As technology evolves and consumer demands spur further innovation, there is often an advantage to adopting a format standard.  When a new technology emerges, it can be confusing for consumers to understand differences between competing formats in next-generation technologies.  Additionally, using the same format as your peers eases consumer problems, as illustrated in the early frustrations with Mac and Windows formats as well as other examples mentioned in lecture.  As such, the diffusion of these innovations greatly influences the ultimate market winners and format standards.

Recent technology history is littered with various format wars where companies and sometimes partnerships compete to establish the standard for the next-generation of various technologies.  These wars have involved video cassettes, video discs, digital audio players, flash memory cards, digital video data compression formats, and many other technologies.  Of these format wars, the quintessential example is the video cassette format war between Sony’s Betamax and JVC’s VHS.

Of course, the free market has many more factors that make this more than a simple case of a cascading network.  Many people have analyzed this competition in search of the explanation for the fall of Betamax.  The most popular explanation for the triumph of VHS seems to be its initially longer recording time, since Betamax was originally limited to a paltry 1 hour.  Further reasons include the manufacturing cost advantage enjoyed by VHS, which allowed higher profits for producers and lower prices on both players and cassettes, plus Sony’s ban on pornography.  The real reason is most likely a combination of many of these factors, but the interesting case is how Betamax fell from a virtual monopoly on video cassettes with nearly 100% of the market to a niche market player thoroughly beaten by VHS.

When VHS arrived, all published cassettes were in Betamax format, with the blank Betamax cassettes as the only home-recording option.  However, as the limitations of Betamax became apparent, some consumers switched to VHS.  These early adopters started the cascade that ultimately led to Betamax’s downfall.  Of course, consumers wanted to maintain compatibility to share their tapes with peers, but soon the diffusion proceeded as consumers and publishers alike wanted to take advantage of VHS.  Eventually, VHS became the majority market leader, relegating Betamax to its last small strongholds outside of the

United States.

Quite similar to the video cassette format war is the young high-definition video disc format war.  This time, Sony is once again a player with its Blu-ray format, while Toshiba and Microsoft are backing HD-DVD.  Currently, this competition is still quite close, but it would not be surprising to start seeing a similar cascading network effect as more consumers upgrade to the next-generation of technology and demand a common format.

Clearly, the understanding of network effects such as diffusion of innovations and cascading is crucial to battling in the technology business world.  As future technologies emerge, companies will increasingly need to examine the past to learn how to tip the cascade in their favor.

http://news.bbc.co.uk/2/hi/technology/6506027.stm

As we have been discussing in class operating systems are one of the products that shows positive network effects with expanding user base. This makes sense because with a larger user base reduceds concerns of interoperability which is a concern for business users. Also once there is a larger user base then more developers will produce programs, luring more users.

The model we look at in class on 4/4 needs some small generalizations to model usage of linux. The “price” of using linux is not in purchasing the operating system, but in time configuring and maintaining linux. The way companies make money on linux is by providing support (RedHat,Novell,Linspire). The second modification that the model needs is the addition of a hard core of users that will use linux regardless of the cost.

This is an important issue because of the prevalence of computers in our society. Most tasks done on computers, web browsing, word processing, spread sheets, and email are fairly basic tasks. It would be very easy to replace all proprietary software for these tasks with open source counterparts. If we assume that a standard microsoft/mac package consisting of the OS, basic software, and an office suite costs ~$300 then for a mid sized company which owns 1,000 machines this is a savings of around $3 million. Similarly large sums of public money could be saved by moving all government computers away from proprietary software.

Reference: “Innovations and Technological Spillovers” by M. Ishaq Nadiri – pdf at http://ideas.repec.org/p/cvs/starer/93-31.html

Recently in class we began discussing the idea of diffusion in networks. This notion of diffusion can be useful for modeling many things including the spread of information or rumors, the epidemiological problem of disease outbreaks, or (as mentioned in class) the acceptance of certain new technologies. The model developed in class thus far has focused on the idea of a network of individual who play coordination games among themselves to determine their optimal behavior. As usual, this model is a simple abstract to which many complications can and should be added to obtain a richer model of these complex processes. One such complication that remains relatively open to further research is the notion of defining the complex network on which these diffusions occur.

