Is there something more accurate and precise than the 90-9-1 rule out there? IMHO, Lorenz Curve and Gini Coefficient. Do you know anything else? The Economics of 90-9-1

My answer as part of yesterday’s Online Community Manager group discussion kind of sums up where I’ve got to after reading Dr Wu’s blog previous post and this latest one:

I like the approach you have using economics-based models. I’ve come at it from a more particpant-observer type sociological point of view, so what I’d like to see is for your analysis to return a new ‘rule of thumb’ based on your in-depth data analysis.

The 90-9-1 rule is useful to community managers because it helps provides a starting point for understanding, as Arantza says above. For example it would be useful to know from a practical point of view whether for more open communities (as opposed to niche market research or project based communities) the 90-9-1 is a useful tool for helping launch a new community.

It’s partly about creating a social dashboard that can explain to a member of senior management why a certain kind of community activity may help or hinder greater participation.

I did this kind of work previously in the National Health Service, creating simple reports on the success of a national public health initiative, which worked well for senior managers (government ministers in that case).

So I come back to the challenge, the age old relationship between lab & fieldwork if you like, what would be the new rule of thumb/thumbs?

I’ve chosen to highlight multiple feedback loops as a useful tool, to help drive top contributors for example (taken from the HP Labs research), but I take your point that for commercial ROI purposes more precision is required. To put it another way in such a dynamic social context how does precision allow you to create heuristics for day to day community management?

1. The Primal Scenario or Basic Datum of Experience: Systems in general work poorly or not at all. (Complicated systems seldom exceed five percent efficiency.)
2. The Fundamental Theorem: New systems generate new problems.
3. The Law of Conservation of Anergy [sic]: The total amount of anergy in the universe is constant. (“Anergy” = ‘human energy’)
4. Laws of Growth: Systems tend to grow, and as they grow, they encroach.
5. The Generalized Uncertainty Principle: Systems display antics. (Complicated systems produce unexpected outcomes. The total behavior of large systems cannot be predicted.)
6. Le Chatelier’s Principle: Complex systems tend to oppose their own proper function. As systems grow in complexity, they tend to oppose their stated function.
7. Functionary’s Falsity: People in systems do not actually do what the system says they are doing.
8. The Operational Fallacy: The system itself does not actually do what it says it is doing.
9. The Fundamental Law of Administrative Workings (F.L.A.W.): Things are what they are reported to be. The real world is what it is reported to be. (That is, the system takes as given that things are as reported, regardless of the true state of affairs.)
10. Systems attract systems-people. (For every human system, there is a type of person adapted to thrive on it or in it.) [eg: watch out for contributors who dominate your community]
11. The bigger the system, the narrower and more specialized the interface with individuals.
12. A complex system cannot be “made” to work. It either works or it doesn’t.
13. A simple system, designed from scratch, sometimes works.
14. Some complex systems actually work.
15. A complex system that works is invariably found to have evolved from a simple system that works.
16. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over, beginning with a working simple system.
17. The Functional Indeterminacy Theorem (F.I.T.): In complex systems, malfunction and even total non-function may not be detectable for long periods, if ever.
18. The Newtonian Law of Systems Inertia: A system that performs a certain way will continue to operate in that way regardless of the need or of changed conditions.
19. Systems develop goals of their own the instant they come into being.
20. Intrasystem [sic] goals come first.
21. The Fundamental Failure-Mode Theorem (F.F.T.): Complex systems usually operate in failure mode.
22. A complex system can fail in an infinite number of ways. (If anything can go wrong, it will.) (See Murphy’s law.)
23. The mode of failure of a complex system cannot ordinarily be predicted from its structure.
24. The crucial variables are discovered by accident.
25. The larger the system, the greater the probability of unexpected failure.
26. “Success” or “Function” in any system may be failure in the larger or smaller systems to which the system is connected.
27. The Fail-Safe Theorem: When a Fail-Safe system fails, it fails by failing to fail safe.
28. Complex systems tend to produce complex responses (not solutions) to problems.
29. Great advances are not produced by systems designed to produce great advances.
30. The Vector Theory of Systems: Systems run better when designed to run downhill.
31. Loose systems last longer and work better. (Efficient systems are dangerous to themselves and to others.)
32. As systems grow in size, they tend to lose basic functions.
33. The larger the system, the less the variety in the product.
34. Control of a system is exercised by the element with the greatest variety of behavioral responses.
35. Colossal systems foster colossal errors.
36. Choose your systems with care.

Many economists believe that investors make decisions rationally, weighing up corporate data and other pricing signals to evaluate gain or risk before buying or selling stocks.

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After years of proposing, theorizing and deducing that there is water on Mars — at least in the form of ice — NASA scientists said yesterday they have finally confirmed it, after the Phoenix lander detected traces of water vapor wafting off a scoop of Martian dirt, researchers said yesterday.

“It’s something we’ve been waiting quite a while for,” said William Boynton of the University of Arizona, describing how an tiny “oven” on the lander heated the dirt until ice mixed with it evaporated. “We’ve now finally touched it and tasted it.”

See they’ve been searching for water for a while now, starting with the Moon.

PS: It’s a solar eclipse today 1 August, which thanks to NASA you could watch online from the UK. But if you missed it here’s my animoto version.