The tightly knit village community is an element with which people from urban and suburban communities may be unaccustomed. Washington Post writer Shankar Vedantam describes the healing power of strong social ties for schizophrenia patients in such communities. Vedantam tells the story of a woman who recovered from schizophrenia thanks to “an embracing village that never excluded her from social events, family obligations and work”. A thirty year study by the World Health Organization concluded that “people with schizophrenia […] typically do far better in poorer nations such as India, Nigeria and Colombia than in Denmark, England and the United States.” Vedantam highlights the difference in the approach to treatment in India versus the United States, explaining that family and a sense of belonging is an element often missing for patients in the US who may be “homeless, in group homes or on their own, in psychiatric facilities or in jail”.

It’s interesting to ponder the differences in the nature of social ties between the suburban lifestyle like that of many people in the US and that in a small village. The tremendous physical mobility that we have due to transportation technologies enables us to have a huge number of contacts, while communications technologies like the internet allow us to maintain ties across wide geographical barriers. In a small village, on the other hand, people are restricted to a tight network of strong ties. My hypothesis is that people in smaller villages have considerably more strong ties than those in suburban communities in part because village dwellers don’t need to expend the time maintaining a network of weak ties. Suburban life offers greater anonymity, but at what social cost? In such an environment I think we need to work harder at developing strong relationships, as they might come less naturally.

The results of this study also remind us that not all answers lie in technology (in this case, drugs and medicine) and that the importance of furthering relationships with one another cannot be underestimated.

• Social Network’s Healing Power Is Borne Out in Poorer Nations

Link to Article

Presidential candidate Barack Obama has decided to launch a social networking site on his campaign site. This will essentially provide users and supporters with a facebook or myspace-like atmosphere. By creating a web 2.0 web site for his current supporters, these supporters will more than likely become a central hub for friends and family (of the supporters) to form a link with Obama.

This tactic will bring in additional support from those that may not have been as interested; allow supporters to converse, share ideas, and post events; and most importantly, create the link between Obama and each person that visits My.Obama. This tactic strictly relies on word of mouth; of course the first few visitors are avid supporters of Obama, but soon after, those supporters will communicate to their friends and families about the site, but the majority of the people they will recruit are the weak links they have formed because they share a common goal- getting Obama into office. This situation is much like the Paul Revere story from “The Tipping Point” where Revere knew who to contact- the weak links, but William Dawes had only a few strong ties who could not help him as much.

I think this move is very smart. I do not follow politics, but I do believe Obama is marketing himself very well to a younger generation. He has not only come off strong and confident by stating that the Chicago Bears will dominate the NO Saints in the NFC Championship game (has association with a team and opinions about sports), but he has also provided his supporters with an online atmosphere that almost harnesses democracy- letting people share their views and discussions (hopefully without moderation). His publicity has certainly caught my eye and I am more eager to learn about his campaign.

Shakespeare Social Networks
     As many of you have read Shakespear’s many plays and write ‘essays’ about them, you are probably familiar with the relationships among characters in his plays. For example, in Antony and Cleopatra, there is a strong relationship between Antony and Cleopatra (even you have not read this play, they are the main characters anyway!).
You can find the relationship graph about the characters on the web site linked above. The web site uses ‘PieSpy’ to detect “Social Networks in Shakespeare Plays.” (Information on PieSpy is on the web) As the stroy of the play goes, many possible relationship graphs appear because, for example, two characters, who did not know each before, can meet at some point.
     Although the society of Shakespeare’s palys are quite differnet from that of our gerneration, we can compare the change of relationship in the play with that of real society. For example, in the play, a relationship between close friends becomes hostile when they fall in love with the same person or when they are involved in a big ‘money’ or a large benefit. In this ‘tragic’ situation, the previous hostile relationship sometimes changes to the friendship for interests of each.
     Similarly, in our society, some people, organizations, companies, or even nations have different relationships in different periods of time. Recall the graph of six nations (Great Britain, France, Russia, Italy, Germany, and Austria-Hungary). Their complicated relationship had changed so many times because of each nation’s own benefits and interests. I believe this kind of ’simple’ tool helps analyze the change of relationships in our society.

Business Week Article The popular online social networking site, Facebook, as well as other social networking sites such as Friendster and Myspace are beginning to open up their Application Programming Interfaces (APIs), allowing third parties to essentially build their own applications using the content and function provided by these online social networks. The most popular of these sites among college students - Facebook - which has 17 million registered users according to the article, is anticipating a major boost in popularity and even more widespread use with the addition of third-party applications.

