In 2000, the International Council of Graphic Design Associations (ICOGRADA) published their first “Design Education Manifesto,” noting “many changes” in design practice, defining “visual communication designer,” and suggesting “a future of design education.” The ICOGRADA manifesto marked a turning point—an international design body addressing change at the millennium. Publishing the manifesto was a significant accomplishment. A decade later, ICOGRADA are updating their manifesto. This essay responds to their request for input.

Framing the manifesto
The manifesto acknowledges change without quite defining it and lists attributes of an emerging practice and education without quite prescribing them. The manifesto does not explicitly define goals or audience. It does not decry indulgences or urge reform. It does not sound an alarm or assert a theory.

Instead, the manifesto asks that we consider our responsibility for harmony, balance, and each other. It invokes oullim, a Korean word denoting resonance and connoting mutual duty. It might also have invoked similar ideas with the Chinese word ren.

In a thoughtful commentary on the manifesto and its development, Sharon Poggenpohl and Ahn Sang-soo acknowledge that “the search was for common ground” and “consensus” and that the manifesto is “somewhat quiet.”

Yet, Poggenpohl and Ahn note, “A manifesto is a form of communication predicated on three beliefs: that a change has occurred . . . that human agency can change circumstances into something more desirable; and the timing is advantageous . . .”

Thus, in relation to the ICOGRADA manifesto, we must ask:
- What has changed?
- What could be better?
- Why act now?

Framing the context
The manifesto begins by acknowledging changes in design. “The term ‘graphic design’ has been technologically undermined…. Boundaries between disciplines are becoming more fluid…. The variety and complexity of design issues has expanded.” We might better understand these changes by understanding their context and causes.

So: What is causing the large shifts in design practice?

Computers? Software tools? The Internet?

Yes, but they manifest a much larger shift in technology, economic structure, and culture. The industrial revolution is ending. A new revolution in information is beginning, on top of which comes another revolution in biology, also largely about information—”understanding how organisms encode it, store, reproduce, transmit, and express it.”

The shift is not only about what’s produced (from things to services) and how they are produced (from long-lead editions to continuous adaptation, from proprietary to open source, from transaction to relationship), it is also a shift in world view (from mechanism to organism), a shift in framing metaphors (from clock-work to ecosystem, from turn-the-crank-linear-causality to feedback-enabled-dynamic-equilibrium), a shift in organizing structures (from individual nodes to webs of links, from top-down to bottom-up, from serial to parallel), a shift in human values (from coherence to responsiveness, from seeking simplicity to embracing complexity).

Thus, we must also ask:
- How will we transform design in the age of information and biology?

Framing design
Design grew out of craft. A craftsman planned-for-making-things and made them. The craft tradition was cut short by the industrial revolution. Mass production separated planning-for-making-things from making them. Planning-for-making-things became design, and design took on some of the assumptions of mass production: notions of objectivity (e.g., framing situations in terms of problems and solutions), an expert or “professional” stance, a concern with “getting things right” (because in the world of mass production, the cost of fixing a design mistake can be quite large).

These assumptions may no longer apply; they may even be dangerous. Problem framing becomes more valuable than problem solving. Software is never “right”. And it’s never done. In software development, delays are often more costly than mistakes. With network-based applications, change becomes continuous. We enter perpetual beta. (For designers who acknowledge that improvement comes from iteration and that ending conditions are arbitrary, perpetual beta may be more comfortable than mass production.)

In the new world of information and biology, design will change. Less common will be situations in which things are designed by designers, in advance of use by users, enforcing a single view. More common will be situations created by participants, during use, enabling multiple views. Today’s users will become designers; today’s designers will become meta-designers, creating conditions in which others can design.

In this world, a media-based focus is less relevant. All design becomes trans-disciplinary. A concern for the form of objects will give way to a concern for the experience of services. A concern for products and things will give way to a concern for networks of interaction and communities of systems.

ICOGRADA has shifted from graphic design to communication design. The new position still focuses on individual products. A further shift to focus on platforms—to design of systems in which communications can take place—might be more consistent with the technological, economic, and cultural shifts we face.

We might even go beyond communication (which implies Shannon’s somewhat mechanical model of delivering messages) and focus on conversation (interactions that converge on understanding, agreement, and action). We might frame design as conversation—with a goal of designing for conversation.

Threats and opportunities
The very basis of graphic design is under assault. Printing is dying. In another 10 years, commercial offset lithography will have all but disappeared, save possibly for a handful of luxury artisans. Mass-production lithography will be replaced by mass-customization ink-jet or other digital printing techniques—or by electronic communications. New printed newspapers, magazines, and books may all but disappear, too.

At the same time, new forms of communications will emerge. Networked tablets will provide an environment for re-inventing the relationship between text, image, motion, and sound. Games, movies, and social networks will spawn new hybrids. E-books will become applications. Data-visualization will become a profession, employing thousands of designers.

We are also finding new ways to apply information technology to design. We are learning that “hardware products want to be web-sites,” and data-driven design is emerging as a new discipline. [5] Computation-based design (the application of algorithms to exploring solution spaces), long a subject of research, is entering practice and promises to become a discipline in its own right. Scan-edit-print, long a framework in two-dimensional design, is becoming a framework in three-dimensional design, and not just for mechanical objects but also for living things.

Given these opportunities, we must ask:
- What skills are required to take advantage of them?

Framing design’s relation to code
Juxtaposing the threat to traditional graphic design with the opportunities of “emerging media” might suggest an easy transition. And many traditional design skills do translate directly. But are they sufficient? Designers will also need to understand computers, networks, and software—as they previously had to understand printing, binding, and other manufacturing technologies.

Yet that industrial-age framing no longer fits. A designer’s relation to a printer is very different than a designer’s relation to a programmer. In both cases, a designer may develop a specification, but both the specifications and what happens next are very different. Printing is all about reproduction and requires little invention from the printer; programming has almost nothing to do with reproduction and requires a lot of invention by the programmer. Consulting your printer during design is a good idea; consulting your programmer during design is a necessity.

Practice has not settled the nature of the relationship between designer and coder, and it is the subject of intense debate among programmers. Alan Cooper has suggested it should be like the relationship between architect and builder. But most buildings are designed by builders, not architects. (And most software is designed by programmers, not designers.) Yet, when the architect is also an excellent engineer, such as Robert Maillart or Toni Kotnik, the results can be amazing.

We’ve also seen amazing results from designers who can code, such as David Small, Lisa Strausfeld, John Maeda, Ben Fry, Casey Reas, and many others. In fact, the best young designers are teaching themselves to code, and the best young engineers are teaching themselves to design. Is this a race? Or will they converge? Can we create schools for hybrids?

End-user programming tools have long promised to shield designers and others from coding, but so far, the best they offer is an easier way to begin. So far, learning mark-up and scripting languages remains a necessity. The best way to convey how you want software to behave is to demonstrate the behavior.

Framing design education
Our notions of design are rooted in the industrial revolution framing of design as planning-for-making-things. Yet our strategies for design education are even older; they remain rooted in the craft era, in the master-apprentice relationship played out in the design studio. In this tradition, students learn by emulating teachers. Almost all their learning is tacit. Response to change is slow.

In the craft world, where change is slow, the master-apprentice system works well. In the post-industrial world, where change is fast, the master-apprentice system tends to fall behind. Often, the apprentice knows more about new trends and new tools than the master. A post-industrial design education system can no longer rely solely on tacit learning. It must also turn tacit knowledge into explicit knowledge—”distill rules from experience, codify new methods, test and improve them, and pass them on to others.”

“The focus on design research at a few top schools is a positive development.” A few design journals publish articles that build lasting knowledge, but they are not widely read. A few design blogs are widely read, but they aren’t building lasting knowledge. Research must inform practice, and practice must inform research. They must co-evolve. This evolution requires invention, for example, fusing the studio and case-study methods.

Research must be more than observation or even abstraction. Research must also invent theory. The holes in design knowledge are huge. We lack theories of conversation, interaction, platforms, products and product management, and service. Filling these holes is an important task for design practice and education. Both must learn how to learn. Both must develop mechanisms to build and share knowledge.

Summary
The manifesto grew out of a recognition of change, mis-alignment, and the need to put things in order. Yet it was circumspect, almost vague. I urge ICOGRADA to greater clarity. Clarity invites response, which can lead to iteration, which can lead to improvement, which is a goal we share.

In the interest of clarity, I propose this summary:

The design practice that grew out of the industrial revolution is no longer sustainable (economically or ecologically). A new practice—one that responds to the information revolution—has begun to emerge. We can see its outlines, but much remains to be invented. For this, we must take responsibility. In addition, we must invent a mechanism (an organic system) through which the discipline of design can learn and evolve.

At the same time, design education still largely reflects design’s origins in craftwork. Simply put: Design education is out of date. What is worse: Change is accelerating, and design education is stuck. It has little means to move forward. We must also take responsibility for re-inventing design education and integrating it into an organic system through which the discipline of design evolves.

And what if we ignore the situation? What if we remain vague? What if we remain stuck?

Design schools will become increasingly irrelevant. But more will be lost: some continuity of history, certain values concerning quality, and perhaps a sense of humanness. The world will fall further under the sway of those satisfied with making things work without making them delight.

This need not be so. Our relationship to our technology is not inevitable. We design it. We have responsibility for it.

I look forward to the conversation that will ensue as ICOGRADA update their manifesto and continue the process of re-inventing design.

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Educom, the main higher education conference for academic computing, was coming up in October 1987. At the time, I was leading the Higher Education Marketing Group, and had been doing so since Steve Jobs and Dan’l Lewin had left Apple to start NeXT in mid-1985. Steve and Dan’l knew all the higher education influencers and decision makers from their time at Apple (Dan’l had led the creation of the Apple University Consortium), and they had been giving sneak previews of various technologies and products that NeXT was building (but had not shipped). NeXT claimed to be focused exclusively on the higher education market. Many of the higher ed influencers and decision makers were saying that Apple had no vision for its future product line. John Sculley was scheduled to be the keynote speaker at Educom, and the stakes were high for us to show some “vision” of where Apple was going. I met with John to prepare for the speech and discuss ideas of what we could do. We planned to incorporate a number of live demos of educational examples of hypertext, multimedia, and interactive learning, using professors and researchers from various colleges to do the demos. John Sculley’s book, Odyssey: Pepsi to Apple, had just come out and John gave me a copy of it to read. I pored through it trying to find some ideas for his keynote. The last chapter was John’s vision he had developed in many discussions with Alan Kay (an Apple Fellow at the time), where he described the Knowledge Navigator and even had a rough sketch of it, with two joysticks on a screen that one would hold to “drive” through libraries of knowledge.

I discussed the concept of the Knowledge Navigator from John’s book with Hugh Dubberly and Doris Mitsch in Apple Creative Services and we subsequently met with John again to review his thoughts. Michael Markman joined our team to write John’s keynote speech. Together with Mike Liebhold in Apple’s Advanced Technology Group, we discussed how we could make a vision video of a higher education example of the Knowledge Navigator. Hugh and Doris wrote the script with input from a number of people (see Hugh’s blog for more detail on other sources of inspiration for the script and all the folks involved in the production process) and I funded the project from the Higher Education Marketing budget. It’s important to note that the Knowledge Navigator vision first articulated in Odyssey morphed quite a bit based on Hugh and Doris’s research, inspiration and contribution from other luminaries in and outside of Apple, and our point of view from working with cutting edge researchers in higher education. We had very little time to pull this off, but sometimes having less time actually focuses the project and keeps the script tight and the length short (KN is only a little over five minutes). Hugh and Doris worked with an outside production company, The Kenwood Group, as a contractor to Apple, and turned the video around in six weeks. John did the keynote at Educom, the live demos came off without a hitch, and we ended with the first showing of the Knowledge Navigator (recall that the example is about a professor coming to work, checking his email, doing some research online, connecting with a colleague in Brazil in a live two-way video a la Skype, etc.). The higher education community received John’s keynote very favorably and felt better about Apple’s “vision,” even though it had nothing to do with our product strategy and had been made in 6 weeks!

