Attempted to find a Meteor developer, unsuccessfully.

Managed to further meteor development myself, particularly in looking at speeding the app up by adding indexes to Mongo. Also completed an (unsatisfactory) graph view.

It’s hard to write about the Creative Citizens conference, it’s given me so much to think about that I can’t corral all the ideas into any sensible shape. A lot of the specific topics – participation, creativity, community, the city – have been in the air for so long that I won’t recount my notes here.

Collectively, the big-picture keynote talks, plus all the on the ground research, snapped into focus a macro view of policy, politics, money and economics in a way that was completely fresh to me.

The panel at the end of the first day, composed of representatives from four think tanks, was the peg on which I mentally hung the rest of the conference.  It was during their discussion that I realised that the measurement of value was, for me,  the concept that tied everything together.

The research presented at Creative Citizens was asking people to value social cohesion, inclusivity, creativity, empowerment.

On another level, the wonks, quite bluntly, pointed out that politicians would evaluate policy by how it helped them claw way across the next electoral threshold – services delivered cheaper, better education as measure by exam results, reduced benefit expenditure etc.

On the third, even more dismal, level everyone accepts that as a society economic value the default setting for measuring everything, which we shorthand as neoliberalism. This is inimical to the Creative Citizens agenda, which is two levels away on my just-invented policy measurement vagueness hierarchy (PMVH?).

When I worked at the (co-operative) council in Lambeth we said the co-design agenda was about, approximately, ‘getting more for your money in the era of austerity’. Very often I think academia gives the same impression, but it’s a bit of charade in both cases – one because it’s not clear how co-design or hyperlocal etc. convert to economic value, and two because I’m not sure that’s what we truly care about anyway.

What Geoff Mulgan’s talk made me think is that what’s really going on is an intellectual rejection of the notion of economic value. We aren’t really interested in hyperlocal media or co-design because it will help eek out the budget, but instead because it’s alternative value system to the remorselessly market based one, a system which we suddenly realised was horribly dysfunctional in 2008.

I heard four different speakers talk about the Occupy movement, regarding things like horizontal organisations, the hyperlocal perspective, what Occupy tells us about participation. But isn’t there a part of us that is interested in Occupy because it was literally manning the barricades against neoliberalism? Surely it’s a factor.

This ties into Adam Greenfield’s talk at LSE of the same week, where he was absolutely frank about his political views. I saw huge crowds thronging to see FT economist Martin Wolf speak on the financial crisis, before finding a more modest lecture theatre for Greenfield’s talk – I now take this to have symbolic significance. His thinking focuses on Creative Citizen themes, but from the perspective of ‘the city’, and I should note that he comes from a very different place on this.

The city, rather than the country, naturally becomes the unit of analysis, because a country, as abstraction, encourages abstract statistical and economic thinking, while the idea of a city makes us think of concrete things – town halls, street parties, the homeless. This is the mode of thought which gives rise to the Creative Citizens agenda, the two are one and then same. Geoff Mulgan and Paola Antonelli both spoke a great deal about projects led by mayors rather than presidents or prime ministers, I think for this reason.

So what should we make of the wonks telling us the Creative Citizen worldview wasn’t sufficiently ‘instrumental’? Creative Citizen ideas promise to serve up a little bit of everything with a selection of intangible benefits on the side, but as I’ve noted, politicians care about social indices – GDP, educational attainment, life expectancy, and in the short term.

Another question – wonkspeak alert – does community-led design “go to scale?”, or, how would it look if you did a lot of it? In my experience this isn’t something co-design proponents are particularly concerned with, but if you want to affect a change, surely it’s an issue?

I sensed that a lot of the audience felt that the think tankers didn’t ‘get it’. But it’s more interesting to assume that they did.

I wish I had a more intellectual reference point, but I kept on thinking of Ian Hislop on Have I Got News For You, along time ago, when Bush was in power. He said that intellectual lefty Americans loved watching The West Wing because it let them pretend the President was a left-wing nobel laureate played by Martin Sheen, rather than confront the reality that he was a neo-conservative malapropism-prone Texan.