Throughout history, social networks have always been somewhat limited in their “connectedness” because it was really impossible to actually be in contact with all of your acquaintances at any given time. Landline telephones revolutionized social networks by allowing people to much more quickly establish (or, in the case of acquaintances, re-establish) contact with eachother than it was ever possible through the postal service. The public introduction of dial-up internet and later the takeoff of home broadband as well as cellular phone service further revolutionized social networks. In a sense, these technological innovations allowed social networks to not only be “more connected” but to expand, as it was much easier to keep logs of people you emailed or save a contact into a cell phone than it was to keep manual records of home phone calls or postal mail sent or received. Additionally, the internet and email allowed for more casual relationships to flourish - people who may not have taken the time to call one another or send cards through the mail to may feel more compelled to stay in touch when all it takes is the touch of a few buttons and very little time.

With online social networking sites, like Facebook, the limitation has always been that you have to be at your computer, on the actual networking site to stay in contact with all of your online “friends.” With the opening of Facebook’s and others’ APIs, and the eventual development of mobile interfaces for these online social networks, social networks should become even more connected and grow even further. People will, in essence, never be “disconnected” from their online friends. As such, casual acquaintances will be even easier to establish and, more importantly, to keep, leading to a more “connected” world than the one we currently live in.

            Earlier this semester, we discussed labeling the edges of a graph. A plus was given if the two nodes were friends, and a minus was given if the two nodes were enemies. This paradigm worked well in describing relationships among people. Not surprisingly, pluses and minuses can also be used to characterize relationships among firms. But one question does arise: how should you assign pluses and minuses to each edge? In “Competition, Mutualism, and Organizational Outcomes: The Effects of Domain Overlap and Non-Overlap on Organizational Performance, Growth, and Survival,” Heather Haveman and Lisa Keister describe a two dimensional relationship structure. This structure can be used in a firm network graph to determine the positive and negative edges. The first dimension describes how similar the firms are, and varies from commensalism (having a lot in common) to symbiosis (being dissimilar). The second dimension deals with relations between companies, and goes from competition to mutualism.[i]

            To see how Haveman and Keister’s classification can be used to determine the pluses and minuses, let’s consider a one industry example with a set of downstream firms and a set of upstream firms. Here, there might only be two types of relations: (1) relationships between firms that have a lot in common and are competing with each other, and (2) relationships between firms which are dissimilar but have mutual interests. The first category would describe companies at the same level of production, while the second would describe businesses at different stages.[ii] Pairs of nodes that fall into number one would get a minus, and pairs that fall into number two would get a plus.

            One thing missing from the diagram is the magnitude of competition and mutualism (i.e. the magnitude of the plus and minus). To address this, a term from Haveman and Keister’s overlap density measure can be placed along each edge[iii]. Let v and u be M x 1 vectors describing the types of sweaters firms v and u make. For example, if v makes a sweater in category 3, a 1 is placed in the third row. Dotting v and u and dividing by the magnitude of v will give you a measure of v’s competiton with u. The same can be done in the other direction with u. Mutualism could also be done in this way, except a 1 would be placed in a supplier’s row if it made supplies for that type of sweater.  

Unfortunately, I could not find an online version of the book chapter; however, I do have links to the authors’ websites:

http://www0.gsb.columbia.edu/whoswho/full.cfm?id=55582

http://www.soc.duke.edu/~lkeister/

[i] Haveman, Heather A., and Lisa A. Keister. “Competition, Mutualism, and Organizational Outcomes: The Effects of Domain Overlap and Non-overlap on Economic Performance, Growth, and Survival.” In The Sociology of the Economy, edited by Frank R. Dobbin.
New York: Russell Sage, 2004. Page 231.

[ii] Haveman and Keister. Page 232.

[iii] Haveman and Keister. Page 241.

Website: http://www.touchgraph.com/index.html

TouchGraph is a business that graphically represents a search term to other closely linked items of interest. For example, I searched for “The Matrix” and was presented with a web of movies related to my search. One such movie was “The Gladiator” which I clicked on and was able to expand my graph–or as the company calls it “visualization”–to include other movies closely linked to “The Gladiator.” See screenshot for a better idea.

Their work may be the underlying power behind “My Recommendation” on Yahoo and other search engine websites. TouchGraph writes: “Visualization goes beyond lists to reveal larger-scale patterns. Results are displayed in context–one can see how they fit into the big picture, how they relate to each other, and how they connect to metadata such as subjects and authors.” [1]. On the left side of the screen, the site has listed suggestions for other movies and books that may interest me based on my search.

While it is unclear how the company creates it’s visulalizaions, a simple visual web may be appealing to shoppers and companies alike.

