“Cisco to Buy WebEx”

http://www.redorbit.com/news/technology/902812/cisco_to_buy_webex/index.html?source=r_technology

In what appears a very strategic M&A deal, Cisco has decided to acquire a company named WebEx Communications for a very large chunk of cash–$3.2 billion. The former is a leading supplier of networking equipment, such as routers and switches. WebEx, meanwhile, is in the applications sector–providing services which allow companies to organize online meetings. Why exactly is Cisco willing to put forth so much money for WebEx?

Is Cisco’s strategy merely to broaden its focus in networking? Basically. According to the article (linked above), Cisco wants to expand into communications, social networking, and other fields in an attempt to help heighten network traffic and ultimately demand for its networking gear. The company has a lot of cash at this point and, in effect, the ability to set such a plan in motion.

What we see here is that Cisco has been playing the “network effect” card, as discussed in class, to perfection. The value of its networking gear to a service provider (and ultimately consumers) increases with the number of network users. Users, of course, grow with the success of WebEx’s service and other useful networking opportunities (Cisco’s goal here). Although the article does not mention how WebEx was valued, I would find it particularly interesting to learn Cisco’s methods in ascertaining the fair market value of its assets.

In our brief introduction to evolutionary game theory, we defined an evolutionarily stable strategy roughly as one that tends to drive a fractionally small population of mutant strategies to extinction over time. Our setting for investigating this idea was the Hawk-Dove game played between behaviorally instinctive animals; here we saw that successive generations of animals will act according to their parent’s behavior. Another setting more closely related to our topics of human social networks deals with a population of players that choose their best response in relation to the others’ actions.

A further variation that is worth merit is a topic explored by H. Peyton Young in his paper “Stochastic Adaptive Dynamics.” As the title suggests, Young discusses the notion of introducing random errors into behavioral mechanism of normally rational players. He shows that in the context of the “Stag Hunt” game (representing it as a Markov chain), that even when players choose their strategy with a small probability of a mistake that a unique stochastically stable solution can exist. That is, as the probability of mistakes tend to zero, the long-run distribution of behaviors concentrates around a single strategy, giving the stochastically stable state. More interestingly, this notion of a stochastically evolutionarily stable solution can persist for non-trivial values of the random error parameter. This conclusion reveals that even in the presence of seemingly mistake-prone players, a long-run evolutionarily stable behavior is attainable.

A seemingly nice property of this type of behavioral analysis is that it allows for random shocks in the normally deterministic choices of the players. This randomness might be interpreted as bounded rationality of the players. This is desirable because most people in the world do not calculate their optimal responses to every situation; more likely, they base their choice on informed (but imperfect) reason and sometimes the outcomes are most certainly suboptimal. Also this variation of traditional game theoretic methods allows for other analysis that simulate the long-run dynamics of these behavioral adaptive processes to test things such as time to convergence. Luckily for this particular form of stochastically played game, the mathematics of stochastic processes are well understood (especially the dynamics of Markov Chains) which detailed alterations to test various aspects of the dynamics.

Reference:

“Stochastic Adaptive Dynamics”, forthcoming in The New Palgrave Dictionary of Economics, Second Edition, Steven N. Durlauf and Lawrence E. Blume, eds. London: Macmillan.

http://www.econ.jhu.edu/people/young/index.html

Using social psychology to motivate contributions to online communities

This paper delves into the problem of under-contribution among online communities. The authors conduct several experiments on a community called MovieLens - a movie recommendation community. In an effort to test various hypotheses regarding what factors effect users’ tendencies to participate in such an online community, the authors of this paper send out emails to various users informing them of various notions and then watch their activity on the site. The results are pretty interesting.

