“How do you set aside the mind space to see patterns, make connections, and read what people want? How do you find the right thing to work on?” Nice piece on the limits of hard work, and the benefits of working smarter and what that means.
I was going to write a long piece about the use of complexity science by innovative airline provider DayJet after hearing the inpsiring Brain Storm! podcast, but then realised that despite the Fast Company piece in 2007 saying they were able to predict the future they weren’t able to keep their head above water, and already have gone under.
Oh well, I could have told DayJet president, CEO and founder Ed Iacobuccithem how to apply complexity in the real world without over-reliance on the maths/technology, but looks like I’ll just have to apply it for my own benefit for now:-)
OK, it’s long list but it’s pretty useful when thinking of designing online communities for example! From John Gall. So as a planning tool how about thinking where your approach might fit into these. Good or bad!
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.
The new Complexity Digest website has the catchy new url: turing.iimas.unam.mx/~comdig. Please update your bookmarks!
Plus read my own choice of article ‘Plectics: The study of simplicity and complexity‘
Murray Gell-Mann, Santa Fe Institute, Santa Fe, USA/Europhysics News 33, 17-20 (2002).
Watching Mervyn King, Governor of the Bank of England speaking live at the Mansion House he said something to the effect that the church did weddings and funerals, and people ignored the sermons. He said the Bank of England did sermons and burials, fluffing the intended analogy. The BBC then reported that the Bank said it wanted the power to ensure its sermons were listened to. Did I get that wrong, but surely the slip of the tongue is indicative; that the underlying reality is that no one driven by profit really listens to the Bank of England? And who can blame them when King can’t even get the fundamental analogy right; surely the precision is important?
PS: The Chancellor said there were no simple solutions. Wrong, there are no easy solutions. Simple is part of complex. There therefore logically must be simple solutions.
Just got hold of this recent study from HP Labs which demonstrates the feedback loop between attention and contributions to online communities. The abstract provides a nice introduction:
A significant percentage of online content is now published and consumed via the mechanism of crowdsourcing. While any user can contribute to these forums, a disproportionately large percentage of the content is submitted by very active and devoted users, whose con- tinuing participation is key to the sites’ success. As we show, people’s propensity to keep participating increases the more they contribute, suggesting motivating factors which increase over time.
This paper demonstrates that submitters who stop receiving attention tend to stop contributing, while prolific contributors attract an ever increasing number of followers and their attention in a feedback loop. We demonstrate that this mechanism leads to the observed power law in the number of contributions per user and support our assertions by an analysis of hundreds of millions of contributions to top content sharing websites Digg.com and Youtube.com.
What’s important about this is helping frame community management strategies to ensure the valuable ‘advocates’ in a community remain active, by ensuring the value of peer attention is factored into sustaining their involvement. This the author’s recognise if paramount as “a disproportionate number of contribution to online peer production efforts are made by a small number of very active users”. And here’s the maths that supports this:
So what sustains this feedback loop? The authors suggest that to answer this question “one needs to look into the constituents of a contributor’s potential audience”, or to put it simply the number of fans/subscribers they have — a feature of both Digg and YouTube — could well be the missing evolutionary link:
Because a considerable portion of attention a contributor receives can be attributed to her fans, the contributor’s publicity (measured by the number of fans) could act as the important missing link between popularity and productivity. A contributor with many past contributions (high productivity) naturally has many fans (high publicity). Her fans naturally pay a lot of attention to her next contribution (high popularity). This in turn incentivizes the contributor to make more contributions.
Conclusion? (1) Have a strategy to support your top contributors. (2) As part of this measurable strategy make sure the means for them to gain attention work well.
Getting these right could make the difference between success and failure in the long term. After all don’t 90% of posts get created by 1% of users, according to Jakob Nielsen?
This is why top contributors matter
Indeed Nielsen adds a useful caveat to this question:
If you display all contributions equally, then people who post only when they have something important to say will be drowned out by the torrent of material from the hyperactive 1%. Instead, give extra prominence to good contributions and to contributions from people who’ve proven their value.
Feedback loops of attention in peer production, Fang Wu, Dennis M. Wilkinson and Bernardo A. Huberman , 2009/05/12, arXiv:0905.1740 (pdf). Thanks to the Complexity Digest for the initial research reference.
Update May 2010: A simple way to boost influencers’ (not precisely the same as ‘top contributors’ but still relevant) credibility with your users is by making their content more searchable, & by promoting it via tweets & bookmarking: http://ow.ly/1FUtM
Nice piece from Francois Gossieaux on the need for new management approaches for using web 2.0; links in with the tough subject of how to use social media for internal organisational change:
Social media allowed the social to scale beyond anything that we’ve ever seen before. To succeed in leveraging social media and the inevitable invasion of the social in everything we do, we need some new management thinking…
A mild-mannered British physicist is trying to render Google irrelevant. Stephen Wolfram, the creator of Mathematica, a grandiosely ambitious piece of software, has come up with Wolfram Alpha, a grandiosely ambitious engine of knowledge.
Grandiosely ambitious, and grandiosely inexplicable. Put simply, Wolfram Alpha, due to launch in May, will “compute” answers to questions, where Google and other search engines merely trawl the Web for pages which might hold the answer.
To do this, Wolfram has had a small army of researchers working on systematically analyzing and structuring the corpus of human knowledge so that a computer might be able to answer questions with concrete answers, such as, “How far will the Earth be from the Sun tomorrow?”, a question Google completely fails to answer.
Er, this human-centred extract from Valleywag reminds me of a quote from Wolfram ‘A New Kind of Science’ from 2002 which I used in a short paper I wrote on the fight against terrorism:
“For our everyday experience has led us to expect that an object that looks complicated must have been constructed in a complicated way…(but) at least sometimes such an assumption can be completely wrong…unlike engineering nature operates under no such constraint.”
Hence humans (complex) vs computer algorithms (complicated) alright? The joke is I actually met some guy at Jane’s Information Group and tried to convince him of the power of complexity; I’m not sure he was that impressed but it was good of him to hear me out;-)
PS: C1E8844DA8344058820E1B0044CB5042
For no reason except I was trying to help Shirley figure out the week ahead I started thinking about the value of conversations, specifically using online communities to full effect, as part of a practically-minded ‘complexity’ approach to business. Then I did a Google search on complexity and conversations and came up with Dr Patricia Shaw’s book on the subject, with the following customer recommendation which is a useful starting point for further thought:
At last, recognition that real change doesn’t happen purely because of top-down, management dictats, but is embodied by real people having real conversations that are not structured by clear objectives, goals and processes. Inherently scary for all those who rely on management as a control process in their organisations and change as a corporately-guided process, this instead looks at the informal organisation and how creating spaces for conversations between like-minded change agents can be the most effective.
This veers slightly too far into complexity and informal processes only for me – I believe that a balance is required between formal change and informal conversations, but this is still an important broadening of the discussion on corporate change.
A physical model of the S & P 500, Dow Jones, and NASDAQ price trend from January to November 2008, demonstrating the value of visuals along the way. Seeing is believing!
The sharp decline in prices has an animal-like feel to it