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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Examples and a good description such as those described by Gowan and Novak (in Learning How to Learn) are helpful for understanding concept mapping. An exercise in which you make a simple concept map (with eight to 12 terms) may also be helpful.

The first step in concept mapping is to generate lists of words related to the main concept. The list can come from research, reading, experts, brainstorming, or any other source. Sharing lists from members of a development team will help generate other words.

The second step is to edit the list. Some terms may be related to the subject, but not in a way that meets the project goals.

The third step is to define the terms on the edited list. This is particularly important with unfamiliar or technical terms. But it also helps with familiar terms, too.

A useful exercise is to create a matrix listing all the terms down one side and repeating the list across the top. The relationship between the terms is noted in the boxes where a row and column intersect. The resulting matrix of relationships provides a checklist for building the concept map.

Another important step is ranking of the terms. Simple “triage” may be sufficient. Some terms are key to defining the concept. Others are clearly details. Some fall in the middle. The ranking provides a way to begin to look at building a structure. Primary terms may be candidates for an armature sentence.

One approach is to ground the primary concept within a sentence that also contains the other two or three most important terms. A first sentence might set context; a second sentence might define the main term branching out at 90 degrees from the first sentence. The armature sentence provides a starting point for the map. From there, you can add secondary terms and then the details.

Another approach, is to look for a structure or model to underlie the concept map. For example, brand is a type of sign. Signs have three components. Those three components become the anchor points of the concept map. Innovation is a process which repeats, oscillating between convention and innovation. The process provides a structure for the concept map.

Making a concept map in an area that is well defined is sometimes fairly easy — if the information space can easily be found and if most authorities agree on it. For more ambiguous topics, a great deal of time may be needed to agree on scope (which terms are in or out) and on structure (how those terms relate). This process can take several weeks or even several months.

Once the terms and structure are agreed to, you can move to a second phase: giving the map an appropriate typographic form — to make the typographic hierarchy support the structure of the content.

Main steps in creating concept maps:

• List terms
• Edit the list
• Define the remaining terms
• Create a matrix showing the relations of terms
• Rank the terms
• Decide on main branches or write framing sentences
• Fill in the rest of the structure
• Revise
• Apply typography to reinforce structure
• Revise

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We are surrounded by things that have been designed—from the utensils we eat with, to the vehicles that transport us, to the machines we interact with. We use and experience designed artifacts everyday. Yet most people think of designers as only having applied the surface treatment to a thing conceived by someone else. Eli Blevis created an illustration to emphasize the gulf between the general public’s notion of design and designer’s views of design (Blevis et al., 2006) (see Figure 19.1).

Figure 19.1 – A caricature of the popular conception of design vs. all other concepts.

Ultimately, everything that has not come from nature has been designed—it just may not have been consciously designed. As early as 1938, Moholy-Nagy described design as more than just facade making. He suggested that design was “a complex and intricate task … and the integration of technological, social and economic requirements, biological necessities, and the psychophysical effects of materials, shape, color, volume, and space’’ (Moholy-Nagy, 1938). Most design definitions also include planning as a critical element. Janet Murray, author of Hamlet on the Holodeck, describes the designer’s role as making ‘‘something new that fits in with what already exists or changes it in a positive way.’’ This description of design is consistent with Herbert Simon’s seminal work in which he says, ‘‘Everyone designs who devises courses of action aimed at changing existing situations into preferred ones’’ (Simon, 1996). Marty Neumeier simplifies further by suggesting that ‘‘design is change’’ (Neumeier, 2009). Of course, change (or the process of change) can be changed. That is, change can be designed; thus, design can be designed.

Service

There are many definitions of service in the literature. On one hand, services are viewed as performances: choreographed interactions manufactured at the point of delivery that form a process and coproduce value, utility, satisfaction, and delight in response to human needs (Zeithaml and Bitner, 1996; Evenson, 2005; Engine, 2006). On the other hand, activities or events in a service process are described as forming a perceivable set or ‘‘product’’ through interaction with designed elements or resources from representatives of the service organization, the customer, and any mediating technology.

For purposes of this discussion, we put forth the definition described by Jean Gadrey and based on Peter Hill’s 1977 work (Gadrey, 2002): ‘‘a service may be defined as a change in the conditions of a person or a good belonging to some economic unit, which is brought about as the result of the activity of some other economic unit with the prior agreement of the former person or economic unit.’’ Gadrey goes on to explain that a service should first be considered a process, and illustrates service as a triangle that includes three primary elements: service provider, customer/client/user, and transformation of a reality (Figure 19.2).

Figure 19.2 – The service triangle as illustrated and defined by Jean Gadrey. (2002)

Are services in support of ‘‘changes in the conditions of a person’’ similar to changing existing situations into preferred ones? Are services change? Are people participating in the service designing as they cocreate the service? The concepts Gadrey presents with respect to service relations, interactions, operation, and activity are well suited for defining service as design.

