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Network effect

Network effect
Diagram showing the network effect in a few simple phone networks. The lines represent potential calls between phones. The classic example is the telephone. The more people who own telephones, the more valuable the telephone is to each owner. This creates a positive externality because a user may purchase a telephone without intending to create value for other users, but does so in any case. Online social networks work in the same way, with sites like Twitter, Facebook, and Google+ becoming more useful as more users join. The expression "network effect" is applied most commonly to positive network externalities as in the case of the telephone. Over time, positive network effects can create a bandwagon effect as the network becomes more valuable and more people join, in a positive feedback loop. Origins[edit] Network effects were a central theme in the arguments of Theodore Vail, the first post patent president of Bell Telephone, in gaining a monopoly on US telephone services. Lock-in[edit]

http://en.wikipedia.org/wiki/Network_effect

Related:  Web & economy

Yahoo! economist rebuilds ad empire with 'Magic Formula' Yahoo! CEO Carol Bartz owns a sweatshirt emblazoned with Preston McAfee's math. McAfee is an economist, but he's the sort of economist who's actually useful. In the early-90s, he helped build the simultaneous ascending auction, a mathematical contraption that governments across the globe have since used to license over $100 million in wireless spectrum.

List of Open Innovation & Crowdsourcing Examples - Best practices Intermediary Platforms Research & Development platforms Innocentive – open innovation problem solvingIdeaConnection – idea marketplace and problem solvingYet2.com – IP market placePRESANS (beta) – connect and solve R&D problemsHypios – online problem solvingInnoget – research intermediary platformOne Billion Minds – online (social) challengesNineSigma – technology problem solvingIdeaken – collaborative crowdsourcingInnovation-community.de – Community of innovators & creators.

Critical mass (sociodynamics) In social dynamics, critical mass is a sufficient number of adopters of an innovation in a social system so that the rate of adoption becomes self-sustaining and creates further growth. It is an aspect of the theory of diffusion of innovations, written extensively on by Everett Rogers in his book Diffusion of Innovations.[1] Social factors influencing critical mass may involve the size, interrelatedness and level of communication in a society or one of its subcultures. Moore's law Moore's law is the observation that, over the history of computing hardware, the number of transistors on integrated circuits doubles approximately every two years. The law is named after Intel co-founder Gordon E. Moore, who described the trend in his 1965 paper.[1][2][3] His prediction has proven to be accurate, in part because the law is now used in the semiconductor industry to guide long-term planning and to set targets for research and development.[4] The capabilities of many digital electronic devices are strongly linked to Moore's law: processing speed, memory capacity, sensors and even the number and size of pixels in digital cameras.[5] All of these are improving at roughly exponential rates as well.

Economists the new hot job category for Silicon Valley tech companies By Mike Swift mswift@mercurynews.com Posted: 11/22/2010 12:01:00 AM PST0 Comments|Updated: 3 years ago In addition to software engineers, computer scientists and Web designers, Silicon Valley giants ranging from Yahoo to Google to eBay are scrambling to hire economists, little-known and increasingly valuable weapons as these companies create new businesses and fine-tune existing ones. In the wake of the example of UC Berkeley economist Hal Varian, who helped Google perfect the auction process behind its multibillion-dollar search advertising revenue stream, big Internet companies are competing to woo economists away from universities or work with them on specific projects. Yahoo has been among the most aggressive, but eBay, Amazon.com, Facebook and other companies also are recruiting practitioners of what used to be called "the dismal science."

50 Ways to Crowdsource Everything This is a blog post by Drea Knufken. Image: Wayne Large/Flickr Want something done quickly and well? Sic the swarm on it. information « relationary.wordpress I was passed this link to a free Knowledge Management Course by a friend today. I gave the entire course a read (it is not that long) and concluded that there was only one thing that the course covered that is not covered by the Six Hats, Six Coats as it has been explained so far. The issue is valuation, how do we know the cost/benefit of any fact. Otherwise, the authors wave the term “knowledge” around with little restraint to the point of its being meaningless. Human-based computation Human-based computation (HBC) is a computer science technique in which a machine performs its function by outsourcing certain steps to humans. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human-computer interaction. In traditional computation, a human employs a computer[1] to solve a problem; a human provides a formalized problem description and an algorithm to a computer, and receives a solution to interpret. Human-based computation frequently reverses the roles; the computer asks a person or a large group of people to solve a problem, then collects, interprets, and integrates their solutions.

Disruptive technology Sustaining innovations are typically innovations in technology, whereas disruptive innovations cause changes to markets. For example, the automobile was a revolutionary technological innovation, but it was not a disruptive innovation, because early automobiles were expensive luxury items that did not disrupt the market for horse-drawn vehicles. The market for transportation essentially remained intact until the debut of the lower priced Ford Model T in 1908. The mass-produced automobile was a disruptive innovation, because it changed the transportation market. The automobile, by itself, was not. The current theoretical understanding of disruptive innovation is different from what might be expected by default, an idea that Clayton M.

Towards a Value-Added User Data Economy Every week it seems like the debate over access to, portability of and privacy over user data on the social web has reached new heights. It's only going to get louder though, just as discussions about other forms of economics will never be resolved. That's a part of what's going on, economics. This is an information economy, after all, and user data is clearly one of the most important currencies in circulation. User data has been sold by ISPs, leveraged by ad networks and horded by social networks for years. User-generated content Many commercial websites rely on UGC. For example,Amazon.com and Trip Advisor rely on users to rate products and hotels and restaurants, respectively.[2][3] These reviews are important part of what the two respective websites offer. When UGC is contained in commercial websites it is often monitored by administrators to avoid offensive content or language, copyright infringement issues, or simply to determine relevancy of the content to the site's theme.

Pro-Ams: The Rise Of The Amateur Professionals, Prosumers, Passionate Amateurs "A number of factors are coming together to empower amateurs in a way never before possible, blurring the lines between those who make and those who take. Unlike the dot-com fortune hunters of the late 1990s, these do-it-yourselfers aren't deluding themselves with oversized visions of what they might achieve. Instead, they're simply finding a way--in this mass-produced, Wal-Mart world--to take power back, prove that they can make the products that they want to consume, have fun doing so, and, just maybe, make a few dollars." Notes on Factors in Collective Intelligence There are probably hundreds of factors we could identify as important for the generation of collective intelligence in different types of human system. We find these factors wherever we see collective intelligence being exercised, and when we support them (especially in combination) we often find collective intelligence increasing. From my work with reflective forms of CI in groups, communities and societies, I find that about fifteen factors stand out most vividly, and I’ve listed them with brief descriptions here.

Related:  Network & Crowd Induced Effects