Oprah and The Secret

This article discusses the best-selling book The Secret. For those who do not know, The Secret, written by Australian talk show producer Rhonda Byrne, is a new self-help book that, in short, says putting out positive energy will bring positive things into your life. There have been many scathing criticisms of this book, often focusing on its ridiculous ideas (i.e. in order to be thin, all you have to do is visualize your ideal self). However, Oprah, on her show, has provided “an approving, wholly uncritical platform”, seemingly supporting the book and its ideas.

The success of this book shows how information cascades can easily get started. As more people start to read the book, others believe that it must be a good read or provide great advice. Instead of reading reviews of the book, people will see it on a best-seller list and go out to buy it. What is happening with The Secret is the same as with The Divine Secrets of the Ya-Ya Sisterhood, as discussed in The Tipping Point. However, The Secret” has one thing The Ya-Ya Sisterhood did not, and that is Oprah. Oprah’s endorsement of the book has no doubt helped its sales and pushed it to an even larger audience. This audience then provides more readers of the book and the cycle continues to increase the popularity of the book until enough people lose interest.

In class, we mostly talked about how several people start information cascades. In this case, there is one, very credible source, who is able to use their influence to convince many people to try something (and possibly go against their original thoughts and signals). In The Tipping Point, Malcolm Gladwell introduces the idea of Connectors, people who know many other people and are able to bring them together. Oprah is a perfect example; she has a talk show that reaches millions, and people love (and trust) her. This characteristic also makes her a great Salesperson. Because so many people trust her opinion, she can easily persuade people to read books, donate to charities, etc. This is the reason why Oprah’s book club is a huge success. These two personality traits make it very easy for Oprah to start information cascades. The question becomes “where does Oprah get her information”? If the information is bad, all those people who trust her are taking bad advice, which starts a bad information cascade. As this article notes, it is important to stay aware of the products, ventures, and people that she endorses and not just trust her completely. This advice will hopefully remind her viewers that they cannot trust everything they see on TV.

Future of Web Searching

The link will lead you to an interesting artucle on the future of web search engines.

It outlines the latest trends that Search engines are doing to improve their engines, and generate more user traffic (So that they can make more money with advertising)

Three main categories that are improving are:

- UI enhancement

- Technology Enhancement

- Approach Enhancement

The UI enhancements are quite obvious, but are not extremely relevant to this course. They are using the newest cutting-edge technologies. However, most of them follow Googles approach of keeping it very simple.

The Technology Enhancement is where things get more interesting:

QDEX is an interesting example, they index pages using a technology known as QDEX (Query Detection and Extraction) this does a semantic analysis of Web Pages, and tries to break the information down to be useful for “meaning-based” searches.

Another interesting Search engine is PowerSet. What this engine does is take into account stop words (by, after, the, etc). While on other search engines asking for something “by dogs” or “for dogs” would return the same results. PowerSet distinquishes using these stopwords.

Finally Some search engines are taking a new Approach:

They are utilizing something known as Vertical Search. This is the most interesting part of the article.

As the internet gets larger and larger, broad-based searche (i.e Google) engines results get larger and larger. The idea behind vertical search is that instead of sending out a general webcrawler to index the pages, You send the crawler out to a highly refined database relevant to a specific topic. They then advertise their search engine as a sort of “niche” search engine. The advertising on these engines are very focused and relevant to whoever is using them.

An important statistic that goes along with this is “A study by JupiterResearch called “Vertical Search: Early Marketers Will Reap Rewards of Low Pricing” found that paid search is densely concentrated within primary categories, or verticals: retail, financial services, travel, and media and entertainment. These accounted for 79% of spending on paid search in the United States in 2004.” This means that companies can focus their search engines on these specific areas. This will give the users more refined results, and also allow the companies to possibly increase their revenue from advertising, since their users will be more likely to be interested in the ads they see.

Informational Cascades in IT Adoption

Xiaotong Li, an assistant professor at the University of Alabama, writes about herd behavior in IT adoption and the decision making process involved. With new technologies continually emerging, businesses place great pressure on IT managers to adopt the latest and greatest to further streamline operations. IT managers often are in the situation where they have limited information in predicting the success of adopting a new technology and will look at the behavior of others in coming to a decision.