We view designing for service as a meta activity: conceiving and iteratively planning and constructing a service system or architecture to deliver resources that choreograph an experience that others design. When a company provides the optimal mix it will have produced a resonating service system and delivers an experience advantage (Evenson, 2005).

Designing for service is a process that brings together skills, methods, and tools for intentionally creating and integrating (not accidentally discovering and falling into) systems for interaction with customers to create value for the customer, and, by differentiating providers, to create long-term relationships between providers and customers.

Figure 19.3 – A service as design triangle. After Gadrey. (1996A)

Experiences Matter

Our lives are shaped by—and emerge from—the experiences we have. How we are greeted when we enter a store shapes the experience that we will have while in the store. When Apple introduced the iPhone, they consciously designed the journey that their new phone customers would have—from learning about the features they would use on the phone in advance of sale of the phone, to making the activation (once a torturous event with most cell providers) a self- service affair that could be done at home with ease. Smart companies work hard to provide the appropriate resources for customers to have experiences that they value.

Pine and Gilmore (1999) suggest that we seek out experiences that fulfill our needs and satisfy our wants. Today (having satisfied many basic needs), people are looking for more (and more meaningful) experiences. Many people are willing to pay more for their coffee or their hotel stays if the brand reinforces their image of themselves. Consider the shift in just one generation’s experience. Many baby boomers grew up in small town America, purchasing through the Sears, Roebuck catalog. In that shopping experience, the catalog arrived and the customer poured over the pages to select just the right thing. The customer either called or mailed an order form back to Sears. Weeks later the purchase arrived and the customer was either pleased or not. If the customer was not pleased, there was a lot of work to be done to return the item and receive credit. Fast-forward to today: Nike offers customers the opportunity to design their own shoes (items that are notoriously hard to fit) online. Zappos also sells shoes online. From the get-go they understood the need for an experience that would exceed customer expectations (Taylor, 2008). They began by offering overnight delivery, which in part was made possible by the technical infrastructure they have in place. Customers report ordering shoes at 8 p.m. and having them arrive at 8 a.m. the following morning. Both examples contrast with the customer experience with Sears decades earlier. Customer expectations have changed dramatically, and if they want to be successful, organizations need to provide the resources for exceptional customer experience. Zappos and Nike are raising the standards for their competitors and for all online retailers.

But not only have expectations changed for online retail, expectations are changing in health care. In a recent McKinsey survey of more than 2000 patients with commercial insurance, ‘‘75% would consider switching hospitals to become better informed about treatments or if appointments were kept on time. If forced to choose between information and timeliness, 3 times as many patients said they valued information more’’ (Grote et al., 2007). Because there is so much more information available generally, people’s expectations have been raised to want better information, tailored for them personally.

People today also want experiences that support their values, whether it is their concern for the environment or their belief in natural foods. Perhaps this fulfillment behavior has gone too far (or at least lacks substance) when people with means can purchase ‘‘carbon offsets’’ to ease their guilt over behaviors that conflict with their personal value of not contributing to pollution. People are seeking meaningful experiences as part of a community as evidenced by the doubling in recent years of people who planned to volunteer on their vacations (Dalton, 2008).

Great experiences are leading to a demand for even better experiences. As expectations for service experiences rise—are the people participating or cocreating those experiences becoming more skilled at leveraging the resources for their experience and designing their service? If so, then what are the implications for designing-for-service experiences?

In designing-for-service experiences we must provide the opportunity for customers to have meaningful, compelling, and fulfilling experiences that address their needs and satisfy wants. We need to provide the resources for people to design, so that they can create their own experiences (Tempkin, 2008).

Given the current cultural, social, and economic contexts, the resources need to meet or exceed people’s expectations, and encourage participation so that customers become advocates for the brand. (In a sense, they invest in the brand, taking ownership and cocreating the brand itself.) The technology is now in place as a key differentiator in service delivery. What happens at Zappos today simply was not possible just a few years ago. They have raised the table stakes for all other companies.

Creating an Experience Advantage by Providing the Resources for Cocreation

Ganz and Meiren (2002) suggest a need for knowledge about social interaction activities. This is due to an intense awareness that service work is ‘‘people work,’’ and too little is known about the human aspect of both the provider and the client in service definition. The consideration of this human aspect is a key differentiator in the design of a service system. People-centered research can drive innovation.

Designing for service, from our perspective, assumes the participants are the starting point or lens for this exploration. This is essential because the service designer is providing the ‘‘clay’’ (or perhaps the potter’s wheel and kiln) for participants to design for themselves. Through the use of creative, human-centered and participatory methods, we model how the service could be performed or provided.

At the same time, service design identifies and integrates the means to provide a service with the desired qualities within the economic and strategic intent of an organization. Collaborators ‘‘visualize, express and choreograph what other people can’t see, envisage solutions that do not yet exist, observe and interpret needs and behaviors and transform them into possible service futures, and express and evaluate, in the language of experiences, the quality of design’’ (Service Design Network, 2005). As a discipline, service design should not be viewed in isolation, but as complement to service development, management, operations, and marketing (Service Design Network, 2005; Mager, 2002; Edvardsson et al., 2000).

