Lone Gunman

New and poten­tially dis­rup­tive music stream­ing ser­vices are hav­ing a hard time break­ing into the mar­ket, with many ana­lysts blam­ing their busi­ness mod­els and oth­ers blam­ing the con­trac­tual demands from labels for the trou­bles encoun­tered. There are also com­plaints about the roy­al­ties paid to artists and poor rev­enues of exist­ing ser­vices.

Michael Robert­son–founder of MP3Tunes and MP3.com–attempts to lift the veil on the indus­try by look­ing at some of the (you could safely say “unrea­son­able”) con­trac­tual demands placed on music stream­ing ser­vices by record labels:

Gen­eral deal struc­ture: Pay the largest of A) Pro-rata share of min­i­mum of $X per sub­scriber, B) Per-play costs at $Y per play, C) Z per­cent of total com­pany rev­enue, regard­less of other busi­ness areas.

Labels receive equity stake: Not only do labels get to set the price on the ser­vice, they also get par­tial own­er­ship of the company.

Up front (and/or min­i­mum) pay­ments: Means large amounts of cash are nec­es­sary to even get into the game. […] This fur­ther sti­fles inno­va­tion in ser­vices and busi­ness models.

Detailed report­ing, includ­ing monthly play counts: Pro­vid­ing addi­tional reports unre­lated to pay­ment, includ­ing over­all mar­ket share of sales in var­i­ous cat­e­gories. […] The labels effec­tively offload their busi­ness analy­sis (and the cost of such analy­sis) onto the music services.

Data nor­mal­iza­tion: With­out stan­dard nam­ing con­ven­tions and canon­i­cal meth­ods for ref­er­enc­ing artist, tracks and albums, the ser­vices are left to try and match artist, track, album names pro­vided by one label with those of another. It’s incred­i­bly inef­fi­cient, as each ser­vice must undergo this process separately.

Pub­lish­ing deals: Once you’ve signed deals with the labels, you then need to cut deals with the pub­lish­ers. […] Although you may have the rights to stream from labels, you some­time can’t get the rights to stream from the pub­lisher, or worse, even find the publisher.

Most favored nation: This is a deal term demanded by every major label that ensures the best terms pro­vided to another label are avail­able to it as well. This greatly con­stricts the abil­ity to work out unique con­trac­tual terms and fur­ther lim­its busi­ness models.

Non-disclosure: This is the main rea­son music ser­vices, not the labels, have been get­ting heat from the artist com­mu­nity. Music ser­vices can’t defend against accu­sa­tions about low artist pay­ments because they pay the labels who don’t dis­close what they’re pay­ing to the artists.

It’s worth not­ing that while Michael Robert­son is a trust­wor­thy writer and likely to have access to peo­ple who know this infor­ma­tion (if this isn’t first-hand infor­ma­tion any­way), he’s also likely to har­bour some resent­ment toward record labels from his busi­ness ven­tures. Still, even with­out a solid ref­er­ence I felt that this was too inter­est­ing to just pass up.

The small sam­ple size of Stan­ley Milgram’s small world exper­i­ment means that the the­ory of ‘six degrees of sep­a­ra­tion’ and the con­clu­sion drawn from it–primarily, the Influential’s the­ory pop­u­larised by Mal­colm Glad­well in The Tip­ping Point–could be deeply flawed. That was the start­ing point for Dun­can Watts’ research that led him to say “the Tip­ping Point is toast”.

So to research how ideas and trends spread virally, Watts (who is author of Every­thing is Obvi­ous, prin­ci­pal research sci­en­tist at Yahoo! Research (he directs their Human Social Dynam­ics group), and found­ing direc­tor of Colum­bia University’s Col­lec­tive Dynam­ics Group) ran large-scale repro­duc­tions of the small world exper­i­ment and hun­dreds of com­puter sim­u­la­tions that brought for­ward two con­clu­sions: the six degrees of sep­a­ra­tion the­ory is cor­rect, but there is no evi­dence for super-connected ‘trend gate­keep­ers’ (such as Gladwell’s ‘Con­nec­tors’):

But Watts, for one, didn’t think the gate­keeper model was true. It cer­tainly didn’t match what he’d found study­ing net­works. So he decided to test it in the real world by remount­ing the Mil­gram exper­i­ment on a mas­sive scale. In 2001, Watts used a Web site to recruit about 61,000 peo­ple, then asked them to ferry mes­sages to 18 tar­gets world­wide. Sure enough, he found that Mil­gram was right: The aver­age length of the chain was roughly six links. But when he exam­ined these path­ways, he found that “hubs”–highly con­nected people–weren’t cru­cial. Sure, they existed. But only 5% of the email mes­sages passed through one of these super­con­nec­tors. The rest of the mes­sages moved through soci­ety in much more demo­c­ra­tic paths, zip­ping from one weakly con­nected indi­vid­ual to another, until they arrived at the target. […]

[His com­puter sim­u­la­tion] results were deeply coun­ter­in­tu­itive. The exper­i­ment did pro­duce sev­eral hun­dred soci­ety­wide infec­tions. But in the large major­ity of cases, the cas­cade began with an aver­age Joe (although in cases where an Influ­en­tial touched off the trend, it spread much fur­ther). To stack the deck in favor of Influ­en­tials, Watts changed the sim­u­la­tion, mak­ing them 10 times more con­nected. Now they could infect 40 times more peo­ple than the aver­age cit­i­zen (and again, when they kicked off a cas­cade, it was sub­stan­tially larger). But the rank-and-file cit­i­zen was still far more likely to start a contagion.