There was no big hullabaloo about Knowledge Navigator in the couple months post Educom (the mainstream media does not attend Educom). For Macworld Expo in January, 1988, Jean Louis Gassee, SVP of Product at Apple, was the designated keynote and was supposed to roll out Apple’s new product strategy. About two weeks before Macworld in late December, Jean Louis informed John that he was not going to be ready to talk about the updated Apple product strategy. John called me and asked if we could do the Educom keynote again at Macworld, tweaking a few things for a more general audience. I called all the demoers again and they all agreed to come out to CA and we tweaked the demos to be less academic. And of course we concluded by showing Knowledge Navigator. (We took advantage of the interval to enhance the screen simulations beyond what we could deliver on the original tight schedule.). It was at Macworld that the general public, including the mainstream media and tech media, saw KN for the first time. And they immediately hailed it as Apple’s new vision. John then started using KN in employee meetings, with the press, etc. He was pictured several months later on the cover of Fortune magazine holding the balsa wood model of the KN that we had used in the video shoot. From that point forward, the mythology around Knowledge Navigator has grown unabated! Among the false legends: that it was produced by George Lucas and that it was produced using Apple’s Cray supercomputer.

Several months later, sparked by the widespread interest and acclaim for Knowledge Navigator, the Higher Education Group and our team in Creative Services led by Hugh and Doris, did another visionary video project called Project 2000, which featured Ray Bradbury, Diane Ravitch, Alvin Toffler, Alan Kay and Steve Wozniak. It is very cool, but never received the same notoriety as Knowledge Navigator.

Bud Colligan Apple’s Director of Higher Education Marketing, 1985 – 1988 November 20, 2011

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In 1980, when I was a college student, I heard Nicholas Negroponte speak about the future of computing. What stood out most was his model of convergence. Negroponte presented the model in three steps. The first slide showed the publishing, broadcasting, and computing industries as separate rings; the second slide showed the rings beginning to overlap; and the third slide showed the rings almost completely overlapped. The publishing, broadcasting, and computing industries were converging and would soon become one.

Convergence 1.0 = Publishing + Broadcasting + Computing.

Convergence has become shorthand for a series of arguments. First, all media will become digital. Second, the analog-to-digital transition will transform media production and distribution, creating opportunities and disrupting existing businesses. And third, and perhaps less obvious in 1980, once media are digital, boundaries between media types will blur and opportunities for interaction will grow, creating new ways for us to make arguments, explain ideas, and tell stories.

In many ways, Apple’s iPad fulfills Negroponte’s prediction; iPad is the first platform to bring together rich media, interactivity, portability, and broad distribution. Paul Saffo tell us that new technologies require about 30 years to move from the lab to consumers’ hands.[1] So: convergence has arrived right on schedule.

Negroponte’s model of convergence has helped me make sense of changes in the computer industry since I was a student. Today, however, the original model is no longer sufficient to describe the emerging world of networked, mobile applications. We need to revise the model.

The internet is becoming a sort of operating system, providing networked services to applications; online social networks are evolving into communications and identity platforms; and boundaries between the virtual world and the physical world are increasingly blurred. These changes are not independent; they are connected and mutually reinforcing. Service, social, and physical are converging.

Convergence 2.0 = Service + Social + Physical.

Convergence 2.0 builds on Convergence 1.0 and helps explain the new types of businesses now emerging. Convergence 2.0 also provides a framework for planning and designing applications adapted to the new environment.

The value of these models (like all models) lies in their ability to explain what’s happening and predict what might happen (or generate options). Value accrues at two scales. The models suggest large-scale changes to industries, e.g., the “death” of silver-halide photography and offset lithography. The models also suggest changes for individual businesses and products, e.g., designers can use the models as frameworks for thinking about new applications.

Convergence 1.0

Convergence gained visibility in the late 1980s and early 1990s, as end-user authoring tools HyperCard and Director led to a boom in “interactive multimedia titles”. Adventurous publishers distributed CD-ROMs (limited to 700 MB of data) through bookstores, but they had no mechanism for previewing, aside from screenshots on the packages.

The web emerged a few years later, offering more efficient distribution and easy sampling. At first, the web was a major step backward from multimedia, a step backward into the mostly-text, not-very-interactive world of HTML1. Over fifteen years, the web caught up. Bandwidth increased; streaming video became viable; and JavaScript and the DOM (document object model) matured. Coupled with mobile devices, such as iPad, the web now provides a convenient way to deliver interactive multimedia.

What else changed during convergence?

Convergence 1.0 = Publishing + Broadcasting + Computing. In 1980, Nicholas Negroponte described the impending convergence of these industries.

Publishing + Computing

Mechanical typesetting became digital; digital typesetting became electronic publishing. The Macintosh, PageMaker, and LaserWriter gave birth to desktop publishing. As desktop publishing tools improved, direct-to-plate publishing became possible. Now, mass-produced lithography is being replaced by mass-customized ink-jet printing.

“Mass” is being replaced by “self” with print-on-demand services like Blurb and Lulu for books and Shutterfly, Snapfish, and the Kodak Gallery for photos. (Kodak is a cautionary tale, an early innovator in digital photography that failed to adapt its core business fast enough to remain relevant.)

Print-on-demand isn’t limited to books and photos; it also includes t-shirt and tchotchke printing services like Zazzle and CafePress. Brian Mathews, Vice President of Autodesk Labs, predicts that the scan-edit-print paradigm of two dimensions will soon expand to three dimensions with on-demand 3D printing and even home fabrication of complex products.

Broadcasting + Computing

Negroponte used broadcasting as a metaphor, a part standing for the larger whole of the music, television, and film production and distribution industries.

Like publishing, filmmaking is becoming digital. Commercial animation is now produced almost entirely with digital tools. And “live action” is regularly merged with digital effects. Human actors have begun to act through or even inside “digital puppets”. Movie theaters are converting from celluloid projection to digital projection.

The convergence of broadcasting and computing includes not only content but also devices—the convergence of consumer electronics and computing. Music players, cameras, phones, and TVs have all become computers. Apple dropped computer from its name while turning itself into a consumer electronics company. The battle for control of the living room has begun.

Distribution has changed, too. First VCRs and then TiVo and other DVRs enabled time shifting and ended the tyranny of broadcast scheduling. VHS and Blockbuster gave way to DVDs and NetFlix. Cable became another means of delivering digital content.

Along the way, video moved online and became “on-demand”. In 2005, YouTube stuffed dancing cat videos into Flash files and embedded them into web pages. Netflix reinvented itself as a streaming video service, followed by Roku, Boxee, AppleTV, and Hulu. The TED Talks and Kahn Academy show the promise of amassing libraries of educational videos. A9’s Block View and Google’s Street View recreated the Aspen Movie Map[2] around the world.

An early sign of the convergence of broadcasting and computing was the video game. Video games have become a $10.5 billion per year business in the US, rivaling the movie industry’s $10.6 billion annual revenue.[3] The two industries are closely intertwined. Disney regularly turns films into games. Harry Potter has spawned hundreds of games. Some movies have adopted aspects of games, and an animation genre, Machinima, has emerged using video games to produce movies.

Video games have affected culture more broadly. Gamification—including game play or game principles in applications and services—has become a way to increase user engagement and has spawned articles, books, and conferences.

Publishing + Broadcasting

Shipping atoms costs more than shipping bits. Printing of newspapers, magazines, and academic journals may largely disappear. Last September, Arthur Sulzberger, publisher of the New York Times, acknowledged, “we will stop printing” as the paper reinvents itself online.[4] [5] In May, Amazon reported “selling more Kindle books than print books . . . hardcover and paperback—combined.”[6]

RSS has disaggregated publishing, and created opportunities for re-aggregators like Google News and Popurls and for attention analyzers like Flipboard, Pulse, and Zite. At the same time, thousands of new voices have sprung up in blogs, microblogs, and tweets.

Broadcast news services are also moving to the web and branching out. The BBC and CNN publish text stories. Meanwhile, the New York Times publishes slideshows with voice-overs and recently began publishing videos. (Newspapers have been slow to move into video, especially given how easy and inexpensive video has become to produce.)

Convergence 2.0

The rise of the internet requires a reassessment of Convergence 1.0. Negroponte developed his model of convergence very early. Personal computers were in their infancy. The internet was a small government experiment used mainly to exchange mail and files. Nothing like the web existed.

Negroponte has acknowledged, that none of us saw the web coming. It took a while to see, as Andy Grove later did, that “All companies will be internet companies, or they will be dead.”[7] Or as Tim Misner put it, “All hardware products want to be web-sites.”[8] Or that most human services will become networked. Or as Tim O’Reilly observed, “Virtually every application is a network application, relying on remote services to perform its function.”[9]

Convergence 2.0 recognizes that interactive multimedia exist within a networked world and depend on networked services. It recognizes that most services have a social component. And it recognizes that people are rooted in the physical world and networks are increasingly connected to things. Convergence 2.0 integrates interactive multimedia with internet-based services, social networks, and the physical world.

Convergence 2.0 = Service + Social + Physical. Combined with interactive multimedia, they provide a framework for understanding the emerging generation of network-based mobile applications.

Services

We often think of the internet as a network of networks. It’s that and more. The internet is also an emerging ecology of services. A network of networks requires interchanges, routing services, and other systems for directing traffic. DNS (the Domain Name Service) and its system of governance is an important and early example. Servers that implement protocols, such as FTP (file transfer), SMTP (email), HTTP (web), SMS (texting), and RSS (feeds) are a few of many examples of a growing infrastructure of network services.

Internet users have also come to depend on higher-level services, such as search, GPS, identification, authentication, and many more. Without networked services, interactive media would be less viable. Amazon’s WhisperNet, the wireless network that instantly delivers books to Kindle readers, is a key element in creating a seamless experience.

Some applications such as Google Maps and Facebook have turned themselves into platforms, publishing their APIs so that others can build on top of them.

A new class of network services is emerging. Services that identify context and gauge relevance—providing the right information and the right tools for the situation—are becoming important.

Social

Social networks are important in all cultures. Online social networks, such as Friendster, MySpace, Orkut, Linkedin, Facebook, and Twitter, tend to increase the probability of finding people with similar interests, the number of interactions between people, and the rate of communications between them.

But “social” is more than just online social networking sites. Wikipedia and its variants, Threadless (a crowded-sourced design service), and open-source software projects are social. Social has become a component in most networked services. Modern search algorithms, collaborative-filtering, and crowd-sourcing are all inherently social, relying on “the wisdom of crowds” to create value.

Physical

Of course, the physical world has been here all along; we’ve just taken a while to see how it will connect to form what Kevin Ashton calls “an internet of things”. “Physical” means providing context: - Where am I? What’s around me? Location—mapping and descriptions - Who am I with? Participants—identity and relationship - What are we doing? Activity—process and current stage - Why are we doing this? Goals—intention and interest - When is it happening? Time—calendar and commitments [10] And “physical” means providing information—labels, summaries, meta-data, and deep descriptions—about the things around us so that we can understand our context.

“Physical” also means providing information from a network of sensors around us, on us, and in us—sensors measuring location, motion, energy use, temperature, humidity, and a wide range of biomarkers.

Designing with Convergence 2.0 In Mind

The convergence model also has practical value; it can help product managers and designers generate options. We can identify opportunities we might otherwise overlook by using the model as a sort of checklist.

How does the application we’re designing (or redesigning) relate to the convergence of the publishing, broadcasting, and computing industries? How does it take advantage of text, rich media, and interactivity? And how does it connect with network services, social networks, and the physical world? Where does integration yield innovation, difference, and value?

Let’s look at an example. What does convergence means for e-books?

Books as digital text: No more stacks of atoms means portability. (books + handheld reader)

Books as multi-media: Don’t just tell me; show me. (books + photos, videos, animation, and sound)

Books as interactivity: Tell me more or tell me less; let me try it myself. (books + games, simulations, linking, and glosses—parallel texts)

Books as services: Access on demand, integration with other systems. (books + continuous updating, expert sources, etc, e.g. Lexis-Nexis)

Books as social nodes: Conversation topics and learning from others. (books + online social networks—shared interests, notes, highlights)

Books as places: The reader device becomes a window on a virtual overlay of the physical world providing details and explanations on demand. (books + objects in the environment, e.g. contents, instructions, history, provenance)

Taking a service-design perspective, we may consider the experience cycle in relation to convergence. Imagine a matrix with steps in the customer journey along the y axis and the six elements of convergence on the x axis. What goes in each cell? What’s the message at this touch-point? What’s best delivered through images, sound, and motion? What interactions are appropriate? Can we gamify? What network services are needed? What are the social components? How can we connect to the physical world? The resulting matrix reframes the business-unit or channel focus of traditional service blueprints, substituting a focus more suited to the emerging digital environment.