In our approach to designing for service innovation, we integrate these activities across a service development process that includes exploratory, generative, and evaluative research that spans the entire development process—from discovery to release The process differs from conventional approaches, such as those defined by Booz and Hamilton (1982), Bowers (1985), Khurana and Rosenthal (1997), and Zeithaml et al. (2006), where strategy is defined prior to investigation, creating an outline of the service that has to then be filled in. We argue that the right strategy cannot be known a priori. Instead of trying to define a service from the top down, we start with exploratory or immersive research to lead to opportunities for innovation in strategy. This, in turn, provides context (or the fill) from which the service can be created.

People-centered Research Drives Innovation

The approach we have taken to service design is based on our experience in interaction design and approaches developed and published primarily in Europe (Erlhoff et al., 1997). At Carnegie Mellon University we have organized our approach within a conventional design process framework, leveraging exploratory, generative, and evaluative research methods along the way.

Exploratory Research—Uncovering and Understanding Latent and Masked Needs.
In exploratory research, techniques are used to define ‘‘what is’’ in the current situation or context. Methods used in exploratory research are typically drawn from ethnography and include shadowing, participant observation, and contextual inquiry. The goal of this type of research is to immerse the researcher–designer in the context of the inquiry and to provide a deep under- standing of not only the category of people under observation, but also their goals and needs.

In a recent project at Carnegie Mellon, students were asked to improve service flow at the Transportation Security checkpoint at the local airport. Students first documented stories of their experiences at the Pittsburgh airport and other airport checkpoints. This directed storytelling exercise immersed them in the context of the experience even before going onsite. After just a few hours of observation, the students uncovered a latent need and documented it. They found that passengers and their friends and loved ones had no place to say goodbye. The service as designed for the critical security-checking goal provided resources for security officials and a few for passengers to participate in the process, but the physical space, in particular the area leading up to the security checkpoint, the communication products such as the signs and cue markers, and the service providers offered little support for another fundamental activity in the traveling process—people simply saying goodbye.

Generative Research—Determining What Is Meaningful.
In generative research, the goal is to verify the framing of the ‘‘what is’’ and assumptions about how to respond to the needs identified with representatives of the service participants. Early on in generative research the activities are more projective and include exercises that help people express ideas, emotions, and desires around the service experience, The exercises are designed to help people express or explore what is usually hard for them to communicate—how they feel about the given service experience on an emotional level. Later activities are more constructive and are designed to validate specific reactions to service concepts, flows, and evidence. Figure 19.4 illustrates the projective and constructive faces of generative research (Hanington, 2007).

Figure 19.4 – Model of generative reserach (Hanington, 2007)

The later activities are usually design collaborations between designers and participants in sessions that may include people, process, and artifacts that encourage creativity and conversations (Sanders, 2000). In these sessions designers and participants engage in the meta-design of the experience resources when they coproduce prototypes and enactments of the service experience. In a recent project with UPMC (the University of Pittsburgh Medical Center) students teams engaged in two very different activities to elicit patients’ emotional needs with regard to their health-care experiences. In the first case, students provided patients with a set of stimulus cards that had images of different environments in which the ambiance ranged from a baby sleeping in a room to a pianist playing in a concert hall. The participants were asked to select images that best represented the experience they would like and to explain why. Another team took a slightly different approach. They provided respondents with sets of four images of the same thing, such as four orange juicers or four magazine covers, and asked respondents to compare the images to what they wanted from the service setting and explain why one of the images was most appropriate and another was least.

The resulting conversations from both of these participatory exercises helped the design team suggest appropriate resources (places, products, and people’s behavior) for the ultimate service users to design a health-care experience that would be right for them.

Evaluative—From Concepts to Recommendations.
Evaluative research helps validate whether the needs and expectations people bring to the service experience are actually met by the resources as designed. Ultimately, the goal is to determine if the resources provided for the experience are useful, usable, and desirable for the intended service users and providers (Sanders, 1992). Methods may be tightly controlled as in a lab experiment or loosely defined as an extension of generative activities (Hannington, 2007). The purpose is to evaluate the resources while they are still easy to change and before major investment is made in producing the service process, service products or evidence, or the setting for service delivery.

An Integrated Service Design Process

An integrated service design and implementation process is key to the success of any service experience. We have found a multidisciplinary effort with a modeling-centric approach to be most effective for service design. The process is illustrated in Figure 19.5 in the context of the previously described people-centered research model. Though the process as shown is illustrated in a linear fashion in practice, it is fluid and iterative.

Figure 19.5 – Integrated design process and people-centered research.

The Five Major Stages in Designing for Service.
There are many models of the design process, and many service design organizations opt for their own variations, while others prefer not to be confined to a single process. We have refined our process through practice, but admit that it is fluid and should change according to the design challenge (Evenson, 2005). The activities in the stages of our current process are described briefly in Table 19.1.

