For the past few months, I’ve been hungrily scouring the web in an effort to find and collect that most breathtaking of obsessive compulsive video montage masterpieces: the pop culture megamix.

Lovingly dedicated to curating and documenting every tiny, weird repetitive tic of celebrities or Hollywood plot device, the megamix genre is a thing of beauty. Like watching glitches in some bizarre pop culture Matrix, it’s unsettling — but really in only the best possible way.

In any case, if you’ve spent any amount of time online, you’ve no doubt seen some of these and heard about others. In my opinion, it’s totally worth going back and experiencing them all (and catching up on the ones you haven’t). I’ve collected all the best I could find and included them all below.

So, enjoy. If I’m missing any that you think should be included, absolutely drop a comment!

(Obscenely) more videos, after the jump…

* “Zoom and Enhance” Moments –

* Ice related puns in “Batman and Robin” –

* Sandra Lee saying “Delicious” –

* Shia LaBeouf saying “No” –

* GET OUT OF THERE –

* David Caruso One Liners –

* Montage of “Problem” Scenes In Infomercials —

* I’m Not Here To Make Friends (2009 edition)

* Sheriff John Bunnell: The Definitive Introduction Compilation (thx Rich)

* All The Secret Ingredients of Iron Chef (thx Alex)

* The Surreally Bad Closing Monologues of “Lopez Tonight”

After the wave of anger following instant personalization, Facebook has since issued an odd, self-justifying blog post and withdrawn to a kinda-sorta seeming compromise on its privacy policy. But generally the lingering question seems to be not if, but when the next attempt by a fallen social media darling to violate privacy goes down.

But even beyond this question, I think there’s an interesting deeper issue lurking here behind all the media hubblaboo of the Facepalm saga. A question that has more to do with the entire ecosystem of social media generally, rather than the specifics of who or what is going to pose a threat to personal information. Namely:

Is Facebook just an isolated case of a company gone wrong? Or are increasing violations of privacy just the typical behavior of a mature for-profit social network?

And, more generally, are privacy violations on the part of traditional social networks just a unique case of social media gone wrong? Or are they just the first in a broad and growing trend among all social media services (thinking here of microblogging, etc etc)?

The key here, might be to examine the rawest motivation of these companies, which, like all companies, is simply the need to stay solvent. And, if advertising revenue is the cornerstone of the business model for social networks, then one of the fundamental engines determining their activity is a simple formula — the formula for advertising revenue:

(Total Impressions of an Ad) X (Clickthrough Rate of the Ad) X (Cost Per Click) = Total Advertising Revenue

From this formula, I’ve been thinking that there’s a way to derive a pretty neat little economic model. A model which interestingly seems to suggest that as businesses based primarily on the revenue from online advertising continue to expand, they inevitably will try more and more intrusive strategies to acquire data about users. In short, that invasions of privacy are just part and parcel of the mature behavior of a certain type of business that makes online advertising a cornerstone. What’s worrisome, of course, is just how many “businesses that make online advertising a cornerstone” implies in the web startup world.

This argument why is a bit involved, so it’s worth going through it step by step (geekery follows).

One of the great natural advantages of social networks is that they enjoy network benefits as they grow. More active users increases the value of the social network, so growth tends to accelerate exponentially if a service is successful. From the impressions point of view, this is a great story. In the initial phase, a virally growing social network provides sufficient impressions to drive a situation where online advertising covers most if not all costs and then some.

Sadly, betting on the side of entropy, impressions are generally doomed over time. Existing users get bored — they stop coming back, or come back less frequently. After reaching sufficient size, there’s also just less potential users to acquire, which slows the rate of growth. As a large multinational company, you can do some things to shore up this inevitable decline — expand internationally, for example, but in any case the story remains the same: acquiring impressions gets more difficult over time. As a result the long-term growth of a successful social network over its lifetime looks something like the “S” curve shown below, with exponential growth in the beginning tapering out to equilibrium later on.

Without any particular efforts to improve cost per click or clickthrough rates (meaning that they’re held roughly constant, then, this simply means that revenue generally stalls over time. This problem gets compounded since the cost side of a social network doesn’t stay constant against revenue. Far from it, beyond maintaining the cost of many millions of users for a free service, factoring in the development and deployment of more (and higher bandwidth) features to keep users interested and impressions inflated tends to actually accelerate costs over time. Barring some unexpectedly improbable generosity from media buyers to shore up social network revenue by agreeing to substantial increases in cost per click (unlikely since declining or stagnant impressions lowers the value gained from an advertisement), the only remaining factor to keep the cash flow strong is the key variable of clickthrough rates.