Whether we are concerned with a network of individuals or of larger economic entities, such as companies or even entire industries, it is equally interesting and important to understand the dynamics of how new technological advances (products or processes) move from one node to the next. In order to study and model these dynamics, however, we must have an accurate representation of the structure across which the technologies are being diffused. Several studies cited by M. Ishaq Nadir in a section on “technology flow methodology” in the referenced article show that this problem is difficult to solve in its own right. These studies have tried to define various network structures including those that span several industries and those that connect firms within a single industry. The difficulty in do these analyses is the lack of a large quantity of reliable data about the borrowing of R&D results. These difficulties aside for now, most often the purpose of these research projects was to determine the rate of return due to R&D spillovers in high-tech industries, a direct product of network externalities. However, these network definitions could also be used to further enhance our models of the diffusion process itself.

Whereas the previously mentioned studies intended to determine the returns to private investors as well as to society derived from the diffusion of new technologies, it would be interesting to build these empirically defined industry networks into the diffusion model. This type of analysis would reveal which firms and industries or types of firms and industries fall into dominant positions within the network. Also it would be useful to measure the strength of each link in the network in terms of the ability of on player’s decision to influence that of his neighbors. Incorporating these specific real-life structures into the diffusion model would certainly make the problem more difficult, but the end product could be a very useful analytical tool that could aid in the decision making of not only individuals and firms but also of the federal government who subsidizes a large portion of the total R&D performed each year.

Overall this does not seem like such a unique proposition. In all of the network models that we have encountered this semester, a clearer understanding of the physical, social, or economic network to which we are trying to apply a simple model will certainly lead to more complicated but more rewarding discoveries. The interesting notion here is that we can apply a method first attempted for a slightly different purpose and enhance it by looking at not only industry network structures but also those that connect industry to academia and industry to consumers to achieve some truly revealing results about the diffusion of technological innovations through a network.

Stock Markets are ideal places to track fads and information cascades because Stock Markets quantifiably merge emotion and information. From the first Tulip bubble in the Dutch Golden ages to the recent Internet stock market bubble, information cascades provide a framework when markets react in non-rational ways. Stock market corrections prior to the Great Depression were even known as panics, and both panics and bubbles are the result of many people chosing popular opinons contrary to prior individul information.

One recent example of this occured last month. As the Chinese bubble dipped, markets all over the world reacted with depressed markets because people presumed that if others believed that the Chinese dip would affect markets all over the globe. Stock markets from  Brazil to South Africa to Europe to America lost significant amount of value because people made panicked decisions based on emotion rather than reason. Indeed, despite the underlying problems behind the Stock Markets of the Roaring Twenties, it was the collapse ot a small bank in Austria that plunged the world in a global depression.

 No example of this was demonstrated as dramatically on Film than the classic 1980’s comedy starring Dan Akroyd and Eddie Murphy. Their aim was to use information cascades to build a fortune to retaliate against scheming caused by their bosses. While http://en.wikipedia.org/wiki/Trading_Places describes the climactic scene in more detail, the key points are as follows:

Assuming a piece of information was true, the two antagonists pay for shares at an inflated price assuming that the price will rise. Despite everyone’s preconceived guesses at what the fair price of Orange Juice may be, everyone buys and buys. Here exists a classic information cascade where despite preconceived notions, a mob mentality is created where people ignore these notions, initially follow the antagonist’ lead and soon everyone is chosing this deicison because everyone presumes that everyone else has better information.

Unfortnately, the information that was give to the antagonists was wrong. Oranges that year was expected to have a healthy crop, and thus prices are far lower than they had become. The protagonists used inside information and the principle of information cascades to make a lot of money as the information cascade had tipped in the other direction. Now the same mob intent on buying price inflated Oranges fighting to sell their shares as quick as possible, simply because other stock brokers were doing the same. The collective opinion on the value of these Oranges have then fluxuated wildly.