I wondered if there is a sense in which advocating small-scale, community-led, DIY policy could be seen as hiding out too, doing well-motivated, beautifully crafted projects, but failing to engage with governmental thinking – instead doing projects that aren’t expected to scale and aren’t persuasive to policy makers.

When I spoke to Leon Cruickshank about the community-led project he led in Lancaster he said that as part of his process he absolutely expected local government experts to have closed meetings where they could use technical language and voice expert opinions. It seems to me that many people wouldn’t always want to highlight that part of their project because it seems to go against the ethos.

But it absolutely addresses one of the points raised by the think tank panel, which was that community-led design ignores the experts who are needed to implement complex and technical aspects of projects. Perhaps these concessions to reality are should be made more of.

I do sometimes admire the brutally prescriptive approach that ‘deliberative democracy’ takes for exactly this reason, although Leon did mention some drawbacks to this approach.  Deliberative democracy also interests me because it seems so on-topic for these types of discussion but it never gets mentioned, perhaps because from an American university?

Anyway… it seems to me measurement could be part of the answer too. If it was possible to articulate measurements of inclusivity or community cohesion perhaps they would become more attractive targets for policy, and move up politician’s agenda. Where economic value and social values are in tension, one could make the tradeoff explicit. Currently, economic value wins because it can often be captured by a number.

Tying this back into my own research, what I’m looking at is studying community cohesion by looking at the digital signature it leaves behind, which I really hope has some potential to make more visible slippery constructs such as community cohesion, and play a part in this measurement idea.

Which again loops back on the Creative Industries workshop I attended in Beijing, where the idea of measuring the economic impact of creativity was discussed in some detail, including the notions of stated preferences as alternative to the revealed preferences of standard economic thought.

The conference ended on the day of the Indyref result, with all of the talk of revivified political culture that bought. Yesterday Ed Miliband proposed breaking up the banks and more local powers, perhaps the economists and the wonks are underestimating the Creative Citizens approach to politics, and it can be part of a new era of civic dynamism.



This week was mostly eaten by preparing for the Creative Citizen conference. One of the key points has been the amount of work of installing even a modest exhibit – in this instance cleaning data that I normally would bother about, and much testing of the Raspberry Pi / Banana Pi  setup.

Despite this, during the install there were still problems with connecting the computers to the Internet via wifi. A key lesson is that any kind of connection that relies on wifi is always a possible source of concern, and multiple fall backs are required. Given how flawlessly Ethernet seems to work, it seems like a sensible idea to use it where ever possible.

Finally there was the issue of documentation, after doing so much work to get the thing installed it’s not that easy to find the energy to get nice photos taken. Ben says: “Friends document each other’s work”.

The conference itself is written up separately.



Having been round in some circles with getting our projects off the ground, in the end I decided to try and get a meeting together between the all the students to discuss the issues, which resulted in Google Doc of questions that everyone agreed needed answering.

I added a new screen to the observatory software, showing the graph and map view for use in the Creative Citizen conference.

I also had to resurrect the JSON API for the Adafruit IOT printer to work. Pleased to report the printer worked straight out of the box.

Also went to the LMI For All hackday at Hub Westminster and finally had a meeting with Elvira Grob and John Fass about finding a research designer for taking John’s pin boards forward.


In 2006 Netflix, at the time an online DVD rental company, ran a competition to improve their recommendation system. Their existing recommendation system, called Cinematch, made predictions about how many stars a user would give a movie, based on how they had the rated movies they had watched before. If Cinematch thought a user would give a film a high star rating, and the user had not already seen it, then Netflix would promote that movie to the user as their next rental choice.

They wanted to improve on Cinematch, so in 2006 they offered a prize of $1,000,000 to any team that could improve the accuracy of predictions by 10%. Cinematch was not a sophisticated system, and teams in the competition quickly worked out how to get a 7% improvement [1].

