Energy Use Study Demonstrates Remarkable Power of Social Norms

An interesting extension of the “information cascade” effect. Researchers found that, given information about other people’s energy usage, people will likely adjust their own energy usage to match the “social norm” as presented to them. A study was done on a group in California to observe how their energy consumption behavior changed when given information about other people’s behaviors. The study showed that people whose intial usage was below the norm tend to increase their own usage with a “whew! I have some extra carbon to burn relative to everyone else” mindset and people whose energy usage was way above the norm tended to curb their own consumption. Because intial predictions vary so much, many of the people increased their own energy consumption when they found out that “most” people were using more than they were. In an information cascade, individuals make decisions about their own actions based on the prior actions of other people. In this particular case, all humanitarian and environmental tendencies aside, it was shown that people will generally adjust their own behaviors to reflect how they view people’s behaviors as a whole, often in a detrimental direction.

What is significant about this pattern is that it displaces many theories formed about American energy consumption. Obviously it has been a hot topic for several decades amongst the environmental and green activist groups, and everyone from lobbyists to preschool teachers have been trying to figure out ways to convince American people to be more conservationist. Here, we see that the phenomenon of information cascades may be a powerful tool in persuading American people to cut back on their SUV usage. If, someone, people were convinced that it is the “norm” to be using much less energy than they are, then they will lower their own usage, and so the norm would be lowered, and so on and so forth. Currently, it would not be surprising to find that energy usage is so high because people are made to believe that everyone else is driving Hummers and leaving their bathroom lights on.

http://wwwlisc.clermont.cemagref.fr/ImagesProject/FinalReport/literature_scientific_bacground/final_rep_1.3_fichiers/final_rep_1.3.pdf

In class we have been touching on the subject of network diffusion, and how it can result in innovation diffusion, with early adopters, etc. The linked paper expands upon those ideas. It takes into consideration several factors, including the risk of early adoption, how the media affects this diffusion, and the idea of “critical mass”. Critical mass is the ammount of people it would take before this innovation propogates to the rest of the population.

The authors use several different types of models to analyze this innovation diffusion. They begin with the classical approach. Where 5 key stages of network diffusion are indicated and discussed. The 5 stages are Awareness, Persuasion, Trial, Adoption, and Consolidation. For more indepth analysis and information about these stages please refer to the paper.

The network approach model, which is more relevant to the class, analyzes the position people play in the social network, and how their position affects this innovation diffusion. They identify “Opinion leadership.” These are the people who become influenced to adopt a product from the media, and use their leadership to spread it into their social networks.

The paper continues on about how centrality, and other sociological network analysis can shed light on who and how innovation diffusion occurs. The paper then critiques the limits of their models, and gives a very nice conclusion.

In a recent issue of Forbes, the meaning of networks was probed and articles were selected covering four different sectors: network breakthroughs, lifestyle, technology, and community. In the community division of networks, an article titled A Small Circle of Friends by Virginia Postrel discusses a particular type of network, the self-help group. The workings of a self-help group incorporate several ideas that have been covered in our networks class.

A self-help group can be a group for those seeking employment, those that have a similar illness, those with problems such as alcoholism. The general thread connecting all these self-help groups is that they have a unified objective. This type of choice of grouping comes from the idea of homophily, which means that people are more likely to have a social network link to others who have similarities. Similarities could be based on a number of different factors such as geography, occupation, religion, age, opinions, interests, health, and social status.

These groups face several questions that resemble those discussed in class: does requiring too many similar characteristics to determine groups seem to make everyone far from everyone else, and how broad should the characteristics be. Self-help networks need to think about how exclusive the groups can be to achieve its goals and at the same time sustain participation. Once groups are formed, they will exhibit behavior of forming clusters and bridges. The author mentions a “bliss point” where the group must be open enough for new ideas, but also tight enough to foster intimacy among members.

Some self help groups contain people considered as “hubs” with similar definition to a web hub, directing users to authorities as discussed in class. In the case of immigration, hubs are people who have strong connections to the motherland and can pick new migrants and refer them to jobs. The “hub” then receives a score based on how the employer likes the recommendation and therefore adds to the score of the entire network. This is equivalent to employers believing certain ethnic groups make better employees than others.

