An article from NewScientist that was published several years ago (May 2004) suggests that a small-world type of network arrangement may be present in the connections between neurons in the areas of the prefrontal cortex that are responsible for short-term memory. The article explains that recent research has suggested that these areas have bistable states of activation–they can be activated into two (or possibly more) distinct self-sustaining patterns of activation. Computational neuroscientists have been struggling to model how this sort of neural activity might occur, and many models of the phenomenon use extremely complex models involving complicated neurochemistry and lots of different types of neurons.

More recently, however, simpler models have emerged suggesting that short term memory could be achieved without so many complicated parameters. These newer models have architectures similar to some of the small-world networks we’ve discussed in class: each neuron connects not only to the neurons around it, but also connect to a few far-away neurons selected at random. These models have been able to exhibit the requisite bistable behavior for short-term memory without much trouble, and similar models lacking the random connections (non-small-world networks) cannot be made to exhibit the same behavior.

One study mentioned in the article found that when 10-20 percent of the neurons used their “short-cut” connections to random distant neurons, self-sustaining bursts of activity would occur. Furthermore, when the activity propagates back to its source, it has an inhibitory effect, and causes the pattern of activation to cease after a short period. This is the expected behavior of a bistable network: it stays in the necessary state just long enough to adequately represent a memory, and then can be quickly cleared away in order for a new to occur in the same place.

Whether such models are biologically plausible is still open for debate, but it is nonetheless interesting to consider applications of small-world networks in this sort of context.

http://faculty.haas.berkeley.edu/berk/papers/disc.pdf

Discrimination in the labor market has always been a hot topic. Characteristics such as race or sex which do not affect the ability of the employee are used in determining whether the applicant gets the job. Statistical discrimination occurs in many ways. In cases where a person applies to a job where they know they are being discriminated against, they apply because they think the profit of getting that job is worth it despite the amount of effort to apply. Being discriminated they have to be over qualified or more qualified than the rest of the applicants to have a chance at being hired. In other cases there is discrimination that favors the worse qualified group. Some of the reasons identified for the discrimination is in the employers ability to access the skill level of the applicant. For example the employer may be more familiar with working with men and is familiar with their abilities at the job so they end up discriminating against women that are applying because they are not familiar with the skill levels of women. This paper focused on two main factors for statistical discrimination in the labor market: (1) differences in the quality of the applicants and (2) differences in the employer’s ability to measure that quality. They found that the second factor is the main reason behind discrimination but favors the applicants where their quality is less known. There are many other interesting findings in this paper along with proofs for those more interested.

While MacBooks and iMacs have experienced drastic growth in the consumer market, their implementation in enterprises has yet to catch up. Most analysts believe that Microsoft will continue to dominate the market, however Roger Ehrenberg points out the many mistakes analysts have made in the past. His article criticizes Wall Street for not foreseeing the downfall of dominant companies. For example analysts used to believe GM, Kodak, and IBM were infallible, however over the past decade all three companies have weakened significantly. Ehrenberg believes Wall Street is making this same mistake with Microsoft, believing that Windows will remain the dominant workplace operating system. Ehrenberg believes that Macs are growing in both the consumer and enterprise market, and thus can eventually overtake Windows in both markets. Despite the debate, it’s worthwhile to note that Macs are experiencing much higher growth in the consumer market than in the enterprise world. As discussed in class, when viewing Macs a better technology dependent on the number of users, the cluster structure of the business world must be hindering Mac diffusion (since clusters are the only obstacles to diffusion cascades). A closer analysis and comparison of the consumer and enterprise markets reveal how network effects play a key role in the diffusion of Macs as a superior technology.

Clusters exist in both the consumer and enterprise markets, however enterprise clusters are much more dense and thus are more resistant to switching to Mac technology. We can imagine a perfect enterprise cluster where each computer is connected to every other one within the company, yielding a density of 1. The idea is that every work computer must be able to communicate with any other given computer in the same enterprise (through email, video chat, or whatever…). A consumer however may be tied to other friends or family members, but there is no definite cluster with a high density; any individual is part of many clusters with varying densities. Hypothetically, if Macs and Windows were completely incompatible, then this cluster analysis alone would explain the discrepancy: for any value of q, it is easier for consumers to switch given their lower cluster densities. However, Macs have become more compatible with PCs and as a result have lowered q. The result is more consumers purchasing Macs because a lower value of q means that it takes less of one’s surrounding computers to convince a consumer to switch. However, it is interesting that given this model, a decreased q need not impact the enterprise market if q is too high to allow Macs to break into any given cluster.

This Article mentions how Apple’s consumer products are already enterprise worthy, outlining the link between consumer and worker diffusion. The consumer and enterprise networks mentioned above overlap: all nodes in the work force are also consumers, who also use computers at home. Macs are growing more popular at home due to Intel chips, better virtualization and compatibility with Windows, and lower price points. As a result, IT Managers and CEOs are realizing the value of Macs in the workplace. The cascading of Macs in the consumer market causes initial users to emerge independently in enterprises, bringing their emerging love for Macs into individual clusters/corporations in the enterprise world. For instance each company may have 5-10 employees who recently switched to Macs because of the consumer network, and then petition their bosses to use Macs instead. In this way the enterprise switch to Macs will not occur as a cascade in the enterprise network, but rather as a displacement from the consumer network. The diffusion in the enterprise network will occur as many parallel diffusions stemming in each company as a result of a few initial users (from the consumer market).