The elements of convergence provide a checklist of options to consider when assessing each step in the customer journey.

Negroponte’s convergence model was a brilliant piece of foresight. The early work of the MIT Architecture Machine Group (a precursor to the Media Lab) enabled him to predict the convergence of publishing, broadcasting, and computing. As a result of convergence, we no longer think of computing only in terms of text; we design with multimedia in mind. Communication is no longer one way; we design with interaction in mind.

But today, it’s not possible to find commercial examples of stand-alone interactive multimedia. Instead, we find it deeply embedded in networks. We find networks increasingly reliant on networked services. We find services deeply intertwined with social elements. And, of course, we find all these things embedded in the physical world. We find Convergence 1.0 deeply embedded in Convergence 2.0. We design with interactive multimedia + service + social + physical in mind.

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Read Cooper Hewitt Museum Director Bill Moggridge’s Blog Post on “Convergence 2.0.”

People invent and revise their conversation midsentence. People assume they understand enough to converse and then simply jump in; all the while they monitor and correct when things appear to go astray from the purposes at hand. This article explores how this adaptive regime works, and how it meshes with less adaptive regimes of machines and systems.

A Tale of Two Stories

A colleague told us a story of two friends discussing euthanasia. At least, that was what one thought they were discussing. The other heard the discussion as being about “youth in Asia.” Remarkably, the conversation went on for more than five minutes before the misalignment was detected.

The “Who’s on first?” comedy routine by Abbot and Costello is based on a similar misalignment. Those master comedians make the audience a knowing third party to the difficulties.

The usual accounts of such conversations would have it that this is an exceptional case, and usually speakers are well aligned. These accounts hold that good (or even perfect) alignment is necessary for conversation.

We explore an alternative perspective: These stories of misaligned conversations are not different in kind from more typical, apparently well-aligned conversations. Rather, we hold that all interactions are necessarily misaligned to some degree, and that the mechanisms that make conversation “good” are not those that bring speakers into perfect alignment, but rather those that maintain a degree of alignment appropriate for the situation. The work of being a good conversant is to produce alignment that is just good enough for the purposes at hand.

Getting Started

If you were starting a conversation with a Martian, you might reasonably be uncertain about what you could assume concerning the Martian’s view of the impending conversation—its views on interactional moves, language, subject matter, even what a conversation is. You would have difficulty knowing where to start.

In contrast, when you meet a colleague in the hallway, you usually get started with little difficulty. You assume that they will speak, using the same language you used yesterday when you two last spoke; that a friendly greeting is a good starting subject matter; and that the conversation will be composed of both of you taking turns, sometimes overlapping, with an end in the not too distant future.

We argue that the starting situations with your colleague and with the Martian are different only in degree, not in kind. In each case, both of you make a set of assumptions about the situation. And then one or other (or both!) of you will simply make some interactional move. The Martian might wave its ears; your friend might say, “Did you have a good weekend?” And as a result of that first move of plunging in, you immediately have all sorts of information that you can use as evidence for or against the assumptions you made about the conversation. Yes, the conversation appears to be talking (rather than ear waving, or crying, or hugging, or…); yes, it appears to be in English (although no doubt you may have on occasion started a conversation with “Bonjour!” to a friend who you know also speaks French); yes, it appears to be starting with social niceties; and yes, we seem to be embarking on a hallway conversation.

There is nothing determined about any of this. The world we live in emerges as we live it, and we have to take it as it comes, and make of it what we can. So you have to start with assumptions, engage in conversation on the basis of those guesses, and subsequently adjust your assumptions as you produce evidence from the engagement.

And at the same time, your partner in this game is doing exactly the same thing: starting with assumptions, engaging, and using your conversational moves as evidence for adjusting those assumptions.

Adequate Alignment

As the conversation continues, both of you make conversational moves and monitor each other to see if you make sense out of each other’s moves. In the normal (normative) case, the moves provide evidence that supports, extends, or incrementally changes the assumptions with which you started.

At the same time, both of you are monitoring each other to see whether you are being “understood”—whether the other person appears to be making enough sense out of what you said. You cannot read their mind. However, their responses are evidence of whatever sense they made of your move.

In a similar vein, when you are listening, in order to provide information for your conversational partner’s use, you may signal that you are making sense of their moves: Maybe you make eye contact, give a nod or a smile, even engage in an overlapping completion of their sentence.

We achieve continued conversation by maintaining mutual assurance that each of us can make enough sense of each other’s moves.

Trouble

However, sometimes making sense is not so easy. In the “Who’s on first?” routine, the evidence of trouble is immediate and profound. In the “euthanasia” scenario, trouble took surprisingly long to emerge.

Confronted with trouble, the next conversational move may address not whatever is under discussion, but rather the difficulty in interacting. This may take the form of “What are you talking about?” or a furrowed brow, or a conversational turn about the trouble: “When you say ‘euthanasia,’ are you talking about assisted dying?”

Conversation analysts refer to such shifts in subject matter from the matter at hand to the conversation itself as “breakdowns.” A breakdown in this sense is a response to a feeling that our interaction is not working well enough, and that the conversation should be interrupted and refocused on the conversation itself. When a hammer handle breaks, fixing the roof stops, and fixing the hammer begins. We shift focus to converse about the conversation and “repair” the breakdown.

Once a repair has been concluded, the conversation can pick up where it left off, but now possibly with improved alignment—a better grip on the mechanics of conversing, the meaning of the terms, even the purpose of the discussion.

Levels

In conversation we always work on multiple levels: We monitor the comfort, interest, and comprehension of our partners; adjust our approach; maybe switch topics, etc. Explicit repair of breakdowns is an unusually clear case of switching primary attention to a different level.

In most situations we shift our emphasis between these levels so easily that we are hardly aware they exist and so we may find it difficult to make our multilevel negotiations explicit.

Sense Making

We are all very good at making sense of situations, fitting things into the context and moving along. The sense we have made may later turn out to be flawed, but we are really troubled only if we can’t make our understanding work well enough for the purposes at hand.

Sometimes we find that new activity is confirming evidence: We can make sense of it without any change to our assumptions or understanding. It fits right into the sense we have made of the world.

Alternatively, we may have to change our assumptions in order to make sense of a move. We might think the sky is blue, and our conversational partner might say, “Looks like rain.” On observation, low clouds in the west are indeed there, so we adjust our blue sky to have low western clouds, and we adjust our assumptions about our partner to reflect that they see the world that way too.

When something doesn’t fit, we tend to look for the smallest (and often most local) changes in our view that will have things make sense. After which, we may opt to move on. But we also often retain a concurrent view of how well we are doing in making sense of things, how much work we had to do, how happy we were with the result, and whether there are loose ends—simply, is the conversation working?

Because it is expensive to drastically reset our assumptions, we are inclined to delay doing so until we are reasonably sure about being unsure. Therefore, the suspicion of misalignment often develops over a number of interactional moves, finally reaching the point where we feel the effort of realignment is worthwhile.

This process of working within common assumptions, noting anomalies, seeking the smallest changes that can get us back on a track that seems to make sense, and sometimes reluctantly accepting the need for a more radical overhaul of our conceptual framework exactly fits the pattern Thomas Kuhn first described in The Structure of Scientific Revolutions—though on a much smaller scale. Each partner has their own tacit, informal theory of the conversational ground, and the interaction proceeds by growing and/or challenging the partners’ theories. In our design conversations, our local renegotiation of meanings often ripples out to shift our design goals, directions, and fantasies, and in the most fruitful cases may pave the way to revolutions.

Repair

When we shift focus to improve alignment, we are working to repair the breakdown: A; “Bonjour!” (start shift) B; “Oh, parlez-vous français?” (start repair) A; “No, but I grew up in Toronto and struggled with French for five years in high school.” B; “Oh, I see. (end repair) OK. (end shift) Bonjour to you too.”

And, of course, shifts and repairs are themselves conversation. You and your conversational partner have to deal with them in exactly the same way as any other conversation—including the ones in which you encountered a breakdown. You have to use the same conversational mechanisms and practices. In tough cases, when implicit coordination breaks down, you have to hope your partner recognizes that you are shifting focus and talking about the talk, not about the weather. You have to make assumptions, monitor, adjust, and continue. You have to work to stay adequately aligned through this sub-conversation and to get back to the interrupted one.

Uncertainty

In talking about the conversation, you are using the same assume-act-monitor-adjust style of communicating as in any other conversation. And you get only circumstantial evidence that you are understanding what sense your partner is making of the whole thing.

When you work on terminology and meaning and philosophical frameworks, you may infer a lot about the alignment of your respective views. However, you cannot ever know for sure what sense your partner is making, nor how closely aligned that sense is to the sense that you are making.

Aligned Enough

Fortunately, you don’t need to know your partner’s sense of the conversation precisely or certainly. You need only enough evidence to stay confident that your alignment can meet the needs of the conversation. Small talk about having a nice day will probably not require exploration of a partner’s sense of the terms of meteorology. But discussion of a hurricane might.

Your understanding of the purpose of the conversation will tell you how much alignment is needed and how hard you need to work at achieving it. And of course your partner will have their own view of the conversational purpose and their willingness to invest in achieving alignment. Their view may be different. How different? Recursively, the answer is: however much each of you find sufficient for the purposes at hand.

Stability

As the conversation continues, confidence in sufficient alignment can build and be reinforced by the success of the preceding talk: The same term continues to be used in ways that continue to make the same sense; conversational moves do not lead to incompatible responses, and any breakdowns are easy to repair. Overall, a feeling of stable convergence can develop.

We may think of this as a “fixed point” of the conversational negotiational activity, in the mathematical sense that the ongoing conversation keeps converging on the same underlying understanding while continuing to add layers and details to that understanding.

Further, stability can accumulate. Each discussion means that the assumptions for starting the next discussion can be better, convergence can be faster, and so forth. This is sometimes referred to as “having good bandwidth” with someone. Indeed, if our communication channel is fixed—for example, face-to-face conversation—we get greater effective bandwidth. Conversely, if we just want to convey a specific point, we can do it with less bandwidth. This metric has been partially formalized in some three level accounts of adaptive communication.

As the background becomes stable, we are increasingly tempted to treat it as if it were frozen forever. This can make it difficult for us to “challenge the brief,” to question and revise the context of our own designs. Great designs typically involve un-freezing and renegotiation of the background.

Codes and Negotiations

Fixed points in conversation remind us of classical information theory, which starts from the premise that communication always depends on a fixed “code” that defines the possible messages and the encoding of those messages in the channel. Information theory was inspired by the experience of building a national telephone network and has subsequently become the standard basis for designing machine-machine interactions.

In our view, this is an optimized case of collapsed negotiation-based conversation, with completely stable fixed points of conversational meaning. This raises two questions for us: Where did the codes come from, and how can codes change?

Where did the code come from? Information theory is concerned with optimizing communication efficiency in a static environment. As mentioned above, in conversations based on stable understandings, fixed points—the codes—can be frozen and sedimented.

How can codes change? In code-based communication there is no place for negotiation of the codes, so system-builders must negotiate outside the code itself to respond to misalignment. Such negotiation mechanisms need to be included in a full account of how codes work in the real world. That is where our “larger” perspective is required.

Consider HTML. A given version of HTML may be viewed as a classical information theoretic code, but in practice HTML is defined by an ecology of roughly compatible codes being generated and accepted by multiple (buggy) software packages, and furthermore constantly being renegotiated at higher levels by developers, standards bodies, and so forth. We have to consider multiple levels to understand the evolution or even the current status of HTML.

While our view is unusual in most parts of computer science, powerful conceptual tools are available to support it. It has been explored in different forms in cognitive linguistics and has been formally analyzed in various ways using game theory.