Clickthrough rates are the function of two things. We have data, on one hand, essentially determining how much we know about users. And “technology,” broadly speaking — which pulls from the data in an effort to get people to click on an advertisement. This embraces everything from using hard Google-style quantitative scripting to target ads based on the interests of users, to the qualitative design work that gets done to create appealing designs. Technology is a machine for converting the value within the data into actual money through users clicking through ads. Knowing my music tastes and the music tastes of my friends, for example, is the data. The fetching felt-owl-Etsy designs and the strategic placement in front of lovers of similar bands on Facebook when I log on, is the Technology.

But, really, a given piece of data (or a big bundle of data) has only so much exploitable value for this purpose. Technology can only go so far in extracting useful information from a set of data. This is due to the limitations of how much design can add to the attractiveness of something, and how good predictive algorithms are at delivering advertising to susceptible people. This problem is particularly exacerbated since nearly all studies show that users are becoming increasingly desensitized to online advertising. Despite wheelbarrows of cold hard cash flowing into research and online advertising coming into its own as a huge industry, we’ve actually seen a decline in clickthrough rates over time. We’ve gone from 2% in the 1990s to 0.28% by 2003, and by 2010, it’s good to get something in the 0.1 – 0.2% range. This is another good reason for Cost to Click to stay steady or even decline as well, since the quality of clicks gained from an ad buy is more likely to contain a bigger proportion of mistakes and other noise. This makes it a less valuable proposition to buyers.

As technology attempts to squeeze an ever declining bit of value from the total number of impressions hitting a site, social networks face the big crunch of increasing costs and slowing revenues. This produces an increasing pressure to find ways of boosting advertising revenue again.

And, conveniently enough, one other unleveraged door remains open: data. That is, rather than expand the technology to pull ever smaller marginal values from data or crudely try methods to boost impressions, it’s possible to just attempt to acquire more complete information about users. Indeed, as the other components determining advertising revenue fall down, the incentives for taking this route get higher and higher.

It’s not controversial at this point to state the obvious: features on a social network or social media are essentially gateways for a service to collect data about users. In addition to improving clickthrough rates by garnering more data, refactoring these features to acquire deeper and broader information about users provides a universal push upwards to all variables in the advertising revenue equation. Specifically, it raises impressions by giving more places for users to see ads and it raises cost per click by potentially opening up some more valuable on-screen real estate.

The conclusion? Maturing social networks, particularly with slowing user base growth and with advertising as a primary business model, become increasingly hungry for data about their users as they age. As a result, they inevitably tend towards invasions of privacy. The incentives increase to find ways to gaining more data about users to increase revenue, at first through non-intrusive and less publicity costly ways. But there’s only so many options to do so, particularly in cases when a user base is unwilling to give up more information more quickly through opt-in features.

As the benign invasions of privacy become less easy to find, the remaining options are to implement increasingly creepier and more intrusive “Instant Personalization”-type strategies. Stated simply, any promises for user privacy are increasingly at odds with the financial incentives of the social network, particularly as it faces higher costs and lower returns. For some companies who become storehouses of information (i.e. Twitter) but don’t want to implement large-scale advertising, incentives also exist to just provide all the data wholesale at a price (i.e. to Google), often to other data-hungry, advertising-centered companies.

There’s three considerations to this logic which make this basic picture more nuanced, but leave the conclusion essentially the same:

1) The Venture Capital Delay: Assuming that a social networking company really has a strong internal commitment to user privacy and autonomy, sufficient influxes of venture capital could stave off this effect in the short run, since it delays the need for resources with all the other factors falling. However, this is merely a delay insofar as major investors are willing to believe that other options for revenue exist, and the collapse of this artificial flow of funds puts a company back into the dilemma. It’s a time bomb for privacy based on investor confidence. Perhaps worst of all: this period of good behavior is habit-forming for users of a social network. They come to develop expectations that the service will be free and non-intrusive, and the social network becomes more “sticky” and hard to leave as more friends join. This sets up a natural conflict when this artificial infusion of money declines, as the business has incentives to behave differently.

2) The Google Exception: Here’s also another possibility: users willingly give social networks their data, which also takes companies out of this flow. So long as they are willing, the engine still runs since the data flows in without ever being truly “intrusive.” This is essentially the Google model, where control over the search market gives an enormously large, regular base for impressions — and where search and other services provide the company with the lifeblood of data to keep clickthrough rates high(er). Given the dominance of Google over key data sources that users are strongly willing to give up information to (Gmail, Google Reader, search itself), however, there’s a good argument that it’s an outlier case in the dilemma. For most other services, willingness to give up data is more limited to the purpose of the site so that the willingness to give up information only exists until the site steps “out of bounds.” For instance, we’re comfortable with giving information about ourselves and our friends to Facebook, but are less comfortable as soon as it starts attempting to seep into every other major site in our online lives.