Getting the full 10% improvement was significantly more difficult. Eventually the prize was claimed in 2009 by a team called BellKor’s Pragmatic Chaos, a collaboration by several competitors who combined their techniques to get over the 10% line.

During the three years the competition ran Netflix had moved on, the system was never implemented. A sequel to the competition was cancelled amid privacy concerns – Netflix had claimed that the data it released about star ratings as part of the first competition was anonymised and could not be tied back to any individual user, but two researches had from Texas proved this claim to be false [2]. However, the company still felt Big Data was going to be the key to their future.

The process of recommending a movie for users can be broken down into two steps. The first is to discover what kind of movies the user likes, the second is to find a movie that fits this category which the user hasn’t seen. What happens if your system says users want to watch, say, a Washington-based big name political drama and they have watched all such existing material?

One answer is to make more, which is what Netflix did in producing the TV series House of Cards a political drama featuring Kevin Spacey and directed by David Finch . While the precise impact of Big Data on the production process of House of Cards is unclear, Netflix certainly gained much press attention for their use of extremely detailed data about movie consumption when deciding what kind of show to make [3]. As a result the media have taken House Of Cards to presage a statistical era of cultural production where Big Data is king, with headlines such as ‘How Netflix is Turning Viewers Into Puppets’ [4], ‘“House of Cards” and Our Future of Algorithmic Programming”’ [5], ‘The Secret Sauce Behind Netflix’s Hit, “House Of Cards”: Big Data’ [6]

In this paper, three problems with the use of Big Data when making cultural artefacts will be discussed:

1) Big data approaches struggle when failure is expensive

2) Cultural output may become boringly repetitive if statistical methods are applied

3) Cultural output is reflexive, and relies on a shifting context that might be hard to capture by analysing existing behaviour

As a result, big data is most likely to be used as a rough guide, rather than to completely specify, cultural artefacts – unlike the way it has been used in other industries – since most creative endeavours already use informal historical data to forecast success anyway. For these reasons, I suggest that In contrast to other arenas, in culture big data will only represent an incremental change in approach.

The need for human intervention

It’s hard to discover exactly how instrumental the use of ‘big data’ was in the the formulation of House of Cards, how much of the programme’s success is due to Netfilx’s marketing power and the other novel features of the production – such as publishing the whole series simultaneously.

Casting a big star with a track record of successful films is not a new idea, so choosing Kevin Spacey isn’t intrinsically revolutionary – nor is it unusual to use well-known directors or target genres that are known to be popular. To know for sure whether Big Data was important we have to know that Kevin Spacey was better than other actors who might have been cast in the role without the use of ‘big data’ statistical techniques.

This is clearly impossible, and perhaps an unreasonable demand. However we can get a feel for the Netflix approach by looking in more detail at the data they assemble and they way the use it.

Ian Bogost & Alexis Madrigal took apart the Netflix system for recommendations [7], reverse engineering it to see how it works – at least the parts of it that are visible on their website. Subsequently Netflix cooperated with them to explain some of their processes, granting some window into the company’s inner workings. In addition to the data they have about who rents which movies, they have have panels of expert reviewers who

…receive a 36-page training document that teaches them how to rate movies on their sexually suggestive content, goriness, romance levels, and even narrative elements like plot conclusiveness. They capture dozens of different movie attributes.

Using this data, films are classified into 76,897 “altgenres” that can then be used for recommendations (eg ‘African-American Crime Documentaries’). Presumably similar data was used to inform the production of House of Cards.

Altgenres frequently include the name of an actor or director – ‘Dramas Starring Sylvester Stallone’ or, more pertinently here, ‘Mysteries starring Raymond Burr’. Raymond Burr, star of 1950s TV Series Perry Mason, is the single most mentioned actor in the categories. Perry Mason director Christian I. Nyby II is the most mentioned director, and Barbara Hale, who starred alongside Raymond Burr is also very high, in fact directly above Clint Eastwood. Madrigal calls this “Perry Mason Mystery” – why does the system rank all things Perry Mason so highly? When questioned about the Perry Mason Mystery, Todd Yellin, the designer of the system, simply says ‘These ghosts in the machine are always going to be a by-product of the complexity’.