 In reality, Stock Markets have safeguards against panics and information cascades. If the price of Oranges fluctuates too wildly, the stock market stops trading. In essence, this is a mechanism designed to prevent information cascades. By preventing people from making quick decions, decisionmakers can then think rationally and independently of other people’s trading decisions to make rational decisions.

“Meet Hollywood’s Latest Genius”

http://www.latimes.com/features/printedition/magazine/la-tm-random27jul02,1,1850294,full.story?coll=la-headlines-magazine&ctrack=1&cset=true

In his article, Leonard Mlodinow examines the unpredictability of Hollywood; he explores how much of a studio’s success is due to executives making the business choices and how much of it is random. Recent research has suggested that films, no matter what the budget, are subject to unpredictable factors arising during production and thereafter. Although Hollywood executives tend to disagree with such an assessment, evidence points to the industry following the law of chaotic systems– meaning that it is nearly impossible to foresee whether a movie will generate revenues.

Going on to discuss information cascades, the article becomes quite relevant to our coursework. Emphasizing that studios are unable to create a strategy that gives them any advantage, Arthur De Vany, a retired UC Irvine economics professor, has theorized regarding the effects of a marketing blitz on a movie with a sub-par plot. He asserts that although marketing helps in a movie’s opening weeks, eventually an information cascade takes over. When we hear good reviews from peers and critics, we go (and might then tell others)—and vice-versa. In effect, for a film to have long-term success in the cinema, it needs to create a cascade that will raise our expectations about its quality.

Can we predict a resultant information cascade? While some feel that screening a movie to a focus group will be very indicative about its long-term revenue-producing capabilities, others feel differently. Mlodinow’s article points out how quickly tastes can change—how the plans of studio executives can suddenly go awry from one week to the next.

De Vany notes that studios did a lot better before the 1960’s because it had power over their products’ distribution. Controlling a film’s release pattern, the studio was able to control the information cascade as well. I found such an observation the most intriguing part of the article. At the same time, I am unsure how relevant the information cascade application is itself. I disagree that the opinion of others have an absolute influence on me. There are some movies criticized by many that I just have to see, while there are others that have been widely applauded that I absolutely refuse to sit through. I am sure there are many others like myself who place a high value on private information and opinion and thus disrupt De Vany’s information cascade theory.

In a recent press release by Google, the company announced that they will be expanding their advertising ideas to television.  The article can be found at Google’s Press Center:

http://www.google.com/intl/en/press/annc/tv_ads_trial.html

By using a similar model of their online advertising, the television ad spots will be auctioned off and advertisers will only pay for how many people viewed the ad.  Google claims that they will utilize “set-top-box technology” to keep track of how many times an ad was viewed and how long it was watched for.  Additionally, advertisers will be able to target specific demographics, time slots, and channels.  According to Scientific American, Google has already made a deal with Dish Network and is currently talking with DirecTV:

http://www.sciam.com/article.cfm?alias=google-talking-to-directv

This new method of advertising will benefit advertising companies, television networks, Google, and even the consumer.  The statistical feedback that Google will provide on each commercial will be invaluable to advertisers.  Companies will receive information about the effectiveness of their ad, the channels on which the ad was most effective, and the target market to which they are advertising.  Television networks will also benefit because they will be able to generate more profit by charging more money for more effective ads.  Additionally, Google will receive a cut of the ad revenues for providing the hardware and software to keep the statistics of an ad.  Ultimately, the consumers will benefit the most because more entertaining ads, specifically directed towards their interests will be aired.  Over time, companies will stop wasting money on ineffective ads and will spend more money on designing create ads that captivate a large audience.  Also, if the new ads become more effective and more expensive, companies will purchase less ad time and there will be fewer commercials.