3) Other Business Models: Obviously this dire situation is made less dire in the case where the company has a different business model. Indeed, sites that survive off a mixture of product sales and advertising can largely avoid some of these incentives to subvert privacy. Regardless, with regards to advertising, the economics remain the same — forcing businesses to attempt to acquire more data or to change to alternative revenue sources over time to grow.

“Traditional” social networks are probably the first place where we seen this phenomena emerge since they, out of all services, fit the pattern of this theory the closest. They confront a universe where online advertising is the overwhelmingly important revenue component, they face the rising costs of maintaining users and features, and the declining factors with regard to clickthrough rates and impressions. In order to continue expansion, there’s an increasing attractiveness to become data hungry and privacy indifferent.

What potentially worrying about this logic is that the problem it gestures at is systemic: in the fifteen years of the popularity of the open web, advertising still emerges as one of only a few reliable business models online (that, and, like, selling t-shirts and books). To that end, all businesses that are founded on the advertising engine and roughly fit the situation described face these incentives, which should make the threats to privacy and personal data numerous as the entire Web 2 ecosystem ages. It’s also worrying because it’s difficult to push hard against these businesses on a regulatory front (as has been suggested by some) due to their dependence on the revenue from online advertising : doing so could deflate cashflow and produce a “big crunch” slowdown of social platforms online and their ability to expand.

The picture that’s drawn here rethinks Facebook and is prescriptive about what should be done. Rather than a single company hellbent on disregarding user privacy, it reframes the social network as just the first player that’s reached sufficient scale and age to face the difficulties of being a late-stage Web 2 service. And, if anything, this model suggests that data hunger on the part of social platform companies is something that we’ll be seeing generally more of into the future, rather than less. If you’re a strong believer in privacy, Facebook isn’t the enemy — but its business model is. Ultimately, like the fears that a massive dependence on fossil fuels negatively impacts the decisions of a society, there’s room to perhaps advocate for a diversification in the revenue engines that play a role in fueling the internet’s social infrastructure.

[Thanks to the very good folks of Web Ecology Camp IV that talked through and critiqued a early version of this idea]

While I agree that the captioning for James Cameron’s new flick “Avatar” could have settled for something more awesome than Papyrus as its font (especially considering they shelled out the money for a linguist to create a totally new language and a new camera just to film the damn thing), Keith Hopper rightly points out that there are way, way worse things it could have been.

He is right.

Image from an emerging conversation with Graham Webster about how to start to map out the intellectual space about the internet beyond the Berkman School by using the old school polysci trick of putting everything into a 2×2 grid. Here, we’re varying the first two pillars/assumptions of the Berkman School, holding all else constant. For the first assumption, we vary whether or not the group of assumptions has relatively greater faith and emphasis on users or institutions in shaping the web. For the second, we vary whether or not the group of assumptions places importance on “The Internet” as a particular set of features and characteristics, or is more agnostic between various forms for different purposes.

Doing so seems to make a neato variety of positions fall out.

I’ve been enthralled lately reading the amazing bit of scholarship that is Randall Collins’ The Sociology of Philosophies. The big idea of the massive 900-page something or another tome, which is pretty intuitive but amazing to see played out across a huge swath of historical research, is that intellectual thought is primarily the product of social processes. To that end, he argues, you can track the course of a school or frame of thinking by closely examining who scholars and intellectuals hang out with and associated themselves with through history. There’s some neat things in there that he argues about the behavior of growing or failing schools of thought, and it’s all pretty great. Collins’ focus is on traditions in philosophical thinking, but I’ve been thinking alot about how this might apply to other fields as well, particularly to scholarship and popular discussion about the internet that’s emerged in the past two decades.

Obviously, the Berkman Center for Internet and Society at Harvard, and its sprawling list of digerati that have passed through there over the past ten years and change, is a nice place to start such a discussion. Much of the conversation and scholarship happening there has influenced a great deal of the popular rhetoric around the web in the past decade, and looks to continue to for the foreseeable future.

So, the question: if there is one at all, what constitutes the Berkman School of Thought? What are the underlying assumptions unfolding and undergirding the community of thinkers that have surrounded the Center?

I think the attempt to do this is a fun exercise on two counts. First, laying out these ideas clearly and mapping them against other schools of thought that might exist seems to be a good way of assessing where the Center has been, where it might go, and what intellectual challenges it might face in the future. Second, the task of reordering and putting the existing scholarship into context seems useful, since it forms the basis for creating an intellectual history of thinking about the internet (which, in turn, would be neatly badass).