In a similar quirk, in a previous attempt I undertook to measure the importance of historical figures using data from Wikipedia revealed that Mircea Eliade, a virtually unknown Romanian historian, as the fifth most linked person on the site [8].

Clearly, it would not make sense for Netflix to cast Raymond Burr in House of Cards, even if he was alive. This highlights the obvious point that Netflix are at most using their data to guide their intuition about the production – they aren’t going make bizarre casting choices just because the system says. This is not step change from how TV production worked before, for example casting directors have always been guided by previous successes, perhaps now even actual statistics, to make their choices.

The T-Shirt manufacturer Solid Gold Bomb is a useful illustration of what can happen when algorithms are allowed to make creative decisions on their own. Their system advertised thousands of different T-Shirts with automatically generated slogans printed on them through Amazon – hoping to find success through weight of numbers. If someone bought one it would be printed on demand to avoid the expense of actually making such a variety T-Shirts, most of which would never be purchased. Unfortunately, their system automatically generated misogynistic slogans such as “Keep Calm and Hit Her”[9], among others, causing outrage and the removal of all their products from Amazon.

Big Data has been most successful in scenarios where occasional failures can be tolerated. For example we can accept a credit card being blocked if a fraud detection system suggests it has been compromised, as long as we can remove the block if the alert is wrong.

In the case of designing T-Shirts, society found the failure of the algorithm morally unacceptable, in the case of producing a TV show failure is too expensive: it’s impossible to imagining a commissioner defying a strong intuition and casting a seemingly inappropriate actor on the basis of statistical evidence.

Converging output

The success of House of Cards will ensure that next time Netflix look at their data, Kevin Spacey, Political Dramas and director David Fincher will seem even more popular. If they were blindly to go by the numbers, they might see that the best thing to make was another House of Cards.

This is a problem that advertising platforms, such as Google AdWords, also face. They want to show the adverts that have been clicked on most, because they are the ones that will most likely be clicked on in the future. However, if this was all they did then there would be no opportunity to expose new, potentially even more effective adverts to users – because a new advert will, by definition, never have been clicked on. This is described as the Multi Armed Bandit problem, and there are a number of mathematical solutions to it.

Another way to understand this problem is as it was posed by Richard Feynman [11]. He thought about the problem of choosing what to eat in a restaurant – should you have the dish you know you like, or try something else, which could be disappointing, or even better?

All of the mathematical solutions to this problem balance some amount of choosing the option currently thought to be best while occasionally trying out some riskier things. When applied to the Netflix problem, it suggests commissioning some shows that are very likely to be winners (House of Cards), but also to try a few riskier things which might prove successful, but whose success is not so well predicted by the data. In the cut-and-dry, high frequency world of online advertising, this might be a useful result. However, in terms of TV commissioning, isn’t that what already happens?


Social scientist Donald T. Campbell formulated the following adage which captures something important about the problems that Netflix and cultural producers more generally might face in formulating statistical forecasts of success

The more any quantitative social indicator (or even some qualitative indicator) is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.

Economists have the same concept, due to Charles Goodhart [12]

Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.

Big hit Hollywood films already follow a well observed formula [13,14], which is frequently derided as mechanical. In this area, we might expect Big Data to allow a fine tuning of ‘safe bet’ mainstream productions – but surely not revolutionising the industries approach, since it’s already so mechanistic.

But in general, even if it is the case that House of Cards made important use of Big Data, it may not be the case that this approach can be continued into the future. Audiences, especially more discerning ones, might come to recognise the kinds of patterns it produces and become tired of them.

More importantly, if Big Data driven content becomes a regular occurrence writers will respond to this new context – they might parody or satarise the phenomena, choosing to deliberately ignore, exaggerate or lampoon certain effects to play on the audience’s expectations – in much the same way as the Spike Jones film Adaptation pokes fun at script writing courses.

