http://en.wikipedia.org/wiki/Utopia_%28online_game%29

Utopia, a highly addictive online multiplayer strategy game, is one of the most popular games on the internet. I used to be a huge fan, back in my secondary school days, and I was helplessly addicted — but it was a lot of fun. Utopia allows for the formation of social networks completely separate from the ones we experience in our everyday lives.

In Utopia, every player controls a province, and can choose a race and a personality, each of which will bestow unique features to his gameplay, both strengths and weaknesses. Provinces are grouped into kingdoms, and each kingdom functions as a collective unit. The player can explore land, build different buildings, train armies, and attack and conquer lands from other provinces. Kingdoms can wage wars and battles include both military campaigns and thievery and magic operations. Each ‘game’ last several months — an age — after which everything resets and everyone starts over. The more organized and successful kingdoms in Utopia depend greatly on teamwork and communication. Every province in the kingdom has a role, and co-ordination is essential for success during wars. There is also a forum within each kingdom that provinces can chat and interact.

Utopia is essentially a huge social network, just like the other multiplayer games World of Warcraft, Guild Wars etc. However, one key difference is that Utopia is real time, meaning actions in the game take time to complete, and the game is updated every hour. This makes it feel more like a real world, and the forum is a huge avenue for socializing and interaction. Utopia is very much like the social networks in real-life — in another world. Organized kingdoms tend to be very close-knit - players controlling the provinces tend to communicate outside of the game, coordinating attacks and operations and discussing war strategies. There are also alliances among kingdoms and a lot of politics played, especially among the top echelon. Since placement of provinces at the start are random, it encourages players meeting people from all over the world.

I was once part of a kingdom that was fairly active. There was a lot of interaction in the forum and I gradually found myself drawn into the game due to the social aspects of it. I mean, yes the game was fun and exciting, but playing it as a collective unit with people that you have gotten to know is the main draw. As a kingdom we each had a role to play in wars — we were each assigned a target, and we constantly communicated via MSN or ICQ, messenger programs. Utopia was a complete world on it’s own, and a social network completely separate from the one in real life. And having a forum meant that interaction between the different players extended to things outside of the game — debates about real-life issues, sharing of personal stories, and even discussions to meet up in real life, across the borders that separated us.

Many people feel that multiplayer games as such are ‘unhealthy’ as they are not real and just offer a form of escape for people from their real lives. I definitely beg to differ. Such social networks are very similar to those that we encounter in our everyday lives, just that they exist in different forms. You still see the usual triadic closures, and information cascades happening in the World of Utopia — meeting new people, spreading different strategies and playing styles. It’s just that all these occur in a world over the internet, with a different way of communication, and separate from the world that we know and are most comfortable in.

My friend told me a little about Network Marketing a few years ago and while I did do some research on it then, my gains were pretty limited. However, now that I’m taking a Networks class, I figured this might just be the right time to re-explore network marketing and see if concepts we had touched on in class could be applied here. While browsing through websites, I chanced upon this article: http://www.homeworking.com/library/mlm.htm which gives a little overview and insight on Network Marketing.

Network Marketing, or Multi-level Marketing (MLM) is basically an alternative method of distributing products to the public, to via wholesalers and retailers. These businesses are essentially a form of personal franchising, making use of networks of people to distribute goods to a huge group of customers, and at the same time introducing more people as business partners. Profit margins increase as distributors deal directly with the consumers, bypassing wholesalers and retailers, and as your network increases in size and you introduce more people to the business as fellow distributors, you stand to earn a bigger share of the profits. MLM makes use of one of the most powerful distribution tools available to all of us — the human networks that we have already built up in our everyday lives. MLM distributors introduce the product and the business to others in their social network, and those who end up joining the network reach out to individuals in their network. This makes for a very effective method of distribution and has the potential of growing into a very large network, much like the concept of the six degrees of freedom.