I’m drawing broadly here from the general swath of classic and new Berkman texts and projects — Wealth of Networks, Free Culture, Cluetrain Manifesto, Born Digital,  The Chilling Effects Project, and so on. The following isn’t meant to be comprehensive — I’m just trying to capture the overall gestalt of some of the basic assumptions that Berkmanites (and their allies) have generally agreed on, and lay the groundwork for them to communicate and collaborate about anything. Obviously, in the same way you use terms like “The Frankfurt School,” or “The Trekkies,” there’s bound to be varieties and nuances in the positions that individuals thinkers take. My attempt here is to sift through and make sense of it all, and approach the outcomes and intentions of the research as forming a bigger picture of what people believe. The use of the word “school” is deliberate here as well — I’m referring to more than the physical Center. It captures everything from the latest speech given by Tim O’Reilly, the student activism around Free Culture, even to the themes underlying BoingBoing — indeed, anything conversant to a certain style of thinking. “Berkman” is just a label here.

Thinking through it, I’m seeing that there’s arguably four pillars to the “Berkman School” or “Berkman Style” of thought about the web.

1) Faith in Users and Emergent Collaboration

The lesson that the Berkman School draws from case studies of projects like Wikipedia is that users can be fundamentally trusted to contribute, cooperate, and occasionally make works of enormous talent. From this also springs an enormous faith in open crowd-sourcing as a way of solving problems, since generous, giving users naturally should play well together. Moreover, crowd-sourcing brings emergent collaboration and amateur innovation to exert enormous effort for a given problem.

Obviously, the standout text on this is The Cluetrain Manifesto, but it’s evident in a whole range of works that have been generated by members of the Berkman School. It also is present in the Center’s general resistance against the outright trolling works of Andrew Keen, and its objections to the more sophisticated challenges presented by people like Cass Sunstein and the Republic.com thesis. It also appears in their approaches to problem solving. The Herdict project plays with this logic rather strongly — trusting that uncoordinated amateur contributions will evolve to assist in the detection of accessibility and inaccessibility on the web.

There’s also a corollary to this, I think, about how the Berkman School downplays the dangers of the web. This is particularly true in the perspective that takes a stand and argues that children, by and large, aren’t at danger online from predators since their nuanced use of the web and selective choices about the information they reveal on it help them avoid threatening situations.

2) Civics as the Center of Attention

The internet, broadly used and influential technology as it is, requires any group of thinkers to settle on what issues are the most important changes wrought and worth studying. To that end, the Berkman School has tended to focus on the web as changing the realm of civics — broadly defined as the relationship of the individual to the organization and operation of the public sphere. This embraces the Berkman Center’s traditional focus on the law and intellectual property, as well as its focus on the human rights issues that surround the proliferation of the web. It also captures the School’s interest in topics as broad as the structure of governance (Sunlight Foundation), the participation of digital natives in the community (Digital Natives Project), and the future of the university.

Needless to say, this does leave out or generally de-emphasize other ways of slicing and discussing the effects of the web as a whole. This applies not only on the more qualitative side — whether it be in examining popular web culture or communities, art, or media theory — but also on the quantitative side as well — social network analysis, for example, is a much smaller part of the Berkman perspective (or even the “Berkman Method”)

3) “The Internet” As Specific Configuration of Features

For the Berkman School, the internet isn’t just computers and the connections between them. It isn’t “just” technology. It’s a kind of technological network that possesses very particular features. When the Berkman School invokes “the internet,” then, they are referring to something quite specific. Digitally, it’s an open space (where behavior is transparent and visible), a free space (in that there are little restrictions on content and behavior and contribution is broadly permitted), and an unfathomably deep space (in that the access to and existence of content is massive and covers nearly everything you might want to experience).

This is what the internet is. Things that fall out of this category are somehow “not” the internet in some sense, a crippled version of what the internet really is. Accordingly, things like the Great Firewall of China are “not” the internet, or at least not the one that we would want. Indeed, the famous John Gilmore’s quip that “The Internet detects censorship as damage, and routes around it,” has this assumption baked right in.

The one outlier here in recent memory is The Future of the Internet, which makes a pretty fierce argument that the current configuration of features that exists now is merely a historical, provisional result. The “internet” could exist otherwise — less free, more tethered, and so on — and his book argues that the prospect of that is real. Zittrain is making a pretty unique jump here — and its worth noting that his book has to uniquely suggest easing traditional absolutes on the net neutrality issue as a result.

4) Faith in Internet as Revolution

For the Berkman School, there’s a very strong normative slant to the nature of the internet in its particular configuration. It’s democratized expression, opened up new ways for people to communicate to one another, and made life more difficult for repressive regimes (a la Benkler). For the large part, there is support for these upheavals among Berkmanites. But even beyond arguing whether or not it is for the good or bad, there’s another deeper assumption at work: the Berkman School asserts that the internet has fundamentally changed things in a real and inescapable way.

The relevant representative text here is The Wealth of Networks, which credits the internet with not only enabling an unprecedented new form of production, but making an entirely different kind of human motivation significant on a central, society-wide level.