The three reasons given above are intended to describe some limitations of big data in the creative industries. One reason why Big Data, as a slogan, has gained so much traction is because of its facility for very directly increasing revenue by improving advertising effectiveness. In this sphere it suffers much less from the problems above: a poorly targeted advert rarely causes offence, algorithms can optimise while preventing convergence, and there is much less reflexivity.

A cynic might think Netflix already knows this. Perhaps their real play is to understand their customers in order to serve better targeted adverts in their films, not to directly shape their productions.


What could big data do?

In describing how Netflix likely uses its data, as a guide to temper intuitions, a new horizon for big data in the creative process may have been opened. Rather than looking movie consumption data as a way of algorithmically generating TV programs, perhaps a more productive approach would be to think of the data as more grist to the writer’s mill.

In this case, why stop at Netflix internal data? Projects such as IBM’s trend forecasting look as though they provide data which is just as relevant. By crunching through social media data, IBM attempt to distill what’s capturing people’s imagination. In two published examples they predict in 2012 (incorrectly?) that 2014 will be the year of Steampunk [15], and more plausibly that cycling would be the flavour of the zeitgeist for 2014 [16]. Even more ambitiously, proposals have been mooted to try and simulate the entire social world [17], which, as well as sounding like the plot of a film, might augment a company’s ability to formulate novel creative output that keys into the collective psyche.

In some sectors, human input is a (prohibitively) expensive, error prone factor to be eliminated, but in the creative world it’s an intrinsic, and valued, part of the product. This does not mean that Big Data cannot play a part, but, unlike other applications, in the case it is to provide novel, inspirational support to the creative – and essentially human – process, rather than a substitute.
[1] Rajaraman, Anand, and Jeffrey David Ullman. Mining of massive datasets. Cambridge University Press, 2011.
[2] Narayanan, Arvind, and Vitaly Shmatikov. “How to break anonymity of the Netflix prize data set.” The University of Texas at Austin (2007).
[10] Chakrabarti, Deepayan, et al. “Mortal multi-armed bandits.” Advances in Neural Information Processing Systems. 2009.
[11] Feynman, Richard P., Robert B. Leighton, and Matthew Sands. Exercises for the Feynman lectures on physics. Basic Books, 2014.
[12] Goodhart, Charles Albert Eric. Monetary Theory and Practice: The UK Experiencie. Macmillan Publishers Limited, 1984.
[13] Snyder, Blake. “Save the cat.” The Last Book on Screenwriting You’ll Ever Need. 1st edition. Ingram Pub Services (2005).
[14] Hauge, Michael. Writing screenplays that sell. A&C Black, 2011.
[17] Paolucci, Mario, et al. “Towards a living earth simulator.” The European Physical Journal Special Topics 214.1 (2012): 77-108.


Last week I attended a week-long workshop in Beijing about the ‘cultural industries’  (The 5th International Doctoral Workshop on Cultural Industries).

From outside all you know are stories about extreme economic growth and Chinese tourists buying expensive handbags. During the workshop we were exposed to one of China’s anxieties: is Chinese contemporary culture OK? Is there enough of it? Why is it still to so fixed on the West?

I often got the sense of a blank canvass – which I found incredibly exciting. What if China is ambitious about creativity as it was about the Olympics?

Culture broad and narrow: the fact that the Chinese consumer still hankers after the design aesthetic of of Apple rather than a local alternative, but also in the narrow, classic,  sense of music, writing, film etc. China understands that it can claim the world’s most venerable heritage, its concern is to reanimate that inheritance.

I suggested that it was only a matter of time before the new middle class generated a richer artistic landscape and demanded alternatives to iPads specific to the Eastern market. More or less, I was told “We’ve been saying that forever!”. There’s no question that an iFetish runs deeper here than in the UK. Someone said they would never think of buying a Chinese designed car and noted the absence of native cultural celebrities.

