Response to the Virtual Class Essay

While I have worked on Google Docs for class projects and assignments in the past, yesterday’s exercise of a completely crowd-sourced, virtual essay was a first for me. Initially I was skeptical about how the process would work, and thought that it would either result in utter chaos or with individuals answering unrelated questions without any true collaboration. Despite being slightly overwhelming at times, I thought that overall the exercise was a positive one, and that contrary to what I thought would happen, there was a fair amount of virtual collaboration between individuals in crafting responses that naturally fit together. Additionally, at least in the A-K document, there was little interaction using Google Docs’ chat feature, and most of the edits seemed to occur somewhat organically. The overall exercise made me think of a virtual discussion based class, where a professor’s questions would be answered online and in real-time, with students able to comment on others’ posts and give other types of feedback remotely. Maybe this is the next step in eliminating the fear of speaking up in public that continues to be an issue for students. Another aspect of the crowd-sourced essay that I found interesting was the discussion that the A-K document group started below our responses. Those comments noted some similarities between the exercise and The Circle, namely the fact that everyone could observe and potentially monitor the other members’ level of participation. The discussion drew similarities between Google Docs and The Circle’s PartiRank program, but it also expanded further and highlighted the potential for other uses of individual’s data, much like how Eggers presents some of the innovations in the novel. Specifically, someone brought up the point that Google could analyze one’s writing style and sell it to colleges and hiring agencies, presumably to build a more comprehensive profile for an applicant. Although these comments were extraneous to the overall objective of the assignment, it was interesting to get some realtime feedback from other participants. While the group was very aware of the connections between the exercise and Eggers’ novel, such an experiment would be interesting to conduct with people who are unfamiliar with The Circle and some of its major social implications.

Personal Information: When Less is More

As I’ve continued to read The Circle by Dave Eggers, I’ve gotten progressively more freaked out by the idea of such an all-inclusive social network. Although this comes from last week’s reading, the scene where Mae is accosted by a superior for not activating her social circle had me sweating, as did her meeting with an offended employee.

This week’s reading (I think; I’ve overlapped somewhat) introduced LuvLuv, an extremely creepy search engine that allows users to (in the world of The Circle, of course), pinpoint another user’s preferences regarding dating. The common theme that these fictional technologies share seems to be the idea that all information about each other must be shared to ensure efficiency, and indeed, this is the company’s predominant slogan: “All that happens must be known.” Sure, in the novel, LuvLuv can only search for information already provided for by the user, but our intentions when posting statuses surely cannot be to have them meticulously combed. As Mae points out in the book, what’s wrong with simply asking someone in person? She, understandably, hates having her information sifted through, and states that perhaps it’s because she doesn’t want her personality to be boiled down to her presence on social media.

The catch-22 is that The Circle wants this to happen, for everyone, but is it truly possible? Can social media effectively convey who someone is through photos and text on a computer screen? Unfortunately for Mae, this is what her company requires, and later, her ex-boyfriend expresses his opinions on the subject, saying that social media only clouds legitimate relationships. I wholly believe this to be the case. Sure, perhaps these websites can offer a small glimpse of someone’s hobbies and interests, but that seems to be about it. This doesn’t even take into account that (thank God), we still have a choice, no matter how thinly veiled, in terms of what we decide to post. As a result, we only post what we want to be illuminated. I can’t imagine a world where everyone knew everything about me. Give me the shadows, because when I decide to let myself out, it will mean that much more.

Hypocrisy at the Circle

One of the most notable themes that can be identified between pages 102 and 205 of The Circle is hypocrisy.  As Mae continues incorporating herself more and more into the Circle and immersing herself in its culture, it seems that she loses a sense of who she is and what she stands for.  Her interactions with both Francis and Mercer highlight this development.

When LuvLuv was introduced at Dream Friday, Mae was horrified to find herself named publicly as the subject of Francis’ affection during a preview of the new service.  Mae was caught off guard and unprepared for the entire experience.  Ultimately, everyone in the audience was given a detailed look into her life, including her allergies, names of restaurants she frequents, rankings of her favorite foods, movie preferences, favorite locations, and more.  LuvLuv was able to take advantage of the data trail she had been unwittingly leaving for years and turn it into a search engine for people who were interested in knowing about her.  Mae, understandably, is furious and finds herself wondering why Francis couldn’t ask her himself what he wanted to know about her.