Every revolution needs adversaries to the newness it presents, of course, and the internet is no exception. This is partially a result of #3. If you believe truly in the internet as a particular bundle of features and qualities (and support those qualities), then people who are out to resist these qualities or change them are “baddies” in a very real sense. They are resisting progress, propping up dead business models, and so on. These two groups are diametrically opposed and destined for clash, with options of cooperation or co-existence unlikely. Accordingly, the ultimate drama of Networks, and the thing that casts it particularly in this vein, is Benkler’s general argument that the motivations of commons-based peer production are at odds with established authority, and that conflict over it characterizes all the various battlefields that the web is engaged in.

It’s also worth noting that this is quite distinct from trying to argue that that the Berkman School is a group of blanket cyberoptimists, blindly believing that the web is going to triumph over all or be good for everything. Indeed, the work of Ethan Zuckerman and Eszter Hargittai point out the many flaws in believing that the internet is in some way uniformly changing everything, everywhere and everyone (and uniformly thinking that it’s for the better). But it seems likely from their work that they would agree that something has changed, and that “something” is remaking the world in a big way that is unlikely to be eventually reversed.

Combining each of these positions defines a particular intellectual space, within which the cadence of discussion and scholarship takes on a particular tone. Indeed changing any one of these base assumptions would bring you to quite a different body of scholarship about the internet and its implications/prescriptions for society more generally. Interestingly, it would also open new vistas in the kinds of tools used in research, and how arguments are made.

This is just an initial cut — I think the next steps would be to sketch out how this might be influenced and challenged by changes in the structure of the web. Or, alternatively, to begin to play around with other versions of these assumptions and try to match them to various other non-Berkman School intellectuals as a way of testing the model or thinking about the other schools of thought in practice.

In any case, this is an ongoing project. Thoughts? Am I missing any pillars here? Are these inaccurate characterizations? Also, another interesting question, using this as a rubric, what are the other major intellectual schools of thought in thinking about the web?

One of the big ironies about social media and marketing gurus is that the prescriptions of their (presumably massive) knowledge and experience are almost always vague and Sphinx-like in their implications. “Listen and engage with your customers,” they offer, unhelpfully. “Why don’t you set up a Twitter account?” suggests another.

Academic research and discussion about social networks suffers from an almost bigger irony: in the midst of an ecosystem of huge amounts data, talent, and quantitative analysis, some of our best prescriptions still don’t give us any good best practices about how to actually take part and consciously shape the social processes they identify.

And, despite huge amounts of work and energy in both worlds, it’s a little bit sad that we still can’t answer some fundamental (albeit by now cliche and boring) questions in a really concrete way. What factors make content become massively popular within certain social groups? What factors lead to social networks/connections between users to take the shapes they do? Are there ways of influencing these social processes? And so on.

The problem with both cases, of course, is a tangible lack of practice and implementable tactics. There’s no good space where people can playtest, experiment, and rapidly iterate on a variety of strategies, particularly where influencing the social space online is concerned. There’s no good place to measure success, or even compare various approaches against one another to assess their usefulness. There’s no way to prove that your methods and data mining can actually produce repeated success. The general question here is: if you’re so sure about the social web and how it works — can you actually put your money where your mouth is?

This is a big problem if we’re ever going to get down to the business of actually figuring out what makes culture and community online tick, and be applied about our knowledge. All these issues came up during a recent WEC, and I’ve been collaborating since with web ecologist Alexy Khrabrov on using games as a way of creating these focused scenarios that people can experiment with.  Beyond being fun, it’s also a way of gathering data around what works and what doesn’t in terms of shaping and influencing social structures online.

Think of it like computer security Capture The Flag, but for social engineering and social hacking.

The general idea is this: judges will specify a “battleground” of unknowing, target users (who aren’t aware a game is going on) that form a common pool competed over by all the participants. Groups of teams will then descend on them, trying to get these people to behave in a particular way or influence the overall shape of the social structure. We keep score. And then teams are ranked accordingly. The hope is that teams will mine data about the targets and try to develop a strategy based around available data about the social terrain of the situation.

I’ve included the rules for a first game that we’ve sketched out below, called “Triangles,” essentially a game built around racing to influence certain users on Twitter to connect in particular, tightly interconnected ways. Check it out, after the jump.

We’re doing this for reals. So if you’re interested in putting together a team or otherwise participating, drop an e-mail to tim AT timhwang DOT org. If you want to get in on the first round, e-mail me by November 18th.

——————————————————————————-

“Triangles” — Version 1.5

Objectives

Can quantitative methods and social network analysis allow us to consciously and precisely craft the shape of social systems online? Competing over the same, narrowly-defined space of real, living users, this game tests this possibility that we might be able to do so. The team to build the most interlocked social infrastructure over the course of the competition wins.

