This week, I'm going to digress from the topic of flow and rank ladders in order to share an interesting talk I heard at a conference. Last week, I was at the C&T2009 meeting at Penn State University with our Chief Community Officer, Joe Cothrel. Although this meeting was rather relaxing for me, because I didn't have to present, I still can't believe it took me 12 hours to get there (9 hours of travel from SFO to State College connecting at DC, plus 3 hours lost from PST to EST). I stayed on campus at the Nittany Lion Inn that is a 5 minute walk to the IST building, the meeting venue location. I will not bother to recap the meeting, since a concise summary can be found in the conference program. However, one talk sparked some thoughts in my head that I'd like to share with you.

Day 2 of the conference opened with a keynote, titled "Knowledge Reuse and Novelty in Community Settings," delivered by Prof. Karim R. Lakhani from Harvard Business School. Prof. Lakhani presented an interesting experiment on collaborative innovation in the form of a MATLAB programming contest for solving an NP hard problem. Each code entry's performance is evaluated, scored, ranked, and displayed immediately with the contestant's name. Since this is a collaborative effort, any contestant may reuse and modify code submitted by others and then resubmit it as their own entry. The contest is closed after about a week and the top score at that time wins regardless of how many times one submits, how many lines of code one adds, or how much performance gain one contributes. The competition is all about reputation, collaboration, and learning; the winner only gets a T-shirt or a cap.

The results of this experiment are quite interesting!

• Novelty per entry is quite low: 3.5% on average.
• Borrowed code per entry is rather high: 71% on average.
• Small chunks of novelty code are often reused, so they tend to have high social values. But code entries that are too novel (have too many novel blocks of code) are often not reused because they are too hard to understand. Therefore their social value decreases.
• In contrary, small chunks of borrowed code are not often reused, so they tend to have lower social values. However, as the sizes (number of lines) of the borrowed blocks of code increase, they become reused more often, so their social value increases.
• Winning entries tend to have few lines of novel code and many chunks of borrowed code. In fact, the amount of borrowed code is twice as predictive of top performance as novelty.
• Finally, collaborative innovation almost always leads to a more optimal solution in shorter amount of time.

Since all communications in a community are persistent and are made available through the internet to the rest of the world, a community is a fertile ground for collaborative innovation. Although the amount of novelty per post is usually negligible, through many iterative refinements by many users from different backgrounds, the solution is often highly optimized and very innovative. This method of innovation and optimization is actually very similar to how evolution optimizes certain biological motifs through natural selection. Computer scientists have found this optimization method so effective that they invented the field of evolutionary computing through biomimicry.

Now, how would you like to run a similar type of collaborative innovation "contest" on your community? Lithium is geared up for a new product that will enable you to reuse the great content in your community, collaborate, innovate and produce highly valuable knowledge base articles. Watch out for our Tribal Knowledge Base (TKB) products announcement soon!

Have you ever experienced a time when you were so immersed in what you were doing that you forgot about your physical feelings and the passage of time? This highly-rewarding mental state is known as flow, and it is studied and characterized by a renowned psychologist Mihaly Csikszentmihalyi. I had the great pleasure of hearing Prof. Csikszentmihalyi himself speak on this topic at the Persuasive2009 conference. The talk was enlightening and made me understand why I sometimes forgot to eat or sleep when deeply absorbed in solving a problem.

According to Csikszentmihalyi, flow is an optimal state that can be attained when the challenges we encounter are matched to our ability. When the task is slightly too easy (or too hard) we fall out of flow and go into a state where we feel in control (or aroused if the task is slightly too hard). When the task difficulty greatly exceeds our skills, we are likely to experience anxiety. And if the task challenges do not come close to our ability, we will often experience boredom (see figure).

As illustrated by the figure, this also implies that when we are in a state of control or relaxation, we simply have to challenge ourselves and pick a more difficult task to get back into flow. However, if we picked a task that is too hard, we must learn and increase our skills gradually in order to move back into flow. Therefore, we learn the most when we are in the arousal state.

Picking a task that is just challenging enough for us to move into the flow state is not easy because the tasks we encounter do not have a continuous range of difficulty. Moreover, the exact level of challenge for a task is difficult to gauge. In an attempt to challenge ourselves, we often pick a task that is too hard and go into a state of anxiety. This is why many people like to stay in the comfort zone of control and relaxation and do not like to challenge themselves. Consequently, flow is not a common mental state.