Table 19.1 – Process Overview

Service designers must account for the complexity of service resources that must be accessible to the appropriate participants to design the service experience for themselves. Methods that service designers use to address this complexity in particular are service ecologies, experience prototyping, and service blueprinting. Service ecologies are maps of the participants and entities affected by a service and the relationships between them. Ecologies or mappings of the research findings reveal new opportunities and inspire ideas, and they help to establish the overall service concept (livejwork, 2004). Experience prototyping brings the service experience to life. First designers, and then stakeholders in the experience, act out the service experience with specific roles and rough props. This is similar to Brenda Laurel’s design improvisation (Laurel, 2003). The goal is theater that enables the designers to better understand the contextual level of the design experience. This understanding is crucial because experience emerges from the activity of persons acting in a setting and is embedded in context and ongoing social practices.

G. Lynn Shostack developed service blueprinting. She states, ‘‘a service blueprint allows a company to explore all the issues inherent in creating or managing a service.’’ She goes on to explain that there are four aspects to the blueprint. They are process identification, isolation of fail points, establishing the time frame, and analyzing profitability (Shostack, 1984). We have extended this approach to include opportunities for service innovations that are derived from immersive research.

Service Design Languages

Just as spoken languages are the basis for our conversations with people, so design languages are the basis for conversations with services—they are building blocks of the service experience. People use spoken language to express themselves; services designers use service design languages to express the service, what it does, how it is to be used, and what experiences or journeys are made possible through it. Service design languages are used to visualize, express, and choreograph the resources that mediate the service experience. A design language consists of a system of elements (with associated meanings) through which designers signal purpose and users ‘‘read’’ intent (interacting with expectations), for example, ‘‘grip here’’ or ‘‘this is a button that can be pressed.’’ A design language also includes a set of organizing principles (the rules and conventions) for combining elements.

Spoken languages consist of words and rules of grammar. Design languages consist of design elements that are combined into constructs, such as a touch point, and the principles for their combination. Spoken language supports the production of meaningful expressions by allowing people to combine well-known sets of words and rules of grammar to create previously unknown but usually comprehensible expressions. In addition, spoken language is generative and inherently open. Research into creating a service language, so it is similarly open, will be invaluable.

With a service design language it is possible to visualize, express, and choreograph the resources for interaction. Design languages are general to a servicescape, such as a coffee shop with a condiment station for tailoring the coffee that has a flat place large enough to hold several drinks, trash receptacles, sugars, creamers, and so on, and specific to a particular brand (e.g., in the way Starbucks expresses a condiment station) (Bitner, 1992). Essentially, design languages are the means by which

• Designers build meaning and create coherence in the service interface
• Service interfaces express themselves and their meanings to people
• People learn to understand and use the service and engage in experiences associated with the service journey
• Companies establish new industry standards for quality, market presence, and customer satisfaction

When an effective service design language is deployed consistently, people who use or access services become fluent in their interactions with the service. Designers and developers are also articulate and skilled at the production of the resources for service delivery. Research into design languages is likely to influence service design in multiple ways. An exploration of service design languages will augment or change existing business process description or blueprinting methods that are used for describing the current state of service experiences. This work is a natural compliment to research into specification, choreography, improvisation, and, most importantly, implementation.

Cocreating and Experience Advantage – Designing Design

Approaching service as designing will lead to new ways of thinking about service innovation. Service as designing means service itself is fundamentally a creative process. As service designers we are engaged in meta-design—designing design—and are producing resources for people to creatively engage with a service. The position explains why the metaphor of choreography that is so often used with service experience may not be a metaphor at all. The choreographer creates a plan for the dance, but the dancer also creates the dance as he brings his own point of view to performing it.

What will the impact of a ‘‘service as designing’’ mindset be on the design of services such as a healthcare experience? In recent projects with the University of Pittsburgh Medical Center and the Mayo Clinic, Carnegie Mellon students have shown that a design approach and design mindset can lead to innovative solutions to serious service challenges. At a small scale it can mean simply better understanding the relationships that are created through interaction around the service. This is illustrated through the suggestion that catheterization lab team members wear ‘‘gear’’ that unifies them as a group and allows the patient and family to see them as their team. On a broader scale, the service as design mindset leads to service innovation concepts that put the patient more in control of their experience—both in proactive and in chronic primary care situations. In this case, the patients would then be provided with the resources to change their existing situations into preferred ones. We hope that more efforts to frame service as design can lead to even more innovative solutions for these and other important challenges.

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

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

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

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

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

Bio-cost measures human effort

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

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

Bio-cost is a function of time

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

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

Bio-cost has physical, mental, and emotional components

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

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

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

Bio-cost reveals trade-offs

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

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

Table 1: Bio-cost components.

Can we replenish our “reserves”?

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

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

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

How do we assess bio-cost trade-offs?

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

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

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

1 Bio-cost for single participant

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

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

2 Bio-cost for cooperative participants

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

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

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

3 Bio-cost for collaborative participants

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

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

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

Bio-cost in business and design

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

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

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

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

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

Why bio-cost is important

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

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

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

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

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edited by Jon Kolko
Oxford University Press

Dubberly Design Office consults on development of software and services. We follow a user-centered process that often involves mapping. We use concept maps to represent factors that influence the product development process. We regularly map user goal structures and user interactions; business models and resource flows; and hardware and software infrastructure and information flows. Increasingly, we are called on to map data models and content domains.