One day later, however, Mercer repeats this same question to Mae when he criticizes her work at and association with the Circle.  Mercer take issue with how Mae interacts and communicates with him, essentially unhappy with how she uses social networks to interact, prioritizing online engagement over personal engagement.  He has problems with how the Circle encourages people to participate online in what he understands to be a system that perpetuates untrue information by way of comments, posts and reviews on businesses.  Mae had believed one of the false reviews about his business that was nothing more than rumor, and he is frustrated that she became instantly angry with him instead of asking him personally to verify the claim.  As a reader its frustrating that Mae can’t see the ironic parallel between her own frustration and Mercer’s. She had been wishing for the exact same thing only a day ago, that Francis ask engage with her personally.  Yet when speaking with Mercer, she decries him as an underachiever for being unwilling to participate and buy into the necessity of an online presence.  If she really believed in the importance of this online presence then she would have no issue with LuvLuv.

Maybe its my natural tendency to distrust things I don’t completely understand (in this case the Circle’s incongruous image as an overlord yet extremely convenient and necessary for social life), but Mercer’s interaction with Mae was the first time during my reading that I wasn’t on Mae’s side.  He advocates for an offline lifestyle and refuses to buy into the Circle’s dominance over communication and interaction, which is refreshing after reading about Mae being blindly enthralled by the Circle’s capabilities and technologies.  She refuses to acknowledge the dangerous side of these technologies, even after she is upset about the LuvLuv incident.  Mercer, I think, provides a needed respite from the Circle and its culture, and at least proves that some characters are immune to it’s powerful influence.

Looking For Love In all the Wrong Places

In this week’s reading of The Circle, an app called LuvLuv is introduced. The app makes all of the data surrounding an individual available for the lover in question to use in order to make wise decisions on dates and ultimately, win over the individual.

I immediately compared this fictitious app to present dating sites including Eharmony and Match.com. I personally do not have much experience with the sites, but they do expedite the process of matching people with similar interests. The main difference between these sites and LuvLuv is that people can edit their profiles on today’s dating sites. This ability often leads to people altering some (Read: most) of their details in order to seem more attractive; in this sense forming an artificial data double. The classic example leads to profile pictures that are taken at a way earlier date or of a completely different person. Additionally, it is questionable whether a perfect match of interests leads to a happy couple (See Video). The saying, “Opposites attract obviously does not apply to dating sites.

LuvLuv ultimately cuts out this flaw entirely, by using truthful data out of people’s everyday lives, leading to a pretty accurate depiction of an individual. Although useful, I think there is another weakness in this system. By making this information known to the general public, it makes dating into a somewhat rigged game rather than real life. If I knew all of a person’s interests before meeting them, I could artificially make myself exactly like them. I could take her to her favorite restaurant, put on her favorite music, and ultimately connect with her using the power of information. The problem the person on that date would not be “me.” I would be just as fake as the 80 year old using an Eharmony profile picture from the 1970s.

Although both methods attempt to precipitate relationships, I think love is something that cannot be created with wires.

Incompatibility of Dataveillance and Relationships

Eggers highlights the complications arising from dataveillance in relationships, seen when Mae’s personal information is revealed to Francis through a search program called LuvLuv. This program utilizes an algorithm to search for Mae’s interests and dislikes, while “[analyzing] for relevance,” in order to create a virtual profile for suitors (Eggers 123). LuvLuv only turns up information that Mae “openly offered” or that was collected through her use of TruYou, highlighting her active role in producing the data LuvLuv aggregates. Understandably, Mae is uncomfortable with “having a matrix of preferences presented as [her] essence,” which she notes is not completely accurate (126).