Not all self-help groups will grow stronger with time. Some will start to disintegrate, but what causes the difference? For some groups, once people have reached a goal, they may leave. Even in choosing when to disintegrate, groups follow the ideas of a cascade where after the first one or two people leave, the rest of the group become comfortable to leave as well. Other groups that don’t survive are those not selective enough, where new members don’t have similarities but join to learn something. This leads one to believe that grouping is more likely to be caused by people who seek out those like themselves, rather than acquiring characteristics of the group after joining for some time.

As social networking sites become more popular, new companies are trying to break away from the computer to create a more mobile, web-based network.  A recent article in the New York Times highlights two new websites that have brought social networking websites to users’ cellphones.   

http://www.nytimes.com/2007/04/30/technology/30social.html

The first site is called Kyte (http://www.kyte.tv/home/index.html).  It allows users to instantly upload videos and pictures from a cellphone to the internet and share them with anyone that has access to their Kyte Channel.  It is even possible to send live streaming videos to the site.  Additionally, anyone who is watching a channel can interact directly with the user through their cellphone.  While some websites have offered these features on mobile phones (uploading photos to facebook mobile, talking on AIM), this is the first time that users can communicate and share multimedia with the same service. 

The second site is called Twitter (http://twitter.com/).  Several blog posts on this site have already discussed the features of this site.  It is basically a website that allows people to text message their current status to a webpage. If one sets up an account, a list of their friend’s statuses will appear every time they log on. 

Mr. Dorsey of Twitter.com makes a very interesting point on page 2 of the article.  “We have a few business models in mind right now. But they’re not interesting until we have a massive number of users,” he said. “We are entirely focused on growth right now.”  Instead of using a business model that most websites have used in the past, Dorsey believes that a large user base is more important than focusing on advertising and marketing.  This has become very true for social networking sites because the only way that they will survive is if they have a large number of users.  Dorsey is essentially waiting for an information cascade to turn Twitter into the next facebook or myspace.

With more and more social networking websites popping up all over the internet, it has become very important to create new, innovative pages that will attract a large user base.  These two websites do just that.  They allow anyone with a cellphone to stay in touch with all of their friends at all times.  Additionally, they allow the user to break free from an office chair and explore the world while still feeding their social networking addiction.  However, some may consider this another step towards avoiding actual social interaction.  In the days of email, instant messaging, and facebook, it is already too easy to talk to someone for days without ever seeing them in person.  It is even possible that people will become less social by relying on small, grainy videos and away messages to substitute actual interaction.

With the recent, high-profile public discourse surrounding the potential for virulent outbreaks—be they from an outbreak of Avian Flu or an incident of bioterrorism—epidemiological modeling has been a vital tool used to assess the threat of an epidemic facing the nation. Epidemiological models often have two key components, a method of modeling the spread of an infection within a population and the spread of an infection spatially, over geographic distances, to different population centers.

Modeling the spread of an infection within a population usually relies on fairly simple, but robust, model involving the movement of individuals in and out of various subpopulations (susceptible individuals, infected individuals, and recovered individuals, etc.) dependent on various factors such as the virulence of the disease and the recovery rate/recovery time. However, modeling the spread of an infection over vast spatial dimensions presents a much more daunting task as it requires deriving a viable model of the human travel network and travel behavior.

The simplest model of virulent diffusion geographically is dependent solely on distance, in which areas closer to an infected region are more likely to also be infected than regions farther away. However, diffusive models based solely on geographic distance do not comprehensively capture the phenomena of modern transportation, particularly air travel, in which infected individual could potentially travel large distances in a short time. To correct this, one could introduce the factor of population into the diffusion equation, as transportation hubs tend to be located in areas of high population. Again though, this is still a generalization that may not provide the level of real-world fidelity necessary.

So how does one solve the problem of generating an empirically based model of human travel?

One could certainly attempt to compile a tremendous dataset of all air and train travel, using data from airlines and rail services. However, travel on the highways via personal vehicles is a far less well-documented phenomenon and certainly cannot be ignored.

Surprisingly, a group of German and American scientists turned to what could be considered an internet novelty in their search for data. Dirk Brockmann, et al., used data supplied by the creator of the website Wheresgeorge.com in order to general a diffusion model for human travel in the United States. The site allows users to enter the serial number of a dollar bill, along with the user’s location, and will present all the prior locations the bill was logged at. The team interpreted the trajectories of 464,670 dollar bills in 1,033,095 reports of bill locations as corresponding to human travel. The result of their work with the data provided by WheresGeorge was published in the article The scaling laws of human travel, which appeared in the journal Nature.