Many of today’s new software applications and online services integrate content more deeply than earlier desktop productivity applications. As Nicholas Negroponte predicted, content, computing, and communications have converged.

A concept map is a collection of terms related to a main idea. Links between terms form a structure—something like an outline, but with some branches connected. Labeling a link with a verb creates a noun-verb-noun chain that can be read as a sentence. Thus, concept maps present a series of propositions related to each other and a main idea. Mapping a content domain—creating a concept map—is an effective way to understand a domain.

Sharing a concept map with project stakeholders is an effective way to identify errors in understanding and reach consensus on content definition, structure, and boundaries. Mapping a content domain is a good way to prepare for designing or redesigning a content rich web site, application, or service.

The Benefits of Concept Mapping

Deepening Understanding

We developed a concept map of Java as a way to understand Java. The map helped us prepare to redesign and re-launch Sun’s main web site for Java developers, java.sun.com. Concept mapping was one of many tools that we used in the design process, including auditing the existing site, reviewing site traffic logs, and interviewing Java developers. This case study focuses on the Java concept map and does not describe the other tools or the larger site redesign effort.

The main question that we faced was this: How should we organize java.sun.com? What should the information architecture be? Answering these questions was not trivial, since the site contained more than 110,000 pages. It couldn’t be reorganized by simply reading a few pages and moving them around. What we needed was a deep understanding of Java—what it is, how it’s used, how it changes, and why it matters.

The trouble was: We knew little about Java except that it was a programming language that runs in many environments. We developed the Java concept map so that we could learn what we needed to know. The knowledge we gained making the map enabled us to propose revisions to the site’s infor-mation architecture with confidence—and helped us backup our proposal with reasoning built on a firm foundation—reasoning built on a definition of the content domain (i.e., the Java concept map) already accepted by the client and his many internal constituents.

Building Trust

Like any large corporate project, the redesign of java.sun.com encountered political issues. First, it was a visible project in a decentralized company. That meant the project had a lot of vocal stakeholders. In addition, java.sun.com was managed by Sun’s Developer Relations Group, which had recently been formed by consolidating several previously separate departments. Not everyone was happy about the new organization.

As we began to meet internal stakeholders, we encountered considerable skepticism about the site redesign project and our ability to execute it. Developing the concept map became a way to engage known stakeholders, discover new ones, and build trust.

We interviewed a series of Sun employees involved with both Java and java.sun.com. We began with a small group, who in turn suggested others. Eventually the number of employee interviews exceeded 50. We also asked the stakeholders to review the concept map as we developed it.

At a project meeting a few weeks into the process, one of the key stakeholders reviewed the map and said, “Not bad. It looks like you’re ready to meet the Java Distinguished Engineers.” Before that, no one had mentioned these high priests of Java; they turned out to be a powerful constituency. The map helped us find them and gave us entree—both permission to meet and something to discuss. Those meetings went well; the Distinguished Engineers were intrigued by the map. (It’s not often someone turns up with a map of your baby.) We also entered the discussions with more credibility than we had at the start of the project, since we had clearly done a lot of homework to get the map to where it was. The organization did us a favor by revealing the Distinguished Engineers only when we were prepared to meet them.

The most important benefit of the map, though, was that we were able to discuss the structure of Java with the Distinguished Engineers (and other stakeholders) on its own terms, apart from its instantiation in the web site. In other words, we were able to discuss the structure of Java and ensure that we understood it, rather than discuss a menu system or page layout, which might have conflated issues—the structure of Java, the site information architecture, and the appearance of the navigation interface.

By separating content from expression—by mapping—we were able to establish relationships and build credibility and trust, before proposing changes to the client’s baby, the Java web site.

Other Uses

While the main goal of the concept map was to help the design team understand Java so that we could reorganize java.sun.com, it soon became clear that the map might have wider uses. Our working version of the map looked like a sketch, which reflected the constant changes we were making. (It was messy.) The sketch form invites comments—where a more polished form may inhibit comments.

When we reached consensus on the content, we formalized the map’s appearance. Eventually, the map went through two printings and was distributed to more than 25,000 Java developers. We also created an interactive in Flash version of the map.

The Process of Concept Mapping

At the beginning of the java.sun.com redesign project, we asked to see Sun’s models of Java. We were unable to locate detailed models, but we did find slides from marketing presentations—”marketectures”, versions of technical architectures simplified by marketing people. One of these marketectures depicted Java as the Parthenon; three steps supported a few columns capped by an architrave and a pediment. This model included less than a dozen elements. It became our starting point.

Set Goals

Setting goals is the key to managing. Rick Robinson points out that all research should begin with a clear goal, what he calls a “hunt statement”. Likewise, mapping should begin with a clear goal. A simple way to clarify a map’s goal is to write a “working title.”

