Sunday, April 25, 2010
Wednesday, March 24, 2010
How Not Achieving Something Is the Key to Achieving It
By: Peter Bregman
3:24 PM Tuesday November 17, 2009 | Comments (40)
Source: HBR
Link
Many years ago, when I first started my consulting firm, a friend of mine, Jane*, who worked for a large company, suggested I speak with her colleague, a man named Fred, who might be in a position to hire Bregman Partners.
So I called Fred, mentioned Jane and asked to meet with him. I'm very busy, Fred told me, let's just talk on the phone.
But I knew the phone wouldn't cut it. How about lunch?, I suggested. Or a drink after work? Or maybe just fifteen minutes in person somewhere?
Fred finally agreed to a short lunch. Then he canceled. We rescheduled. He canceled again. We rescheduled again. He canceled again. It was clear he didn't want to meet with me. I almost gave up.
Here's what I realized though: if I could avoid reacting to my feelings of frustration or hurt, then the cost to me of rescheduling the meeting was a two minute phone call with Fred's secretary. And the upside was potentially enormous.
So I kept rescheduling until, one day, several months later, Fred didn't cancel and we had lunch. Which was very quick, of course, but long enough for me to ask him to let me submit a proposal. A couple of weeks after I sent it to him, he left me a short message explaining that I had missed the mark but he'd keep me in mind. Right.
I felt affronted. All that work I put in and all I got in return was a voicemail? Again, I almost walked away.
But instead I called and asked for another lunch to understand what I misunderstood. He declined but suggested I speak with his colleague, Lily, who was in a different department and might have a need for my services.
So I set up a meeting with Lily. Who canceled. As I prepared to reschedule I noticed something unexpected: I started to enjoy the process of trying to get in, the challenge of making the sale. It became a game to me and my goal was to keep playing until, at some point, I'd say the right thing to the right person and get my foot in the door. I was, surprisingly, having fun.
And I was starting to be good at it. Scheduling. Rescheduling. Finding a way to keep the conversation going. You'd think it wouldn't be something hard or useful to become good at but you'd be wrong on both counts.
Most of our jobs hinge on repetition. That's how we become good at anything. The problem is we give up too soon because anything we do repetitively becomes boring.
That is, unless we have a peculiar taste for the task; if it captures our interest. For some reason, maybe we don't even understand — and we don't have to — we enjoy it.
That's how I learned how to do a handstand. It always seemed completely out of reach for me. But then someone told me they learned as an adult. So I figured I could learn too. It took six months but now I can, somewhat reliably, stand on my hands.
Which has led me to believe that anyone can do anything. As long as three conditions exist:
You want to achieve it
You believe you can achieve it
You enjoy trying to achieve it
We often think we only need the first two but it's the third condition that's most important. The trying is the day-to-day reality. And trying to achieve something is very different than achieving it. It's the opposite actually. It's not achieving it.
If you want to be a great marketer, you need to spend years being a clumsy one. Want to be a great manager? Then you'd better enjoy being a poor one long enough to become a good one. Because that practice is what it's going to take to eventually become a great one.
In his book Outliers, Malcolm Gladwell discusses research done at the Berlin Academy of Music. Researchers divided violin students into three categories: the stars, the good performers, and the ones who would become teachers but not performers. It turns out that the number one predictor of which category a violinist fell in was the number of hours of practice.
The future teachers had practiced 4,000 hours in their lifetime. The good performers, 8,000 hours. And those who were categorized as stars? Every single one of them had practiced at least 10,000 hours.
And here's the compelling part: There wasn't a single violinist who had practiced 10,000 hours who wasn't a star. In other words, 10,000 hours of practice guaranteed you'd be a star violinist. According to Gladwell, 10,000 hours of practice is the magic number to become the best at anything.
Which is why you'd better enjoy trying to achieve your goals. Because you'll never spend 10,000 hours doing anything you don't enjoy. And if you don't enjoy the trying part you'll never do it long enough to reach your goal.
Eventually, after five or six cancelled meetings, Lily and I met for lunch. Which, as it turned out, was perfect timing. When we finally met, she had a real need, which hadn't existed when we first started scheduling a meeting.