However, there are certain problems with their model.  Google’s biggest obstacle will be estimating how many impressions that a certain commercial has made.  An ad that is aired during a commercial break could make a wide range of impressions.  There is no way to tell if the TV is on with nobody watching it, or if ten people are watching a particular commercial.  One solution would be to have the consumer press a button each time he or she watched a commercial before the show returns.  Additionally, the new advertising model brings up many privacy issues.  Google stated that relevant ads will be only based on “demographic, daypart, and channel”, but once Google starts tracking which commercials that you watched, they are collecting information about your personal interests.  Eventually, Google could sell this information to companies, so advertisers can air different commercials specifically directed towards millions of different viewers. 

Although there several problems with Google’s new advertising approach, overall, it has the potential to start a new advertising revolution.

Imagine a network in which one person has several contacts. These contacts, for the sake of argument, are not connected despite the likely influence of triadic closure, and thus stem from the person in question like spokes from a wheel. The people at the end of these spokes have their own friends who may or may not know each other and this large network component branches out from our single, central individual.

I would like to make a point about the importance of reaching the “right” person with a product, advertisement, or viewpoint. In our current class lectures about popularity, we have seen that it only takes one person to change their mind about a product, program, idea, or person to set off a trend of people making similar choices. The more people change their mind, the more people will change their mind.

Given these insights, it seems that reaching some people in a network might be more profitable than others. For example, using the IM chat programs example mentioned in class, suppose that a person at the far end of one of the branches mentioned earlier decides to convert to a different program as the result of a special promotion. In theory, this decision might influence those directly surrounding to change their IM program as well. Eventually, assuming the program maintains momentum, it might spread throughout the entire network as people convert to match those people closely connected to them.

Now imagine that the central figure in the network changes IM chat programs. This change will produce a much larger and faster effect on the network as a whole. Each of the people on the spokes will be dissatisfied and change and then everyone in their branches will likely change. Rather than starting at the extremities of the network and slowly creeping across, a change which is initiated at the center of the network produces much larger initial effects and is therefore less likely to sputter out before reaching its full potential of users.

Put simply, if the most popular person uses the product, it is more likely to be picked up by others. This can be seen in the use of popular athletes endorsing athletic products or when celebrities wear certain clothes to promote a specific designer. These examples however are not the limits of the potential for this phenomenon. If producers can find a way to map network activity relative to the product they intend to market, then they may increase the likely success of their product by targeting a central figure in the network. Email sites might target a person sending the most emails to the most diverse group of people. Car companies might target people who drive more frequently to expansive areas or in high profile areas for exposure. Shampoo companies might target people with reputably good hair for their ads. Many other examples may be formulated similarly and many are already in use, but I felt that this was a noteworthy thought about popularity in play.

Sites referenced:

http://www.moneycontrol.com/india/news/pressnews/juxtconsultshaadicom/shaadicomadjudg/market/stocks/article/271619 

www.shaadi.com

Shaadi.com is the number one matrimonial website in the world. A quick look at the website reveals it has recently won a spate of titles, including most user friendly matrimonial website in India, the subject of the article posted. Shaadi.com has capitalized on the role of the matchmaker in arranging in Indian weddings with its access to over 9 million users worldwide, one of the world’s largest matrimonial databases. How did Shaadi.com become so successful? By getting into the market early and capitalizing on a network effect. As they state on their website, “the number of members is directly proportionate to your chances of finding the person you are seeking.” As more people join, the worth of a Shaadi.com membership increases, and people put a higher premium on having a Shaadi.com membership, leading to higher potential profit margins and an exponential increase in membership. To get past the initial tipping point in getting people to purchase a membership in the first place, Shaadi.com allows users a limited profile and messaging opportunities for free with the idea that if you find someone you like, you can upgrade your membership in order to find out more about them and possibly even chat online with them. This strategy appears to be effective; Shaadi.com is enormously profitable, so obviously this limited trial version has convinced enough people to purchase a membership to get past the tipping point. However, this strategy could backfire. Having too many people with limited free profiles will cause congestion; these people are essentially freeloaders benefiting from the positive externalities of the network, being able to browse the full profiles of paying members without paying themselves. Since freeloaders neither interact with others to form matches nor pay membership fees, they clutter the system and lessen the efficacy of the matchmaking system, which will eventually discourage people from joining. If too many freeloaders amass, the site will cease to be profitable. In the meantime, however, Shaadi.com is doing well with 723,549 successful matches and counting.