What makes IT adoption decisions in relation to information cascades interesting is the network externalities presented when joining the herd. Regardless of whether the herd is making a “good” decision” or not, benefits of joining the herd often include increased technological compatibility. For instance: Microsoft launches Microsoft Office 2007. A company might want to delay the adoption of the new Office suite in order to thoroughly evaluate the impact an upgrade would have on its operations, but by joining the herd of people eagerly upgrading to Office 2007, they benefit by the interoperability that two people using Office 2007 can take advantage of. To quote Li’s article, “IT adopters may find that following the majority is the best strategy if the benefits of joining the herd dominate the benefits of learning.” These “benefits of joining the herd” are these network externalities such as interoperability.

When you have adopters joining the herd to reap the benefits of network effects, information cascades are reinforced. Li warns that when adopting a “wait-and-see technology”, it is important to differentiate between blind signals from the cascade and actual informative signals.

 Check out the article for charts and more details about the decision making process that goes into IT adoption and the effects that others’ decisions has on your own. Understanding the dynamics of IT adoption will better enable you to make successful technology decisions and avoid investing in inferior products.

FON is a company that seeks to exploit network effects to supply free wireless across the globe. The working premise is that people often have wireless internet access at home, usually from a wireline internet service provider (e.g. cable or DSL). However, when away from home, people often have to pay to go online (e.g. T-Mobile hotspots). Other access points are visible, but they are often secured or locked from access (typically personal routers with WEP encryption).

FON tries to work around this problem by encouraging people to join the Fonero network. To join, you get a “La Fonera” router that you install at home. This wireless router will broadcast two access points: one for your personal use and one for public consumption. Once you have registered your Fonera, you are now allowed free access to any Fonera router in the world.

This is a clear example of a network effect where the value of your FON membership is directly related to the number of FON users worldwide. The more people that join FON, the more likely you will run across a Fonera router during your travels. FON also employs several of the techniques mentioned in class to push the membership over the tipping point. For example, if you are a FON member, you can have free Fonera routers sent to any of your friends. FON also promotes “specials” for people to join the network for free, such as free FON routers for anyone living within wifi distance of a Starbucks.

One could also argue that the FON router has some intrinsic value of its own. For one thing, the FON router is a perfectly functional wireless access point, regardless of the global FON network. The price for the router ($0) is certainly less expensive than similar offerings from Linksys, D-Link, etc.

Another interesting feature is that the FON router also has an inverse network effect! People who do not have a FON router at home (and thus not FON members) have to pay to use a public FON access point. The fewer members there are in the FON network, the more likely that people logging into your FON router will pay money for internet access. Under certain arrangements, you (the owner of the FON router) get a portion of any revenues generated from your router.

While the demand curve is unknown, there is likely a nonzero value at 0 users (due to the two arguments mentioned above: inherent value, inverse network effect). Similarly, the supply is set at $0 or close to $0 (depending on whether you qualify for any “specials”). This suggests that the FON network should tend toward the larger equilibrium point very rapidly. However, it remains to see if FON can continue to supply routers at such a low cost, since it may be unsustainable for their business to produce more free routers.

On a local note, there are several FON access points in Collegetown and around campus.

http://www.cjpf.org/crime/crime.html

There is a controversy over our current correctional system. Is it an effective deterrent to make people not commit crimes or not? There is evidence to show that it can go either way. For example we talked about in class how if clean up petty crimes people are less likely to commit bigger crimes, so we have a cascade effect of people not committing crimes and therefore our jail system works. However, this does not work in general. I once read an article describing inner city neighborhoods that have a significant percentage of their population in jail. The article argued that when an area reaches a critical percentage of their population in jail that it actually start to break down the society, and creates more crime, and leading to a cascade effect of putting more people in jail. It breaks down for several reasons. Let’s say that now we have greater percentages of households without a father. This has been shown to have a negative effect on the children. In addition if everyone you know has been to jail then it becomes normal to go to jail.