So both perspectives are necessary; they complete each other. Negotiated systems can gain efficiency from stability when it has emerged, and code-based systems need negotiation, so that they can be responsive to a diverse and changing world.

Change

Because conversation does not depend on preestablished agreements, and the mechanisms of monitoring and repair help us handle a partner’s conversational moves that we can’t understand, this conversational practice is also suitable for dealing with a changing world.

If a partner changes their mind about something—and that change is relevant to a discussion—the mechanisms for conversation have the capacity for detecting the mismatch from the conversational moves, shifting focus, negotiating adequate realignment, and resuming.

Agreement

We often say we “reach agreement” with others on some matter. We talk as if there is a view that we then all share (a “common ground”). In contrast, our view is that the idea of “reaching a shared view” is a linguistic gloss, shorthand for something much more complex and powerful. Agreement is not a single ground. Rather, it is a commitment to continue to work together to maintain coherence.

We would say the parties to an agreement interact with each other until they each can construct senses for themselves and for each other that are aligned enough, so they anticipate that their subsequent individual actions will be coherent enough to achieve their goals.

A common failing of meetings is that participants engage in “collaborative misalignment”—working hard to get language that all can agree to but avoiding testing whether the inevitably disparate senses carried away will lead to collectively coherent action. Another failing is that on later encountering a world that was unanticipated during the meeting, individual action is based on personal understanding alone rather than on the personally aggregated sense of the disparate understandings of all.

Coherence, Responsiveness, and Scale

Finally, we see this perspective as strongly supporting the need for systems to both be responsive to many particular viewpoints and also to achieve coherence in activity, and to do so even as scale increases. Consider scaling the achievement of conversational alignment over many people doing many things. Meanings, purposes, and negotiations are local, but because of overlapping alignments, they begin to cohere into a commonality that we think of as the meaning of language—again, at risk of reverting to the one level code perspective. We believe it is important to stay aware that this sense of commonality is a gloss for a vast dynamic network of local exchange and negotiation of meaning. Our systems must support both the efficient use of commonality and the renegotiation of meaning when the commonality is inadequate to the needs of participants.

Design

Unlike communications systems, people interact with each other without first agreeing on communication protocols. This is possible because they start and continue to act on the assumption that they understand enough to communicate—and then they interact. All the while they monitor and correct when things appear not to be working well enough for the purposes at hand.

As designers, conversations are at the center of our practice. Now we must challenge ourselves to design systems that accept and support users’ conversations. Machines cannot yet negotiate alignment, but they can and should help their users carry on conversations, recognize breakdowns, and negotiate meanings to meet the needs of a heterogeneous and changing world.

About the Authors

Austin Henderson’s 45-year career in HCI includes research, design, architecture, product development and consulting at Lincoln Laboratory, BBN, Xerox (PARC and EuroPARC), Fitch, Apple, and Pitney Bowes. He focuses on technology in conversations in a rich and changing world.

Jed Harris started out exploring cultural anthropology, linguistics, philosophy of science and artificial intelligence research, and then spent forty years in research and development at SRI, Stanford, Xerox PARC, Data General, Intel and Apple. He’s now happily meshing technology with the human sciences.

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This article examines The Model-View-Controller Pattern in a Rails-Based Web Server.

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A design innovator argues that design learning is a prerequisite for design thinking.

You have said that design is stuck. What do you mean?

Design practice does not learn. As a profession, we don’t even know how to learn.

We’re stuck. Trapped in the past. Unable to move forward. Unclear on what forward might mean. Lacking mechanisms to build and share knowledge. Lacking even a model of design knowledge.

In fact, the problem is so structurally embedded, so pervasive, so deep, that we don’t see it.

Can you give an example?

In 1985, in Boston, the AIGA held its first national conference; speakers included Nicholas Negroponte (a famous technologist) and Milton Glaser (a famous designer). Twenty years later, the AIGA conference returned to Boston and again included Negroponte and Glaser.

In his 2005 speech, Negroponte talked about the One Laptop Per Child project. Glaser showed some beautiful posters and talked movingly about human rights.

What struck me was how much things had changed in Negroponte’s world and how little things had changed in Glaser’s world.

During the intervening twenty years, computing power, storage capacity, and network speeds doubled more than ten times, while costs remained roughly the same. Personal computers grew from toys to necessities. Mobile phones, the internet, and social networks arrived.

During the same twenty years, the big changes in design were not about design; they were about technology—computers and the internet— changes forced on Glaser’s world by Negroponte’s world.

What this examples shows is that the world of computers evolves. Like the worlds of biology and physics, it has learned how to learn. It bootstraps existing knowledge to create knew knowledge.

That’s what academic disciplines do, but it rarely happens in design.

Why not? What’s holding design back?

The short answer is art schools. Most design programs are housed in art schools. And art school teaching still follows a medieval model: Master and apprentice.

Studio courses are mostly about socialization— sharing and creating tacit knowledge through direct experience. Students learn by watching one another. Teachers rarely espouse principles. Learning proceeds from specific to specific. Knowledge remains tacit.

Practice is much the same as education. Over the course of a career, most designers learn to design better. But what they learn is highly idiosyncratic, dependent on their unique context. The knowledge designers gain usually retires with them.

Rarely do designers distill rules from experience, codify new methods, test and improve them, and pass them on to others. Rarely do designers move from tacit to explicit.

But aren’t things changing?

Slowly. Publishing has become a requirement for tenure in design programs at major universities, but studio work remains the overwhelming factor in tenure decisions.

Publishing matters less in second-tier universities and independent art schools. And it is almost a black mark in for-profit design schools, where practical experience remains the main criterion for hiring.

Making things worse, art school tenure committees include non-design faculty, with little appreciation of design research.

The focus on design research at a few top schools is a positive development, (e.g., IIT/ID, CMU, NCSU, Royal College, Delft). Journals such as Design Issues, Visible Language, and Interactions publish interesting articles. But design journals are not widely read. And design research rarely affects practice or teaching.

(A few design blogs are widely read, but they aren’t building lasting knowledge.)

Why isn’t design research making a difference?

Design doesn’t have feedback loops that include funding, research, publishing, tenure, and teaching, These feedback loops ensure quality. Without them, design will remain stuck.

In contrast, engineering, medicine, and biology have strong feedback loops. Government and industry fund research, which leads to military, health care, and commercial applications. Peer reviewers look for breakthrough papers and filter out those that tread old ground. Tenure can be awarded on merit. And graduate students and professors are able to attract VC funding, start companies, and apply their ideas (e.g., Sun, Netscape, Yahoo, Google).

Setting up strong feedback loops for building design knowledge will be difficult. Existing institutions are unlikely to change. We need new ones.

What’s the solution?

Visually-oriented design programs should be left to do what they do well. Design should move out of art schools and into its own professional schools, alongside schools of business, law, and medicine.

Drawing and form-giving are not the essence of design. Seeing patterns, making connections, and understanding relationships are.

Modeling, mapping, and visualizing information should replace figure drawing. Systems theory and process management should replace 2D and 3D foundation courses. Social sciences and communications theory must be part of design curricula (e.g., ethnography, cognitive psychology, economics, rhetoric, semiotics).

Instruction should shift from an emphasis on making to a balance of making, observing, and reflecting.

The case-study teaching method works well in law, business, and medicine. We need to write and teach design cases. We need to integrate design cases and other research into studios.

Why does this matter? What are the practical consequences?

Value is created by developing new products and services. But we don’t really know how to design products, services, or organizations. That great products occasionally emerge is something like magic. (Design thinking remains a special form of this magic.)

Product management is not yet a discipline. It isn’t taught in design schools or in business schools. We have no theory of product management. We don’t even have a theory of products.

Those are giant holes.

What’s more, design is no longer concerned only with things. Increasingly, design is concerned with systems—and now systems of systems or ecologies.

In a sense, these systems are alive. They grow and co-evolve.

Designers and product managers cannot always control them. Instead, they must create conditions in which they can emerge and flourish.

All this requires new thinking and new knowledge. It requires design practice to learn.

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Designers often speak of design as a process. Typically, design thinking leads to design making, which leads to artifacts. Yet the design process also leads to something more—to new knowledge. Thus, we might characterize designing as a form of learning.

Curiously, the converse is also true. We might characterize learning as a form of designing. That is, the process of observing, reflecting, and making (and iterating those steps) may aid learning. Several designers and teachers have recognized the link between designing and learning and are bringing designing into curricula not just in college but also in high school and even elementary school. See, for example, a recent New York Times article, “Putting New Tools in Students’ Hands” [1].

I acknowledge framing designing as learning (without providing further explanation) may be little more than trading one black-box process for another, but if we can find robust models of learning, they might prove useful in designing and might suggest ways to improve the design process.

The connection between designing and learning was brought into sharp focus for me last summer while editing an article by Maurício Manhães [2], who wrote, “Design and innovation are both knowledge creation processes” [3]. What struck me about Manhães’s article was that he introduced the SECI model of knowledge creation and explicitly applied it to analyzing and improving the design process. I was further struck by the similarity or even isomorphism of the SECI model and the analysis-synthesis bridge model described in this forum in the March + April 2008 issue [4].

Introducing the SECI model

The SECI model comes out of research in “knowledge management,” which is related to “organizational learning,” “business administration,” and “information systems.” SECI stands for socialization, externalization, combination, internalization—a model of knowledge creation proposed by Ikujiro Nonaka [5]. (It’s interesting to note that Nonaka received his MBA (1968) and Ph.D. (1972) from UC Berkeley, when West Churchman was teaching in the business school and offering seminars that included design-methods pioneers Horst Rittel and Christopher Alexander, who were on the faculty of the UCB College of Environmental Design. The problem of managing knowledge created in the design process is described by Horst Rittel in his work on Issues Based Information Systems (IBIS), which helped spawn an area of research in computer science known as design rationale [6].)

Nonaka sees ongoing knowledge creation as the source of continuous innovation and continuous innovation as the source of sustained competitive advantage. “When organizations innovate, they do not simply process information, from the outside in, in order to solve existing problems and adapt to a changing environment. They actually create new knowledge and information, from the inside out, in order to redefine both problems and solutions and, in the process, to re-create their environment.”

Nonaka considers knowledge “as a dynamic human process of justifying personal belief toward the ‘truth.’…This understanding emphasizes that knowledge is essentially related to human action….As a fundamental basis for the theory of organizational knowledge creation, we focus attention on the active, subjective nature of knowledge represented by such terms as commitment and belief that are deeply rooted in individuals’ value systems.” [7][Italics are from the original.]

“The basic argument is that knowledge creation is a synthesizing process through which an organization interacts with individuals and the environment to transcend emerging contradictions that the organization faces” [8].

The process moves from tacit knowledge to explicit knowledge and back. “Tacit knowledge is personal, context-specific, and therefore hard to formalize and communicate. Explicit or codified knowledge, on the other hand, refers to knowledge that is transmittable in formal, systematic language” [9]. Tacit knowledge tends to be specific to a context (available in a particular time and place), practical, routine, and procedural. Explicit knowledge can transcend a specific context (and is transferable to other times and places) and tends to be rationalizing, theoretical, and declarative.

Nonaka postulates four modes of “knowledge conversion that are created when tacit and explicit knowledge interact.”

• Socialization (tacit to tacit) “is the process of converting new tacit knowledge through shared experiences in day-to-day social interaction.”

• Externalization (tacit to explicit) is a process whereby “tacit knowledge is articulated into explicit knowledge…so that it can be shared by others to become the basis of new knowledge.”

• Combination (explicit to explicit) is a process whereby “explicit knowledge is collected from inside or outside the organization and then combined, edited, or processed to form more complex and systematic explicit knowledge…The new explicit knowledge is then disseminated among the members of the organization.”

• Internalization (explicit to tacit) is a process whereby “explicit knowledge created and shared throughout an organization is then converted into tacit knowledge by individuals…This stage can be understood as praxis, where knowledge is applied and used in practical situations and becomes the base for new routines.”

Successive iterations of the process form a spiral, with each loop amplifying the knowledge to a higher-level knowledge-creating entity; the process moves from individual to group to organization to community of organizations.