References:

[1] TouchGraph’s Company Overview. (n.d.). Retrieved February 1, 2007, from http://www.touchgraph.com/company-overview.html

http://www.beyonddiscovery.org/content/view.article.asp?a=3681
The Federal Communications Commission (FCC) auctioned off a slice of electromagnetic spectrum for the use of wireless communication devices in July of 1994. What was estimated to be worth about $10 billion, turned out to garner $42 billion by early 2001 and there are still more licenses to be sold. What was successful about this auction was in its design and in the rules of the auctions. The FCC split the spectrum into thousands of licenses to be auctioned off piece by piece. There were many complexities, such as choosing the type of auction: open or sealed bid, the planners were careful to also avoid loopholes that the firms might exploit, such as conspiring to keep the prices low. To solve these issues, the FCC hired mathematical experts of game theory to figure out the best strategies in this competitive arrangement, predict how bidders would behave in different types of auctions.

Since October of 1993, the FCC has invited telecommunication firms to submit proposals on the structure of the auction, gathering preliminary reports and important papers of auction theory. Since there were over 2500 licenses to disperse, and many companies needed to buy more than one license at a time, the FCC decided to auction off the licenses altogether instead of one at a time, to prevent bidders from fearing the winner’s curse of paying too much; this decision made running the auction more complicated than selling each spectrum license one at a time. The FCC also decided to use an English auction, since it would raise the most revenue, but with a twist: the bids in each round of the auction would be placed secretly in enclosed booths, and the FCC would then announce the new high price without saying the bidder’s name. Concealing the bidder’s identity this way lessened the possibility of retaliatory bidding or conspiracies to keep prices down.

The outcome of this careful planning resulted in a great success that yielded in not only more money than anticipated, but it also sold licenses to the companies that would use the spectrum efficiently: within two years of the first auctions, wireless phones were seen in the market.

Careful planning and design based on game theory are often needed for high-price auctions such as this. Many auctions have tried to follow the FCC for similar success. While many were successful and yielded high prices for licenses, some have been exploited by bidders who’ve found loopholes in designs, such as the telecom licenses Turkey auctioned off in 2000 that resulted in a company monopoly, as well as the 10 blocks of spectrum Germany tried to sell in 1999, resulting in a collusion between two powerhouse bidders, Mannesman and T-Mobil. Auction theorists work hard to prevent such abuses from happening, mostly just through the simple idea of putting themselves into the minds of their opponents, the bidders.

This article discusses the designs of various telecommunication spectrum license auctions, in particular those by the FCC. It also talks about the basic idea and general history behind game theory, discussing many things we have learned in class, such as Nash equilibrium, zero-sum games, types of auctions, and the Winner’s Curse of paying too much. It’s a great article to see the application of game theory in real-life: not only applied to yield a specific result, but one that involves such large sums of money. Also, it emphasizes the basic concepts that we’ve learned in class, a great review for those that would like one.

The date was September 5, 2006, and Facebook, the enormously popular social networking website, unveiled its newest features: the “News Feed” and “Mini-Feed”. The first of the features appears on every Facebook member’s home page, showing their friends’ latest Facebook activities. The second feature, the Mini-Feed, is a module on each member’s profile page, displaying a log of his or her own Facebook activities.

Although members were allowed to manually delete items from their respective Mini-Feeds, the News Feed offered no such privacy controls. Whether or not a user wanted his friends to know about what he or did on Facebook, his actions were broadcast to the News Feeds of every person on their friends list. Many users found this “Facebook Facelift”, as it was popularly dubbed, “stalker-ish” and “creepy”. Even though these features did not allow users to access any more information than they had been able to before, many people thought that by compiling so much information in one place, Facebook had made stalking too easy.

The day of the release many Facebook groups arose in protest of the new features. The most successful of these groups popped up just hours after the release when Northwestern University junior, Ben Parr, created the group “Students against Facebook News Feed (Official Petition to Facebook)”. Within a few days, the group’s membership had proliferated to over 700,000 people, roughly 8% of Facebook’s immense user base. At its height, there were roughly 500 users joining the group every minute.

But how could this Facebook epidemic spread so quickly? The beauty of the situation lies in its irony – that the News Feed itself made such a pandemic possible.

But before I describe just how the News Feed catalyzed its own chorus of disapproval, let me explain the basic architecture of social networks on Facebook. Unlike in face-to-face relationships, Facebook does not differentiate between strong and weak ties. Virtually everyone a college student meets results in a Facebook “friendship”. For example, I have over 300 “Facebook-friends” even though I only actually consider about 10 of them to be my close friends. Accordingly, the vast majority (roughly 97% in my case) of “Facebook-friends” are weak ties.

In predominantly face-to-face relationships, people are more likely to share information through their strong ties. Since strong ties generally occur within densely connected groups of friends, this tendency makes it much less likely that the information will spread to many people. As illustrated by Granovetter’s paper, The Strength of Weak Ties, sharing information with weak ties increases the probability that it will spread quickly. However, since people are by nature less likely to share information through weak ties, this outbreak of information happens only infrequently.