In class a large focus has been on links and how they increasingly make the world seem to be a smaller space. An issue that comes up because of this is the smaller seperation or space a node is able to take up the less privacy that node has. Privacy is also affected by the increasing forms and abilities of communication devices. One example is cell phones that have the capibility to capture both still images and video photage. Individuals then often abuse such a capibility by posting videos of others on websites or web networks that compromise what the other individual is actually comfortable with letting others know or see about them. Situations of photographs or videos emerging have been as popular as the American Idol scandal this year and as damaging as causing college students their athletic opportunities.

The specific incident I read about in this article had to deal with a college student who upon graduation was no longer given the collegiate degree they had spent time earning. The decision to deny the female student her degree in education and instead give her one in english came on the eve of graduation when a photograph came in to light that had the young women drinking from a plastic cup and that had the heading “drunken pirate”. The young women has now sued the university.

http://www.usatoday.com/tech/news/2007-04-29-myspace-school-suit_N.htm?csp=34

Over a month ago, a blog post written about customer reviews on carpets made it to the front page on Digg.com with 569 diggs. The blogger noticed on HomeDepot.com that three comments posted within the span of three months seemed to be very similar. He noted:

“I suppose it could be the same person who made all three comments. It’s not uncommon for people to move three times in the span of three months right? So she was in Saskatchewan, then hopped over to Toronto, decided that wasn’t for her and moved to Sudbury…And of course she only identified herself as “Sally” while living in Toronto.”

So why am I picking on this random blog post about some guy’s random observation? What struck me was that a blog post that had almost no conclusive evidence about the posting of fake reviews to increase the sale of rugs at HomeDepot.com, and the post made it to the front page of digg—which is no easy feat. I think the popularity of this ordinary rambling exemplifies our increasing skepticism of consumer of reviews on the internet and our emotional reaction to feeling cheated. I think that this increasing cynicism will change the signals in information cascades that affect consumer buying behavior.

With the internet, consumers now have access to large source of information to learn about products before they buy them. Some of the most useful and persuasive information include consumer reviews because of lack of connections between companies and more personal nature of writing. However, as companies learn of the power of consumer reviews as a tool to drive sales, they will be more tempted to seed fake reviews—especially in the beginning of a product lifecycle to induce an information cascade. The post brings up the question of whether we can trust consumer reviews and whether our skepticism of reviews will prevent an information cascade from happening. While we would normally find a good consumer review to be a buy signal in the cascade model, what happens if we believe it’s a fake and decide to disclose our speculation to the rest of the world as this blogger did? The emotional effect of feeling cheated might make us want to never buy from the retailer again. This new level skepticism will put companies in a very dangerous situation. Overall, I believe it can have a positive impact for consumers because companies will be discouraged to writing fake reviews. But since companies now know the way consumers analyze reviews, what prevents them from censoring reviews that are not palatable to us?

When reviewing the class blog, one can see that most of our news stories come from very few sources. Major examples being, Wired.com, Slashdot.org, CNN.com, and other major news sources. This means, that by the time I read an article online, and then go to check the blog to post about it, *someone* has already beaten me to it, by a matter of minutes. This is an increasingly frustrating phenomenon. Thus I figured, I would write about the small segment of news sources that all of our stories come from.

Thus what I am questioning would be does all critical news end up on these sites? How can one find unique information from all these sources, or rather, how can one compile the most information from the various sources. That is to say for example, reading an article on yahoo.com and then reading the news article on the same story from CNN.com, how similar are they and how would you rate one as more informative than another. While the entire premise of news and news articles are the opinions of the writer, there must be some way to differentiate which news stories are more “worthy” than others. So in the future, when I want to compile many news sources together, I can pick some sort of criteria (more factual than opinion, etc.) for the type of news stories that I so desire. My basis here is based on a different approach than RSS feeds, which just feed you all news. I am trying to suggest a more detailed system.

A random thought would be are we creating our own information cascade? I mean, I read the same news sources as many other students do, and pick an article that I find interesting either independently at first but then on the basis of not picking something that was already discussed on the blog. I can see the decisions of all previous people and make a decision based on that. Does that mean we’ll go through all the interesting news with enough students participating in this blog? We already have repetition in some ways of information.