By this time, I was familiar to her and the company even though I had never done any work for them. I had been around for months and they trusted me because I followed through on every commitment I made to them.
That year I signed a large contract with Lily's company. Twelve years later, they're still a big client of Bregman Partners. And they still cancel lots of meetings with me.
*Some information has been changed to protect people's privacy.
3:24 PM Tuesday November 17, 2009 | Comments (40)
Source: HBR
Link
Many years ago, when I first started my consulting firm, a friend of mine, Jane*, who worked for a large company, suggested I speak with her colleague, a man named Fred, who might be in a position to hire Bregman Partners.
So I called Fred, mentioned Jane and asked to meet with him. I'm very busy, Fred told me, let's just talk on the phone.
But I knew the phone wouldn't cut it. How about lunch?, I suggested. Or a drink after work? Or maybe just fifteen minutes in person somewhere?
Fred finally agreed to a short lunch. Then he canceled. We rescheduled. He canceled again. We rescheduled again. He canceled again. It was clear he didn't want to meet with me. I almost gave up.
Here's what I realized though: if I could avoid reacting to my feelings of frustration or hurt, then the cost to me of rescheduling the meeting was a two minute phone call with Fred's secretary. And the upside was potentially enormous.
So I kept rescheduling until, one day, several months later, Fred didn't cancel and we had lunch. Which was very quick, of course, but long enough for me to ask him to let me submit a proposal. A couple of weeks after I sent it to him, he left me a short message explaining that I had missed the mark but he'd keep me in mind. Right.
I felt affronted. All that work I put in and all I got in return was a voicemail? Again, I almost walked away.
But instead I called and asked for another lunch to understand what I misunderstood. He declined but suggested I speak with his colleague, Lily, who was in a different department and might have a need for my services.
So I set up a meeting with Lily. Who canceled. As I prepared to reschedule I noticed something unexpected: I started to enjoy the process of trying to get in, the challenge of making the sale. It became a game to me and my goal was to keep playing until, at some point, I'd say the right thing to the right person and get my foot in the door. I was, surprisingly, having fun.
And I was starting to be good at it. Scheduling. Rescheduling. Finding a way to keep the conversation going. You'd think it wouldn't be something hard or useful to become good at but you'd be wrong on both counts.
Most of our jobs hinge on repetition. That's how we become good at anything. The problem is we give up too soon because anything we do repetitively becomes boring.
That is, unless we have a peculiar taste for the task; if it captures our interest. For some reason, maybe we don't even understand — and we don't have to — we enjoy it.
That's how I learned how to do a handstand. It always seemed completely out of reach for me. But then someone told me they learned as an adult. So I figured I could learn too. It took six months but now I can, somewhat reliably, stand on my hands.
Which has led me to believe that anyone can do anything. As long as three conditions exist:
You want to achieve it
You believe you can achieve it
You enjoy trying to achieve it
We often think we only need the first two but it's the third condition that's most important. The trying is the day-to-day reality. And trying to achieve something is very different than achieving it. It's the opposite actually. It's not achieving it.
If you want to be a great marketer, you need to spend years being a clumsy one. Want to be a great manager? Then you'd better enjoy being a poor one long enough to become a good one. Because that practice is what it's going to take to eventually become a great one.
In his book Outliers, Malcolm Gladwell discusses research done at the Berlin Academy of Music. Researchers divided violin students into three categories: the stars, the good performers, and the ones who would become teachers but not performers. It turns out that the number one predictor of which category a violinist fell in was the number of hours of practice.
The future teachers had practiced 4,000 hours in their lifetime. The good performers, 8,000 hours. And those who were categorized as stars? Every single one of them had practiced at least 10,000 hours.
And here's the compelling part: There wasn't a single violinist who had practiced 10,000 hours who wasn't a star. In other words, 10,000 hours of practice guaranteed you'd be a star violinist. According to Gladwell, 10,000 hours of practice is the magic number to become the best at anything.
Which is why you'd better enjoy trying to achieve your goals. Because you'll never spend 10,000 hours doing anything you don't enjoy. And if you don't enjoy the trying part you'll never do it long enough to reach your goal.