How the SECI Model Maps to the Analysis-Synthesis Bridge Model

The goal of this article is to introduce the SECI model to the design community. At the same time, this article also argues the SECI model and the analysis-synthesis bridge model are not just similar but also isomorphic. That is, they use different terms to describe essentially the same process. More precisely, the analysis-synthesis bridge model and related models [4] (Robinson model, Kumar innovation model, Kaiser/IDEO model, and Suri/IDEO model) are specific instances of the more general SECI model. (The Beer model and Alexander model are slightly different, though still roughly analogous. The 1966 Beer model is interesting in relation to SECI, as it describes the process of applying scientific models to managerial situations, a special form of knowledge creation.)

The analysis-synthesis bridge model describes a four-step design process. It begins with 1. directly observing a current situation, 2. reflecting on observations of the current situation to create a model representing essential elements, 3. reflecting on the model of the current situation to create a second model representing essential elements of an improved situation, and 4. instantiating the second model in a physical form or prototype. The process described by the analysis-synthesis bridge abstracts essential characteristics of both current and improved situations as a “scaffold” for moving from researching to making in the design process; using models as a bridge may be especially useful in complex areas of practice, such as software design, service design, and systems design, where the path from researching to making may often be unclear.

The four steps of the analysis-synthesis bridge model correspond to the four steps of the SECI model:

Step 1:
Observing the current situation is a form of socialization. Insight-gathering methods or problem-finding methods, such as ethnography, often rely on acquiring tacit knowledge through inhabiting a specific context and interacting with others in that context. Nonaka writes, “The key to acquiring tacit knowledge is experience. Without some form of shared experience, it is extremely difficult for one person to project her- or himself into another individual’s thinking process.”

Step 2:
Modeling the current situation is a form of externalization. Sharing one’s experience and insights with others, for example, writing an ethnography, requires abstracting and generalizing. Nonaka writes, “Externalization…is the quintessential knowledge-creation process in that tacit knowledge becomes explicit, taking the shapes of metaphors, analogies, concepts, hypotheses, or models.” He adds, “To make a hidden concept or mechanism explicit out of accumulated tacit knowledge, abduction, or retroduction is effective rather than induction or deduction.”

Step 3:
Modeling a better situation is a form of combination. A designer looks at aspects of what is and imagines combining them with other things that he or she has experienced or imagined. Nonaka writes that combination “synthesizes knowledge from many different sources in one context. The combination mode of knowledge conversion can also include the ‘breakdown’ of concepts. Breaking down a concept…also creates systemic, explicit knowledge.”

Step 4:
Instantiating a model is a form of internalization. Prototyping requires working out many details and determining many relationships, creating a new level of knowledge of the model on which the prototype was based. Nonaka writes, “Explicit knowledge, such as product concepts or manufacturing procedures, has to be actualized through action, practice, and reflection so that it can really become knowledge of one’s own.”

The bridge model is a specific instance of the SECI model.

Rotating the SECI model 90º counter-clockwise aligns it with the bridge model—so that they both “begin” in the lower left corner.

By rotating the SECI model, we can see that Socialize and Externalize tend to look backward at more known situations, and Combine and Internalize tend to look forward to less known situations.

Like the SECI model, the analysis-synthesis bridge model comprises four quadrants of a two-by-two matrix. In the SECI model, step 1 is in the upper left corner. In the analysis-synthesis bridge model, step 1 is in the lower left corner. Rotating the SECI model 90 degrees counter-clockwise aligns the two models. Nonaka does not label columns or rows in the SECI model. However, the analysis-synthesis bridge model labels the bottom row “descriptive/concrete” and the top row “interpretive/abstract.” The left column is “researching a current situation,” while the right column is “prototyping a future situation.” It’s not much of a stretch to apply these labels to the rows and columns of the SECI model.

The SECI model explicitly describes the iterative nature of the knowledge creation process by including a spiral. The analysis-synthesis bridge model does not refer to iteration directly, though the authors assume readers understand the design process as iterative. However, the Kaiser/IDEO model, which is isomorphic to the analysis-synthesis bridge model, includes a loop. And Kumar’s innovation model, which is also isomorphic, does explicitly include a spiral!

The SECI model is just one part of Nonaka’s theory of knowledge creation, which also comprises Ba and dialectic. Ba is a shared “place” or context—loosely bounded and evolving—that “enables a dialectic process among the actors.”

“A firm can be viewed as an organic configuration of various Ba, where people interact with each other and the environment based on the knowledge they have and the meaning they create.” This notion is similar to the Geogeghan and Pangaro notion that a firm is a collection of conversations for understanding, agreeing, acting, and learning [11]. Nonoka argues that “knowledge is created through the synthesis of the contradictions between the organization’s internal resources and the environment.” His notion of the dialectic spiral of synthesis of contradictions is similar to Rittel’s notion of designing as a process of reframing and argumentation.

While outside the scope of this article, Nonaka’s notion of Ba and his insistence on Ba and dialectic as parts of the knowledge-creation process suggest further opportunities for applying his work to designing.

Conclusion

Today, the practice of software and service design—indeed most design practice—is ad hoc, performed on an ‘as-needed’ basis and adapted to whatever context the designers encounter. Most design work still proceeds on an industrial-age model of ‘edition’ and project, in which design is ‘finished’—rather than on an information-age model of continuous improvement, multi-year beta, and organic growth, in which design is never finished. In the future, successful software and service organizations will recognize that software and service design are ongoing processes. Each design iteration and implementation leads to new knowledge. We need systems to identify, capture, and build on that knowledge in an ongoing process, if we are to develop a design practice appropriate for an information and services economy [10]. Applying the SECI model to designing is a step in the right direction.

If both the SECI model and the analysis-synthesis bridge model reasonably represent their subjects—learning (or knowledge creation) and designing—and if the models are isomorphic, then we may say that learning and designing are isomorphic, at least from one frame.

This conclusion has profound ramifications for both business practice and design practice. For business practice, it suggests that since knowledge creation is a central activity of the firm then designing is also a central activity of the firm. That is, designing is an important form of knowledge creation and thus the heart of value creation within the firm. For design practice, it suggests further study of the mechanisms of knowledge creation and knowledge management and their relation to traditional and emerging notions of designing. That is, learning is an important part of the design process, not just in design education and academic design discourse, but especially as design is practiced.

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Editor’s Note:
After a long career in systems engineering and design, John Rheinfrank died on July 4, 2004.

John’s Ph.D. dissertation explored what he called “organic systems theory,” or what’s now called “complex adaptive systems”—bridging multiple disciplines and theoretical frames (e.g., biology, computing, economics, psychology, and sociology). John spent most of his professional life applying principles derived from living systems to designing systems for people—from design languages that could serve as the foundation for a broad range of reprographic machines for Xerox, to personal information and communication appliances for Philips. In essence, he wanted us to design systems that are alive.

In the years since John’s death, complex systems have become deeply engrained in our everyday lives, from Facebook and Twitter to the interconnected financial systems that plunged us into the credit crisis. When John learned he was sick, he began working on a book on the relationship between design and systems. Sadly, he never finished, but some of his core ideas were preserved in a presentation on moving from static to adaptive worlds. John saw adaptive worlds as a new way to frame interaction design, which makes it an important topic for interactions. This presentation was his way of helping us make the leap from the present to the future he could already envision. Working from John’s presentation slides and a tape of his talk, we have summarized his ideas.

—Hugh Dubberly

The history of design is mostly the history of designing static worlds—objects, messages, and spaces that are fixed and invariant.

In a static world we are forced to adapt to the object. For instance, this chair is this height. It doesn’t matter if you’re short or tall—the height of the chair remains the same. Most chairs are still like this today. They are static objects. Your back and your butt must adjust to the chair. It is an object that we adapt to.

Along comes a new kind of chair. Not only can you turn in it, but you can also raise and lower it—or even tilt it to a position that is right for you. The designer’s role expanded from that of arbitrator of form to creator of resources for interacting with the chair. This meant the designer had a whole new range of representations to account for and choices to make. Because now that we can adjust the chair, we are more comfortable as we work.

Many early interaction design efforts focused on making things simple for naïve users. The idea was to take resources for decision-making away from people—automating features in cameras, for example—to reduce cognitive load or how much users would have to think about what they were doing. Reducing cognitive load often comes at a price: limiting choice and possibilities for expression. For example, point-and-shoot cameras with one wide-angle lens, fixed focal length, single f-stop, and single shutter speed. This approach should raise ethical concerns for designers, especially when it “de-skills” people.

In the early days of the photocopier, the machines would often fail. Problems as simple as a paper jam (identified by a cryptic “Error E31” on the display) would require calling a trained service professional to come on site to open and repair the machine. A team from design consultancy Fitch-Richardson Smith, led by John Rheinfrank, helped Xerox shift its cultural paradigm away from training service professionals to embedding information in the machines so that the machine users could fix problems themselves, quickly and effectively. In essence, the redesign provided information that helped people learn to use the machine as they used it, by offering a rich set of resources for managing the process (in this particular case, in the event of an equipment malfunction).

Adaptive Worlds

A new order of systems is emerging, that adapt to the worlds in which they play a part. Although the form they take varies widely from example to example, these systems all have in common some means for: 1. “perceiving” two or more states of the environment in which they are embedded; 2. creating, based on these perceptions, a “model” of the environment around them; and 3. adapting, based on this model, in a fashion to best meet the performance objectives of the system in the face of a changing environment. This need not be a one-shot event—it can occur continuously over time. For example, multilevel digital games (the system) have access to the score achieved by a player (perception of the environment), and, knowing the level at which that score was achieved, can assess the player’s skill level (creating a model) and adjust game difficulty in a way that keeps the player in the flow between boredom (this is too easy) and frustration (this is too difficult), which, ultimately, is the game designer’s goal (adapting to meet a performance objective).

It is not much of a stretch to go beyond this simple adaptive system loop to incorporate additional means of manipulating a system’s characteristics by the users engaging with that system (e.g., in our game example, consider the qualitative expansion afforded by Second Life). Dynamically co-constructed adaptive worlds give both creators and consumers the ability to design or improvise new activities that honor specific abilities as they emerge. In John’s words: “In this framework, we start to build worlds that collaboratively participate in the (co-evolution) of our individual and collective abilities. At the simplest level, we no longer are forced to adapt to the worlds in which we live, play, learn, or work. The worlds now shift to meet our abilities, to anticipate whatever they are or what we want them to be” [1].

John saw this “ability centered” framing of interaction as a way to enrich the user-centered notions that currently drive much of design. He felt that “user centered” focused on modeling explicit, articulated needs. Evaluating designs (usually specifications) against the articulated model was often seen as sufficient. With ability-centered design, the question is not What qualities of the user will allow them to perform this task in the easiest fashion? but rather, What are the latent, masked needs, the unobservable, inconceivable needs? In ability-centered design, functional prototyping and evaluation by end users are paramount.

What characterizes dynamically enabling adaptive worlds, and how can we even hope to design for them?

While static worlds are about objects and interactions, adaptive worlds are about flow and emergence. In a static world, objects are inflexible—they don’t have the ability to change or adapt built into them. In an adaptive world, objects and processes modify themselves based on information gleaned from people, either through sensing or explicit input. A service experience in an adaptive world would feel as though it had been custom-designed for every person. As each visitor entered the store, the environment would sense them and inform the staff of their preferences, their past purchases, etc.

Dynamically Enabling Flow

When John spoke of flow, he was referring to Mihály Csíkszentmihályi’s notion of flow [2]: a correspondence between you and what you’re doing—where the challenge you face matches your ability. You enter into a separate mental space, and you move in a different way. You’re in the “zone.”

Now, as we shift to co-constructed adaptive worlds [3], there are the beginnings of an adaptation between the things that fill my world and myself. There’s also a shift in learning from passive to active. Passive learning is the assumption that people are empty vessels for instructors to pour information into—instructors deliver content and students receive or digest [4].

Active learning involves not just hearing or reading facts, but also doing things that put them to use in a way that makes them real for the learner. Active learning requires a participatory learning environment where learners and instructors “play,” leverage their shared context, and co-construct knowledge in relation to each other and their experiences.