What makes the News Feed such an effective way of sharing information is its equalization of strong ties and weak ties. The News Feed makes a Facebook user’s activities available to all of his “Facebook friends” regardless of tie strength. What’s more, since the News Feed is on every Facebook user’s homepage, the information it provides is virtually unavoidable. This information ranges from relationship status (e.g. “in a relationship with…”, “single”, etc.) to events that a user is attending to groups a user joins. This brings me back to my original point: how the News Feed perpetuated the virtual riot against itself. When users joined a group opposing the News Feed, the fact that they had joined would appear on all of their friends’ News Feeds, thereby spreading the news like wild fire. (In my case, the fact that I joined one of these groups, and therefore the knowledge of the group itself, was broadcast to hundreds of people.) As a result, people discovered the protest groups from their News Feeds and joined them, thereby disseminating them further at a mind-boggling pace.

Even today, now that the dissonance has died down, the News Feed still serves as an extremely potent avenue of spreading information. Late one night, for instance, a close friend of mine and a boy she had liked decided to enter into a formal relationship. Without telling anyone, she and her then boyfriend updated their Facebook relationship statuses so that the change would appear on the News Feeds of their “Facebook friends”. Within hours, almost everyone in their social networks (and well beyond) had learned the news.

The importance of this sort of information dissemination should not be overlooked. In a society obsessed with information, the News Feed is an extremely effective source for both gathering and propagating information. It gives rise to a vastly different way of examining how information spreads, and indicates that we still need a great deal of research to examine how such technological capabilities change the ways people interact with information and with each other.

http://www.time.com/time/nation/article/0,8599,1532225,00.html

http://www.time.com/time/nation/article/0,8599,1533289,00.html

We’ve looked at social networks extensively in the class, but mostly looked at isolated instances of social interactions within groups or organizations. Much interest lies in how social networks actually shape our society and the world around us. They Rule examines the elite who manage Fortune 500 companies and connects them to each other and the organizations they also sit on, such as the Council on Foreign Relations or the New York Stock Exchange. The site then, of course, tries to get you to take political advocacy after seeing the interconnectedness of big business with government and monetary institutions.

For our purposes, however, it is just interesting to use their elegant interface to explore the ties between people and find the degrees of separation, etc. The “load maps” feature shows a multitude of pre-drawn graphs showing connections such as the half of the J.P Morgan-Chase board dubbed the “Magnificent 7″ — for obvious reasons once you see the fan of connections going to everything from PepsiCo to Halliburton and everything in between. This is an example of social networking at its best: seeing how the connections between people could literally be used to control the business world and beyond.

They Rule - http://www.theyrule.net/

In “Social Networks in Silicon Valley,” a chapter of The Silicon Valley Edge: A Habitat for Innovation and Entrepreneurship, Emilio J. Castilla, Hokyo Hwang, and Mark and Ellen Granovetter profile the social networks and institutions that make Silicon Valley a unique social and economic model (http://www.stanford.edu/group/esrg/siliconvalley/docs/siliconvalleyedge.pdf). Their study is a project of the Stanford Valley Networks Analysis Project (http://www.stanford.edu/group/esrg/siliconvalley/papers.htm), which operates out of Stanford University, next to Silicon Valley, ground zero for the tech boom of the late 20^th century. The chapter attributes the area’s success to the structure of its person to person social networks and the impact of neighboring institutions on those networks.

The social networks of Silicon Valley are a huge topic, and I will only be able to cover a few of the chapter’s main points. To start, the tech firms witness a lot of “labor mobility,” or employees switching between firms and fields of employment (tech, venture capital, finance). Based on Granovetter’s study, which we read in class, the writers suggest that this mobility is dependent upon weak bonds between the techies (mostly engineers) who feel greater loyalty to one another and to the advancement of technology than to individual firms. Because the social network is dense (rich in weak and strong ties) and decentralized (people switch fields of interest), the flow of information and, therefore, innovation is sped up. Information that flows between finance, venture capital, and technology firms also encourages a culture of spin-offs. In fact, the writers refer to a “genealogy chart” of Silicon Valley that shows how most current firms are “descendents” of firm 0, the Schokley Semiconductor Labs, founded 1956. In a spin-off, leaders use their experience from previous firms, but are allowed more freedom to try out new ideas and innovations. In addition, the institutional connections to Stanford also encouraged innovation through the university’s industrial park, intellectual capital of students and professors, and affiliates. In so doing, Stanford became a sort-of shared focus for many of the Silicon Valley tycoons. Also, the writers cite the influence of venture capital firms, which hired many techies in addition to financiers with a result of increasing connecting edges between the network.

The Silicon Valley network proves how density of weak ties and high levels of betweenness (because of the flow between different types of firms) can improve flow of information, money, and innovation. In addition, by acting as a mock-focus, Stanford University enabled the area’s success. Venture capital firms reinforced these bonds. This chapter is a great read and makes the main ideas from lecture, Granovetter, and Gladwell come to life!