While it may seem that MLM is a great marketing and retail idea, many people have raised numerous concerns about it. Many claim that MLM is a scam as many companies concentrate too heavily on recruiting fellow distributors rather, while neglecting the retail half of the business. Since new distributors have to pay a fee of some sort, this allows them to make a quick buck, but the inherent backbone of the network — consumers — are sorely lacking and it makes the network unstable as no one actually uses the product. This is also known as ‘pyramid-building’, and is unfortunately one of the most common reasons why MLM companies fail, and why many have a bad impression of what MLM really is.

While I feel that MLM does seem like a fresh and possibly effective business idea, one main concern that I have is whether the network would quickly get saturated. MLM involves finding both consumers and distributors and it would mean that the number of distributors of the product is huge — would profit sharing ensure that there is decent money to be made by everyone?

It looks certain that MLM will continue to grow and more companies adopt this marketing strategy to reach out to a greater group of consumers. However, it is important that these companies take care to avoid going down the path of pyramid-building, as that would most certainly lead to failure.

After reading the notes on the power distribution, I read further into the topic by looking at one the of references. What fascinated me about it, was the practicality of it in the business world, obviously walmart would pick a sales distribution like the normal that would die off quickly rather than, a power distribution and limit their sales to certain threshold. My question is how would they pick this point and know when to limit it? Trial and error, some sort of advanced statistics? Why not buy it from someone who knows?

It would be very profitable for the companies that actually observe the entire demand distribution to sell their market data, even if overall it is much smaller, it is much more relevant as it provides data that other larger retailers can not possibly have access to. Instead of picking the threshold at which to stop providing goods and determining by actual sales if it is correct, why not use some else’s more complete data and save money. Netflix does not have much market collusion with Walmart, unless walmart expands a lot more online. It would be very profitable for them to sell such data, without any individual user information to a large retailer.

In addition, it probably is possible to use the pareto distribution for some minimum value, and actually fit data more practically for this application.

 What’s in a name? As the Internet continues to increase its presence in the lives of people around the world, a new Wall Street Journal article discusses just how important a role your name and its Google results can have.  For a while now, most savvy social-networkers have realized that ill-conceived content on sites like MySpace or Facebook could lead to unexpected consequences, and many others have discovered this inadvertently, to their own chagrin.  With over 80% of executive recruiters reportedly searching for the names of potential new hires online, and an estimated 7% of search-engine queries being names of people (no word on what fraction of these are celebrities or similar), it should come as no surprise that the search results returned by your name may not only say a lot about you, but could even influence your social status and career.  People with common, nondescript names can become faceless nobodies adrift in a crowd of other John Smiths, and fail to stand out to prospective employers or old friends. For those unlucky ones who have the misfortune to share their names with murderers, sexual predators, or other unsavory characters, the consequences can be even more disastrous.  As the article notes, some parents are even taking this into consideration when naming their children, discarding names that produce too many results on Google in favor of less common monikers, in a manner reminiscent of a world-wide anti-coordination game.  (While I have no problem with this per se, if I hear of one more child being named Krystynna or Rylee I’m going to push for federally mandated spelling classes for new parents.)

While the Wall Street Journal offers no predictions for the longevity of this trend, I think it will only continue to strengthen over the years.  Over the past decade or so, search engines have gone from primitive sites that produced laughably bad results to well-known cultural phenomena, and in recent years social networking sites have become important touchstones as well.  The Internet is now the primary source for researching other people, and the most ubiquitous method for using it is still name-based searching.  Unless that changes in the future - which seems unlikely - names will continue to retain their power, and everyone, parents included, would be well advised to keep that in mind.

Our class discussion on information cascades and pluralistic ignorance reminded me of a situation that occurred several months ago that exemplifies these issues.

Towards the beginning of Winter Break, a high school friend used Facebook to invite around 140 people to a party at a beach house. The invitees included the entirety of our high school class, along with 25 or so of the host’s friends, who belonged to other, separate, social networks and were generally not known to those in our high school class.