Out of 6 hypotheses they make, one is that a user who is acknowledged by others (or in this case by the website itself) as unique is more motivated to contribute and play his/her “unique” role. Emails were sent out a random selection of users telling them that they had been selected for a campaign to rate more movies and that they were selected because they had rated movies that only 2 or 3 other users had rated. Other emails were sent out to users telling them that they’re tastes were more typical and that this made them more valuable to the community. The results were that the users who had been told that they were unique and valued rated more movies thereafter than those users who had been told that they were chosen because they’re tastes were similar to the majority of users. Specifically overall movie ratings increased 18% among the unique users than the non-unique users and when considering only the obscure movie ratings, unique users rated 40% more than non-unique users.

Although there are 5 other tested hypotheses in this paper, the lesson that can be taken away from this one is that users are more likely to contribute if they feel unique or special. Just having a site that is customized for each user (for example Amazon’s recommendation system) might not be enough. Specifically being told that you are unique makes the difference. The paper makes a good point that this kind of feature (one that tells users how unique they’re activity is) is pretty much non-existent. Whether this is a possible feature to implement is another question.

 http://www.fortcollinsweekly.com/index.php?option=com_content&task=view&id=647&Itemid=35

Colorado Representative John Kefalas is looking to bring new voting methods to his state for the November 2008 election to try and eliminate the problems with the current voting system. Currently, in Colorado, citizens vote for one candidate for each opening position, however it is often the case that no candidate receives more than 50 percent of the vote. With independent parties on the ballot, such as the Libertarian party and the Green party, it makes it hard for one candidate to win a decisive majority. This dilemma is often refered to as the ”Nader problem” or the “Perot problem” as both independent candidates Ralph Nader and Ross Perot have run in presidential elections and have been accused of “stealing” votes away from the front runners. 

In January, Kefalas first introduced House Bill 1162 to the Colorado state legislature which he helped bush through congress until it died last week in the House Appropriations Committee. The bill would have created a 13 member committee to examine many alternative voting methods. These methods include: instant runoff voting,  approval voting, range voting and proportional representation.

Instant runoff voting, which Kefalas is in support of, allows each voter to rank candidates on their ballot. If no candidate receives a majority of first place votes, the candidate with the fewest first place votes is eliminated from the ballot and those votes are reallocated to voters second place votes. If that still doesn’t result in a candidate with a majority of votes the process is repeated for multiple rounds until one candidate emerges with a majority.

Range voting allows each voter to rank candidates with a numerical value from a scale ranging from either 1 to 10 or 0 to 99. Scores for each candidate are then tallied up across all ballots and the person with the highest aggregate rating is elected. Approval voting is a form of range voting in which voter votes “yes” or “no” for each candidate. A candidates “approvals” are then tallied and the one with the most “yes” votes is elected.

 Proportional representation is different in that voters vote for a party list of candidates and seats in a legislature are awarded in proportion the the percentage of votes each party list receives. This is already practiced in Israel, Iraq and in most European parliaments.

Range voting is very similar to the alternatives to majority rule voting that we discussed in class. Range voting is  similar to the Borda Count in which numerical rankings are assigned to each candidate and aggregate rankings are used to determine a winner. However, in range voting, voters are allowed to assign a number based on their opinion and degree of approval for a candidate rather than just assign a numerically ordered ranking. This means that the vote is more qualitative than the Borda count and may be more indicative of public opinion. A range voting method would allow voters to give support for more than one candidate but in different degrees. It would allow voters to vote sincerely based on their opinions without worrying that their vote for an independent candidate could potentially “steal” a vote away from the next best alternative.

Going back to the topic of page rank, and the illusive Google search algorithms, did you know that your website could end up in “Googlehell”? Googlehell refers to the supplemental index which has been created to “store” websites that the Google search algorithm deems of “low quality” or websites that seem they are artificially designed to increase their own search results. As we discussed briefly in class, there are now consultants who specialize in improving Google search results for websites. They specialize in manipulating aspects of websites that they think Google’s page rank and search algorithm look for when ranking websites. Even though this is a topic that is discussed quite often, with good measure, the power of Google’s page ranking system is monumental. Websites who’s livelihood is solely based on the traffic their sites garners from a Google search can be devastatingly affected by a poor ranking. Google themselves publish this list of guidelines.