The article that I link to describes some of these effects. It goes beyond the damage that is done to the society by taking members out; it talks about how it changes the people in the correctional system. It says “First, the prison culture extends respect to more serious offenders. The culture values the transmission of increasingly sophisticated and more remunerative criminal techniques.” So once a person leaves jail it becomes hard for them to be reintegrated, and during the reintegration process might create other future felons, and thus a cascade that creates further crime.

In class, we’ve started to study network effects and externalities.

In this post, I’m primarily looking to explore two questions. Firstly, how can we better describe network effects in terms of quantifying them? Secondly, what would it look like to filter the first question through a web lens?

It turns out that the process of quantifying the value a network effect offers is not yet completely understood. The model we’ve discussed in class follows from an intuitive derivation that layers a network effect atop a supply and demand graph; similarly, Metcalfe’s Law can be simply and intuitively stated “the value of a network is proportional to the square of the number of users of the system”. Several revisions have been made to the law, but none that change the fact that the network’s value can be considered ‘Big O’ proportional to the square of the number of users.

Much of the difficulty in quantifying the value of a network effect is based on the difficulty of defining value. For example, when trying to apply Metcalfe to the semantic web, value must be related somehow to the value of the objects described on the web and the (number of) links that connect them.

Reed’s Law plays with this ‘what-is-value’ dynamic by making a distinction between the number of possible pair connections in a network and the value of the the network itself. Reed looks at the value experienced by a user connected not to just the larger network as a whole, but also to many subgroups. Value is estimated to be higher than with Metcalfe’s Law. In contrast, Odlyzko and Tilly have developed a model suggesting Metcalfe overestimates the value of adding connections by pointing out that users do not find all (or even most) of the n connections valuable, either in the network as a whole or in most subgroups.

The following graph shows how each of the above-mentioned theories value networks with a network effect (larger version):

Moving on to the second question, it is immediately clear that network effects play a large role in the success of modern web applications. Indeed, network effects are the real secret sauce of web 2.0. Some commentary, however, suggests that in assigning value to the social, BYOC (bring your own content), web-2.0 web, the theories of Metcalfe and the others above do not hold. They suggest that such laws do not allow for the nuances or levels of interaction and features within the modern socially-enabled application. For example (from Stutzman’s The Network Effect Multiplier, or, Metcalfe’s Flaw):

I’ll try to illustrate a comparison. Indeed, Myspace’s network provides two options to you - you can either join or not join the network. If we wanted to apply Metcalfe to Myspace, this is where we’d stop. However, the value in Myspace is much more nuanced than simply being on the network; you can take value from the many things you can do on the network. The network offers a myriad of associations, including friending, grouping, messaging, browsing, stalking - actions that create a compound value that is unique for each network entrant. Indeed, each new entrant to Myspace offers others in the network the chance to create these relationships, but these many types of relationship create a value continuum - which is different than a value binary.

In a similar way to our exploration in class of a model economy without network effects, Stutzman attempts to separate what he calls the ‘core’ or ‘real’ economic value of a web service from the value of its network effect. He uses as an example the (as it has been called) massively multiplayer online photo sharing site Flickr. The core value of Flickr would be that it is a very high-quality image host and archive service. The network effect derives from the fact that Flickr is socially enabled: it offers comments, tags, favorites, messages, contacts, individualized page views, etc.: the perfect example of a BYOC architecture defining web 2.0.

Stutzman suggests that the true value of Flickr is its core value multiplied by the network effect (what he calls the network effect multiplier). He is quick to point out that Flickr is relatively light-weight in its network effect multiplier compared to services like MySpace; he suggests his thought experiment exhibits a balancing function in that MySpace’ large network effect multiplier is multiplied by a pretty small core value: a small personalized home-page.

As a brief but interesting side-note, it is intriguing to hear Eric Costello (founder of Flickr) talk about Flickr’s roots and subsequent growth. Flickr grew out of a massively multiplayer online game called The Game Neverending before becoming a real-time photo sharing and chat application built in Flash. Photo storage was added next, and quickly became more popular than the social aspect; it wasn’t until more recently that Flickr became socially-enabled once again with features like commenting, tags, and more atop its photo archival core.