Materials

1) A cluster of target users.
2) Sets of teams, numbering no larger than five (5) persons.
3) A team of game managers.

Phases of Play

A) SETUP

On a pre-set date, the game managers will release simultaneously to all teams a list of fifty users, the “battleground” of targets within the game. Depending on the mood of the managers, these users will variably selected for their interconnectedness, strong or weak ties, or other structural features. These might also not exist all on one platform. They will however, be unaware of the presence of the game. Following this release, there will be a two week period for these teams to plan and analyze the data on these users and their interconnections. They will also declare an “ego” account, which they will create on the initiation of the mobilization phase and attempt to attract connections towards.

B) MOBILIZE

On a given day and time, the teams will simultaneously create “ego” accounts, and begin the attempt to forge connections between their account and the “battleground” accounts in the mobilization phase by whatever method they see fit. Their actions are constrained only be the “Illegal Plays,” defined below.

C) SCORING

At the end of the mobilize phase (a period of time specified at the beginning Setup Phase), all players will stop activity on all accounts they control. Game Managers then scrape user data at that point to score the game. Points are awarded based on the number of “triads” that exist between the user and two of the targets on the battleground. This is to say, the “ego” account must not only be connected to A and B, but A and B must themselves be connected to one another for a team to score a point. These connections must be new connections as of the beginning of the mobilize phase. Note that this does incentivize teams to watch for new connections built by other users or emerging among the battleground users and swoop in to take advantage of them.  When played on Twitter, these connections must be mutual to be counted. That is accounts must both followed and be following, in order for a “connection” to be registered.

Illegal Plays

The following is a list of plays and strategies not allowed in Triangles. For each violation, judges will choose to deduct points or disqualify a team, based on the severity of the violation. Any team can call for a judgement on an opposing team’s action by contacting the game managers, who are responsible for acting as soon as possible to the complaint.

Hackers Not Allowed – This wargame is designed to test the ability to shape and engineer social relationships, not the formal representation in code. To that end, the hacking of accounts, compromise of computer systems or other exploitation of the technical terrain precluded by the platform’s terms of service will be considered cheating. Fraudulent reporting of opposing team accounts as “spam” are also prohibited. However, it is worth noting that this rule does not preclude the creation of multiple accounts beyond the target “ego” account, the use of automated bots, or the discrediting of other “ego” accounts.

Fight Club: Teams may not reveal publicly or to the “battleground” targets that the game exists or is in progress. Doing so will result in disqualification.

No Financial Misrepresentation: While teams are able to represent their “ego” account however they wish (including representing them as fictional identities), they are not allowed misrepresent financial for connecting with users or having them connect with one another. While not fully illegal, saying “You could win five dollars for being friends with me!” when no money actually exists is considered bad form and grounds for penalty (beyond opening a play for opposing teams to discredit the ego account).

No Head Starts: All teams may not bring accounts that they already control into play during the course of the game. No team may create an account they will use for the purpose of the game before the start date of the Mobilize phase.

Variants

1) Robots: Lengthening the Setup Phase, teams script and launch bot scripts. They may not make any edits to these once the Mobilize Phase is in motion.

2) Invisible Mode: Teams are unaware of each other’s existence and which “ego” account teams are defining as the one to be scored.

3) Offense / Defense: Two round game. In one round, a group of teams attempts to foster connections, while the opposing teams aim to inhibit their activity. In the second round, these teams switch roles. Final score is the winner.

This weekend was, at once, the weekend of the Web Ecology Project’s quarterly research conference, a Window 7 Launch Party, my 23rd birthday, and a screening of von Trier’s incredibly intense (and incredibly great) new film, “Antichrist.” Needless to say, it was crazy, awesome, exhausting, and full of metric shit-tons of enthusing things.

Saturday night, in the haze of all the excitement, a pun came to me when I was about to go to sleep.

It went like this –

“Sad to hear you got laid off man, what are you doing nowadays?”

“Not much, been sitting at home, mostly JOSS READIN”

Hilarious. And perfect for a serious bout of image macro shooping (see above).

It turns out that the pun is surprisingly extensible. I spent the rest of the weekend lovingly crafting more that I’ve aggregated in this post. I’m leaving them here to serve as a testament to the human condition, as a repository, and to solicit you, my way-cleverer readers, to comment and fill in any others that I’ve missed.

So, without further ado, the Compleat Joss Whedon, organized alphabetically, after the jump.

