The process begins when external pressure or internal decay disturbs the relation between a community and its context or environment, a relationship maintained by some convention. The original convention no longer “fits.” Perhaps the context has changed, or the community, or even the convention. Someone notices the lack of fit. It causes stress and increases bio-cost. It creates enough friction, enough pain, to jump into people’s consciousness.

Perception of misfit almost simultaneously gives rise to proposals for change, for reframing. It creates the opportunity for insight. Insights only move forward when shared, articulated, prototyped. Sharing is a test: Does the insight resonate with others? Proposals for change compete for attention. Most are ignored and fade away.

The changes that survive are by definition ones the community finds effective. They spread because they increase fit, because they create value.

The map suggests a cycle moving from fit through misfit and back again. The vertical axis loops back on itself, reflecting the cycle.

The yellow loops: the role of feedback

Of course, innovation processes are rarely linear. The map includes several feedback loops, suggesting the role of iteration and the recursive nature of the process. At a basic level, innovation involves experimentation, making something new and testing it. To some extent, the process may be trial and error. The process may lead to new insights. Or it may prompt reframing of goals, consideration of new approaches, new generative metaphors. Success also leads to change: new beliefs, actions, and artifacts.

In turn, these lead to second-order change. Innovation in one place affects related conventions and may reduce their fit, hastening further innovation.

Ethnography and other research techniques can help identify opportunities for innovation. Design methods can increase the speed of generating and testing new ideas. But new ideas are still subject to natural selection (or natural destruction) in the marketplace or political process.

Variety: a regulator

The map posits variety as a regulator of innovation. Variety is a measure of information. (Ashby, 1956) Here, it is the language available to an individual or community. Language enables conversation; conversation enables agreement; agreement enables action. Language constrains action.

Pressure to increase efficiency creates pressure to reduce variety. (Maintaining less variety requires less effort or saves time.) Reducing variety decreases the number of options a community can discuss. Conversely, increasing variety increases the number of options that can be discussed— increasing the likelihood of insight. (In practice, an increase in variety may be required for some insights to be found.) A community seeking to increase variety must integrate individuals who can increase the community’s language, provide new points of view, draw on additional types of experience, foster new conversations, provoke action. (Esmonde 2002)

Horizontal axis: the importance of individuals

The map posits individuals as drivers of innovation—and the source of insight. But to succeed, individuals must participate in a community, where they contribute variety.

Individuals who drive innovation also have a sense of what is not known but necessary for progress, and they understand how to find it. Individuals who drive innovation also seem to possess a healthy measure of optimism. They are motivated by the value innovation creates (which need not be monetary).

Innovation remains messy. Even dangerous. Luck and chance, being at the right place at the right time, still play a role. Like the vertical axis, the horizontal axis also folds back on itself.

An invitation to interaction

The story above describes one path through major points on the map, but the map offers multiple paths and invites closer reading.

While this model is not a recipe, it hints at ways we might increase the probability of innovation. But more importantly, it invites further thinking. Alan Kay noted, “we do most of our thinking with models.” (Kay, 1988) They are “boundary objects,” enabling discourse between communities of practice. (Star, 1989) This is what makes models powerful.

The poster includes an invitation to react and participate in improving this model of innovation. Just as quality is founded on the feedback loop of ‘plan-do-check-act’ and feedback loops are necessary for successful innovation (cf. the poster), we seek your insights and feedback as well. The team’s hope is for this model to spur thinking and discussion—interaction among readers. We hope it leads to other, more useful models.

This sequence of images separates the model into components.

The map places an innovation between two conventions, the one that precedes the innovation and the one it becomes. The map provides an “exploded view”” of innovation—zooming in on innovation—as indicated by the yellow triangle.

The map proposes that innovation entails insight/change/value. In other words: Innovation is a process in which insight inspires change and creates value.

An armature can aid development and reading of large concept maps. For example, a horizontal axis may set context, and a vertical axis may define the main concept. In this model, the vertical axis describes the process of innovation, wherein fit is disturbed and then restored. The horizontal axis places the source of innovation with individuals. The axes intersect at insight. Both axes loop, connecting the right edge back to left and bottom back to top, indicating that the innovation process cycles. Convention is overturned by innovation, which becomes a new convention, which is overturned by a new innovation.

In the left-most column, convention mediates between a community and its context. As a rule, a concept map should not repeat terms. This map intentionally repeats community, convention, and context, indicating that all three change as time passes.

At the center of the map are four nested feedback loops, emphasizing that innovation is not a linear, mechanical process. First is the simple iteration of prototyping and testing. Second is the design process, incorporating insight to drive new prototypes. Third is the learning process, in which problems or goals are reframed. And fourth is creative destruction, wherein an innovation in one area hastens change in other areas. (Schumpeter, 1942)

Another set of loops fill out the right side of the map. These loops hinge on variety. (Ashby, 1956) Variety is the language available to an individual or community. Pressure to create efficiency reduces variety. Yet increasing variety increases the likelihood of insight. A community seeking to increase variety must seek out individuals who can increase the community’s language and enrich its conversation.

This section shows 12 sketches developed during the design process. More than 50 were printed at full size for discussion. The sketches are arranged in chronological order.

June 29, 2006
The team began with research, reading all the articles and books they could find on innovation. During the process, they developed three collections: existing models related to innovation, prior definitions, and a list of words related to innovation. The first step in mapping was to group related words and begin to prioritize. An early hypothesis was that innovation involves a change of goals.

July 11, 2006
This version is one of the first that links concepts, though many are still in lists. It posits innovation as “a process of purposeful change.”

July 21, 2006
This version posits innovation as one of several processes organizations learn as they grow. An interesting idea perhaps, but it does not fulfill the assignment of creating a concept map.