The country’s vehement approach to progress of all kinds has lead to a tranche of academic research to find out what can be done, as well as a policy drive. We were told that China is building 100 new museums a year; sometimes without much to put in them (I couldn’t confirm this, it’s rather easy to suspend your critical faculties when it comes to the ‘China builds X new Ys every Z’ trope). Blair-era Cool Britainia was frequently mentioned as case study in cultural policy, apparently without any of the nails-down-a-blackboard rictus it connotes for me.

The connective theme that came out for me was the relationship between the market and culture – perhaps unsurprisingly given the topic and the fact that that two key speakers were an economist and a marketing professor.

Neo-liberal Cultures

There was a relatively uncritical attitude towards the market, especially perhaps in the presentations the students each gave – for the most part a tacit assumption was that cultural growth would be stimulated by an unfettered market, and that profit was a legitimate goal of cultural output. If national politics is a pendulum, it’s understandable that China’s momentum is to the right.

If you stood up at our own institution, the RCA, and spoke of money and profit and the free market in relation to culture in the way we heard in Beijing, you could expect a furious Q & A, at least from those who hadn’t walked out. More than an observation about Chinese culture, it reminded me of how easy it is not to stop questioning your own political context, although in the next section I do some work to defend the assumption that markets and cultural creativity have an ambiguous relationship.

Creative, Cultural, Industrial?

All of the speakers addressed the issue of how to define the ‘creative’ and the ‘cultural’, a prerequisite for talking about them; they are widely used in policy discussions. Intuitively we  kind of know what counts: books, theatre, music etc. But is Apple a creative, or cultural, company? Advertising? Type-setting?Defining  a meaningful subset of activity without encompassing everything, or nothing, and such that each member of the set can meaningfully be talked about collectively isn’t trivial.

Mostly, the answer was an extensional definition, using the  pornography: you know it when you see it principle. I didn’t find this especially intellectually satisfying. Given that ‘cultural’ and ‘creative’ seem roughly similar but both fraught with difficulties, for my own mental model I’m happy to abandon the creative industries as a category. It seems to me almost any job at all could encompass some creativity, and the more senior the position the more scope for coming to novel solutions. Taking the term literally, as ‘one who creates’ surely a plumber is more creative than an Arts Council executive? The answer that only certain kinds of creation count leads to the question of which ones, where, it seems to me, the answer is ‘cultural’.

So what are cultural activities? My first thought was that cultural outputs are ones that most directly address our higher mental faculties, reflection, understanding, aesthetics etc, as opposed to the more basic concerns: staying fed, watered and sheltered. Clearly though, the whole concept is fraught with value judgements. My time in Bristol came to mind, where the council constantly fought distribution of fliers and posters for club nights, but when an opera was staged the council suddenly found it acceptable to drape banner adverts all over the place. For them, contemporary music (for which Bristol is actually famous) did not deserve support, while classical music (for which Bristol will never be famous) did.

Maslow’s Heirachy was mentioned, and for me that captures the most important aspect of what any program of ‘cultural’ promotion might encompass. Some of David Throsby’s research into the “Work Preference Model” in Australia, which this I found absolutely fascinating, pointed in this direction. In a standard model, the more you pay people to do something, the more they will do it, unsurprisingly. Many people who consider themselves artists have to take on “portfolio careers”, working outside their artistic practice to pay the bills. But, as they are paid more for that work, in contradiction to the standard Work Preference model, they do less of it. Rather than become richer, they reduce the paid hours of work and focus on their practice.

Combined with discussed unwillingness to assign monetary value to cultural output (discussed in detail below) a case starts to build that money and culture are hard to reconcile. You might be able to use money to access the lower levels of Maslow’s hierarchy, but not the higher ones. In my view, these thoughts might offer some pointers on how cultural policy might proceed. If I’m arguing it’s not about the market, a natural thought is to turn to the government to support cultural output: here I’m stuck, because that doesn’t seem to be an answer either, taking us back to the wave of nausea that is Cool Britania.  My favourite cultural gestures are two-finger salutes to politics and the market.