We set six goals for the Java concept map:

1. Develop an understanding of Java shared among the java.sun.com redesign project’s stakeholders.

2. Inform both the logical organization of java.sun.com and its integration with other sites.

3. Develop a framework by which changes to Java can be understood.

4. Open a dialog with senior Java stakeholders.

5. Provide an overview of Java to people familiar with computing but unfamiliar with Java.

6. Develop a map that an average Java programmer would consider accurate.

Identify Terms

The first step in developing a concept map is to identify terms that could be included. In this phase, the goal is to quickly explore the domain. Write down whatever you find or think of. Editing comes later.

Our first list of terms came from the team’s own experience, from glossaries of Java terms, and from the indices of books on Java.

We kept our list of terms in a spreadsheet. We printed each term on a label and affixed the label to a colored sticky”, so that it could be moved and grouped later. We then placed the stickies on a 4-by-8-foot foam-core board, so that we could move the whole group around the office easily.

• Our initial list included roughly 400 terms.

Figure 1: Early grouping of Post-it notes.

Prioritize Terms

We prioritized the terms, creating more manageable clusters:

• 11 first priority
• 45 second
• 157 third
• 136 fourth
• 51 fifth

Triage is a similar strategy. Which terms are critical? Which terms can we deal with later? And which terms are not relevant?

Figure 2: Armature study.

Figure 3: Armature with next level of elements added.

Define Terms

We defined each first-, second-, and third-level term, adding definitions to the spreadsheet. The list of definitions served as a foundation for later work. In discussions with reviewers, the definitions allowed the team to focus on individual words, without referring to the map. The list of definitions was particularly useful in conversations with reviewers who didn’t understand that map, especially when they reviewed early versions.

• 205 definitions were collected from 8 sources.

Figure 4: Early composie.

Figure 5: Final composite; content basis for final design.

Organize Terms

We organized the first-, second-, and third-priority lists into a single outline. We experimented with several variations. For the most part, category titles in the outline were first-priority terms.

Test Armatures

When the number of terms in a concept map exceeds 9 or 10, introducing levels or hierarchy may make reading easier. Large concepts maps (more than 50 terms) are almost impenetrable without attention to both semantic and visual hierarchy.

We like to organize large concepts maps around an “armature”, a primary sentence or two. A good place to start is with a horizontal sentence placing the main concept in a context; then add a vertical sentence defining the concept. Other terms link off the armature.

An armature should include the terms most fundamental to the concept being mapped. These fundamental terms and relationships serve as the backbone for the rest of the map, providing structure and hierarchy. The armature is often a starting point for readers.

We experimented with several armatures. The client and the design team chose the armature with the most meaningful relationships and the one that provided space (both physically and logically) for the rest of the terms.

Add Terms

We added second- and third-priority terms. New terms suggested changes to the armature.

Review and Revise

Once we had an armature fleshed out with second-ary terms, we reviewed the map with the client and a small group of Java experts. They suggested additional reviewers. From this early stage, reviews were ongoing. We continued to interview stakeholders while we developed the concept map, asking them to review and comment on the current version.

Reviews took place in one-on-one interviews, on the phone, or via email. We sent drafts of the map to groups within Sun. We also posted large, printed copies in high-traffic areas at Sun; reviewers wrote directly on the map or attached yellow stickies.

Marked-up maps were returned to us. Several people reviewed the map multiple times.

• 36 people reviewed the map in one-on-one interviews.
• 10 people provided feedback via posted maps.

Subdivide Large Maps

As we added terms, the map became unwieldy and difficult to consider as a whole. So: We divided the map into logical sections.

Subdividing the map increased efficiency. We distributed sections to members of the team who refined their sections simultaneously. They added terms and modified relationships, and, in some cases, created secondary armatures. We reassembled the sections around a refined armature, paying special attention to relationships between the sections.

• At its largest, the map measured 3 x 8 feet.

Refine the Typography

Jim Faris and Harry Saddler proposed several options for the form of the map. The team adopted Sun Sans as the primary typeface, conforming to Sun’s corporate identity standards.

Early sketches produced some new typographic devices that were eventually applied to the map. One device was a sort of footnote or hypertext link, which allowed us to indicate more relationships without drawing more long lines across the map.

• Refining appearance required 7 complete revisions.

Check Again

Throughout the project, we worked with a copy editor. She checked each comprehensive revision for spelling, grammar, and sense. Sun’s legal and trademark department also reviewed the map several times, as did an attorney working for our client’s department and two subject-matter experts.

At the end, Sun’s marketing department asked for a few visual changes—and we faced a nerve-racking few days when a senior manager questioned whether the map contained too much proprietary information. Luckily we were able to show that the information was all already available on java.sun.com.

• The map went through a total of 53 numbered versions—releases.

Figure 6: Early visual style sketch.

Figure 7: Final poster.

Print and Distribute

The map was printed at Color Graphics in San Francisco. Sun initially distributed the map at the JavaOne conference in Japan.