In exploring network effects on the web, it is interesting to come across discussions of negative network effects. Negative network effects might result from resource limits (such as congestion on highways) or provider complacency (such as with Microsoft’s OS). In regards to negative network effects on the web, Dion Hinchcliffe writes in Hacking the Web’s Network Effect about those individuals tempted to exploit the power of network effects. The stronger the network effect multiplier (to use Stutzman’s term), the greater the frustration and damage spammers and their kind can cause us. His graph displays this nicely (larger version):

The value network effects yield is still not completely understood, and the socially-enabled web is still in its infancy. What role will network effects (positive and negative) play in the web’s near future?

Your thoughts? (Go on, get involved with the BYOC, socially-enabled, network-effecting, expert-voices, Cornell-204 thing you’re reading right now by adding a comment!)

In the mid-90s, when Larry Page was still a graduate student at Stanford University, he started pondering about the Internet and its structure. Before he ever dreamed of creating the world’s greatest search engine, Page was interested in the Web for its mathematical qualities, its network structure. With his deep roots in academia, Page was more than familiar with the importance of peer review, and consequently, of citations. In the academic world, publishing is driven by the concept of rank. In fact, there is an entire field known as bibliometrics that studies citation structure and attempts to create a quantifiable metric for the scientific impact of individual papers.

While many such metrics have been devised, the one most influential to Larry Page was created by Gabriel Pinski and Francis Narin, published in their 1976 paper, “Citation Influence for Journal Aggregates of Scientific Publications: Theory, with Application to the Literature of Physics”. In the paper, Pinski and Narin propose a citation-based metric that recognizes the fact that not all citations are created equal. The authors submit that a journal is “influential” if, recursively, it is extensively cited by other influential journals. In other words, a citation from a so-called influential journal holds much more weight than one from a paper that has never been cited itself.

Accordingly, Page saw a parallel between paper citations and hyperlinks, that they both form unidirectional edges between nodes of a graph. Page (as well as our own Professor Kleinberg) posited that, in a sense, links acted much like citations from one webpage to another. An Internet user, however, can only follow outbound links, from one page to another. Since this would only indicate the page creator’s value of the other pages he links to, rather than the value of the original page itself, Page found information to be largely trivial. What he hypothesized would be meaningful were the links leading to a given page, a page’s backlinks.

Thus, BackRub was born. BackRub, which would later take the name that we all know, was designed to trace all the links on the Web, reverse them, and republish the data so that anyone could see who was linking to any given page on the Internet. However, Page needed a way to make sense of the structure that BackRub found. So he applied Pinski and Narin’s logic to the Internet, and invented PageRank. Along the same lines of Pinski and Narin’s model for citations, PageRank holds that quality pages link to other quality pages, and that a page is of high quality if it is, recursively, linked to by other pages of high quality. With PageRank applied to BackRub, much of the information on Web became readily accessible and useful. The rest is history.

Sources:

The Search by John Battelle

“Authoritative Sources in a Hyperlinked Environment” by Jon Kleinberg

“Citation Influence for Journal Aggregates of Scientific Publications: Theory, with Application to the Literature of Physics” by Gabriel Pinski and Francis Narin

The success of the iPod is a result of information being passed through a network and some point, around the 3rd generation iPod the tipping point occurred and the iPod fad spread like wild fire. Other products with similar features and functionality are finding it difficult to compete with the Apple and the iPod. Today, an enormous range of iPod accessories and iPod compatibility products can be seen across markets beyond audio and media, integrating music into more and more aspects of life. The iPod and Nike collaboration of the iPod nano along with an exercise monitor is a great example. The continued success of the iPod can be in part attributed to the variety of add-ons that are available for the iPod and not other music players, like the iPod and Nike creation.

http://www.cnn.com/2007/TECH/ptech/03/27/apple.tv/index.html

This article discusses the Apple TV, Apple’s newest iPod gadget that allows the user to play whatever is available on his/her iPod on the TV. As a result of the success of the iPod, many other companies make products that are compatible to iPods, which increases the positive externalities when one purchases an iPod.