Googlehell was recently broadcast in an article in Forbes showcasing the economic effects that this phenomenon has had on websites who depend on their search results. Although Google is seemingly not doing this intentionally, it just shows how their algorithm has some aspects which could use improvement. I am curious how Google will handle this phenomenon if an overwhelming increasing number of websites end up with this predicament. The article claims that the business owner sent an email to Google asking for some information why his website may have ended up in the supplementary index even after readjusting his website to ensure it conforms with the put in place “guidelines.” What makes this problem more complex to solve is the fact that websites that have been artificially created to look real are increasing in sophistication and amount. It will be that much more difficult for the Google search algorithm to differentiate properly. It would be an amazing job to be on the cutting edge of making their search algorithm be able to handle not only this complication but others that are sure to arise.

Another point the article makes is the fact that the guidelines as published above are quite vague. How much detail should Google provide about its ranking system? In many ways, it makes perfect sense for Google not to publish any information that could help websites manipulate their rankings (since it causes problems, and for the obvious reason that is it wrong) but should websites such as those broadcast in the articles have to be condemned to Googlehell and perhaps there are numerous other legitimate websites that have not even noticed. The article detailed that his one business owner did not notice that he was in “Googlehell” until six months after he noticed significant sales drops, after the economic damage is done. One of the easiest fixes would be for Google to increase the times they evaluate their supplementary index. Did you ever wonder if it’s someone’s job to sit and check the supplementary index for legitimate sites that may have accidentally been placed there? If it’s not, there should be. The reason I am writing about this is because of the fact while I knew the algorithm was not perfect yet, I didn’t realize the economic effects it has had on other businesses. I wonder how much money Google has indirectly cost websites such as these?

Anyways, the point of this was to highlight manipulations of page rank, and the possible negative economic side effect. What are your thoughts?

The article, What Makes A Winning Social-Networking Site?, summarizes a large panel discussion titled “Social Networking Winners and Losers.”  Representatives from Facebook, MySpace and other social networking sites took turns to discuss what makes a social network “successful”.  What they found out was that the definition of success varies from person to person.  Some feel like success is only attained when you have the ability to “exit” and sell off the network; however, other people only want to build the company/network without creating such an “exit strategy.”  Now if you define success with the size of the social network, you will be interested to know that then according to this panel discussion that then a social network’s success is indeed a fluke.  According to Mike Speiser (who created a talent competition social network and sold it to Yahoo), every social network creates its base in the same way: “by telling everyone at the start-up to send a link to the site to everyone in their address book.”

Personally, I was very disappointed in this article/panel discussion, because I feel like they didn’t really analyze exactly why certain social networks are growing and others are not.  I mean, if everyone is creating it’s social network in the same manner (”send the link to your friends”), why in some cases do people feel inclined to send the link and in others people just ignore it?  I feel like Facebook expanded so rapidly, because they expanded in a logical fashion.  Facebook first started as a project at Harvard University.  Once half the undergrads had joined, then it was expanded to other Boston schools which was smart since Harvard students probably know plenty of other people that go to other schools in Boston (just like I am sure a large number of Cornell students know people at Ithaca College).  Then Facebook expanded to include the rest of the Ivy League.  This was another smart move, since in high school, classes are segregated by intelligence levels, so people who ended up going to Harvard probably took high-school courses with (and were friends with) people who went to other Ivy League schools.  Each time Facebook expanded its online community; it made sure that it expanded to a new network that had plenty of connections to current network of users.