Facebook, along with Evite and other social event planning sites, has a very interesting feature for events, which is that, by default, the attendee list and RSVPs are public. All guests are listed in one of four categories: confirmed guests, maybe attending, not attending, and awaiting reply (the person hasn’t yet RSVPd). Because the party was to be held at a beach house about an hour’s drive north of the city, it was essential that guests decide in advance whether to attend. And because, as was said in class, when it comes to a party, political protest, or any other network decision-making problem, “If you don’t know how many people are going to show up, you’re not going to show up,” the guests at my friend’s party relied on Facebook’s RSVP data to see who else was planning on attending.

Normally, when deciding whether to attend an event, people ask only their own circle of friends whether they plan to attend. However, there is more than a strict information cascade at work here: people may also try to convince their friends to attend if they would like to attend, pressuring entire clusters of the network to come. However, Facebook, Evite, and similar sites break this pattern: in addition to the high/low signal from one’s close friends, one receives a signal from the entire network in the form of RSVP data. If few people have indicated that they plan to attend, then one may not even bother to talk to their friends and attempt to convince them to come. On the other hand, if many people RSVP that they will attend, a cascade of attendances could be created, and a large number of people will accept the invitation. Essentially, the public display of RSVP data makes the first few respondents crucial, because the responses of the entire network are visible to all invitees. If the first few responses are unfavorable to the other guests, a negative cascade will be created and few are likely to attend, or if the first few responses are favorable, will may attend.

So what happened to my friend’s party last December? Unfortunately, a negative cascade quickly developed. Initial responses generally fell into two groups: negative responses from those who would be out of town or unavailable, and a small number of positive responses, almost entirely from the host’s friends who belonged to separate social networks and were unknown to many of the guests. As a result, invitees viewing the event page saw a number of familiar names not attending, and a small number of unfamiliar names attending. Given this, most concluded that they were uninterested in the party, and avoided responding entirely: a negative information cascade. When I asked my own circle of friends on the day of the event about their plans, nearly all expressed ambivalence, in most cases wondering outright whether anyone we knew was coming. Many seemed to want to attend, but Facebook’s display of RSVPs led them to receive a negative signal and avoid attending. Relatively few people actually attended the party.

In this case, the additional information provided by Facebook and the public display of RSVP data actually resulted in less information being available. Rather, the RSVP data created a sort of pluralistic ignorance where attendees were afraid to commit publicly to attending or not attending, leading everyone to assume that no one was attending. This created a cascade of negative signals and therefore few attended. This lesson should be heeded by social networking sites and their users in deciding whether to publicly display RSVPs on the site.

My paper topic was to look at social phenomenon through the theoretical and visual tools established by nonlinear optical theory. If interested you should read the section of the paper that gives a brief introduction about optics. The thrust of it is simply that Social Systems can be considered complex nonlinear dynamical systems and so fall under a regime where it is possible to apply theory from other nonlinear systems to model and visualize it. One important concept in nonlinear system is that of the Soliton, a structure that can propagate indefinitely in time and space due to a balancing effect between dispersive and nonlinear, self-focusing effects.

Stable social structures such as cities and people groups can be thought of as a social Soliton. In my paper I only looked at the behavior of just one such structure. Now I will consider how two might interact to see if the analogy can indeed by extended to group interactions. Do they model a friendly get together, a war, or simple coexistence.

Soliton as the name may suggest are self-sufficient and solitary by nature. They are very happy being by themselves. The fundamental property of their collision is that they asymptotically preserve their shape. This means that when two solitons collide there may be distortion of one or both of the solitons, but with time those distortions will be ironed about and the solitons will return to its original shape. In a high resolution simulation of a collisions between two solitons, one larger than the other, the solitons were found to emerge with the larger one slight larger than before and the smaller one slight smaller than before and both carrying small perturbations on the trailing edge, [Lamb 1998].