David Throsby

David Throsby is a Australian economist who took us through various ways of measuring the economic benefit of cultural output. I love the analytic clarity of  economic thought. Firstly he looked at the microeconomic picture. Traditionally, economists use ‘revealed preference’ as an approach to discovering value. If you are willing to pay £5 for one thing, and £10 for another, then economists assume that the thing you will pay £10 is something you value more than the £5 thing. However, many people feel that cultural output does not obey this model – that even though people aren’t willing to pay very much for it, they actually value it highly. Strong evidence in support of this was presented. An answer comes in the form of the ‘stated preference’ approach. This methodology is a sophisticated version of asking people what they value in survey form (hence ‘stated’ rather than ‘revealed’). From outside of economics that might seem like a fairly innocuous step, from inside I suspect it looks more inflammatory. If economists can’t assume that monetary value is a good measure of actual value the whole discipline would unravel. It would be like the medical profession discovering that people preferred being unwell. None the less, when you have compelling evidence that the revealed preference method is not successfully capturing value then something has to give.

In regard of the macro picture he showed that cultural output is an is not well measure at the national level. The crux is that the standard national accounts, which every nation produces, do not include cultural output in the way that they do fisheries or mining. The answer is ‘satellite accounts’, which take the standard national accounts and attempt to synthesise a measure of the value of the cultural economy by adding up the output of publishers, theaters, the music industry etc. – categories that do exist in the national accounts. This approach is perhaps in tension with the the fact that much of the value of the cultural industries is not captured in any standard economic stat, as outlined in the micro-view. Perhaps unsurprisingly, these satellite accounts indicate that politicians probably undervalue the cultural sector.

Any economic account of culture is vulnerable to a interesting criticism: that the value of cultural is literally incomparable. Economists are normally trying to make comparative judgements, as in revealed preferences, often by the market. What’s the best change we could make to improve the situation? What’s better: A new train station, or better roads, or cleaner air?

Perhaps it’s simply a category error to try and address the question “better poetry, more police, or better health care”. David Throsby acknowledged this question during his talk.

Peter Stephenson-Wright

As a professor from a business school with a background in marketing, Peter gave us an insight into the world of the cultural mercenary: the advertising creative. Uniquely among the speakers he also addressed big data very directly; he made the point that creative decisions are likely in the near term to be guided by big data, but not made by algorithms, as I believe is often hinted at in the hype around data. As is often noted, Apple got bad results in focus groups about the iPad, but it still worked out: even if big data has more scale, it’s still not going to tell you which products will sink and which swim.

Peter also noted, in line with the research by David, that creative activity will not be straightforwardly motivated by money, job titles or bigger offices, and suggested that varied work was also an important motivator. He gave the example of rotating briefs between different teams in advertising companies, to keep everyone excited.

One remark which I found hard to ascent to concerned the work of Damien Hirst, and Peter’s former employer Charles Saatchi. He appeared to suggest that Damien Hirst’s status as an eminent artist was supported by the fact that he had made a lot of money for himself and Mr Saatchi. Further, he indicated, by presenting controversial works Hirst gave journalists something to write about, apparently making him an even better artist.

In fairness Peter did say that Hirst was also a good artists because people enjoyed seeing his work. An alternative story where Saatchi used his marketing savvy to inflate the value of unexceptional works, and that the role of journalists is not simply to fill their pages with manufactured scandal occurs to me – though perhaps that’s me being an idealist. I’m not attempting to evaluate Hirst’s actual artist value, just questioning whether making money for your patron has a bearing on it, or whether your output feeds journalists with a neatly defined controversy really matters. If making money is part of Hirst’s shtick fair enough, but it’s part of his output, not the measure of it.

What next?

China is an laboratory for what is possible in cultural ambition; if money, policy, scale or brute force can squeeze cultural vibrancy out of the ether then China is sure to make it work. But if there is some imponderable required, if culture can only exist as a descenting voice, as a rejection of market logic, then perhaps China’s cultural policy will be best captured by the symbolism of hundreds of understocked museums. Looking out from the train on the journey from Beijing to Xian at the endless tower blocks full of people with increasing disposable income and time, presumably hungry for excitement and novelty, it’s hard not to sense a vacuum waiting to be filled. I imagine some jazz-era-style, elicit musical explosion, a symbolic, non-specific rejection of the system in the manner of Beatles drug-tinged edginess or hiphop style life-on-the-streets audacity - only western analogues are all inappropriate because it will be its own thing with its own rules, and will have, of course, to negotiate the political landscape in which it finds itself.