Project Stats

The final map contains:

• 235 terms
• 425 links (relationships)
• 110 descriptions

We began the map in October 2000 and delivered printed copies in September 2001.

The process required:

• 49 weeks
• more than 50 interviews
• more than 100 meetings
• more than 2000 emails

The team that created the map included:

• Audrey Crane, project manager, interviewer, researcher, mapper

• Paul Devine, content expert, mapper

• Hugh Dubberly, interviewer, mapper

• Jim Faris, mapper, graphic designer

• Paul Pangaro, our client

• Harry Saddler, content expert, mapper, graphic designer

• Ylva Wickberg, interaction programmer

More Information

For more on concept mapping, read Gowin and Novak’s Learning How to Learn.

For more on teaching concept mapping, read Dubberly’s The Baseball Project: A Step-by-step Approach to Introducing Information Architecture.

See the Java Technology Concept Map which inspired this article.

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

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

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

What Isn’t Conversation?

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

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

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

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

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

What Is the Process of Conversation?

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

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

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

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

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

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

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

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

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

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

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

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

What Does Conversation Offer?

Conversation enables participants to:

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

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

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

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

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

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

“That still works for me.”

“OK, good.”

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

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

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

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

What Are the Limits To a Conversation?

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

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

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

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

Types of Participants

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

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

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

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

Why Does Conversation Matter?

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

Requirements for conversation

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

Marketplace example from user perspective

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

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

Summarizing, conversation is infrastructure for commerce because:

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

What Can Designers Do?

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

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

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

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

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

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

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

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

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

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

Invest in a better understanding of conversation:

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

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

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

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

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

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

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

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

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

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

Invest in design of conversations via prototyping:

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

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

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

The Impact

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

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

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

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

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

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

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

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

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

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

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

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

Making Sense and Guiding Action

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

Observations can be a source of new models.

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

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

Our models guide our actions.

Learning As Forming and Reforming Models

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

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

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

1) Reject the data

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

2) Accept the data

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

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

We evolve our models as we test their predictions.

Learning involves:

Creating new models

Revising existing models

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

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

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

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

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

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

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

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

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

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

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

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

1. Primary—the activity at hand
   understood through models
   

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

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

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

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

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

Limits and Costs

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

Our models affect what we see.

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

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

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

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

Agreement and Understanding

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

Models are explained by stories; stories build models.

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

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

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

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

Models in Design

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

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

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

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

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

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

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

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

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

Sharing a model may also change it.

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

Questions to Ask When Making Models

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

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

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

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

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

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

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

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

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

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

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

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

Write a working title for the model.

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

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

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

Revise your proto-model.

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

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

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

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

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

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

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

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

When Judging Visual Representations,
Consider 5 Primary Criteria:

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

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

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

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

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

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

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

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

A Design-Theory View

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

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

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

An HCI View

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

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

Man-Machine System

Gulf of Execution and Evaluation

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

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

Seven Stages of Action

Interaction

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

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

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

A Systems-Theory View

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

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

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

Of course, the steam engine does not operate entirely on its own. It receives its “goal” from outside; a person sets the speed of the wheel by adjusting the length of the linkage connecting the fly-ball governor to the steam valve. In Haque’s terminology, the transfer function is changed.

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

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

Types of Systems

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

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

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

Types of Systems

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

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

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

Water Cycle

Linear system

Self-regulating system

Learning system

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

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

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

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

Kenneth Boulding distinguishes nine types of systems [13].

Levels of systems

System Combinations

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

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

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

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

A special case of 0-0 has the output of the second (or third or more) systems fed back as input to the first system. Such a loop might form a self-regulating system.

0-1 Regulating
The output of a linear system provides input for a self-regulating system. Input may be characterized as a disturbance, goal, or energy.

Input as “disturbance” is the main case. The linear system disturbs the relation the self-regulating system was set up to maintain with its environment. The self-regulating system acts to counter disturbances. In the case of the steam engine, a disturbance might be increased resistance to turning the wheel, as when a train goes up a hill.

Input as “goal” occurs less often. A linear system sets the goal of a self-regulating system. In this case, the linear system may be seen as part of the self-regulating system—a sort of dial. (Later we will discuss the system that turns the dial. See 1-2 below.)

Input as “energy” is another case, mentioned for completeness, though a different type than the previous two. A linear system fuels the processes at work in the self-regulating system; for example, electric current provides energy for a heater. Here, too, the linear system may be seen as part of the self-regulating system.

1-0 is the same as 0-1 or reduces to 0-0. Output from a self-regulating system may also be input to a linear system. If the output of the linear system is not sensed by the self-regulating system, then 1-0 is no different from 0-0. If the output of the simple process is measured by the self-regulating system, then the linear system maybe seen as part of the self-regulating system.

0-2 Learning
The output of a linear system provides input for a learning system. If the learning system also supplies input to the linear system, closing the loop, then the learning system may gauge the effect of its actions and “learn.”

On the other hand, if the loop is not closed, that is, if the learning system receives input from the linear system but cannot act on it, then 0-2 may be reduced to 0-0.