Although flow is not common, Prof. Csikszentmihalyi has mentioned that they are more prevalent in creative professionals, such as artists, composers, poets, scientists, mathematicians, etc... This is because these professions require much self-challenge to create something novel and original. Due to the distinctive gaming heritage of Lithium, we know another group of people who often experience flow. Can you guess? Yes, they are the gamers. If you know friends who are into gaming, or if you have teenage children who are addicted to computer games, you will know what I am talking about. They will play tirelessly for hours, if not days, straight.

So what is it about video games that enable people to move into flow so easily? Actually, games in general (not limited to video games) can create an artificial environment where the task difficulty is well-controlled and increase gradually. This makes it much easier for gamers to pick a just-challenging-enough game to move them into flow (B2 in figure). Even if a gamer accidentally chose something too difficult, it would most likely not be something totally beyond his skill. So, they would experience arousal (B3) rather than anxiety or worry (B4), which is undesirable. In the arousal state, gamers only have to learn a little bit to increase their skills sufficiently to move back into flow (C). This will in turn encourage gamers to take on more challenges. This feedback dynamic is what makes so many gamers addicted to playing their favorite games.

As a practitioner of this theory, Lithium knew all along that the reason a superuser would spend 8 hours online answering questions is precisely the same reason that a gamer would play for days without sleeping. In fact, the Lithium platform is built upon our deep understanding of various gaming and social dynamics. The control--arousal--flow dynamic is just one of many that are deeply ingrained in our rich and flexible reputation engine. This is the reason we are able to attract and keep those superusers who will spend many hours on our communities. Moreover, because flow is inherently a rewarding and desirable mental state, superusers are often happy to volunteer their time and effort. To them, it's just like playing a game.

Despite my personal rediscovery of the connection between flow, gamers, and superuers, I must clarify that I am not claiming that a superuser answering questions online is necessarily experiencing flow. Whether superusers truly experience flow is a research question that can only be addressed via the scientific method. I was just inspired by Csikszentmihalyi and wanted to share the spark in my mind.

Having discussed the relationship between flow, gamers, and superusers, next time we will apply the theory of flow to help us design the optimal ranking structure that engages the superusers. Stay tuned at mich8elwu.

Couple weeks ago, I was invited to participate in a panel at the Persuasive2009 conference. The panel was on new metrics for engagement and I was to speak about the community health index (CHI). However, the audience was primarily social psychologists from both academia and industry. And all of them have a common interest in Persuasive technologies, which is defined by its inventor, Prof. B.J. Fogg, in his book to be any technology "that is designed to change attitudes or behaviors of the users through persuasion and social influence, but not through coercion." So, I was challenged with the task of relating CHI to engagement and persuasion. As a scientist, I did my homework. I read up on the most authoritative research papers in this field and came up with the following strategy.

First, I evaluated the Lithium platform by a persuasive system evaluation framework. This framework was published by Prof. Harri Oinas-Kukkonen in last year's conference proceedings and has already been adopted by researchers in this field. So I thought this would be a good place to start. In addition to some basic requirements, the paper outlined 28 persuasive design features that are grouped into 4 categories. To my surprise, the Lithium platform actually met all the basic requirements. Moreover our platform currently has 25 out of the 28 persuasive features. This allowed me to confidently conclude that our community platform is in fact a very persuasive system!

The next step was to relate all this to CHI. Since the panel was on metrics for engagement, I have reinterpreted the 6 health factors of CHI as measures of engagement: 

1. Traffic: is a measure of passive engagement.

2. Content: is a measure of passive engagement.

3. Members: is the conversion rate from passive to active engagement.

4. Liveliness: quantifies the likelihood of any user to engage actively.

5. Interaction: is an estimate of the scope of the engagement.

6. Responsiveness: measures the quality of the engagement.

By reinterpreting the health factors as measures of engagement, CHI can take on a whole new meaning. Since every engagement provides an opportunity for persuasion, CHI is actually a measure of "persuasibility". Although the Lithium community platform was not designed nor thought of as a persuasive system, it can certainly be used as one. In addition, we can now measure the persuasibility of this system.

My research really paid off at the end, because it has made the Lithium platform and CHI very digestible to the audiences. I was pretty thrilled to find my framework for CHI was tweeted and blogged, by Maury Giles from Pursuit, another panelist at the conference.

Next time, I will tell you a bit more about some of the fascinating things I've learned at the conference. For updates, come and follow me at mich8elwu.

Photo by David Lin