“Man, you just fell down the stairs, are you alright?”
“Yeah, it’s cool dude, I’m JOSS BLEEDIN”

“How are you doing in your arranged marriage, man?”
“It’s terrible. There’s a huge lack of feelings there, it’s like we’re JOSS BREEDIN”

“Nice! Monopoly! I love this game. How are you doing?”
“It’s early in the game, at this point, we’re JOSS DEEDIN”

“You’re looking pretty lame in those pocket protectors and tweed jackets”
“It’s my Halloween costume, man! I’m JOSS DWEEBIN”

“That X was great! Are you coming off your high?”
“Yeah, at this point, it’s pretty much JOSS FLEEDIN”

“Thinking about training to be a baker”
“It’s easy, JOSS KNEADIN”

“You should stay for a drink, man, haven’t seen you in awhile”
“It’s okay, we’re JOSS LEAHVIN”

“Ooh boy, is it a date?”
“Come on! We’re JOSS MEADIN”

“It’s spring, Farmer Brown, whatchu been up to?”
“I’m JOSS SEEDIN”

Had the honor last week of speaking on a panel at the VRooM “Getting Personal With Data” panel, hosted by the typically brilliant and insightful Keith Hopper and Doc Searls. My fellow panelist was local entrepreneur Ben Rubin. His company, Zeo, is in the business of personal informatics — sleep, in particular. They produce slick alarm clock units that sync with a headset that the user wears at night. The machine tracks a variety of really neat stuff for the obsessive lifehacker in all of us: when you sleep, how much REM you’re getting, whether or not your sleep is being disturbed, and so on.

One point of discussion emerged from Ben, a position that you’ve heard a great deal if you’re anywhere near conference-inclined for the tech-open data-free/open source software world. This was the looming threat of the Data Dilemma, a common paranoia of businesses involved in handling large quantities of information. The general idea is this:

Businesses are majorly screwed with regards to choosing to make their data about users widely available or strictly controlled. Whatever they do, they will lose.

More eloquently:

Businesses that collect and archive data face a crucial choice in design. One is to open their data up — allow users to pull it down at will, and provide easy systems to port to other services. This is great on many levels — users are empowered to create new uses for their data, . Though for businesses, this is a real immediate opportunity cost on one level: you lose the chance to charge users for access and premium services around their data. In addition, there’s a menagerie of potential (though likely) other threats: beyond letting vertical competitors into the game that might build new services on top of your data, it also allows users to exit your service if they want to join a horizontal competitor that offers the same features.

However, to keep it closed and play it “safe” is an equally unsustainable strategy. True, you save yourself from the palpable negatives above. But walking through this door risks the possibility that your users will just get cleverer than you at hacking around your restrictions and getting what they want (something that we’ve been happen time and time again). And, even assuming that you could perfectly defend against this, there’s a bigger threat on the horizon. In particular, your willingness to close the data creates incentives for new businesses to get into the game and steal your customers by offering the open data that your customers want. The more iron-fisted, the bigger the incentive. Supply meets demand, and your profit takes a trip south. In both cases, time grinds down your advantage like so many novelty glass plates in a bull shop.

In parallel: it strikes me that the data dilemma is a smaller, meaner little cousin to the looming copyright dilemma that businesses hem-and-haw over repeatedly as well. It goes like this –

Permissive licensing like Creative Commons is great: it promotes content, encourages remixing, and otherwise. But, like the Data Dilemma, choosing to be permissive means that you lose out on business opportunities to charge people for the content. Plus, you open it up for other people to appropriate your work — inviting competitors and imitators.

However, to vigorously threaten legal action, and to protect restrictive copyright regimes, come with many of the same threats as time passes. Your users, instead of being deterred, might merely become ever more clever at pirating your stuff. Your competitors, seeing an opportunity to serve your market and steal your business, have incentives to design business models that feature permissive use of content.

Now, in the world of Free Culture and the variety of people concerned with personal data, there’s a couple of classic responses to these worries. We emphasize the inevitability of technological change, saying that it’s all a matter of time before The Big Bad Internet will come and erode the advantages of closed data. Or we emphasize the good, arguing for the benefits of allowing user innovation and community engagement. Or we deploy an arsenal of anecdotes, citing customer loyalty and other factors as reasons why its worked for this-or-that business in the past.

I admit that I kind of sympathize with businesses here. Easy for you to say, they might rightly throw back at people like us. And it’s true: we’re not the ones going down with the ship if the business fails, and there’s no good way to tell if it will work for them the same way it works for the odd success story. From their perspective — someone’s telling you to jump off a cliff with largely only a vague reassurance that a genie will pop out of your ass and you’re going magically going to start flying. I mean, really, who would?

While I can see where they’re coming from — as people interested in the overall ecology of the web, we worry about the aggregate effects of this reluctance towards opening data. Many businesses, choosing to remain closed, create a huge collective action problem and a systemic risk. This concern is partially Lessig-ian: siloed data and content lowers the possibility for innovation and cultural expression. It’s also partially just a worry about the logic of straight economics: popular, first-mover closed services have a huge advantage against competitors. Why move all my data from Zeo to another service, for instance, if it means I’ll lose all the data that’s already been collected? Closed ecosystems of data might remain closed (or become increasingly closed).

