We worked with Zume Life to create interaction design prototypes for a new service that helps individuals manage their chronic illnesses. The service integrates hardware and software to improve the relationship between individuals and health professionals.

Our service design work for Zume Life includes:

1. Interaction for handheld device (named Zuri) which enables patients to monitor and record health information
2. Web-based application for managing patients
3. Branding and Identity System
4. Quick start guide

Written for Interactions magazine by Hugh Dubberly and Shelley Evenson.

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.

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.

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.

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.

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DDO collaborated with Steve Barretto to help create an identity for Soar, an online service that offers dynamic screening of medical tests over time. We helped Soar to analyze their business, pin point key adjectives, and then create an identity that expressed them.

When screening for prostate cancer, one test is not enough. Soar provides dynamic screening for both physicians and patients to enable comparing of tests throughout time. Dynamic screening leads to earlier detection, and higher cure rates.

Soar is developing techniques for analyzing proteins—with a view to comparing changes over time and using results to measure aspects of patient health. The service seeks to engage “the whole person” in behavior change that can moderate or even reverse conditions and improve quality of life and extend length of life.

Created in collaboration with Satoko Kakihara, Jack Chung, and Paul Pangaro.

This model is built on the idea that play is a type of conversation. It involves two individuals, who might also be teams, or points of view with in a single person, or a virtual person and a real person. Through conversation, they create a shared world in their imaginations, which leads to fun.

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Written for Interactions magazine by Hugh Dubberly, Shelley Evenson, and Rick Robinson.

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.

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.

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.

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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Nikon my Picturetown is the only photo storage and sharing site created by a camera manufacturer. Wi-Fi-enabled Nikon camera users can upload images directly to the site—essentially eliminating the PC from the equation—creating an integrated hardware + software service.

We worked with Nikon to design the interaction and visual design of the site.

In addition to the online service, we designed mobile versions of the site which worked on select U.S. phone and the majority of Japanese market DoCoMo phones.

Originally published in Sketching User Experience by Bill Buxton, 2007.

We made the Knowledge Navigator video for a keynote speech that John Sculley gave at Educom (the premier college computer tradeshow and an important event in a large market for Apple). Bud Colligan who was then running higher-education marketing at Apple asked us to meet with John about the speech. John explained he would show a couple examples of student projects using commercially available software simulation packages and a couple university research projects Apple was funding. He wanted three steps:

1. what students were doing now
2. research that would soon move out of labs, and
3. a picture of the future of computing.

He asked us to suggest some ideas. We suggested a couple approaches including a short “science-fiction video.” John choose the video.

Working with Mike Liebhold (a researcher in Apple’s Advanced Technologies Group) and Bud, we came up with a list of key technologies to illustrate in the video, e.g., networked collaboration and shared simulations, intelligent agents, integrated multi-media and hypertext. John then highlighted these technologies in his speech.

We had about 6 weeks to write, shoot, and edit the video—and a budget of about $60,000 for production. We began with as much research as we could do in a few days. We talked with Aaron Marcus and Paul Saffo. Stewart Brand’s book on the “Media Lab” was also a source—as well as earlier visits to the Architecture Machine Group. We also read William Gibson’s “Neuromancer” and Verber Vinge’s “True Names.” At Apple, Alan Kay, who was then an Apple Fellow, provided advice. Most of the technical and conceptual input came from Mike Liebhold. We collaborated with Gavin Ivester in Apple’s Product Design Group who designed the “device” and had a wooden model built in little more than a week. Doris Mitch who worked in my group wrote the script. Randy Field directed the video, and the Kenwood Group handled production.

The project had three management approval steps:

1. the concept of the science fiction video,
2. the key technology list, and
3. the script.

It moved quickly from script to shooting without a full storyboard—largely because we didn’t have time to make one. The only roughs were a few Polaroid snapshots of the location, two sketches showing camera position and movement, and a few sketches of the screen. We showed up on location very early and shot for more than 12 hours. (Completing the shoot within one day was necessary to stay within budget.) The computer screens were developed over a few days on a video paint box. (This was before Photoshop.)

The video form suggested the talking agent as a way to advance the “story” and explain what the professor was doing. Without the talking agent, the professor would be silent and pointing mysteriously at a screen. We thought people would immediately understand that the piece was science fiction because the computer agent converses with the professor—something that only happened in Star Trek or Star Wars.

What is surprising is that the piece took on a life of its own. It spawned half a dozen or more sequels within Apple, and several other companies made similar pieces. These pieces were marketing materials. They supported the sale of computers by suggesting that a company making them has a plan for the future. They were not inventing new interface ideas. (The production cycles didn’t allow for that.) Instead, they were about visualizing existing ideas—and pulling many of them together into a reasonably coherent environment and scenario of use. A short while into the process of making these videos, Alan Kay said, “The main question here is not is this technology probable but is this the way we want to use technology?” One effect of the video was engendering a discussion (both inside Apple and outside) about what computers should be like.

On another level, the videos became a sort of management tool. They suggested that Apple had a vision of the future, and they prompted a popular internal myth that the company was “inventing the future.”

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Created in collaboration with Sean Durham, Ryan Reposar, Paul Pangaro, and Nathan Felde.

This model is built on the idea that innovation is about changing paradigms. The model situates innovation between two conventions. Innovations transform old into new. It is a process—a process in which insight inspires change and creates value.

Read the Interactions Magazine Article which explains the process of creating the map.

See also our collection of Innovation Models.

