Unfortunately I can’t be at this months Brrism (sorry Micheal @kobb). However, one of the topics will be around ancient scrolls of wisdom (or anything over 5 years in Internet time).

Which got me thinking about what I was up to 10 years ago.

At the time I was a junior PhD researcher at the University of Plymouth. Bizarrely my old homepage is still live, though mercifully I’d updated it in 1999 with fewer flashing GIF’s and roll-over image maps, though the obligatory animated email gif is still there (the email isn’t live however).

A large part of my research (1997-2000) was looking at systems theory as it applied to ‘human activity systems’, so I’m going to cheat (slightly) and reference some work that wasn’t directly about Social Media (or even web-based technologies). Though I was based in the School of Computing, my background is in engineering and the research group was mostly engineers, economists and psychologists. We were interested in how systems theory could be applied to particular social groups (mostly engineering companies in this case) and in particular the processes that those social groups used to achieve certain aims (generally converting some specification into a manufactured product). However, I believe there is good reason to think that much of that research can be applied to social media application in other business endeavours.

Squared Circle Mosaic: Fibonacci Spiral with Hue Twist - Uploaded on January 27, 2005 by krazydad / jbum

Prof Peter Checkland (Lancaster University) is a Chemist that worked in industry on complex engineering problems and eventually moved to the most complex systems of all, those involving humans (unfortunately his seminal work is a book, 1981, not an online article).

Systems are generally recognised by some fundamental principles;

• Boundaries – there’s a bunch of stuff that’s “inside” the system, and a bunch of stuff that’s “outside” the system (there will usually be an argument over where to draw the boundary but that almost defines the fact that you’ve got a system)
• Inputs and outputs – stuff crosses the boundary, this can be physical or non-physical [you can have a 'closed' system but they're generally rather boring and hypothetical]
• There’s some transformation, i.e. difference, between the inputs and outputs
• There are components within the system; a single component is not a system
• Systems are nested; within large systems are smaller systems

and most importantly

• Emergent Properties – you can’t describe the performance of the system just by analysing the component parts

Checkland is important because he was one of the first people to try and describe the messy company and organisational situations he was working within from a systems perspective (building on much of Bertalanffy’s work between 1934 & 1969) . He identified boundaries and within those boundaries the sub-groups that actually made the company work. He identified information and flows of power within the organisation, and across those boundaries. He was able to sketch out the ‘actual’ human activity system, rather than the business or computer information system. It was the systems characteristic of emergent properties that led to them not performing as planned, and gave rise to the law of unintended consequences (previously identified in social sciences by Robert Merton (1936) but not explained).

But what does this mean for the social media strategist?

Well it means that, despite our shiny shiny toys, there is quite a bit of good research and clear thinking about how people work in groups and in particular how we can design such systems. No matter how carefully we design them, there will be emergent properties; but that doesn’t mean we shouldn’t design them in the first place.

There will always be boundaries, some stuff will be “inside”, some stuff will be “outside”. You don’t want everything to be “inside“; even planet earth isn’t a closed system.

Stuff needs to cross your system boundary, and it needs to be transformed en-route to becoming an output. I put my details into Facebook (as do 350m others) and a whole bunch of RSS feeds, and I get a ‘useful’ homepage about my ‘friends’. I can put photos, comments, stories, whatever in, and people can further transform them with additional comments, links to other people, etc. True, you can’t automagically get that data out of Facebook, but you can log in and read what’s there. And reading what’s there is one form of taking information out of the Facebook system, as are social networks, etc.

Of course there are lots of component software chunks and sub-systems within Facebook. Each casual game on Facebook is its own system, nested within Facebook. Each fan community is a nested system. Each discussion board is, potentially, a sub-system, nested within a fan page, nested within Facebook, nested within the Internet, etc. Depending on where you draw the boundary, everyone that’s on Facebook is also part of the system…

And of course there’s a ton of emergent behaviour that wasn’t predicted (or predicable).

So what can we do about/with it?

The first thing to note is that, from my experience, most people aren’t very good at meta-cognitive thinking about systems theory. That is, they are used to living within systems (social, organisational, leisure) but they don’t actually spend a lot of time thinking about those systems, and even less thinking about how they are thinking about them.

This means that people will usually try to apply existing social behaviours and norms to on line systems, and if that doesn’t work they get frustrated/angry/disillusioned/etc. You either build your on-line social media system exactly like ‘real’ world (but then why would anyone be interested in your system?) or educate people  into the operating of the new system. That’s why all games have tutorial / training built in.

In order to develop a training programme you need to understand, and be able to communicate, the designed purpose and functioning of your system. One way to do this is draw a picture of it, not a UML diagram or a wireframe, but a human activity system diagram. You don’t need to use any ’standardised’ modelling nomenclature, so long as you and your team understand it and it covers the basics above.

Conclusion & Caveat

In conclusion, some thinking time about the network you’re trying to build is valuable. The tools you employ should come afterwards; twitter is not a social media strategy. There are lots of good, well established frameworks to think about social networks, systems of activity, etc. You don’t need to follow slavishly the minutia of their particular quirks and peculiarities but you should understand why they are there and why you are ignoring them.