And if that’s the picture for narrow culture, as I defined it, what about broad culture – where will China’s Apple come from? Perhaps it’s worth remembering that both Steve Jobs and Steve Wozniak were formed by the foment of San Francisco counterculture, not the output of a university program shaped by cultural policy.




This week doesn’t really exist because it’s mostly about coming back from Germany and going to China. Some small developments:

1) Considering writing a little sketch in D3 to help me understand network algorithms

2) Started to get into the network literature by reading this:

3) That post pointed me onwards to this:, which I have printed out and will read on holiday.

4) Bugs continue to come out of the mill for the Hounslow project.

Network Tiles

John Fass’s cork tile / rubber band / drawing pin methodology for eliciting social networks has become a focus of my work in terms of providing a context for people to discuss their communities, as well as the direct network information it generates.

However, the finer points of the methodology are not yet fixed, and there is also a question as to how robust the process is. Will two people, describing a similar network, generate similar results? If one person is asked to complete the process twice, will they produce similar results? Do these questions matter?

To address some of these questions, I took the opportunity of being on holiday with my family to get them to have a go. I asked them to fill out a network around where they lived, indicating “people, places and organisations”. I interviewed each person separately, half received more detailed instructions, half were shown a photo of a completed tile.

In terms of stability, I found the tile process quite variable. For example, my Mum & Dad have fairly similar networks, but produced very different tiles. My Sister and her two daughters also have overlapping networks, but also produced quite different output. This is in part down to the differing and (deliberately) vague instructions. Of course, it also represents the fact that different people will see the same things differently.

Overall, I felt that showing people a photo of completed tile didn’t help them understand the process very much. The single most helpful instruction (which I was omitting to start with), was to ask participants to put a drawing pin in the middle of the board to represent themselves. Although it is possible that someone might feel they are not the centre of their own network, making this instruction leading, it guides users past the intimidating blank canvass they are otherwise faced with.

Further thought is also required about the hypergraph aspect: should users be able to use one rubber band to link multiple pins?

Roland Burt Visualisations

Reading Neighbor Networks: Competitive Advantages Local and Personal, and looking at his formula for various measurements of the network (density, connectedness, access to structural holes), I was struck by how they could be explained visually in the style of Bret Victor. This may be a project for the future, it would be a great way for me to get my head around Burt’s work.

Continuing development of the Network Observatory

As ever, writing a web app is always a bigger project than you think. It’s the first time that someone else has tried to use the Network Observatory code, and it’s proving a challenge. For some reason, deploying the code to the Modulus hosting services proved to make it much slower, which is annoying, because part of the appeal of the Meteor framework is that it’s fast.

(Which reminds of me of another point – that writing for Meteor has meant a lot of difficulty in terms of not having a relational database, I’ve spent ages writing what is automatically handled by the Rails ORM. There must be a way round this)

Password reset emails weren’t working, the interface does unexpected things, and it doesn’t give enough feedback when users carry out actions.

Hopefully, I’ll tackle these and the app will start to become more mature and usable.

Art Vs Science

I’ve been working on an essay for ages, about the culture clash between the arts and the humanities, but it doesn’t seem to have taken much shape yet. For a long time I’ve been trying to fit it around the structure of an update on CP Snow’s Two Cultures book / lecture. It’s been something of a breakthrough to realise that this is not the correct starting point at all. A project to return to in the future.

What I’m Doing

I’ve wanted to update the headline version of my PhD for a while, but every time I do it, it turns out to be extremely time consuming. Now it’s done, I hope it will be a helpful way for me to describe my work to people; it’s also helped clarify things for me.