I can’t help but find it some­what ironic that, writ­ten almost four years ago, this argu­ment hasn’t really gained much trac­tion and Gladwell’s ideas are still dis­cussed ad nau­seam.

Five neu­ro­sci­en­tists trav­elled into deep­est Glen Canyon, Utah, to con­tem­plate how tech­nol­ogy has changes their behav­iour. Some were scep­tics and some were believ­ers, and by tak­ing this forced break from their com­put­ers and gad­gets (there was no mobile phone recep­tion or power) they were deter­mined to find out whether or not mod­ern tech­nol­ogy inhibits their “deep thought” and can cause them anx­i­ety.

This bit of self-experimentation and cog­ni­tive reflec­tion is a bit too light on the con­clu­sions for my lik­ing, but this arti­cle, from The New York Times’ Unplugged series that exam­ines “how a del­uge of data can affect the way peo­ple think and behave”, does have this that’s worth think­ing about:

[By day three] the group has become more reflec­tive, qui­eter, more focused on the sur­round­ings. […]
The oth­ers are more relaxed too. Mr. Braver decides against cof­fee, bypass­ing his usual rit­ual. The next day, he neglects to put on his watch, though he cau­tions against read­ing too much into it. […]

Mr. Strayer, the believer, says the trav­el­ers are expe­ri­enc­ing a stage of relax­ation he calls “third-day syn­drome.” Its symp­toms may be unsur­pris­ing. But even the more skep­ti­cal of the sci­en­tists say some­thing is hap­pen­ing to their brains that rein­forces their sci­en­tific dis­cus­sions — some­thing that could be impor­tant to help­ing peo­ple cope in a world of con­stant elec­tronic noise.

“If we can find out that peo­ple are walk­ing around fatigued and not real­iz­ing their cog­ni­tive poten­tial,” Mr. Braver says, then pauses and adds: “What can we do to get us back to our full potential?”

“Third-day syn­drome”. I like that, and it rings true. Week­ends away to nearby cities don’t do it for me in terms of dis­en­gag­ing and allow­ing free thought; I need at least four days.

One more com­ment that was a bit too close for comfort:

Tech­nol­ogy has rede­fined the notion of what is “urgent.” How soon do peo­ple need to get infor­ma­tion and respond to it? The believ­ers in the group say the drum­beat of incom­ing data has cre­ated a false sense of urgency that can affect people’s abil­ity to focus.

Arti­cle and book pitches — both suc­cess­ful and unsuc­cess­ful — can give you a small insight into an editor’s selec­tion process and the sales-side of a writer’s mind, as well as help you learn to write more effec­tively. As such I’ve started to col­lect sites fea­tur­ing pro­pos­als and pitches.

A recent addi­tion to this list is the pitch data­base from The Open Note­book; a col­lec­tion of writer-submitted pitches for sci­ence arti­cles that have been accepted for pub­lish­ing in many of my favourite places, such as Ars Tech­nica, Atlantic, Lapham’s Quar­terly, This Amer­i­can Life and Wired.

Of par­tic­u­lar note is a pitch from David Dobbs, writer of the Neu­ron Cul­ture blog. Pitch­ing Atlantic edi­tor Don Peck, Dobbs wrote an exten­sive pitch for The Orchid Chil­dren that led to the pub­li­ca­tion of a fan­tas­tic arti­cle, The Sci­ence of Suc­cess. Those who fol­low Dobbs’ blog will know that this in turn led to a book deal for The Orchid and the Dan­de­lion, Dobbs’ forth­com­ing book.

User inter­face design­ers (and par­tic­u­larly icon design­ers) could learn a lot from comics and the the­ory behind them.

Tak­ing his cue from Scott McCloud’s excel­lent Under­stand­ing Comics, Lukas Mathis looks at how for opti­mum recog­ni­tion and in order to aid under­stand­ing, user inter­face ele­ments must find the sweet spot between uni­ver­sal­ity and real­ism. Like when draw­ing cer­tain comics, it’s about find­ing the opti­mum com­prim­ise between too lit­tle detail and too much.

Peo­ple are con­fused by sym­bols if they have too many or too few details. They will rec­og­nize UI ele­ments which are some­where in the middle.

The trick is to fig­ure out which details help users iden­tify the UI ele­ment, and which details dis­tract from its intended mean­ing. Some details help users fig­ure out what they’re look­ing at and how they can inter­act with it; other details dis­tract from the idea you’re try­ing to con­vey. They turn your inter­face ele­ment from a con­cept into a spe­cific thing. Thus, if an inter­face ele­ment is too dis­tinct from its real-life coun­ter­part, it becomes too hard to rec­og­nize. On the other hand, if it is too real­is­tic, peo­ple are unable to fig­ure out that you’re try­ing to com­mu­ni­cate an idea, and what idea that might be.