Eventually, after five or six cancelled meetings, Lily and I met for lunch. Which, as it turned out, was perfect timing. When we finally met, she had a real need, which hadn't existed when we first started scheduling a meeting.
By this time, I was familiar to her and the company even though I had never done any work for them. I had been around for months and they trusted me because I followed through on every commitment I made to them.
That year I signed a large contract with Lily's company. Twelve years later, they're still a big client of Bregman Partners. And they still cancel lots of meetings with me.
*Some information has been changed to protect people's privacy.
Tuesday, January 12, 2010
The Danger of Entrepreneurial Passion
5:34 PM Wednesday January 6, 2010
by Daniel Isenberg
Passion is up there with innovation in what people think entrepreneurs need in order to succeed. I doubt it. My experience as entrepreneur, entrepreneur educator, and venture capitalist tells me that the more scarce and valuable commodity is cold-shower-self-honesty. Sure, it takes huge commitment, energy, and stamina to get a new venture off the ground. And of course you have to believe, sometimes with little data, that you can succeed against the odds. But passion is an emotion that blinds you.
Mixing the oil of self-belief with the water of dispassionate assessment is probably the entrepreneur's toughest task. Here are some guidelines:
Beware of praise. Experienced entrepreneurs learn to clearly distinguish between real success and the many proxies which mean little but can turn your head. There is a huge crevasse between first place in the business plan competition, winning the Ernst and Young Entrepreneur of the Year award, or being selected by the Red Herring 100, and having paying and profitable customers and an organization that can satisfy them. Experienced entrepreneurs know how to use these proxies effectively in marketing and to get investors' attention, but don't be confused between praise and success. As blogger Mark Suster puts it, don't drink your own Kool-Aid.
Stop lying to yourself. It is amazing how much lying is a part of life, and business is no exception. But before focusing on deciphering customers', employees', investors', suppliers', and competitors' true intentions behind their words, it is best we focus first on the worst and most insidious lies of all — the lies we tell ourselves. So when you are unsure of what to do, close the door, make sure no one is around, look in the mirror, and tell yourself the truth. Is that really the best investor to have? Is that really the best VP candidate despite your board's recommendation?
Bind yourself to the mast. Ulysses had it right: in order to endure listening to the seductive-but-deadly sirens, not only did he have himself bound to the mast, but he ordered his crew to ignore his demands to set him free. The result: the venture survived Ulysses' passionate implorations which would have driven them to ruin. For the entrepreneur, that means surrounding yourself with people who will do what is right for the venture, not what your feelings dictate. It is very difficult for the strong-willed entrepreneur to really listen to critics; if you find people who will be painfully honest with you, get them on board.
Know when to fold 'em. One of the reasons qualified people don't make the entrepreneurial choice is that they don't trust themselves to know when or how to press the restart button. Although perseverance in the face of adversity is often ranked as the most important entrepreneurial characteristic, experienced entrepreneurs actually learn how to manage risk by failing fast and small, regrouping, and starting down a different path. This is what two of my students learned when they tried to implement their HBS prize-winning business plan. As they wrote:
Dear supporters: After more than a year of work, we have decided to close. This is a difficult decision but we believe it is the right one, and we are glad we reached it prior to taking in any third-party capital....While this has been one of the most difficult decision...we feel fortunate to be able to shut down ...early. Many startups realize their business isn't viable at a point when ... too much money has been invested and too many lives have been affected. We could have ended up in this position. "Failing fast" and learning as much as possible from the experience is the second best thing an entrepreneur can do.
And as Joseph Conrad wrote years ago, "Any fool can carry on, but only the wise man knows how to shorten sail."
So, entrepreneur, leave your passion in the bedroom. And when you are launching your venture, let nothing get in the way of sober, hard headed, objective assessment.
Daniel Isenberg, PhD, is a professor of management practice at Babson College.
by Daniel Isenberg
Passion is up there with innovation in what people think entrepreneurs need in order to succeed. I doubt it. My experience as entrepreneur, entrepreneur educator, and venture capitalist tells me that the more scarce and valuable commodity is cold-shower-self-honesty. Sure, it takes huge commitment, energy, and stamina to get a new venture off the ground. And of course you have to believe, sometimes with little data, that you can succeed against the odds. But passion is an emotion that blinds you.