Advanced robots are just beginning to “learn” and adapt to the terrain around them, while constantly monitoring and adapting to mission-significant objectives like threats and the people they have to rescue. Another example of this new kind of system is financial tools geared toward consumers. These days, when you bring your money to a well-designed bank, the system evaluates your “life stream,” a constantly changing model of how you and people like you act in your progressing stages of life. So as you and your life change—for example, you buy a car or begin a family—bank services are reconfigured for you. PNC Bank’s Virtual Wallet, based on the money mind-set and financial lifestyle of Gen Y, takes on some of these qualities. Today the wallet helps people plan and save. The resources behind the wallet grow and change over the life of the user.

John believed “this dynamic can apply locally at the smallest scale and globally to the composite of forces that shape our lives. Until recently, we’ve done this coarsely, marginally, and at tremendous cost and over extraordinarily long time periods. We add handicap-access ramps to old buildings and design new buildings that seek to be barrier-free. The limitations of these world-shaping objects define what it means to be disabled. The objects—our designs—quite literally create the disability. This need not be so. The objects we can make today and tomorrow are no longer dumb in the exclusive light of our intelligence.”

What John called “emergent systems” are an ecology or community of these adaptive systems, in which elements in the system learn, adapt, and share the knowledge they gain about the world with other pieces of the system. An example of this type of system is Google. The system shares knowledge from Web search to Maps search to Images search, to help you find the thing you are looking for as easily as possible. Many organizations behave as emergent systems, for instance, the governing system of the Internet. Each node doesn’t hold enough power to sway the entire system, but as events arise, the standards and systems adapt to the emergent needs.

To explain the technology and trends that are enabling adaptive worlds, John mapped the elements across two axes. The horizontal axis is a continuum of materiality, from human being, thinking, and doing to machine being, thinking, and doing. The vertical axis represents the nature of the action you are undertaking, from understanding to doing. The elements on the bottom help you understand the system or the world better, while the elements on the top enable to you do things better. John provided four examples of trends that are moving toward complex adaptive systems.

Universal design is an example of a carbon-based emergent system. OXO created a line of kitchen tools specifically designed for the arthritic or the handicapped, but they ended up appealing to a much broader audience. People soon realized the OXO tools felt fantastic in the hand and made performing functions much easier and more enjoyable.

Ubiquitous computing is an example of a silicon-based trend. Ubiquitous computing is the increasingly embedded nature of sensors, processors, and networks in the physical objects that surround us, from medical equipment to our mobile phones.

A good example of assistive or augmentative technology is Dean Kamen’s iBOT-powered wheelchair. The unique technology and orientation was intended to enable those with severe mobility problems to ascend stairs. Anyone who uses this device daily will also mention the unintended appeal of the device: the mechanism can readily lift the user to be eye to eye with anyone they interact with.

Finally, an example of devices that help one understand better is the work done on Xerox machines. As noted earlier, the machine evolved into something that could teach users how to diagnose problems and quickly return the machine to a fully operational state.

John saw three different resources enabling adaptive systems. The first are the platforms for creating experiences, “the auto-catalytic foundation for co-constructing fluid, extensible interactions and meaningful relationships between people and hybrid physical/virtual worlds that matter to them.” The second is the people, places and things that contribute and benefit from the adaptive worlds, what John referred to as the material substrate. Last are the underlying elements that make up the networks within adaptive worlds, infrastructures that are self-organizing rather than guided by outside forces. At least one component from each of these clusters is required for any adaptive emergent system.

We now have the capacity to design and build objects that are active, semi-autonomous, evocative, emergent, mixed-initiative partners in the (re)formation of worlds that are magical by today’s standards.

Eric Schmidt, CEO of Google, recently suggested “people are not ready for the technology revolution that’s going to happen to them.” He was referring to the ubiquitous role of technology and the collection of data to enrich our interactions with the artificial. Many organizations, including Google, are under fire for provoking privacy concerns over the handling of their users’ data. The complex adaptive systems that are beginning to emerge are a testament to the benefits of systems that can learn and engage in a dialog.

What does this mean for interaction design? More broadly, what does this mean for the systems we will interact with in the future?

John, when speaking to a group of present and future designers, said: “Don’t be satisfied with my native abilities. Provide a setting in which my ability is extended… Help me reveal my potential.” He envisioned a world in which systems weren’t designed for specific interactions, but instead designed for the latent potential abilities that exist in everyone. John continued, “Let me feel that it’s alive. Don’t hide it from me. Don’t make it transparent.” Living systems are inherently fallible and magical: We make decisions that end up being mistakes or happy accidents. One of the qualities of biological systems is their ability to acknowledge and react to these events. John believed that complex adaptive systems should react the same way: They should evoke the same feeling of “alive.”

About the Authors

Shelley Evenson is a design manager at Microsoft leading a team that explores real-time communication products that engage and connect people in new ways to help them communicate and collaborate. Before Microsoft she was an associate professor teaching interaction design at Carnegie Mellon University. Evenson taught courses in designing conceptual models, interaction, and service design, and collaborated in projects with colleagues from the Tepper School of Business and the Human Computer Interaction Institute. She jump-started the study of service design in the U.S.—designing courses, energizing students, and hosting the first international conference on service design—Emergence. Before joining the faculty at Carnegie Mellon University, Evenson worked for more than 25 years in multidisciplinary consulting practices on a wide variety of design and development projects.

Justin Rheinfrank is an interaction designer at gravitytank, an innovation consulting firm in Chicago. He has helped visualize and define exciting new interaction and service concepts for organizations like Google, Samsung, Mayo Clinic, and NASA. Justin graduated from Carnegie Mellon University with Masters and Bachelors degrees in Human-Computer Interaction and a Bachelors in Industrial Design.

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Less common are models of the domain of design—models describing the scope or nature of practice, research, or teaching. (I have found only about a dozen such models.) Such models may be useful for locating individual processes, projects, or approaches and comparing them to others. Such models also help clients, colleagues, and students understand alternatives and agree on where they are (or want to be) within a space of possibilities.

Typically models of a domain are of three types:

1. Timelines

   • Lists of events from the domain’s history
   • Links between events suggesting influences

2. Taxonomies

   • Lists of sub-domains
   • Trees branching into categories and sub-categories and so on

3. Spaces

   • Venn diagrams indicating overlapping categories
   • Matrices defining the dimensions of a space of possibilities or area of potential

Among the very few spatial models of the domain of design is Jay Doblin’s 2 x 3 “Matrix of Design.” The rows are performance and appearance; the columns are products, unisystems, and multisystems.

Doblin wrote, “A continuum exists between pure performance and pure appearance. Some products, such as crowbars or paper clips, are clearly performance products. Others, such as Christmas ornaments, medals, and trophies…are purely appearance products. Still others, like automobiles, cups, and chairs, are combinations of both. The essential point is most products (and messages) can be conceived as primarily performance or appearance oriented.”

“Products, the simplest kind of design, are tangible objects, which can be touched, photographed, and comprehended. Objects such as cars, chairs and spoons and messages such as brochures, signs, or ads are all included.

Unisystems are comprised of sets of coordinated products and the people who operate them. They are more complex in design, perform more complex operations, and are not as readily discernible as products alone. A kitchen, an airline, a factory, and a corporation are all types of unisystems…the important concept in unisystems design is…the relationships and interactions between the items involved.”

“Multisystems are comprised of sets of competing unisytems. The retailing field or the office equipment market are types of multisystems…Sears goes against JCPenny, K-Mart, department stores, and hardware stores…IBM, Xerox, Digital, Wang, Apple, and Canon are all pitted against each other” [1].

Multiplying columns and rows yields “six types of design problems that are fundamentally different.”

1. Performance Product Design—The realm of product engineering, where “performance is quantitative.”

2. Appearance Product Design—The realm of product “styling” and style, “not easily quantified.”

3. Performance Unisystems Design—The realm of technical planning and methods, often associated with infrastructure, government, or military projects. (The Design Methods Movement grew out of this type of project.)

4. Appearance Unisystems Design—“Environments that…deliver a satisfying experience…usually designed by impresarios with an holistic approach. Projects begin with an overall vision of what the consumer’s experience should be, then the details of the experience are painstakingly worked out.” He cites as examples restaurants, worlds fairs, South Street Seaport, and Disneyland. (Doblin’s emphasis on experience prefigures discussions of experience design and service design by several years.)

5. Performance Multisystems Design—Groups of competing unisystems. Doblin gives no examples of performance multisystems.

6. Appearance Multisystems Design—Also groups of competing unisystems. And again Doblin gives no examples, nor does he distinguish performance multisystems from appearance multisystems. In fact he says, “design approaches for these two types of multisystems are similar.”

This comment is odd given that one of Doblin’s goals for the model is to “discusses how design methods and design specialists can be matched to the problems.” He notes, “Just as there are six distinguishable types of design, there are six different kinds of designers. It is a rare designer who is competent in more than one design type. The capability and experience required in one arena may actually obstruct a designer’s competence in another.”

Yet, Doblin himself questions the distinction between performance and appearance, “Unfortunately, the threshold separating performance products from appearance products can be fugitive, and is sometimes confused when the designer has one goal, the user another.” Of course, no product or system is all about form or all about function; all products and all systems have formal and functional aspects—and other aspects, too.

Perhaps we need to reconsider Doblin’s y-axis.

I propose substituting Charles Morris’s model of “sign function,” which he describes as having three levels: syntactic, semantic, and pragmatic [2].

Thomas Ockerse has argued that the result of any design process is a sign (in the semiotic sense). That is, anything that has been designed acts as a sign—loosely, it stands for something [3]. (Rhetoricians might say anything that has been designed makes an argument or arguments, including arguments for itself.)

If the result of the design process is a sign, then we may apply Morris’s model of sign function to things that have been designed—or more broadly to the space of things that can be designed.

1. Syntactic—The form or grammar of the artifact. How will this be? How are we making it? In Morris’s terms, “the formal relation of signs to one another.”

2. Semantic—The meaning or definition of the artifact. What is this? What are we making? What does it do? In Morris’s terms, “the relation of signs to the objects to which the signs are applicable.”

3. Pragmatic—The context (from which an artifact emerges and in which it will be used) or need (which it will meet). Why does this matter? Why are we making it? Who will use it and for what purpose? In Morris’s terms, “the relation of signs to interpreters.”

Matrix of Design + Examples – After Jay Doblin

In a rational design process, we might begin by understanding why something is needed—who will use it, where, and to what end; that understanding might help define what is designed—the structure and features that make it meaningful; and definition of what’s needed might help drive how the artifact looks and even how it’s made.

Of course, the process is rarely so neat or linear. Discussion about what may also change the way we understand why, and prototypes of how very often affect the way we understand what and even why. Still, we seek not just coherence within each level but also between levels. The structure of form must map to the structure of meaning, and the structure of meaning must map to the structure of the context. These mappings do not flow in just one direction; they are reciprocal. The design process involves iteration, adjusting structures at each level to achieve coherence throughout.

In the late 1970s, Ockerse explicitly organized RISD’s graphic design curriculum around Morris’s model: the first year introduced students to form-giving exercises; the second year added greater attention to meaning; and the third year added practical considerations.

Meredith Davis has criticized this approach to design education, arguing that the distinctions are artificial. She has proposed a curriculum that engages students in issues of form-giving, meaning-making, and context-negotiating simultaneously.

In practice, however, the distinctions often correspond to commonly found responsibilities or “degrees of freedom” of operation.

Young designers typically find themselves working within a team structure where senior designers, managers, and clients have already negotiated many of the practical business issues. The problem at hand is “simple” in Horst Rittel’s terms, well understood—and already agreed—by the constituents. What remains is the working out of the solution within the established framework.

Space of Design + Examples

Also likely is that the message or feature set—the content, the information architecture, or the interaction sequences—have already been decided by others. Our young designer’s role is to “make it look good” or “professional” or “appealing” or even “sexy”. Doing so requires skill and benefits from training.

And this is where most design schools start (and quite a few stop). A typical problem in a graphic design class asks students to design a poster. The teacher provides the context—perhaps a poster promoting a concert for the Boston Pops. The teacher also provides much of the message—the copy to be included. The teacher may even specify the size, particular colors, and typeface. All that’s left is for the student to arrange the elements. Each student should produce half a dozen or more variations.