July 27, 2006
This version focuses on ways of classifying innovation, reprising taxonomies from several authors. It posits innovation as “insight applied.”

July 28, 2006
Sean Durham suggested a straight-forward, journalistic approach: who, what, when, where, why, and how. It introduces the idea of consequence, which later became value.

September 1, 2006
This version (one of many related studies) frames innovation as insight + change + value. Change is at the center with innovation behind it, sandwiched between two conventions. Innovation and convention are out of focus, suggesting the blurring of boundaries. The vertical axis defines the innovation process.

September 4, 2006
Nathan Felde suggested a number of improvements. He also sent his own version. And he urged the group to meet.

September 10, 2006
Hugh Dubberly, Nathan Felde, and Paul Pangaro met in Pittsburgh (at CMU’s Emergence Conference). They went back to the beginning, rehearsing the arguments and creating a rough outline using Post-It notes. Over two days, a new consensus formed with the team agreeing on the structure of their argument and a series of propositions.

September 12, 2006
After the Pittsburgh meeting, Ryan Reposar created this version, documenting all the propositions. He also counted the number of times terms appeared in a proposition, creating a measure of their relative importance.

September 19, 2006
Next, Ryan linked the terms so that none repeat, creating a version that was a “true” concept map.

February 4, 2007
The next step was to give typographic form to the model. It still places the old convention at the top and the new one at the bottom. Terms and propositions continue to change.

February 24, 2007
This version is relatively close to the final. The armature is in place, as are the feedback loops. But they are not differentiated from the rest of the terms. Innovation is still the same size as convention. Insight, change, and value have not been called out. The color metaphor of a spotlight shining on innovation is not in place.

This page shows a series of sketches developed by Nathan Felde. They too are in chronological order.

July 25, 2006
Nathan sent this wonderful poem early in the process. Sean Durham later turned it into an animation.

September 4, 2006
This version responds to the map created on September 1. Together, they illustrate a central tension in the team’s discussions: Can innovation be defined?

Nathan wrote: “I guess what I am concerned about [in prior models] is the representation of innovation as cut and dried. Fear, greed, need, perplexing situations and the associated behaviors and anxieties are messy and volatile.

I realize that the progress of business requires order and command and control, but the chaotic flux within which or at least from which the seeds of innovation are sown needs some depiction in our rendering of the map / diagram / output of this discourse.

Can anyone do it or can it be taught are questions that have come up. Have we resolved that or is that a starting premise to be confirmed or denied?

Are we at a juncture that mandates innovation ourselves? Is this a predicament that fosters innovation? It appears to me that a fault or fault line discloses the opportunity to innovate, although the activities take names like think, wonder, search, toy, rummage and guess.

Design: A guessing game.”

February 14, 2007
Nathan proposed this playful version in response to the grid structure of the February 4 version. He described this one as “my structural engineering interpretation of the latest round.”

February 14, 2007
Nathan’s assistant, Purnima Rao, created this version. It contains a number of very interesting ideas. Change is literally at the center of a whirl. It posits “motive, opportunity, and means” as necessary for change. (Does that suggest a crime?) It also describes innovation as “a label we assign after the fact.”

Another View

by Paul Pangaro

‘Innovation’ has frustrated me for some time. Does ‘innovation’ mean ‘new idea,’ ‘invention,’ ‘design concept,’ ‘product revision,’ or ‘game-changing revolution on-the-order-of general relativity’? Making a concept map is a good way to decide what we mean. In the process of collaboration to build this map, I felt that coming to the core entailment—“Innovation is an insight that inspires change and creates value”—was an insight of its own about innovation. I sensed that if this insight countered the dilution of meaning and inspired a change in use of the term, that it would create value. An innovation about innovation. But, as with any innovation, saying does not make it so—it actually has to change a convention, and for the better. (‘Value’ means ‘positive value’). There was a point where that core entailment was lost in revision, one of many twists and turns in the process. This shows that the process of innovation can be fragile. Perhaps because I was a participant, I feel the story of making the map is as interesting as the outcome. Reviewing the spreads reprinted here retells some of that story, though flipping through 50+ full-sized prototypes retells it fortissimo. What neither tells is the tug-of-views across cities, threads of email, and fields of post-it notes. One key argument was: What parts of the process of innovation are messy, unpredictable, ineffable, mystical, magical, intuitive? (The more innovation is those things, the less we can help the process and make a deliberate innovation; at one extreme, that phrase becomes an oxymoron.) Conversely, what parts of innovation are predictable, likely, improve-able, or even deterministic? (We certainly resist the idea that the source of inspiration, the source of hypotheses, can be fully known, reduced to algorithm.) While we explored those questions, I learned that bringing about innovation, in addition to being creative, is about being stubborn. Without stubbornness, obsessiveness even, why would an individual rage against the lock-in of current convention—spend all that time in the patent office and on trains, in thought experiments outside of prior language in order to see anew? So, this is the unpredictable part: getting to the moment of genuine insight when a new means to solve a problem (a new metaphor for framing the problem- solution) breaks the lock-in of convention. This is the inventor’s phase of innovation.

Yet innovation requires a second form of obsessiveness: inspired by the possibility of bringing value, there must be drive to do something with the inventor’s insight. This role can be called ‘the innovator,’ and often it’s a different person. Propelled by demonstration of possibility, the innovator moves from insight to demonstration to fruition—to creating value.

Is it inevitable that, once invented, an insight with real potential brings about valuable change? It would seem so, though timelines and paths are not predictable. The innovator’s phase seems more understand-able, plan-able, work-able from experience. These are the aspects we can understand better, and foster, and improve.

See the Innovation Concept Map which inspired this article.

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