Mixing the oil of self-belief with the water of dispassionate assessment is probably the entrepreneur's toughest task. Here are some guidelines:
Beware of praise. Experienced entrepreneurs learn to clearly distinguish between real success and the many proxies which mean little but can turn your head. There is a huge crevasse between first place in the business plan competition, winning the Ernst and Young Entrepreneur of the Year award, or being selected by the Red Herring 100, and having paying and profitable customers and an organization that can satisfy them. Experienced entrepreneurs know how to use these proxies effectively in marketing and to get investors' attention, but don't be confused between praise and success. As blogger Mark Suster puts it, don't drink your own Kool-Aid.
Stop lying to yourself. It is amazing how much lying is a part of life, and business is no exception. But before focusing on deciphering customers', employees', investors', suppliers', and competitors' true intentions behind their words, it is best we focus first on the worst and most insidious lies of all — the lies we tell ourselves. So when you are unsure of what to do, close the door, make sure no one is around, look in the mirror, and tell yourself the truth. Is that really the best investor to have? Is that really the best VP candidate despite your board's recommendation?
Bind yourself to the mast. Ulysses had it right: in order to endure listening to the seductive-but-deadly sirens, not only did he have himself bound to the mast, but he ordered his crew to ignore his demands to set him free. The result: the venture survived Ulysses' passionate implorations which would have driven them to ruin. For the entrepreneur, that means surrounding yourself with people who will do what is right for the venture, not what your feelings dictate. It is very difficult for the strong-willed entrepreneur to really listen to critics; if you find people who will be painfully honest with you, get them on board.
Know when to fold 'em. One of the reasons qualified people don't make the entrepreneurial choice is that they don't trust themselves to know when or how to press the restart button. Although perseverance in the face of adversity is often ranked as the most important entrepreneurial characteristic, experienced entrepreneurs actually learn how to manage risk by failing fast and small, regrouping, and starting down a different path. This is what two of my students learned when they tried to implement their HBS prize-winning business plan. As they wrote:
Dear supporters: After more than a year of work, we have decided to close. This is a difficult decision but we believe it is the right one, and we are glad we reached it prior to taking in any third-party capital....While this has been one of the most difficult decision...we feel fortunate to be able to shut down ...early. Many startups realize their business isn't viable at a point when ... too much money has been invested and too many lives have been affected. We could have ended up in this position. "Failing fast" and learning as much as possible from the experience is the second best thing an entrepreneur can do.
And as Joseph Conrad wrote years ago, "Any fool can carry on, but only the wise man knows how to shorten sail."
So, entrepreneur, leave your passion in the bedroom. And when you are launching your venture, let nothing get in the way of sober, hard headed, objective assessment.
Daniel Isenberg, PhD, is a professor of management practice at Babson College.
Saturday, October 31, 2009
Cranfield Awesome Experience
Twenty Four years, MBA, Worked for more than an year and back to studies.
Honestly, I was a little skeptical initially - will I be able to adjust to assignments, examinations, new cultures, new country...
But Phew! I realized in less than an hour that I landed, that am really going to do well here. No missing home, mom, feeling scared and add to it no jet lag too!
The course am doing here is Knowledge Management for Innovation - something I wanted to do from the past 3 years. Am really glad I chose Cranfield to the only other two universities offering this course in the world. Infact, am really glad that I took this decision in the first place...
This place is awesome - it totally ROCKS!
It's fun and studious at the same time. My classmates especially, all 14 of them are amazing.
I've never been a fan of working in groups - sometimes it was irritating too. But this is the first time am realizing as to how much fun it is to work as a group, learn and share.
Europeans(wouldn't want to generalize for sure) are but incredible! They are beautiful people in and out. Am learning Spanish just to learn more about their culture and also to increase my chances of staying back in Europe!
I do not think I would want to go back to India for atleast a five years from now. Wouldn't for sure enjoy it.
And yes - my room! It's one of the largest rooms students can get in Cranfield University and offers this really incredible view. Everyday, I sleep under the stars and wake up to see the first rays of the sun. Had a huge influence my decision of joining the Astronomical Society!
The weather so far has been pleasantly cold - impatiently waiting for the first snowfall though!