These results have two important features: exchange of energy and residual perturbations. When two people groups interact, say in some form of competition, whether its war or a friendly game of soccer, we can expect that one wins, the other looses and both are shaken up a little. The energy exchange can come in the form of popularity, influence as well as money and booty, as in the case of war. We also should not be surprised that the larger soliton stole from the smaller soliton.

Yet both are shaken up, not simply the smaller one. Even the larger powerful countries or parties cannot avoid ripples in its structure it if wants to pick a fight. Yet it takes the smaller structure longer to iron itself out where as for the larger the ripples are nearly inconsequential.

Here I presented a brief exploration of social collisions through the formalism of soliton collisions. Two important conclusions are that in any such interaction there is a power exchange from weaker party to the stronger party, and residual oscillations for both parties which are ironed out with time. This model has a tunability such depending on the relative magnitude of the two solitons and the resulting energy exchange and residual, it can model both peaceful coexistence to all out war.

Kevin G. Lamb (1998)

Are Solitary Internal Waves Solitons?

Studies in Applied Mathematics 101 (3), 289–308.

doi:10.1111/1467-9590.00094

Although it’s about three years old at this point, a group of researchers at MIT and IBM have an interesting paper (pdf, Google HTML version) showing interesting visualizations of Wikipedia edits. The visualization they use is derived from the same data as an annotate command in CVS or Subversion would give: it displays which user contributed which line. They color each user’s contribution’s a different color, and shows how these lines appear in the order they are in the article over time.

One interesting part is the analysis of vandalism, which is certainly a central issue to any wiki, especially one that aims to be a general encyclopedia. In some of the visualizations, one can see “holes” in the graph; these are places where a vandal deleted all or most of the article. However, changing the horizontal axis from equally-spacing edits to an actual time scale makes these disappear. They did not survive long enough to even show up when the graph was viewed in this way.

Wikis are an inherently democratic system; things that survive are those that everybody looking at the page can agree on. In this way, we can see visually how the actions of editors affect other editors. One example of this, in a sort of information cascade, is that the text put down by the person who created the article tends to survive much better than text added later. They explain this by saying that the first person sets the tone of the article, so everyone editing the article after them does things in the same way. There is also a very interesting picture of a zig-zag pattern demonstrating an edit war.

On a related note, here is a time-lapse video of the first 12 hours of the article on the Virginia Tech shootings.

The Grand Unified Theory on the Economics of Free

Techdirt recently featured an article summing up the economics of goods when scarcity is removed. Specifically, the author recommends that content creators actively encourage dissemination of their content for free. The idea is that unhampered content distribution will allow your market share to expand greatly beyond what you would have had otherwise. Moreover, not attempting to do this is also risky — if someone else does this successfully, you lose much of your potential market.

This dilemma can be modeled from a game-theoretic perspective. Suppose you have two content creators A and B. They can each follow one of two strategies. Strategy X is to charge for their content. Strategy Y is to distribute their content for free. If A uses X and B chooses Y or vice-versa, the creator that chose strategy Y will have a huge competitive edge, and thus a much greater payoff than the other creator. If A and B both choose the same strategy, they are on equal footing in the sense that neither content creator has a competitive edge over the other, assuming the content quality itself is equal. Thus, A and B will have equal payoff P. But if A and B both choose X, P will be greater than if they had chose Y. The reason for this is that you cannot charge for content you distribute for free, and you no longer hold exclusive copyright on your work.

The Nash equilibrium of this game is the case where both A and B choose strategy Y, but this is not the socially optimal situation. (The social optimum is the status quo, where both A and B choose strategy X.) However, the Nash equilibrium does benefit the consumers, who enjoy more accessible content.

Color Popularity in Cars

http://www2.dupont.com/Automotive/en_US/news_events/article20061129.html

The above link is an article about DuPont’s 2006 Color Popularity Report, which is the automotive industry’s standard for analyzing and predicting consumer choices in vehicle color. This decade, silver won its spot as the most preferred vehicle color in the world after overtaking green in 2000 and remaining the top consumer choice of color for 7 years. According to the article, the entire world is somewhat synchronizing globally into some mixture of neutral colors with green, red and purple, with network effects driving this information cascade of global convergence.