Another reason that Facebook’s expansion methodology worked so well was because it was based on eligibility rather than just “send a link to the site to everyone in [your] address book.”  Just think, Facebook is new, you are at Harvard and have exhausted all of the possible people you can “friend.”  All of a sudden, MIT is added to Facebook and now, all of these new potential friends are eligible to join and become your friend.  My first reaction would be to check if these friends had joined yet and if not, send them an IM to join.  Facebook was able to create the desired result of “[sending] a link to the site to everyone in [your] address book” without directly saying it.  So in conclusion, I feel like there is more to the growth of an online social network than what this article describes, and that the successful social networks have taken more steps to ensure their growth than just telling people to email everyone in their address book.  

The notion of six degrees of separation has infiltrated popular culture to a very wide extent, particularly for connecting celebrity individuals, i.e. six degrees of Kevin Bacon. It is not suprising that this theory has mainly been applied to high-profile people, as the original Milgram experiment was oriented towards a New England stockbroker. However, as we all know, networks are not confined to the realm of human nodes. Can a small-world phenomena exist in a non-social network? How about a network of words/ideas? The website ’Six Degrees of Wikipedia’ allows users to find the degrees of separation between Wikipedia entries.

Here are some examples:

There is 4 degrees of separation between ’George Bush’ and ’Crack Cocaine’

There is 2 degree of separation between ’Mongolia’ and ’Richard Feynman’

There is 3 degrees of separation between ’skiing’ and ’sex’

There is 4 degrees of separation between ’viagra’ and ’mackerel’

Try it out! http://tools.wikimedia.de/sixdeg/index.jsp

[1] http://www.techcrunch.com/2007/02/16/dell-pays-tribute-to-digg-with-new-ideastorm-site/

[2] http://www.mozilla.pk/blog/2007/05/02/linuxopensource-grows-ubuntu-to-be-packaged-in-dell-desktops/

[3] http://atomiq.org/archives/2006/01/how_much_wisdom_is_there_in_digg.html

As the semester is winding down, the latest topic to arise in the course is voting, and its implications within social networks. A great example of this, and its influence on contemporary social networking, can be viewed with Dell’s new IdeaStorm site. [1] The site, opened in February, has given the public the ability to vote on what it feels should be the company’s direction or next endeavor, a la digg.com. Although the site has only been open for two and a half months, it has already proven to have an effect on the company. On May 1st, Dell announced that it would support Ubuntu Linux as an alternative to Microsoft Windows on its desktops and notebooks directed towards home and business users. This announcement was not only an important step in the open-source movement, but a direct result of the IdeaStorm website being “deluged in its first few weeks with requests for Linux PCs.” [2] Allowing the public to vote on what it feels should be the company’s direction, gives an incredible amount of control back to the consumers about what they want and how they want it. It has become popular within online social networks to belittle how companies have recently been doing business by blaming them for not listening to the consumers. Such has happened with the recent launch of Windows Vista, as well as with Facebook’s modifications late last year. Other companies, like Yahoo!, are experimenting with social voting to determine new desires and trends within technology.

Although Dell’s decision to go with Linux doesn’t stem just from the IdeaStorm website, the site proves how valuable voting has and will continue to become in social networks and consumer-company interactions. The IdeaStorm website proved to Dell, beyond a shadow of a doubt, that Linux was gaining popularity was in demand, something the company’s CEO, Michael Dell, was unsure of back in 2005. The implications from voting still haven’t been fully realized, but some issues, like cascading information problems can be seen. Just like with digg.com, others can see the total votes garnered so far by each topic, fostering people to be swayed by already popular topics, although this hasn’t been researched fully yet. [3] Like with any kind of voting, however, problems arise and as discussed in class, there is no guaranteed way of reading the rankings, as discussed in the Arrow Impossibility Theorem. Also, since the companies haven’t divulged how voting is tabulated or taken into account, right now it’s a gamble as to whether the public’s desires will be fully realized. However, as with the Linux adoption by Dell shows, there is hope.

The combinations of information cascade, social and blogging networks, as well as voting bring in all these aspects of this course as well as point us in a direction to see the future unfold and changing paradigms, not only in networking, but in how consumer-driven businesses operate and research.