Everything is great here - but (there is always a "but" ) strangely it's the Indian guys am having a problem with. Most of them are young - just passed out of their college and over time they will learn a lot of things. In the mean time, I'll just keep complaining about it!
I haven't spent the amount of time i wanted to on studies this week - mostly it was due to my sleep timings and also a lot of "gappe maarnaa". I'll improve on both of them over the next few weeks.
Impatiently waiting for my electric cooker and pan. Once they arrive, I'll have no problem cooking, eating, sleeping and studying!
That's it from me now - Adios!
Divya
2:54 AM
Saturday
31/10/2009
Cranfield
Honestly, I was a little skeptical initially - will I be able to adjust to assignments, examinations, new cultures, new country...
But Phew! I realized in less than an hour that I landed, that am really going to do well here. No missing home, mom, feeling scared and add to it no jet lag too!
The course am doing here is Knowledge Management for Innovation - something I wanted to do from the past 3 years. Am really glad I chose Cranfield to the only other two universities offering this course in the world. Infact, am really glad that I took this decision in the first place...
This place is awesome - it totally ROCKS!
It's fun and studious at the same time. My classmates especially, all 14 of them are amazing.
I've never been a fan of working in groups - sometimes it was irritating too. But this is the first time am realizing as to how much fun it is to work as a group, learn and share.
Europeans(wouldn't want to generalize for sure) are but incredible! They are beautiful people in and out. Am learning Spanish just to learn more about their culture and also to increase my chances of staying back in Europe!
I do not think I would want to go back to India for atleast a five years from now. Wouldn't for sure enjoy it.
And yes - my room! It's one of the largest rooms students can get in Cranfield University and offers this really incredible view. Everyday, I sleep under the stars and wake up to see the first rays of the sun. Had a huge influence my decision of joining the Astronomical Society!
The weather so far has been pleasantly cold - impatiently waiting for the first snowfall though!
Everything is great here - but (there is always a "but" ) strangely it's the Indian guys am having a problem with. Most of them are young - just passed out of their college and over time they will learn a lot of things. In the mean time, I'll just keep complaining about it!
I haven't spent the amount of time i wanted to on studies this week - mostly it was due to my sleep timings and also a lot of "gappe maarnaa". I'll improve on both of them over the next few weeks.
Impatiently waiting for my electric cooker and pan. Once they arrive, I'll have no problem cooking, eating, sleeping and studying!
That's it from me now - Adios!
Divya
2:54 AM
Saturday
31/10/2009
Cranfield
Thursday, August 06, 2009
How Knowledge Can Hurt Innovation
SCOTT ANTHONY INNOVATION INSIGHTS RSS Feed
How Knowledge Can Hurt Innovation
2:28 PM Thursday July 23, 2009
Tags:Customers, Decision making, Innovation
A meeting I had recently with some folks at Gillette highlighted an important issue facing the would-be innovator — the "curse of knowledge."
Chip and Dan Heath described the curse of knowledge nicely in their 2007 book Made to Stick (highly recommended to all innovators). The basic problem: people who have deep knowledge about a topic sometimes assume other people have that same knowledge. That can lead to major missteps.
The brothers Heath bring this to life by describing a simple experiment run by a Stanford doctoral candidate in the early 1990s. The researcher gave subjects a list of popular songs like "Happy Birthday" and asked them to tap those songs out on a table. Another person had to guess the songs. The researcher asked the "tapper" to predict the percent of songs the "listener" would guess correctly.
The tappers — who could hear the song in their heads as they tapped — assumed that people would get 50 percent right. They actually got 2.5 percent right.
What does this mean for innovation? Managers who have spent their entire lives working in an industry often suffer from the curse of knowledge. They assume customers know more than they do. This curse can blind managers to opportunities and threats.
During my meeting at Gillette, one group member described how "of course" the last place you should shave is around your mouth. As I tend to shave my chin last, I asked him why.
"Well, that part of the face has the most nerve endings," he explained. "So you need to give more time for your shave prep [lotion or gel] to work."
As that was news to me, I wondered if I was alone in my naivety. So I launched a quick survey.
Twitter and Innosight friends and family produced about 100 responses in 24 hours. Turns out only about 30 percent of people claim to shave around the mouth last (the neck was the most popular choice).