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

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

A History of Connections Between Cybernetics and Design

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

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

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

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

Designs Shift to Service-Craft

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

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

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

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

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

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

Hand-Craft

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

Service-Craft

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

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

The Need for New Language in Design

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

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

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

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

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

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

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

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

Cybernetics as a Source for New Language in Design

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

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

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

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

Cybernetic Frameworks for Modeling What We Design

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

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

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

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

1. First-Order Cybernetic System

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

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

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

2. Requisite Variety

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

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

3. Second-Order Cybernetic System

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

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

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

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

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

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

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

5. Bio-cost

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

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

6. Autopoiesis

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

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

7. Evolution

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

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

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

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

Cybernetic Frameworks for Modeling How We Design

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

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

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

1. First-Order Cybernetic System

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

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

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

2. Requisite Variety

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

3. Second-Order Cybernetic System

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

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

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

4. Conversation

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

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

5. Bio-cost

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

6. Autopoiesis

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

7. Evolution

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

A Constructivist View—Design as Politics

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

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

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

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

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

A Call for Curriculum Change

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

Design educators need to respond to these changes.

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

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

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

About the Authors

Hugh Dubberly and Paul Pangaro co-teach a course, “Introduction to Cybernetics and the Design of Systems,” in Stanford’s Department of Computer Science. The course introduces cybernetic frameworks within the context of designing systems for computer-human interaction. Dubberly runs a design consultancy focused on information modeling, interaction, and service-craft. Pangaro consults at the intersection of information technology, marketing, and organizational behavior.

Footnotes

[1] Nortbert Wiener lectured at the Hochschule für Gestaltung Ulm (HfG Ulm). Ulm required students to take a course in cybernetics. Herbert Simon noted the relationship of cybernetics to design in Sciences of the Artificial. Stewart Brand recommended books on cybernetics right alongside those on design theory in his Whole Earth Catalog.

[2] Two founders of the design methods movement, Bruce Archer and Horst Rittel, explicitly mention cybernetics in their writing on design. Rittel incorporated cybernetics in his courses on design methods at UC Berkeley.

[3] In 1972, Horst Rittel proposed a second generation of design methods in “On the Planning Crisis: Systems Analysis of the ‘First and Second Generations.’” He stressed the difficulty of maintaining an objective view of design, and he presented the second-generation approach as an expert-less argumentative process that is inherently collaborative and political.

[4] In a 1972 lecture, Heinz von Foerster proposed second-order cybernetics. He noted the role of the observer in describing systems, and he too stressed the difficulty of maintaining an objective view.

[5] Ashby, W. Ross, An Introduction to Cybernetics, Chapman & Hall, Ltd., London, 1957.

[6] While design has many similarities to architecture, architectural practice has a separate history, which we will not cover here.

[7] Evenson, Shelley, “The Future of Design? Designing for Service”, presentation delivered at Emergence Conference: Service Design, Pittsburgh, Pennsylvania, September 8, 2006.

[8] Kelley, Kevin, Out of Control: The New Biology of Machines, Social Systems, and the Economic World, William Patrick Books, 1994, page 27.

[9] Mager, Birgit, Service Design: A Review, Köln International School of Design, Köln, 2004.

[10] Alexander, Christopher, Notes on the Synthesis of Form, Cambridge, Massachusetts, Harvard University Press, 1964.

[11] Buchanan, Richard, “Design and the New Rhetoric: Productive Arts in the Philosophy of Culture,” Philosophy and Rhetoric, Vol. 34, No. 3, 2001, The Pennsylvania State University, University Park, Pennsylvania.

[12] Alexander, Christopher, op cit.

[13] Pask, Gordon, “Introduction”, Soft Architecture Machines, (by Nicholas Negroponte), The MIT Press, Cambridge, Massachusetts, 1975.

[14] Geoghegan, Michael, and Esmonde, Peter, Notes on the Role of Leadership and Language in Regenerating Organizations, Sun Microsystems, Menlo Park, California, 2002.

[15] Pangaro, Paul, “Participative Systems,” a presentation available at Paul’s Web Site

[16] Geoghegan, Michael, op cit.

[17] Varela, F., Maturana, H., and Uribe, R., “Autopoiesis: The Organization of Living Systems, Its Characterization and a Model,” Biosystems, Vol. 5 (1974), pp. 187–196.

[18] Geoghegan, Michael, op cit.

[19] Discussion between the author and the individuals cited.

[20] Shewart, Walter A., Statistical Method from the Viewpoint of Quality Control, 1939.

[21] Simon, Herbert, Sciences of the Artificial, The MIT Press, Cambridge, Massachusetts, 1969.

[22] Cooper, Alan, The Inmates Are Running the Asylum: Why High-Tech Products Drive Us Crazy and How to Restore the Sanity, SAMS, 1999.

[23] Discussion between the author and the individual cited.

[24] Pirsig, Robert, Zen and the Art of Motorcycle Maintenance: An Inquiry into Values, William Morrow and Company, New York, New York, 1974.

[25] Rittel, Horst, “On the Planning Crisis: Systems Analysis of the ‘First and Second Generations,’” Bedrifts Økonomen. 8 (1972): 360–396.

[26] Rittel, Horst, “Issues as Elements of Information Systems,” Working Paper No. 131. Berkeley: Institute of Urban and Regional Development, University of California, 1970.

[27] von Foerster, Heinz, “Disorder/Order, Discovery or Invention,” in Disorder and Order, Proceedings of the Stanford International Symposium, Paisly Livingston (Ed.), 1981.

[28] Rittel, Horst, and Webber, Melvin, “Dilemmas in a General Theory of Planning”, Panel on Policy Sciences, American Association for the Advancement of Science, 4 (1969): 155–169.

[29] Gordon Pask died in 1996; Heinz von Foerster in 2002; Horst Rittel in 1990, and Bruce Archer in 2005.

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