This passage highlights the benefits of having a certain level of initial ignorance when in a relationship, as personal information must be learned through getting to personally know someone. Even though she provided this information publicly, it seems as though forgoing the process of becoming close with someone is unnatural. Moreover, simply collecting information about someone does not equate with truly getting to know that person, which requires time and trust. As Mae’s ex notes, Circle creates programs that “manufacture unnaturally extreme social needs,” highlighting how this technology is overstepping the bounds of normal dating behavior (134). Although one could argue that people use dating websites today, which essentially reflect LuvLuv’s goals, I would say that the active collection and creation of an online profile by the individual differs from an external source doing so for you.

How Computers Trawl a Sea of Data for Stock Picks

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An example from the Wall Street Journal of more data improving efficiency – in this case, allowing firms to more efficiently allocate resources in our economy to bring about the best growth. By leveraging increasingly large and diverse datasets, they can get a more accurate picture of the world’s needs and various risks. The equality of who gets the gains from these efficiencies can be a matter of debate, but more data are unequivocally more efficient than less data.

Two Sigma’s funds all take a big-data approach. Among its data sources are news bulletins, National Weather Service reports, market data, tweets and information from smartphone users who have agreed to be tracked by a retail-trend-analysis company.

Thirty years ago, it was easier to make investment picks because the world wasn’t as interconnected, Mr. Siegel says. “Here’s the problem: What affects the price of a share of Apple stock? The answer: Pretty much everything. Absolutely every little thing has some effect. Every sale, every earthquake.”

To comb through data 24 hours a day, the firm has more than 100 teraflops of power—more than 100 trillion calculations a second—and more than 11 petabytes of storage, the equivalent of five times the data stored in all U.S. academic libraries.

The Cost of Perfect Dataveillance

For most of the semester, I have typically argued that society’s constantly improving ability to monitor and record data about our daily actions would be beneficial, provided that the information would be more or less equally accessible to everyone.  For example, if car insurance companies had perfect information available to them to evaluate how risky drivers are to insure, they could naturally develop a socially optimal pricing structure (in addition to creating incentives for people to be safer drivers).  However, what I read from pages 101-205 in the Circle began to make me question that assumption.

If we view the Circle as a closed network in which everyone who works for the Circle has more or less perfect access to their electronic information about one another, we begin to see some of the potential problems that could arise in this society with perfect access to information.  One of the biggest problems that arises in this scenario is the fact that the can be large transaction costs associated with collecting the information.  If the opportunity cost of Mae filling out various social media profiles (the revenue she could have generated for the company if she had been working in Customer Experience for the time it took her to fill out the profile) exceeds the “Community Value” that The Circle gets from having this additional information, then obtaining this information was not efficient for the company.

In addition to this, The Circle also creates an adverse incentive for people to use social media to inefficient levels.  The Circle may value the data it obtains very highly, but if the data was created for the sake of creating data (for example, people trying to climb up the popularity rankings), there is no guarantee that the data is accurate and/or useful.  For example, when Mae is doing her best to race to the top of the popularity rankings, the book mentions that she commented 33 times on a single page.  From the context given, it seems fairly safe to assume that her comments were made solely for boosting her ranking, and not because they reflected her actual opinions.  Therefore, returning to the issues of transaction costs that perfect dataveillance could potentially incur, it seems as though The Circle would have been better off if Mae had been working in Customer Experience than commenting on that random page.  The value of these comments ties back in to our previous discussions about what types of communication are considered “good” communication.

One of the best examples of this in The Circle is the concept of “Zinging” someone.  When someone receives 10 “Great Job” Zings for completing a relatively menial task, it’s ultimately a waste of time from the people who sent the Zings.  This wasted time is ultimately the largest cost that was created when The Circle implemented the popularity rankings.

The common theme that ties the past three paragraphs together is that perfect dataveillance is inefficient, because the additional benefit of having access to 100% of information instead of 99.9% is relatively small, but the costs of obtaining that extra 0.1% is relatively high.  While this does not entirely refute the position that I initially believed, that perfect dataveillance is not detrimental to society provided that is it not applied asymmetrically, it definitely reinforces the fact that we should not attempt to achieve perfect dataveillance purely for the sake of perfect dataveillance.

He Talks the Talk but Does He Walk the Walk?