A class of 25 students produces 150 variations, which provide the basis for a critique—a discussion about the student’s proposed form and perhaps its relation to the given message. Through prototyping and discussing, students come to understand the space of possible solutions—the degrees of freedom open to them—and the tradeoffs between various factors.

Projects like designing the form of a concert poster remain the reality of most graphic design classes at the undergraduate level today—and quite a few at the graduate level. Such formal projects are also the reality of much of practice. Not just for graphic design, but also product design, interaction design, and architecture.

As young designers gain experience, they may get opportunities to affect the way projects are defined. At first, that may mean simply having visibility into new projects and being able to express interest. Later, they may sit in on planning meetings and then client meetings. Eventually, they may take on responsibility for “running” a client engagement. In function, if not name, they become managers. Here they can affect at least how a design team organizes a project.

However, clients still constrain the level of engagement. Figuring out what product to build or which markets to serve are pragmatic business issues—the third level of the matrix—issues typically decided by the CEO or other “C-level” officers. Such issues are almost always outside the hands of even the product manager—and the designer.

It’s always good to remember at the beginning of each project to explicitly confirm the level of engagement:

• Is the focus here making icons and skinning this interface?
• Or do you want us to look at the interaction as well?
• Who’s writing the copy? Or developing the content?
• Is the product positioning “locked and loaded”?
• Do you have user research to share?
• Or would you like us to talk to users?
• How will the product be distributed?
• Where is value added?
• How does the product pay for itself?

Mimicking this growth path with design class exercises is difficult. Critiquing formal issues is easier—simply less time consuming—than critiquing semantic issues. Asking design students to create content means asking them to write. That means the teacher needs to read and review what the students write. It’s difficult to imagine teachers like Armin Hoffman or Wolfgang Weingart commenting on student writing. Even Paul Rand, who seems to have written rather well, never gave assignments that required students to write.

But why not extend our Boston Pops poster assignment? Shouldn’t students discuss the copy as well as the typography? Shouldn’t students discuss what makes an effective poster? Or whether a poster is the best way to attract people to a concert? Or perhaps even what the role of the Pops might be in Boston, in New England, in the US, or the broader music community—today and 10 years from now?

Rather than ask students to redesign (reskin or even reorganize) the Pops website, wouldn’t it make more sense to ask how the internet will affect the Pops’ long-term future?

That’s some of what moving from the bottom row up to the top row might mean.

Let’s come back to Doblin’s x-axis: product, unisystem, multisystem.

I propose replacing product with object, because product may suggest a thing to be sold, while the result of a design process need not be sold. Object also seems to be in the same family as system.

Unisystem and multisystem are terms of Doblin’s devising. While diligent readers may be able to decipher them, they are not immediately accessible. System seems clearer than unisystem. Likewise ecology (or Meredith Davis’s term, community of systems) seems clearer than multisystem. Ecology also suggests the dynamic, even living quality of a system of systems. In sum: Ecologies are composed of systems, and systems are composed of objects. The examples Doblin gives of multisystems are all competitive spaces or markets, but as Pytor Kropotkin noted, cooperation may be as important as competition in evolution [4]. Multisystems or ecologies need not be seen only as markets. Many large organizations (e.g, conglomerates, universities, and governments) are themselves multisystems or ecologies. And even some product offerings are multisystems or ecologies, (e.g, the Univers family of typefaces is a system of systems; so are integrated systems of hardware, software, networked applications, and human services, such as Apple’s iTunes and iPhone environments).

Traditionally, designers have focused on the lower left corner—crafting the form of objects. Such work can be direct and largely unmediated. Individuals work material in highly intuitive even idiosyncratic ways.

In the past 20 or 30 years, practice and theory have evolved. Ethnography and research about users and use are regularly incorporated in design processes. We might represent this change as expanding focus from the lower right and moving up the y-axis. At the same time, many designers have become involved in the design of systems and ecologies (or designing conditions in which ecologies may arise and thrive). We might represent this change as expanding focus from the lower right and moving across the x-axis. Such work is often indirect and mediated by models or maps. Teams collaborate, often by sharing explicitly defined processes.

Doblin noted, “For years, most design problems could be solved by using a combination of design training, experience, and applied intuition. But as the world and its design problems have become more complex, traditional approaches have become less effective.” Differentiation and value may be created more easily by expanding beyond form to meaning and context and by expanding beyond objects to systems and ecologies—moving up and to the right. This shift reflects interest in design thinking and emergence of cross- or trans-disciplinary practices and educational initiatives.

Still, none of this diminishes the value of good form. Designers who love to make things look good should feel no compunction to expand their practice. We still need beauty.

Direction of Change in Design Practice

Areas of interest and concern of the design office, the client, and society. – Charles Eames

Among the models of the domain of design, perhaps most well known is Charles Eames’s diagram of the overlap between the areas of “interest and concern” of the design office, the client, and society. Eames’s model is sometimes erroneously described as “a diagram of the design process.” While Eames notes that the “areas are not static—they grow and develop as each one influences the others,” his model does not describe how design is (or should be) practiced; it describes where “designers can work with conviction and enthusiasm” [5].

Space of Design Constraints – Brian Lawson

Brian Lawson has proposed a model of the space of design constraints, defined by three dimensions:

1. The generators of constraints: designers themselves, clients, users, and legislators. On this continuum, designer-generated constraints are the most flexible; client- and user-generated constraints less so; and legislator-generated constraints are the least flexible.

2. The domain of constraints, which may be internal to the thing being designed or imposed from outside.

3. The type of constraint, which he bases on function:

   • Symbolic: related to meaning
   • Formal: color, texture, shape, etc.
   • Practical: related to production
   • Radical: fundamental, related to the main purpose

Lawson reminds us that many constraints are self-imposed and that their flexibility varies considerably. His matrix provides a framework for cataloging a project’s constraints, a useful starting point [6].

The Matrix of Inquiry – Richard Buchanan

Richard Buchanan has proposed a model of the space of design research, “The Matrix of Inquiry” [7]. Rick Robinson summarizes it nicely.

The vertical axis . . . is asking what drives a particular inquiry—from the immediate needs of production, through questions of (design) practice out to questions generated by theory. [Most research skews toward the bottom.]

The horizontal or ‘scope of inquiry’ dimension presses a slightly different question upon us. By ‘clinical’ Buchanan refers to work primarily based on case studies. Again, were we to plot relevant work in the field, ‘skew’ would be a barely adequate description of the result. A single case study is often a powerful thing. But theory cannot be built on cases alone, especially when one case is rarely connected to the next. It is, as Buchanan’s diagram implies, a limited ‘scope of inquiry.’ If case studies are the only fodder for the conversation, there is no extension, little reach beyond the immediate, and no larger patterns or emergent issues for theory to make sense of. . .

But I think the single most important thing to draw from this model is found on his z-axis: past, present, and future as the ‘direction’ of inquiry. Future has this little paren after it: “(theory)”. What does that mean? Obviously, it could be prediction, in the sense of extending our understanding of the current situation into likely sequelae in the future. But there is also a much more potent way to understand it: that in this space—the ‘here’ . . . —theory of the future also develops the future, conditions the future.

In the gap between what is (now) and what might be, theory is action. This is especially true of the representations of theory we develop and deploy. Because we are in this conversation with the people and organizations who will populate the future with artifacts, affordances, tools, and ways of thinking, we are actively engaged in shaping the future. We are not simply observers, describers, or contemplators of it” [8].

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Editor’s Note:

Improving healthcare is a wicked problem [1]. Healthcare’s many stakeholders can’t agree on a solution, because they don’t agree on the problem. They come to the discussion from different points of view, with different frames. Wicked problems can be “solved” only by reframing, by providing a new way of understanding the problem that stakeholders can share [1]. This article describes a growing trend: framing health in terms of well-being and broadening healthcare to include self-management. Self-management reframes patients as designers, an example of a shift also occurring in design practice—reframing users as designers. The article concludes with thoughts on what these changes may mean when designing for health.

—Hugh Dubberly

What is health?

From the point of view of today’s healthcare system, health is largely about minimizing illness. The healthcare system has evolved primarily for treating acute conditions. Despite flaws (including high cost and limited access), the system does a good job of curing infections, repairing injuries, and responding to emergencies. The healthcare system does less well in treating chronic conditions. It provides resources for managing aspects of systemic problems, such as statins for cholesterol, ARBs and ACE inhibitors for high blood pressure, and insulins for diabetes; but in most cases that means merely slowing the rate of decline. Yet health is “not merely the absence of disease or infirmity.” In contrast, the World Health Organization defines health as “a state of complete physical, mental and social well-being” [2].

Health as well-being depends not just on healthcare but also on employer practices [3], social policies [4], and self-management, the main subject of this article. Of course, health is “not the objective of living”; health is a resource contributing to the quality of our everyday living [5].

Traditional healthcare focuses on treating acute problems.

Traditional health management applies the tools of acute care to stabilizing chronic conditions.

Health is more than eliminating or managing disease; and its requirements extend beyond traditional healthcare.

Health is a means to higher goals — “a resource for everyday life, not the objective of living” — World Health Organization (WHO)

Identifying the Frame of Healthcare

The way we usually think about health today is bound up in the language of our healthcare system. We call individuals “patients.” We call physicians healthcare “professionals” (HCPs). Professionals “care for” patients—by observing symptoms, diagnosing diseases, and proposing therapies. Their proposals are not just suggestions; they are prescriptions or literally “physician orders.” Patients who don’t take their medicine are not “in compliance.”

In the relationship between HCPs and patients, HCPs dominate. HCPs do whatever is necessary, with patients playing a relatively passive role [6]. In some ways, the system reduces patients to the status of children—simply receiving treatment. The power imbalance may grow out of illness. When we feel ill, we may seek comfort or aid from others. When we feel afraid, we may hand responsibility to a confident expert. In a medical emergency, letting a physician take charge is probably the surest way to stabilize things and return to normal.

A heart attack requires quick action; it’s not the best time for discussion. The time for discussion is before a heart attack occurs—and after—finding ways to avoid the heart attack in the first place or at least avoid another one.

Yet the language of acute conditions (the frame of healthcare) is ill suited to managing chronic conditions or preventing disease (often framed as behavior change). The American Heart Association reports, “The No.1 problem in treating illness today is patients’ failure to take prescription medications” [7]. Patient behavior does not change on a physician’s orders. To expect behavior change on command is to misunderstand human nature. To blame patients (who respond to the very present pressures of busy lives rather than less tangible long-term risks) is unhelpful, unkind, and perhaps unethical. (Blaming patients—or clients—suggests that one doesn’t understand or respect their context and constraints and doesn’t share responsibility for outcomes.) According to social epidemiologist Leonard Syme, “We need to pay attention to the things that people care about, and stop being such experts about our risk factors” [8].

The language of acute conditions (the frame of healthcare) limits what we imagine. Discussions about improving healthcare focus mainly on improving assessment of patient conditions, improving HCP education, and improving therapies—since surviving a crisis depends mainly on the patient’s condition, the HCP’s skill, and the medical technology at hand.

We debate how to have more of the same rather than something new. We debate how to be more efficient and reduce cost rather than radically increase effectiveness and eliminate causes. Our goals remain modest. We seek little more than increased patient compliance and more knowledgeable consumers. We can do better.

The language of acute conditions (the frame of healthcare) is ill suited to achieving well-being (the frame of self-management). By its very definition, healthcare almost assumes both a present problem and an expert who intervenes. In that sense, well-being lies outside the scope of our current healthcare system. Wellness is more than absence of illness: It’s is a way of living. Well-being requires its own language, its own frame.

Self-management does not replace healthcare; rather it acknowledges the limits of what healthcare can accomplish and seeks structures that go beyond those limits.

Imagining the Frame of Self-management

Foucault attributes “the birth of the clinic” to the Enlightenment, when early versions of the current healthcare paradigm displaced a medieval paradigm [9]. The language of health had a beginning; it was invented. And like other languages, it can evolve; we can reinvent it [10].

Imagine reframing health so that it includes self-management.