Today much of computer-human interaction is characterized by a learning system interacting with a simple linear process. You (the learning system) signal your computer (the simple linear process); it responds; you react. After signaling the computer enough times, you develop a model of how it works. You learn the system. But it does not learn you. We are likely to look back on this form of interaction as quite limited.

Search services work much the same way. Google retrieves the answer to a search query, but it treats your thousandth query just as it treated your first. It may record your actions, but it has not learned—it has no goals to modify. (This is true even with the addition of behavioral data to modify ranking of results, because there is only statistical inference and no direct feedback that asserts whether your goal has been achieved.)

1-1 Balancing
The output of one self-regulating system is input for another. If the output of the second system is measured by the first system (as the second measures the first), things are interesting. There are two cases, reinforcing systems and competing systems. Reinforcing systems share similar goals (with actuators that may or may not work similarly). An example might be two air conditioners in the same room. Redundancy is an important strategy in some cases. Competing systems have competing goals. Imagine an air conditioner and a heater in the same room. If the air conditioner is set to 75, and the heater is set to 65—no conflict. But if the air conditioner is set to 65 and the heater is set to 75, each will try to defeat the other. This type of interaction is balancing competing systems. While it may not be efficient, especially in an apartment, it’s quite important in maintaining the health of social systems, e.g., political systems or financial systems.

If 1-1 is open loop, that is, if the first system provides input to the second, but the second does not provide input to the first, then 1-1 may be reduced to 0-1.

1-2 Managing and Entertaining
The output of a self-regulating system becomes input for a learning system. If the output of the learning system also becomes input for the self-regulating system, two cases arise.

The first case is managing automatic systems, for example, a person setting the heading of an autopilot—or the speed of a steam engine.

The second variation is a computer running an application, which seeks to maintain a relationship with its user. Often the application’s goal is to keep users engaged, for example, increasing difficulty as player skill increases or introducing surprises as activity falls, provoking renewed activity. This type of interaction is entertaining—maintaining the engagement of a learning system.

If 1-2 or 2-1 is open loop, the interaction may be seen as essentially the same as the open-loop case of 0-2, which may be reduced to 0-0.

2-2 Conversing
The output of one learning system becomes input for another. While there are many possible cases, two stand out. The simple case is “it-referenced” interaction. The first system pokes or directs the second, while the second does not meaningfully affect the first.

More interesting is the case of what Pask calls “I/you-referenced” interaction: Not only does the second system take in the output of the first, but the first also takes in the output of the second. Each has the choice to respond to the other or not. Significantly, here the input relationships are not strict “controls.” This type of interaction is a like a peer-to-peer conversation in which each system signals the other, perhaps asking questions or making commands (in hope, but without certainty, of response), but there is room for choice on the respondent’s part. Furthermore, the systems learn from each other, not just by discovering which actions can maintain their goals under specific circumstances (as with a standalone second-order system) but by exchanging information of common interest. They may coordinate goals and actions. We might even say they are capable of design—of agreeing on goals and means of achieving them. This type of interaction is conversing (or conversation). It builds on understanding to reach agreement and take action [16].

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

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

The coordination of goals and actions across groups of people is politics. It may also have parallels in biological systems. As we learn more about both political and biological systems, we may be able to apply that knowledge to designing interaction with software and computers.

Having outlined the types of systems and the ways they may interact, we see how varied interaction can be:

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

We may also see that common notions of interaction, those we use every day in describing user experience and design activities, may be inadequate. Pressing a button or turning a lever are often described as basic interactions. Yet reacting to input is not the same as learning, conversing, collaborating, or designing. Even feedback loops, the basis for most models of interaction, may result in rigid and limited forms of interaction.

By looking beyond common notions of interactions for a more rigorous definition, we increase the possibilities open to design. And by replacing simple feedback with conversation as our primary model of interaction, we may open the world to new, richer forms of computing.

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

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

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

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

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

How is design changing?

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

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

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

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

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

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

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

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

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

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

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

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

Models of change

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

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

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

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

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

A Concern for Users

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

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

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

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

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

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

The Rise of Service Design

The shift from industrial age to information age mirrors, in part, a shift from manufacturing economy to service economy. In the new economy, as former WiReD editor Kevin Kelley put it, “commercial products are best treated as though they were services. It’s not what you sell a customer, it’s what you do for them. It’s not what something is, it’s what it is connected to, what it does. Flows become more important than resources. Behavior counts [11].”

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

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

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

We also noted, “hand-craft has not gone away, nor is service-craft divorced from hand-craft. Hand-craft plays a role in service-craft (just as in developing software applications, coding remains a form of hand-craft). While service-craft focuses on behavior, it supports behavior with artifacts. While service-craft requires teams, teams rely on individuals. Service-craft does not replace hand-craft; rather service-craft extends or builds another layer upon hand-craft [13].”

Characterizing Services

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

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

Sustainable Design

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

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

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

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

Early Parallels

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

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

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

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

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

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

What do these changes mean for design education?

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

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

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

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

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