The caveat: feedback loops are a feature of systems. The huge difference that digital technologies have brought is the near frictionless feedback loop. There is almost no transactional cost to publishing a comment and for that comment to then be republished to +350m people (it happens both automatically via services like posterous and twitter-bots, and through the retweet/comment feature in all social media services), and re-re-published endlessly. That is something that we haven’t modelled effectively yet. The good social media marketeers amongst us know how to achieve this, even if they don’t fully understand the why.

Uploaded on October 23, 2006 by Hamed Saber

Well 2010 kicked off in the UK with snow, ice, sub-zero temperatures and general chaos as public services ground to a halt.

But not Open Coffee and the entrepreneurs of Bristol.

Fortified by the best coffee that the Boston Tea Party on Park Street has to offer we gathered on their first floor to catch up after the break and discuss the future. By the end Steve Cayzer (HP Labs, LinkedIn), Rupert Russell (Carmen Data, LinkedIn), Helen Davies (For Effect, website), Sam Machin (Orange, personal website), Nigel Legg (Katugas Social Media, website) and Andy (who surname I’ve unforgivable forgotten, sorry).

Conversation covered the various tax implications of company car ownership, developing new brand images for the new year (and the difficulty finding a good printers these days), online marketing for small tourism companies and the challenge of getting good geo-location data, and that was just at my end of the tables!

The general opinion was that while the weather and economic climate might be a bit inclement (or just down right awful) there was business to be done and opportunities to be exploited. Business cards were swapped and a couple of new collaborations initiated.

So the New Year is off to a great start and looks to get better.

Look forward to seeing you at the next Open Coffee Bristol on Tues, 26 Jan from 8.30am in The Boston Teaparty on Park St.

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Always a good evening, the next Brrism looks to be a real barnstormer (6.30pm on Wednesday 20 Jan in the Pervasive Media Studio, Bristol).

There promises to be some great presentations on social media tools (Colin Rainsforth, twitter) and the fine line between time management & time wasting (Lee Cottier, twitter) but I’m guessing the hot topic will be the Digital Economy Bill.

Paul Smith (Labour Prospective Parliamentary Candidate for Bristol West) is leading the discussion. The bill is about to enter the Committee Stage in the House of Lords, so there’s no guarantee that it’ll be law before the election, but this is a critically important bill for the UK and Bristol’s digital economy so come along and can make a positive contribution.

Doctor-Patient contact time is vitally important for diagnosis and treatment.  To aid doctors thousands of ‘tools’ have been developed for use in various fields of medical practice.  But as new technology (including physical assessment and validated questionnaires) is introduced assessment tools become redundent and others are introduced. However technological advances tend to be expensive and time consuming to train medical staff to use.  It is also the case that they often require additional physical space, time to administer and a level of health of the patient.

The aim of Dr Rupert Jones and collegues (including me) was to derive a multi-component assessment index for use with patients with COPD to gain a measure of severity. The index was intended to include items that are clinically important, applicable to all grades of disease severity and all healthcare settings, and simple and clear to use.

Traditionally, the forced expiratory volume in one second (FEV1) has been the main measure of COPD severity for clinicians and still has a prominent place in international guidelines. While patients are mainly concerned with symptoms, exacerbations and functional capacity, airflow obstruction is important to clinicians in order to measure the lung damage and determine treatment. A composite measure could account for various dimensions of the disease, and take into account both the patient’s and the physician’s perspectives.

One highly regarded composite measure is the BODE index which was originally designed to predict mortality in COPD. However, the BODE index involves a Six Minute Walking Test (6MWT) which limits its use in routine clinical settings as it takes time, supervision, and space. Another validated prognostic index, the COPD Prognostic Index is also cumbersome to use in routine clinical settings as it includes seven items, one of which is a health status questionnaire.

Therefore we have derived and validated a composite index of severity in chronic obstructive pulmonary disease, which has recently been accepted for publication by the American Journal of Respiratory and Critical Care Medicine.  We hope the index will be widley adopted by the medical profession.

If you are unsure of its relevance or usfulness to your practice, below is the abstract associated with the journal article.

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Rationale: COPD is increasingly recognized as a multi-component disease with systemic consequences and effects on quality of life. Single measures such as lung function provide a limited reflection of how the disease affects patients. Composite measures have the potential to account for many of the facets of COPD.

Objective: To derive and validate a multi-component assessment tool of COPD severity which is applicable to all patients and healthcare settings.

Methods/ Measurements: The index was derived using data from 375 COPD patients in primary care. Regression analysis led to a model explaining 48% of the variance in health status as measured by the Clinical COPD questionnaire with four components: dyspnea (D), airflow obstruction (O), smoking status (S) and exacerbation frequency (E). The DOSE index was validated in cross-sectional and longitudinal samples in different healthcare settings in Holland, Japan, and the United Kingdom.

Main results: The DOSE index correlated with health status in all datasets. A high DOSE index score (> = 4) was associated with a greater risk of hospital admission (odds ratio 8.3 (4.1 – 17) or respiratory failure 7.8 (3.4 – 18.3). The index predicted exacerbations in the subsequent year (p ≤ 0.014).

Conclusions: The DOSE index is a simple valid tool for assessing the severity of COPD. The index is related to a range of clinically important outcomes such as healthcare consumption and predicts future events.