So, for those concerned about the ecology of the web, how do we provide more than reassurance to worried businesses squaring off against this dilemma? How do we make it worthwhile and persuasive for businesses to open up their data?

Here’s my plan: you pay me, repeatedly.

Look at it this way, the Data Dilemma implies that businesses face risk and uncertainty. Since the option of keeping data closed is only temporarily tenable, they would take a path of opening up data if they knew more certainly that they would benefit from taking this option.

But the data dilemma is not a special situation: businesses (and, for that matter, individuals) face analogous risky situations all the time. Do I trade with vendors online and run the risk of them being fraudulent? Do I move into a house and run the risk of it burning down? Do I ship this extremely expensive heirloom through the mail and run the risk of it being lost? How do I reap the advantages of living, and deal with the risk that I might, you know, die at some point?

The point here is that we have a recently unpopular though still critical instrument that we commonly use for things like this. It’s called insurance. Why couldn’t we do the same for businesses and their technology policy decisions? It’s tough out there for a business that wants to play it geek-friendly. Why couldn’t we offer, in a phrase, geek insurance?

So here’s my plan, again: you pay me, repeatedly. In exchange, I issue a data openness insurance policy. We’ll evaluate your business at regular intervals, and, if your business dips below a certain threshold, or competitors come to occupy a certain percentage of your market share as a result of your actions in allowing users to get their data — we will pay you, in full, for the entirety of potential business lost from opening data.

Interestingly enough, if it turns out that we are in fact right, that empowering your users with portable and interoperable data really is a win-win from the point of consumers and businesses — geek insurance should be, in fact, enormously profitable as a business (and as a sector). What we would be insuring against is risk, but importantly, only perceived risk — an insurer’s wet dream. Businesses would pay us for their policy, but we should rarely ever have to give a big payout back, since they should succeed by being open.

Like real insurance providers, this plan would also give some control over the process. Like the fire insurance policy that doesn’t apply if you’re experimenting with flamethowers in your basement, we could also have certain terms under which data openness was intelligently done. A final benefit, too: it’d also give us in turn some really hard data on something that we’ve only been until this point discussing in the talking shop. What kind of businesses succeed when they open their data? What kind fail? What are the differences between the two? I’d love to see the actuarial tables on success in data openness, the same way we’d calculate life expectancy of someone who regularly exercises.

So, any takers?

Sometimes, you find the best stuff in the weirdest places. Absentmindedly picked up this novelization of Atari Missle Command for chuckles in a roadside antique shop right outside Mount Rushmore for $1. Imagine my surprise when I discovered a vinyl audiobook tucked into the back. The cover promises, “SEE the pictures. HEAR the story. READ the book.”

And the audio is, well, incredible. Complete with the ridiculous bleeps-and-bloops background music loop and William-Shatner-knockoff narrator. Been talking about it for a long while, but thanks to the help of Fred Owsley, it’s finally now available online in its pure .wav glory:

* Atari Missile Command Read-Along (Side 1)

* Atari Missle Command Read-Along (Side 2)

If you’d like to read along, I’ve also captured the book and all the illustrations here (which are kind of great in their own right). The best part is that the inner flap of the cover suggests that there’s similar vinyl-novelizations of Pac-Man and Marmaduke. Marmaduke?

Update (11/15/09): Thanks to the ineffable Mac Cowell, we now have a compiled and cleaned up version! He rocks.

I’m glad to announce today the release of “Uncertain Futures,” our political analysis of the FCC and its future. While public attention has largely missed the activity surrounding the nominations (particularly in the face of the Sotomayor hearings), the recent FCC commissioner confirmations have huge implications for the communications infrastructure in the US. Plus, no well-researched overview seems available online for researchers in the space.

To that end, we’ve gone through and done our homework. We prepared a basic briefing which reviews the background of the five newly minted commissioners of the FCC. Then, using what we know of the breakdown of their positions across various issues, we’ve made some educated guesses about the direction of the Commission in the coming years, and which policies will dominate going forwards.

The big news: while there is accordance on a great deal of issues, the controversial, divisive (and most important) points of policy will be determined by the critical vote cast by Mignon Clyburn, whose positions on issues are still largely unknown.

You can read the full report here (pdf).

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Other Key Findings and Predictions:

* Drawing from what we know of Mignon Clyburn and her father’s  political connections, it appears that the principle of net neutrality may be at risk in the coming years.

* The most contentious issues among the commissioners will be net neutrality and media consolidation, issues that have created intense  debate and were the focus of the Senate Committee hearings.

* The FCC will put an end to any further discussion of the Fairness  Doctrine, which is opposed by all five of the commissioners and President  Obama.