An interesting look on news sources can be found on this website which shows the sources of the news stories from the Google News page. It also does a ranking and score based on a few factors such as prominence on the Google news page, number of appearances, and etc. to estimate referrer traffic to the source. I found this site on www.searchengineshowdown.com which is a search engine user guide. Thus this site already sort of implements a system of ranking that might be useful into integrating into some portal where I as a user could decide how I want to see my news. Do you think this is the way of the future of online news? Or will we be doomed with too many sources and not enough strategies to break it down into segments worth reading? ( Based on what you consider worth reading? )

In a recent Wired interview[i], Fred Vogelstein asked Google CEO Eric Schmidt about the current number and future scale of Google’s data centers, to which Schmidt responds, “I think my overall description would be in the dozens. There are a few very large ones, some of which have been leaked to the press. But in a year or two the very large ones will be the small ones because the growth rate is such that we keep building even larger ones, and that’s where a lot of the capital spending in the company is going.”On data center operations alone, Google spent $204 million in Q2 2006 compared with $181 million in Q1 2006. Projected by DataCenterKnowledge, Google’s data center investment is on track to exceed $800 million this year.[ii] 

In the 1960’s Stanley Milgram performed an experiment[iii] where subject individuals were given a letter and asked to send this letter to an individual unknown to the subject through a chain of personal acquaintances. Even though, the subjects had no knowledge of the destination besides a name and a general location, the letters on average took only six steps to reach the desired recipient.

Leveraging the massive amount of computational power and storage availability connected to the internet at any one time through the global user base, searching for information stored across this network could be accomplished efficiently and reliably using a relatively short number of steps from a querying user without impacting the centrally stored repository of internet data.

That is, save for exceptions in certain exceptional circumstances where information stored across the user base is not stored locally enough to the querying user, or the information is so irrelevant to 99.9% of searches such that spreading that data across the user base would be detrimental to the informational integrity of the data stored on each available user node.

In the centralized model, searching for the number 42, the answer to the universe, for example, produces approximately 1,700,000 hits. The question to answer is how best to store this relevancy and ranking information in a readily retrievable manner such that the data is received quickly and in a marginally correct ranking order within some error bounds.

[i] Vogelstein, Fred. “Text of Wired’s Interview with Google CEO Eric Schmidt.” April 9, 2007. http://www.wired.com/techbiz/people/news/2007/04/mag_schmidt_trans.

[ii] “Google Data Center Spending to Accelerate.” http://www.datacenterknowledge.com/archives/2006/Jul/21/google_data_center_spending_to_accelerate.html

[iii] Travers, Jeffrey and Milgram, Stanley. “An Experimental Study of the Small World Problem.” Harvard University, City University of New York.

http://arjournals.annualreviews.org/doi/full/10.1146/annurev.soc.27.1.415?cookieSet=1

This paper published by sociologists McPherson, Smith-Lovin, and Cook touches on a number of topics that have been discussed in class and, for me, brings the study of social networks around full circle. In their paper, these researchers discuss the existence of homophily in the following categories: race and ethnicity, age, religion, education, occupation, social status, and sex and gender. I’ve chosen to focus on the latter, sex and gender, seeing that it provides the most all-encompassing example social networks that have been discussed in class.

First, I’d like to give a brief definition of homophily according to the authors. “Homophily is the principle that a contact between similar people occurs at a higher rate than among dissimilar people.” Furthermore, the existence of homophily entails that any “social entity that depends to a substantial degree on networks for its transmission will tend to be localized in social space and will obey certain fundamental dynamics as it interacts with other social entities in an ecology of social forms.” In short, the notion of homophily explains why we seek companionship of those like us and also why we prefer the company of those who are similar. That being said, I’d like to discuss its application to sex and gender.

In the paper, the researchers discuss previous findings concerning studies of gender in young children’s social networks. The most interesting information I found was the fact that triadic closure was more likely to exist between boys but not girls, an issue we never considered in class. It was actually found that “girls are more likely to resolve intransitivity by deleting friendship choices, while boys are more likely to add them. For example, if A likes B and B likes C, a young boy would be more likely to add an A–C relation to resolve the intransitivity, while a young girl would be more likely to drop B as a friend.” This fundamental principle when applied later on in life, can actually explain a lot of the social activities that differ between males and females. For instance, this explains why boys form larger, more heterogeneous cliques while girls form smaller, more homogeneous groups. “This tendency is especially marked in the early grades and abates as adolescents move into the romantic ties of puberty.”

While reading this paper, I had several “aha” experiences. There were a number of issues and relationships that were typified in the paper that, only after reading about them, are seemingly obvious. It strikes me as odd yet intriguing that so many relationships can be explained so easily using homophily… I always thought our social ties were much more complicated.