Our handouts on information cascades showed how, by using a series of observations of other peoples decisions people often come to conclusions about what decision they should make. The handout on network effects then showed how positive externalities also helped to ensure the dominance of a particular strategy. The novel “The Tipping Point” by Malcolm Gladwell shows us a series of different ‘enablers’ who can nearly single-handedly create information cascades and network effects through their actions.

It is easy to understand the power of these people in causing such changes. For example, consider the example of the Maven. If you know that a particular person’s recommendation (signal) is much much more valuable than a typical signal, the probability that you will follow their signal is much higher than a traditional signal. Look at a more personal situation- are you more likely to listen to two or three friends about a car that they like, or your neighbor the mechanic who studies cars fanatically? Clearly the quality of signal plays a role in decision making. The model which we use is by nature very simple, and doesn’t take this effect into account. How could we modify it to do so? The simplest way seems to enable a signal from a strong source (SS) to as multiple normal signals or multiple actions.

How do these influential people arrive at their decisions to begin with? For a few it comes from direct experience, but in many cases, through other information cascades! Your resident Maven might read a half dozen or dozen articles from critics and reviewers before coming to a conclusion. This decision is also more likely to be based on the real payoff, because there is no sequential or dependent aspect to their evaluations. People who are in contact with them are doubtless aware of this, and may follow the maven irregardless of what the more ‘popular’ decision is.

Then again, influential people can cause changes by themselves without the need for other reinforcing actions or signals, or even a basis in fact. Take this article by satirist Maddox. Since posting, over 2.3 Million people have visited the site. It even earned its own Wikipedia Footnote. Orbitz was flooded with thousands of letters, and undoubtedly lost a number of customers (obviously a hard thing to prove given their reticence to divulge information related to the incident, but likely nonetheless) I have no personal experience about whether Orbitz is a spectacular company or a terrible one, but I find myself much less likely to use them in favor of other alternative options. By Gladwell’s model Maddox might most closely resemble a ‘Connector’, with the exception that Maddox neither knows nor cares about the majority of his fans. His influence is well above and beyond his personal level of connections. The larger a persons popularity and/or perceived reliability, the higher their capability to single-handedly cause these information cascades and network effects.

http://news.bbc.co.uk/2/hi/technology/6615047.stm

Last night Digg.com had a user rebellion: tens of thousands of users bombarded the digg homepage with the AACS’s HDDVD encryption key. Digg had previously said it would delete all references to the key - like wikipedia and boingboing, among other blogs, have. But, users revolting. For nearly five hours last night, every single story submitted on digg was about the key and every story on the front page was about they key too. Users did not want to be censored. Every story on the first 2-3 pages had nearly 5000 diggs - a new record for anything on the front page. Finally, digg crashed under the stress of the attack.

Later, Kevin Rose posted a blog entry at the Digg Blog that says:

 

Today was an insane day. And as the founder of Digg, I just wanted to post my thoughts…

In building and shaping the site I’ve always tried to stay as hands on as possible. We’ve always given site moderation (digging/burying) power to the community. Occasionally we step in to remove stories that violate our terms of use (eg. linking to pornography, illegal downloads, racial hate sites, etc.). So today was a difficult day for us. We had to decide whether to remove stories containing a single code based on a cease and desist declaration. We had to make a call, and in our desire to avoid a scenario where Digg would be interrupted or shut down, we decided to comply and remove the stories with the code.

But now, after seeing hundreds of stories and reading thousands of comments, you’ve made it clear. You’d rather see Digg go down fighting than bow down to a bigger company. We hear you, and effective immediately we won’t delete stories or comments containing the code and will deal with whatever the consequences might be.

If we lose, then what the hell, at least we died trying.

Digg on,

Kevin

Digg is still down as of this morning. This just goes to show how information cascades can form. No one even knew of the key before last night and now millions of people do. It will be interesting to see if the AACS still sues digg and what kind of implications this has for the DMCA and internet copyright laws.