Some minor background in color is needed to understand the article’s analysis of trends in color preference and why the authors think it is so. DuPont’s trend show last year in 2006 was called “Continental Chroma,” showing a growing emphasis on that aspect of coloration. The chroma of some coloration is a measure of its purity, so when the article cites “high-chroma” colors it means something like pure red, pure blue, etc. On the other hand, there are neutral or “achromatic” colors like silver or gray, which may look somewhat duller. The other terms used to describe coloration are hue (the classification of a color as a mapping to a specific set ~ you can have an “orange hue” for example) and value (the intensity of a color, which affects lightness (tint) and darkness (shade)).

The article describes the rest of the decade trend between 2007-2010 to be a broadening of the range of tones in silver and gray. What this means is that although silver, a neutral color, is the top global choice currently, there are signs of a shift toward cars with more high-chroma intonations even within a neutral color. A top contender to the long-popular silver is a gray with color hues fused in. This fusion is usually with high-chroma colors, and the popularity of mixing neutral with high-chroma colors is fast rising because the “boldness allows for smaller vehicles to make a strong statement.” There is also a general desire by consumers to personalize their vehicles. All these coloration ideas are being shot across the vast network of car consumers around the world. While in the past few years DuPont’s Color Popularity Report has shown (as usual and expected) cultural differences in color preference between different countries, this year in Japan and Korea, the neutral silver emerged as a dominant choice. In fact, most of the vehicle-driving world reports silver as the dominant color, with only European countries that primarily prefer black. This certainly suggests an inter-continental cascade of some sort, as even the Color Popularity Report itself cites that “cultural tastes merge, economies strive for competitive parity and information flows instantaneously.”

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1361135

The above link goes to a 2005 article titled “Testing for statistical discrimination in health care” that appeared in the journal Health Services Research.

The article describes a study by Balsa, McGuire, and Merdith that was motivated by a 2002 report by the Institute of Medicine. The Institute of Medicine report, titled “Unequal Treatment,” had described a discrepancy in between the healthcare provided for ethnic minorities and the healthcare provided for whites. Even after controlling for such “social factors” as socioeconomic status, insurance, and geography, there still is a significant discrepancy in the quality of healthcare provided to patients of different ethnicities. Thus, there appears to be a prevalence of racial discrimination by doctors during the “clinical encounter.” The Institute of Medicine noted three possible explanations for a doctor’s ethnic discrimination: 1.) straight-up racism, 2.) stereotypes about the “health-related behavior of minorities,” or 3.) statistical discrimination in diagnosis based on the prevalence of the illness within the racial group.

Balsa, McGuire, and Merdith (2005) attempt to determine which of the three alternative explanations best explains most cross-racial differences in healthcare. To do this, the authors looked at medical records from doctor visits of 11,000 patients. While the patients were of various ethnic backgrounds, all the doctors were white family physicians. Each record included the patient’s symptoms and the doctor’s diagnosis.

The researchers found that there were systematic differences in doctors’ diagnoses depending on the race of the patient. With regard to certain illnesses, differences in diagnosis were predicted by regression models that involve Bayesian statistics and either of two different forms of statistical discrimination. For racial differences in the diagnosis of hypertension and diabetes, a model that incorporates Bayesian priors about the prevalence of the illness among people of a particular race simulated doctors’ differences in diagnosis. For racial differences in the diagnosis of depression, a model that accounts for miscommunication (by just adding noise) between doctors and patients with different language and cultural backgrounds accounted for the discrepancies among diagnoses.

Differentiating between statistical discrimination and outright racism is important. In the case of racism, doctors are not acting the best interest of her patient. But in the case of statistical discrimination as described above, the doctors are “doing the best they can, given the information available.”