Digital Common Law

I was reading one of my favorite thought leaders on system-level IT (Irving Wladawsky-Berger blogs here) and I was compelled to read the paper he referred to and tell you all about it.  The paper is by ID3’s executive director John Clippinger and policy strategist David Bollier and it was presented at a recent D.C. workshop on the legal implications of digital media innovation. 

The premise of the discussion is one of my favorite topics to gripe about.  Government is too slow to have any chance of regulating a single fast moving technology domain effectively.  Imagine what would happen if they tried to govern the whole thing!!! 

So Clippinger and Bollier propose what they are calling Digital Common Law.  It is a bottom-up self-regulating approach that takes the need for big government out of the picture.  Let each digital community create its own rules and regulations, imbed it in the software, and make it transparent to users.  Give users enough control to manage their own privacy and security.  Make sure the technology infrastructure enables trusted relationships and permeable boundaries with other systems (I think this means low switching costs so if you are unhappy with the community you could easily take your business elsewhere). 

Some additional recommendations they make are to leverage the Big Data approach to get both business and social value out of the data that the system collects.  And to use bottom-up (even within each individual community) management so that users have control of the terms.  Disputes could be resolved internally or if necessary at the small government level. 

Irving WB notes at the end of his blog that he is “struggling to wrap my head around the implications . . .”  If he is, I don’t expect to be able to explain all of the details, benefits, or dangers of this approach.  But I agree wholeheartedly with him that it has the potential to be transformational. 

An amusing implication of the Peter Principle wins an Ignoble Award

The most recent Ignoble Award given out for research in management (awarded in 2010) caught my eye.  The study is available here and the official reference is: Pluchino, A. Rapisarda, A.; Garofalo, C. (2010). "The Peter principle revisited: A computational study". Physica A: Statistical Mechanics and its Applications 389 (3): 467–472. 

In essence, they proved the Peter Principle.  For those of you unfamiliar with the Peter Principle  it is the practice of promoting people when they are really good, with the consequence that you are taking them out of the job they are good at and putting them in a different one.  Simple probability predicts that they will be less good at their new job then they were at the previous one where they excelled.  This research found that organizations would do better if they promoted people at random.  This way they would be equally taking people out of jobs they are good at and out of jobs they are bad at. Again, it is simply the law of averages at work.

But doesn’t this imply that the BEST way to award promotions is always to promote the worst performers?  It would be kind of scary to find out that this is actually the best strategy. 

Do you think it would?  Let me know in the comments.

Can gamification save the world?

One of the messages of the book is that games/gamification can have an impact on the long term health of society itself.  The basic reason is that it gives us an opportunity to learn and practice some skills that are essential to deal with the complex world we have created for ourselves.

1.  Games allow us to practice with long term goals without having to wait such a long time for the learning to bear fruit.  In games we can accelerate the passage of time, experiencing many cycles in quick succession. 

2.  Similarly, games allow us to practice ecosystems thinking.  Ecosystems thinking involves grasping the complex interplay of several components of a system.  In a game, we can make the interactions more transparent.  We can also start a new player out with small interactions and then scale them up to make learning easier and more accurate. 

3. We can also facilitate pilot experimentation, which is practice with no real consequences.   We don’t want pilot trainees to crash any real planes while learning to fly.  But practice in a flight simulator is great (assuming it is designed for real learning as well as gamified).

4.  Massively multiplayer games give us large sample size simulations.  At the moment, we don’t actually know how to model complex systems like the environment, or international trade.  We change one thing and all kinds of crazy unexpected side effects happen.  Pretty soon we have a Black Swan like the real estate market crash/financial crisis in 2007-10.    Instead of practicing on the real world, we can recruit 100,000 people to play a gamified simulation.  Because they are real people, they will react a lot more like real people in the real world and then give us a better idea of what will happen.  We can prepare in advance or just do something different in the first place.

So it we create a game that simulates some aspect of the real world, recruit 100,000 people to play, and then mine the results for actionable hypotheses, perhaps we can figure out what to do about the environment (World Without Oil) or some other potentially catastrophic situation we are getting ourselves in to.

Fundmantal attributes define a game.

I really liked this part of the book.  She identifies six different kinds of games.  These are different not because of superficial attributes, but because they are fun/effective for fundamentally different reasons.  I like this so much because it has fantastic implications for designing games, gamifying other systems, and understanding the psychology behind games.  As usual, I will add my two cents for each one.

1.       Busywork games (bejeweled): something to distract you, refresh your right brain, give you rapid and frequent successes, guaranteed feeling of productivity to counteract a lack of productivity in real life.

2.       Knowledge/strategy games (crossword puzzles): large sense of accomplishment due to personal strengths. To counteract failure or lack of feedback in real world.

3.       Physical games (sports): endorphins of fatigue and challenge of competition. To counter boredom in real world.  Lots of opportunity for Fiero.

4.       Discovery games: investigate and explore new worlds (physical, virtual, knowledge) to counteract repetitive determinism in the real world.

5.       Teamwork games (ropes courses): collaboration, cooperation, division of labor, reliance.  To counter solitude in the real world. When your activity is linked to the team, you can’t quit without hurting others (peer pressure to keep going).  When rewards are linked you win or lose together, not competitively.

6.       Creative games  (Sims): make meaningful decisions that create something good (art or function). To counteract transactional activity in real world.

I used some human factors technical terms here, so if you want more information on any of these, feel free to ask in the comments (or shoot me an email).

Human Factors basis for why games are fun.

Charles Mauro published an incredibly insightful case on what makes games work – using Angry Birds as a demonstration.  This is my interpretation of the major points he makes (to see my basic philosophy about gamification, see here and here). I am sure he will let me know if any of it is way off.

Games make a system engaging when/if they:

·         Use a simple basic interaction model that allows users to build a robust schema quickly (during their first user experience).  This reduces the need to learn the interaction model, which most users find frustrating.  It is not as fun as learning content.

·         Have a response time that is fast enough for effective interaction (users get what they need before they need to use it), but slow enough and variable enough to create mystery and anticipation.  The response/feedback should also be slow enough to enable learning (time to process the feedback) and error correction (time to change before an action has irreversible negative consequences) when appropriate.

·         Have some variation just for observation – no learning is necessary but again adds more mystery and anticipation and keeps it from getting stale.

·         Never require more working memory than the user has available. But don't require so little that it becomes boring.  This can be done by including variations that are not essential for use, but enhance use.  This means users can learn them at their own pace and can forget them without penalty.  This keeps it from getting stale.  It also allows users to allocate more working memory when they want to, but less when they need to.

·         Provide breaks periodically to prevent WM and attention from fatiguing.   This is especially effective during phase shifts (when a user makes it to a higher level, there is often a pause to “load” the next one).

·         Enhance mystery by having some responses that appear without any user input and at random intervals.

·         Use audio to give advance warning of pace.  But have the audio vary enough not to get boring.  The mean pace can be equal to the interface pace but vary a little faster and little slower along the way. 

·         The audio is synched with interface actions as well as its pace.

·         The visual complexity matches the game interaction complexity.

Most of these are true for enhancing user experience in general.  They create a sense of "flow".  But they are even more critical for games because without flow a game is just work.