The iPod exemplifies the effects of direct payoff interdependencies in a network. One of the great selling factors of the iPod is the wide variety of services, compatibility and choice of accessories. When someone purchases an iPod, he or she is not only getting a media player, but also access to iTunes (music, movies, TV shows), perhaps a device that will compliment the iPod connector and holder in their car, something to store an address book and planner and compatibility to new devices that Apple or other companies will come up with. The first person to purchase the iPod did not benefit from these externalities. These externalities arose of a result of the widespread of the iPod. Apple and other companies realized that a profit could be made by selling accessories to the iPod. In turn, these externalities attract more users to buy the iPod. In comparison to the first person who purchased the iPod, a person purchasing their iPod today would have much more to gain from it. This shows the direct payoff interdependency in a network because when one person buys an iPod he or she positively affects other individuals.

http://blogs.business2.com/business2blog/2005/11/prices_law_and_.html

The network effects that explain the huge growth of peer-to-peer production and organizations should not be overestimated. Metcalfe’s Law and Reed’s Law both give overly optimistic formulas for calculating the net value of a network. Metcalfe’s Law dictates that a network’s value is equal to square of the number of nodes connected within the network. Calculating by Reed’s Law gives an even higher value, stating that a group’s value is equal to the number of subgroups by connecting any subset of nodes within the network. With the recent popularity of online networks like Facebook, MySpace, and Flickr, the drawbacks of network effects should not be overlooked. A lesser known law, called Price’s Law, counters the overestimation of Metcalfe’s Law and Reed’s Law by stating that half of a group’s output will be produced by only the square-root of the group, while the other half will be provided by the rest of the group. “As a field expands, value doesn’t necessarily grow exponentially, instead a greater percentage of the whole is simply less productive.”

Therefore, as a network increases in size, an increasingly larger amount of people will be able to benefit from the efforts of a few. The article gives the photo-community site of Flickr as an example, in which a small group of photographers get an in-proportionally large amount of exposure based on number of links to their photos. Thus, not everyone must be equally productive and not every addition to a network increases its value substantially. However, as networks’ members reach into the millions, all of their marginal contributions add up, and in the case of digital networks, the cost of adding is negligible. These are all important concepts to consider when addressing the issue of networks based on member-generated content.

http://www.shmula.com/197/digg-as-a-game

This article discusses how Digg is similar to something called the “Urn Game”. This “game” quite simply shows the ideas of information cascades. In short, there are two identical urns, W (filled with mostly x-colored balls and some y-colored balls) and Z (filled with mostly y-colored balls and some x-colored). Everyone takes one ball from the same urn and makes a guess at which urn they picked from. Obviously, after hearing some people’s guesses, your answer will be affected by what they say, leading to an information cascade and conformity even if your ball signals the opposite urn but everyone else’s points to the other. Digg is similar to this game because a few “powerful” early voters can vote for (or against) a story and everyone else’s votes will start to follow.

As we learned in class, the users on Digg may be making rational choices given all the signals they are getting. However, the signals (which is the number of votes a story has gotten) give little information about why the users are voting for it (perhaps they are just following the signals they saw). Therefore, the user’s own feelings about the story may be ignored or at least not as highly considered. More importantly, information cascades on Digg are less fragile than the ones we discussed in class. Because Digg only shows votes in the positive direction, voters get no signals that anybody is voting against (burying) the story, which means you can never start an information cascade against a story.

The article then suggests a two-part way to help Digg avoid such strong information cascades in order to create a less biased news. First, users must all be equal. This suggestion could be good or bad. It makes it harder for people to have the power to push a bad story to the top because the “credible” sources don’t have more weight than anyone else. However, if the “credible” source does somehow have better information, it is not recognized at all. The second part is to only show profile and number of votes for the top stories, thus eliminating the number of votes impacting new stories. This idea prevents people from voting with the crowd, as they won’t know what the crowd thinks until it’s on the front page. Keep in mind, the author notes that implementing his solution would make Digg less social, however, as social connections become stronger, you are more likely to be influenced by and thus conform to the choices your connections make. Therefore, there seems to be a give and take relationship between a social site and a news site that provides many diverse viewpoints.