There is a balance between personal signals and decisions of past users for most people in a decision making process. However, I question what can be said for the initial individual to accept or reject something.

Theory has it that the first individual makes a decision based on their personal signal. So thereafter the other individuals don’t have to rely on their own signals, but can outsource that consideration to the group that has decided before them. For the next person to join the group, the decision becomes easier, and it is somehow admirable to operate like that. But, what if there was a certain type of person that didn’t always rely on the group?

Apparently Francis Galton, before his information cascade conversion, felt that only a few people were keeping society at large healthy. This of course carries some political implications. Because if we could identify this type of person, then we would make him or her president or king.

But we can’t (or at least we think we can’t) identify these people. So we have political campaigns and elections to make sure that we have a consensus on who would make the best candidate to bring health to society.

But we find ourselves hitting and missing in the democratic regions of the world. Some elected officials were healthy for society, while others deteriorated our condition. But this shouldn’t be in a democratic nation. The cream of the crop should always rise to the top in an information cascading political world. Because the voters are assuming that the decisions of the majority are a safe bet despite their individual signals.

Well then we should go back to our original statement and say that the reason we are hitting and missing as a democratic society is because we believe that there is such a health bringing individual, that will start a cascade of blooming prosperity. I believe it to be true myself.

This type of individual can be found by looking at their history of decision making. First of all they would have to be among the first in a group to take an action. Then we should be able to observe whether the action they took brought about some benefit. Then we should choose the individual who initiated the most benefit to society to be the president or king.

This, in my opinion, is where western society is going. For now we have been looking at the voting history of senators and officials to decide who is the best. But the time will come that surveillance will afford us to see the decisions of every member of society. Things like movie rentals, and grocery lists will be reviewed to see what information cascades individuals are instrumental in starting, and whether those lead to health or harm. Leaders will be selected like this rather than democracy. But there are flaws to this system as well, such as whether such surveillance will be accepted by the public.

A wonderful, recent example of the fragility of an information cascade is the demise of the Atkins Diet craze that occurred earlier this decade. Its official website is www.atkins.com, but I have used its Wikipedia article – http://en.wikipedia.org/wiki/Atkins_Nutritional_Approach – as my main source of clarification. As the article explains, the diet was first created in the early 1970s by Dr. Robert Atkins. Books about it existed from this point through the end of the century, but nobody took very much notice of it. In 1989 Dr. Atkins founded the company Atkins Nutritionals to promote his diet. Finally in 2003 and 2004 it reached its tipping point and became a huge phenomenon. Its magnitude can be fathomed by looking at this 2003 article – http://www.usatoday.com/money/industries/food/2003-12-25-subway-atkins_x.htm or this 2004 article – http://media.www.thenews.org/media/storage/paper651/news/2004/01/30/News/Atkins.Diet.Prompts.Menu.Adjustments-592444.shtml. They basically explain how powerful restaurants such as Subway, Hardee’s, TGI Friday’s and Burger King all took steps to assist customers with following the Atkins Diet. This shows that the diet was widespread enough that the companies respected it as either 1) leverage to draw in more customers or 2) something unavoidable to sustain the number of customers they were receiving.

This popularity, which began in 2003 with the cascade model we examined in class (when people and businesses alike were suddenly and in a very brief period of time obsessed with it), diminished very quickly by the end of 2004. In 2005 the company that Atkins founded was required to file for chapter 11 bankruptcy. This sudden turn of events shows how just one or two nodes in a cascade can stop and cause the “Domino Effect” to reverse itself. At the moment the Atkins company still exists but it is no longer a force of any type. I was unable to find the word “Atkins” anywhere on the Subway or T.G.I. Friday’s websites. This is something that is very simple to see since it happened just a few years ago. I vividly remember long food commercials lauding their adherence to Atkins principles. But now the word is hardly heard at all and low-carbohydrate diets are seldom mentioned.