Self-management suggests a fundamental shift of responsibility. Patients reclaim their role as adults responsible for their own well-being. The relationship between HCP and patient becomes more symmetric (at least outside of medical emergencies). Issuing orders gives way to discussing and collaborating. HCPs become coaches and assistants, shifting their stance from dispensing knowledge to learning from patients. As Melanie Swan reports, “a collaborative co-care model is starting to evolve for healthcare delivery…the patient’s role may become one of active participant, information sharer, peer leader and self-tracker, while the physician’s role may become one of care consultant, co-creator and health collaborator” [11].

In the parlance of “design for service,” HCPs begin to think of themselves as “co-producing” health and well-being with their patients. Imagining healthcare as a designed service is another way to reframe it. Kaiser and the Mayo Clinic employ design innovation teams; UPMC has teamed with CMU design students to reimagine patient experiences [12].

Self-management also suggests setting goals and measuring progress—the basis for managing and improving quality. Individuals decide what’s important to them, what well-being means, what they want to work on. Individuals record their actions; for example, meals eaten, exercise completed, medications taken, hours slept, time spent working or playing or commuting, and perhaps even interactions with others and media consumed (e.g., music played). Individuals also measure results; for example, hard values such as their weight, pulse, blood pressure, cholesterol, and blood glucose; and softer values such as energy, stress, pain, happiness, or mood.

Then they repeat the cycle. If they’ve made progress toward their goals, they may continue the same course of action or even speed up. If they’re diverging from their goals, they may change course. Individuals find and maintain a “healthy balance,” one that’s comfortable for them. They take an active role in their body’s process of homeostasis—including physical, emotional, and social dimensions.

This process is directed trial and error—experimenting, something like the Shewhart-Deming PDCA cycle, a simple application of the scientific method, a version of the design process.

Imagine patients as designers—conducting billions of tiny self-experiments, prototyping their own well-being. That’s the essence of a self-management approach to health [13].

Far-fetched? An impossible change?

Emerging Trends Support Self-management

Self-management has always existed. Americans spend billions of dollars each year on health foods and diet programs. A doctor reported, “20% to 30% or my patients are into some type of supplements or ‘nutraceuticals’” [14]. Deloitte reported that 20 percent of consumers used alternative therapies [15]. Kaiser reported that 33 percent of consumers had “relied on home remedies or over-the-counter drugs instead of seeing a doctor” in the past 12 months because of cost concerns [16].

Several factors have begun the process of reframing health as self-management. The U.S. healthcare system is out of control; managing costs requires a focus on what the medical profession calls outcomes. The public has a growing awareness that well-being is more than healthcare. The fitness and exercise movement, elements of the DIY (Do-It-Yourself) movement like the Quantified Self group, behavior-change programs like Weight Watchers, and more progressive programs for managing chronic conditions like the Stanford Cardiac Rehabilitation Program [17], all point the way to self-management.

The shift to self-management is also supported by changes in the Internet and related technologies. Melanie Swan reported, “Individuals are becoming more engaged in a variety of self-testing and self-management of conditions, symptoms, genomics and blood biomarkers, behaviors and personal environmental factors. Individuals could dramatically expand their use of web-based tools, devices and health-based social networking platforms as their awareness increases, costs drop, financial incentives arise and automated tools proliferate” [11]. The Internet and related technologies are also making it easier for people to have conversations that support self-management.

Imagine online social-network applications creating communities of support around diseases, chronic conditions, and fitness. Of course, health-based social networks have already begun; what’s surprising is just how many there are [18]. Other social network applications serve broader audiences while also offering health-related components [19].

Social networks are dynamic; they can generate collective action. In addition to individuals experimenting on themselves, groups of people with similar conditions—people joined together through online social networks—may sponsor or conduct research. Already, online social networks have begun to affect clinical trials, helping researchers find participants and helping participants compare outcomes.

Imagine several sensors monitoring each person. Already nearly continuous monitors are available for pulse, steps walked, and blood glucose, at relatively low cost. More types are on the way. Many of these sensors send data to the Internet, either directly or through mobile devices or desktop computers, which forward the data. Withings sells a Wifi Body Scale that sends your weight to Twitter each time you weigh yourself [20].

The sensor revolution will change the way we view data and ourselves. Children born in the next decade may look back across a lifetime of data. We won’t be able to ignore how we’re doing; we’ll always know. Continuous feedback may provide micro-motivation—the ongoing awareness we need to live healthier lives.

Imagine personal-health dashboards, applications for tracking your sensor data based on the Web or mobile phones. (Your mobile phone may become a server at the hub of your body-area network.) Health dashboards will provide trend graphs, comparisons with goals and norms, and alerts when things change suddenly or move toward unsafe levels. Health dashboards will be just one of several dashboards in our lives, including those for finance like mint.com, home networks like Pie Digital, and home energy management like the demo Intel showed at CES 2010. In a way, social-network sites, like Facebook, are also dashboards—for friends and message management. Health-based social networks and personal health dashboards seem likely to combine and reinforce one another.

Imagine big data-mining software learning from all the data stored in health dashboards. (Big data is computer-industry jargon for huge databases of information generated on the Web; data mining is jargon for the process of correlating data to generate value. Google’s page-rank algorithm, which bases relevance on counting links to a Web page, is a classic example of big data mining.)

Data that individuals collect will establish a baseline for comparing future measurements. Identifying personal norms is important, especially when we’re not average. For some, 98.6 may indicate a fever, especially as normal body temperature decreases with age. Collecting data will also enable individuals to compare themselves to others—to the entire population or to those sharing similar characteristics, such as age, sex, height, weight, conditions, genes, environment, and even behavior.

Ian Shadforth points out that once health data collecting begins in earnest, we can quickly generate population-wide norms and norms for many sub-groups. By collecting data on a range of age groups simultaneously, we may need just a few years to generate a picture of what’s “normal” across a lifetime [21].

The growth of online health-based social networks, bio-medical sensors, personal health dashboards, and health-focused big data mining applications will not of themselves or even in combination force a shift to self-management. They simply make measurement and tracking a lot easier. They lower the bio-cost of self-management. And they make visible—perhaps even cool—the practice of measurement and tracking. In this way, technology may set off a process of bootstrapping, which can lead to the broader changes we describe.

Parallels with Changes in Design Practice

Reframing health as self-management parallels similar trends in education, where we increasingly recognize that students manage (or design) their own learning, and design practice, where we increasingly recognize that users manage (or design) their own experiences. Perhaps these changes are part of larger trends, the democratizing of professionalism and the shift from a mechanical-object ethos to an organic-systems ethos [22].

Good teachers do more than pass on facts; they help students learn how to learn, so that teaching becomes what Paulo Freire calls the “practice of freedom,” a means to deal critically with one’s living and discover how to transform the world [23].

Freire also insisted on symmetry in the relation between teacher and student—or at least “deep reciprocity.” (Good teachers learn from their students.) Freire’s position echoes Horst Rittel’s assertion that the participants in a design project (all the stakeholders including professional designers) share a “symmetry of ignorance” (or knowledge) regarding the problem. Rittel’s point is that design problems are always “owned” by someone [1]. Design problems have no objective definition; their definition reflects the owner’s point of view. Here, Rittel challenged the orthodoxy of professional problem solving and opened the door to the design process, inviting users and other stakeholders to step inside.

The 1990s saw the flowering of user-centered design. Ethnography and other forms of research about users became standard practice in software design.

Some professional designers began to see their work as engaging stakeholders in a discussion. Liz Sanders and others have begun to advocate for participative design and co-creation—not just designing for users, but designing with them. Co-production has become a watchword in the emerging field of service design (or design for service), as designers recognize the integral role of “consumers” in producing services.

Shelley Evenson and others talk about creating conditions in which users become designers—creating spaces in which people can learn and grow. That means professional designers become meta-designers, designing open-ended systems, languages, platforms, APIs, construction kits, or kits of parts, which others configure or re-configure to their own ends. Wooden blocks, Legos, and train sets are classic examples, kits of parts with which we may play—and design. Herman-Miller’s Action Office is a kit of parts designed for others to design offices. (Sadly, it gives little design control to the office’s occupants.) Programming languages and code libraries like Java and Flash are kits of parts for others to design software. (How much design control can the resulting applications give end-users?) Even simple services like restaurants offer a menu of choices from which patrons may design a dish or a meal. Starbucks and Mini-Cooper offer a dizzying array of choices from which customers can design.

As with health (and education), reframing design will not be easy. For designers who have spent years perfecting their craft and who delight in making beautiful form, the notion of user as designer and designer as facilitator can seem frighteningly foreign. Yet this transition offers the opportunity to make the world richer—to create more options for everyone, including professional designers (and HCPs and teachers).

As the era of mass production ends, design practice must adapt to the new era of information. In order to create value, designers will increasingly have to frame their work in new ways.

Design for Health

As healthcare becomes a larger part of the economy and as healthcare practice and research biology both converge with computing, opportunities to design software and services for health abound. We should keep in mind that health is a means to a goal—one of the things that supports the quality of our everyday living.

Designers should ask their clients: How should we frame health in this engagement? Are we bound to the frame of traditional healthcare? Or can we apply a broader frame, such as self-management?

Designers should also ask themselves and their colleagues: How should we frame design in this engagement? Are we designing artifacts or services? Where might we create opportunities for users to design?

If the user is both designer and implementer (combining first- and second-order agency), what is possible? How can we help users act? Track results? Set goals? How do we “scaffold” tiny self-experiments, learning, and sharing?

Designers should also help users discover and understand both the short-term relationship between action and result (incremental changes that the individual can actually make) and the long-term consequences (big outcomes that matter over time).

Creating opportunities for users to design requires not only giving them responsibility for means and goals but also enabling conversations for:

• overcoming the barriers (bio-cost) of making incremental change through…
• making results, trends, and projections visible and…
• providing emotional support (such as family and community engagement) to maintain…
• higher-level strategic views of the entire process, to maintain goals and momentum, that in turn…
• create learning across time and circumstances that can be shared…
• improving the system for others

We’re on the brink of something new—the intersection of health and computing, design and service. What will we invent as these processes converge? What happens when health self-management meets meta-design?

About the Authors

Hugh Dubberly manages a consultancy focused on making services and software easier to use through interaction design and information design. As vice president, he was responsible for design and production of Netscape’s Web services. For 10 years he was at Apple, where he managed graphic design and corporate identity and co-created the Knowledge Navigator series of videos. Dubberly also founded an interactive media department at Art Center and has taught at CMU, IIT/ID, San Jose State, and Stanford.

Rajiv Mehta consults on exploring and commercializing radical innovation, driving ideas from concept to market. His work has ranged from photography to lasers, computer vision to wireless, and health, at companies from Adobe and Apple to Symbol Technologies and Zume Life. He studied at Columbia, Stanford and Princeton.

Shelley Evenson recently joined Microsoft’s FUSE (Future Social Experience) Labs as a principal in user experience design. Before FUSE, Shelley was an Associate Professor teaching interaction design at Carnegie Mellon University. Shelley taught courses in designing conceptual models, interaction, and service design, and collaborated in projects with colleagues from the Tepper School of Business and the Human Computer Interaction Institute. Shelley jumpstarted the study of service design in the U.S. designing courses, energizing students, and hosting the first international conference on service design-Emergence. Before joining the faculty at Carnegie Mellon University, Shelley worked for more than 25 years in multidisciplinary consulting practices, working with on a wide variety of design and development projects.

Paul Pangaro is the CTO at CyberneticLifestyles.com in New York City, most recently working for clients in consumer internet and mobile computing. He has designed a search engine for poetry, interactive information strategies for medical services, and a framework of ontogenetic sharing for social networking. Paul has lectured at London’s Bartlett School of Architecture, São Paulo’s Instituto Itaú Cultural, École Nationale Supérieure des Mines de Paris, and MIT’s Media Lab and Sloan School of Management on design process, conversation theory applied to interaction design, and the cybernetics of innovation. He was CTO of several startups, including Idealab’s Snap.com, and was senior director and distinguished market strategist at Sun Microsystems. Paul has taught at Stanford University and teaches in the MFA program on interaction design at the School of Visual Arts, New York City.

Continuous Cycle of Health Self-management.

Self-arrangement augmented by conversation with others, sensors, and services.

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