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Tuesday during the beginning of class Prof. Sample specifically mentioned his desire to move around the class more in order to keep our attention. The above is his movement for class on March 31st (Tuesday) during lecture time. As we can see Prof. Sample went on 2 long trips around the room, but most of his time was spent going between his main desk, his podium, and the 2 front whiteboards.

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Thursday we had a lot less lecture time, so as expected their is less movement from Prof. Sample during his lecture. Again we see 2 larger trips around the room, but this time without his podium he spent more time going between the two front whiteboards, and spent a little more time near the rear one.

Observations: Eye contact with Dr. Sample by table

Tuesday March 31

Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8
1:45 0 0 1 0 0 1 0 1
1:50 0 1 0 2 0 0 0 0
1:55 1 1 0 1 2 0 0 2
2:00 0 2 2 1 1 0 0 0
2:05 0 1 1 2 0 0 2 0
2:10 0 0 0 0 0 0 0 0
2:15 0 0 0 0 0 0 0 1
2:20 1 0 0 0 0 0 0 0
2:25 1 0 1 0 0 0 0 0
2:30 0 0 0 0 0 0 0 0
2:40 0 0 0 0 0 0 0 0
2:45 0 0 0 0 0 0 0 0
2:50 2 0 0 0 0 0 0 0
Total 5 5 5 6 3 1 2 4

 

Thursday April 1

Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8
1:45 0 2 0 0 0 0 0 0
1:50 0 0 0 0 0 0 0 0
1:55 2 0 3 0 3 1 0 0
2:00 1 1 0 2 1 0 0 0
2:05 0 0 0 0 0 1 0 0
2:10 1 0 1 0 0 0 2 1
2:15 0 0 0 0 0 0 1 1
2:20 0 1 0 0 0 0 1 0
2:25 3 2 2 1 1 2 3 4
2:30 1 1 2 0 2 0 0 0
2:40 0 0 0 0 0 0 0 0
2:45 0 0 0 0 0 0 0 0
2:50 0 0 0 0 0 0 0 0
Total 8 7 8 3 7 4 7 6

For my observations I separated the tables into groups and measured how many students at each table were making eye contact with Dr. Sample at 5 minute intervals. The Table Guide shows what number correlates to each table in the classroom. The purpose of these observations was to measure attentiveness to Dr. Sample and the lecture. I should have recorded more intervals if I wanted to get a true reading on which table pays the most attention to Dr. Sample because recording every five minutes does not provide enough data. In addition this is a flawed methodology because I am measuring attentiveness based on eye contact, so this method assumes that if you aren’t making eye contact than you are not paying attention to the lecture, which isn’t necessarily true. From the observations, I can’t reach any substantial conclusions on which table focused the most and least on the lectures of March 31 and April 1.

 

 

 

 

 

 

Are We Happy?

For my observations, I attempted to gauge the class happiness, by tracking and analyzing smiles. I chose to observe the class in 15 minute intervals, and count the number of people that were smiling during each of these checkpoints. I also noted what the class activity currently was, to see if I could find any trends in my data.

Smiles on Tuesday, March 31

1:40- 3 (Lecture)

1:55- 0 (Lecture)

2:10-1 (Lecture)

2:25-9 (group work)

2:40-0 (death discussion)

2:55- 4 (legos)

Total- 17

Smiles on Thursday, April 2

1:40-0 (Lecture)

1:55- 3 (Lecture)

2:10- 7 (group work)

2:25- 3 (Lecture)

2:40- 4 (group work)

2:55-2 (presentation)

Total- 19

After analyzing my data, it was apparent that there were slightly more smiles on Thursday. However, there were trends that existed in my observed data. First, it became clear that there were more recorded smiles during group work periods than lecture periods. Given that we have no assigned seats, most people sit with there friends. Group work gives students a chance to interact with there friends, leading to more smiles occurring. We had more extensive group work time on Thursday, so this could be a reason for more Thursday smiles.  Also, during a tuesday lecture, we spoke about the media mourning and death for about a twenty minute period. Given this somber subject, nobody smiled during this time interval, another reason that Tuesday smile counts were down. It is very evident that class activity and content affects smile count and potential happiness.