Further, only about 25 percent of the people who shave around the mouth last said they did so in order to let their shave prep work or because the area is sensitive. Other answers (it was an open-ended question) ranged widely, with my favorite answer being, "Best for last?"
How do you break free from the curse of knowledge? Spending a lot of time with customers helps. The more you listen to what the customer says and doesn't say, the more you can make sure that your intuition is attuned to the customer's knowledge base. Recognizing the curse helps as well. Make a regular habit of asking questions such as, "Is this our view, or the view of our target customer?"
Finally, bring in outside voices who can ask the innocent questions that can expose the curse of knowledge.
The 2004 Boston Red Sox showed how curses can in fact be broken. Don't let your own knowledge blind you to threats and opportunities.
How Knowledge Can Hurt Innovation
2:28 PM Thursday July 23, 2009
Tags:Customers, Decision making, Innovation
A meeting I had recently with some folks at Gillette highlighted an important issue facing the would-be innovator — the "curse of knowledge."
Chip and Dan Heath described the curse of knowledge nicely in their 2007 book Made to Stick (highly recommended to all innovators). The basic problem: people who have deep knowledge about a topic sometimes assume other people have that same knowledge. That can lead to major missteps.
The brothers Heath bring this to life by describing a simple experiment run by a Stanford doctoral candidate in the early 1990s. The researcher gave subjects a list of popular songs like "Happy Birthday" and asked them to tap those songs out on a table. Another person had to guess the songs. The researcher asked the "tapper" to predict the percent of songs the "listener" would guess correctly.
The tappers — who could hear the song in their heads as they tapped — assumed that people would get 50 percent right. They actually got 2.5 percent right.
What does this mean for innovation? Managers who have spent their entire lives working in an industry often suffer from the curse of knowledge. They assume customers know more than they do. This curse can blind managers to opportunities and threats.
During my meeting at Gillette, one group member described how "of course" the last place you should shave is around your mouth. As I tend to shave my chin last, I asked him why.
"Well, that part of the face has the most nerve endings," he explained. "So you need to give more time for your shave prep [lotion or gel] to work."
As that was news to me, I wondered if I was alone in my naivety. So I launched a quick survey.
Twitter and Innosight friends and family produced about 100 responses in 24 hours. Turns out only about 30 percent of people claim to shave around the mouth last (the neck was the most popular choice).
Further, only about 25 percent of the people who shave around the mouth last said they did so in order to let their shave prep work or because the area is sensitive. Other answers (it was an open-ended question) ranged widely, with my favorite answer being, "Best for last?"
How do you break free from the curse of knowledge? Spending a lot of time with customers helps. The more you listen to what the customer says and doesn't say, the more you can make sure that your intuition is attuned to the customer's knowledge base. Recognizing the curse helps as well. Make a regular habit of asking questions such as, "Is this our view, or the view of our target customer?"
Finally, bring in outside voices who can ask the innocent questions that can expose the curse of knowledge.
The 2004 Boston Red Sox showed how curses can in fact be broken. Don't let your own knowledge blind you to threats and opportunities.
Saturday, August 01, 2009
What is Interaction?
By Hugh Dubberly • Published 07/24/2009
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].
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].”
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.
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.
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].
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.
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].
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].”
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.
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.
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].
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.
Friday, July 31, 2009
UI/UX Design Pattern Repository
Lets create a repository of all UI design patterns at one place. This will help us if we need refer any patterns from one repository rather than googling around.
To start with here are some which I know
http://ui-patterns.com/
http://uipatternfactory.com/
http://quince.infragistics.com/UX-Design-Patterns.aspx
http://developer.yahoo.com/ypatterns/
http://www.welie.com/patterns/
http://www.designingsocialinterfaces.com/patterns.wiki/index.php?title=Main_Page
To start with here are some which I know
http://ui-patterns.com/
http://uipatternfactory.com/
http://quince.infragistics.com/UX-Design-Patterns.aspx
http://developer.yahoo.com/ypatterns/
http://www.welie.com/patterns/
http://www.designingsocialinterfaces.com/patterns.wiki/index.php?title=Main_Page
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