Macs are designed to please the user and provide an outstanding computing experience regardless if one is at home or at work. As a result more and more people are adapting to Macs, and wanting to bring this technology from the home world into the work world. So as Macs become more and more popular amongst consumers, we can expect consumer preferences to be mirrored in the enterprise market. Despite Wall Street’s analysis of corporations’ IT structures and past histories, no amount of prediction and calculation can account for people’s sheer love and enthusiasm of the Mac.

In this blog the author Scott Sanders discusses how the rapid advancement of new technologies is allowing a new type of collective swarming to occur. In his post Sanders argues that newer communication tools such as blogs, wiki, and mobile phones have allowed individuals to reorder their lives and coordinate actions in a previously impossible manner. According to the post, this availability of many-to-many forms of communication creates a necessary condition for the production of smart mobs, mobile ad hoc social networks, which are the result of individuals using personal communication technologies to coordinate collective action. Smart mobs seem to share at least some characteristics with the emergent behavior of swarm systems. However, there is one important difference between characteristic swarming behavior and that of smart mobs; individual human beings have considerable intellect. While there are several documented examples of smart mobs displaying emergent behavior, it is most important to consider the cognitive mechanisms and the network structures that create the prerequisite conditions for smart mob formation.

Sanders theorizes that the diffusion of mobile phones and SMS (text-messaging) services has been a significant factor in the development of smart mobs. His reasoning is that first, SMS is an asynchronous communication which allows its users to exert more cognitive effort on expressing and editing thoughts. Second, technological constraints force messages to be brief and to the point allowing them to be created and sent quickly. SMS technology allows brief written messages of a maximum of 160 characters to be sent and received via mobile phones. Furthermore, messages can be sent or forwarded to everyone in an individual’s address book. This is allows a message to be disseminated more quickly than a traditional phone tree where each individual would have to be contacted separately, thereby allowing large networks to be mobilized relatively quickly. Finally, SMS messages are sent to mobile phones rather than email inboxes or hard lines.

Threshold models of collective action is the explanation Sander’s uses as to why individuals would participate in smart mobs in the first place. The argument hinges upon individuals conducting a cost-benefit analysis which weighs the rewards of engaging in the behavior against the possible repercussions. The more people that choose to participate in particular actions the less likely an individual will be held accountable for their behavior. Some individuals require very few individuals to participate prior to joining in, while others may wait for a majority of the population to engage in a behavior before to taking action. So these collective actons would appear to have a cascading effect, but Sander’s post goes on to suggest that socially identifying with other members of the smart mob aids in its success. While it is true that in standard swarms with thresholds it is not necessary for the nodes to share social characteristics, in smart mobs it seems to be a defining characteristic. The participants in smart mobs usually do not know each other directly, so they connect with each other by forming a group identity that they associate with and use as the reason for participation.

Both proximity and homophily may provide partial explanations of how smart mobs form. If mobile phone address books are merely subsets of our social networks, then small world network structures that take into account proximity suggests that a bulk of message recipients will be geographically proximal. This is important because smart mobs gather relatively quickly. Proximal individuals would be able to reasonably travel to the appointed destination without undue effort. Furthermore, this structure also suggests that individuals who would be constrained from participating in smart mobs by geographical distance would be less likely to receive the initiating message. Of course, proximities constraints on smart mob participation likely play little role in online smart mobs. Homophily may also play an important role in the spread of the initial smart mob message. If individuals are more likely to form social linkages with others that they perceive to be similar to themselves then individuals whose social identity is activated by a message are more likely to pass along that message to others than those who do not share that social identity. Not only would homophily facilitate the dissemination of messages but it might also act as a filtering mechanism to prevent people to whom the message would be irrelevant from receiving it. People who do not identify with the message may be less likely to pass the message on to others. As a result, the message is spread among those for whom it activates a social identity, and disregarded by those for whom it does not.

Smart mobs have been used purposefully to accomplish tasks impossible for a single hierarchical organization. Many of the same conditions that allow smart mobs to form also create conditions in which they can display emergent behavior. First, the nature of many-to-many communication mediums means that leadership is decentralized. This means that disabling one node in the network will not cripple it. A notable example of this is the Direct Action Network which used mobile communication devices to coordinate protest of the World Trade Organization in Seattle. Arresting “ring leaders” did not slow the attacks or seriously hamper coordination of efforts. Second, human beings are autonomous and make the choice to submerge their personal identity in favor of a social one. Furthermore, as individuals they have street level data and do not have an overall picture of the scenario. The conciseness of text messages mean that complex strategies cannot be laid out in detail but must evolve as events unfold. Finally, the high connectivity provided by SMS technology allows individuals to coordinate action by converging on a target from many directions and then dispersing just as quickly. This phenomenon has been labeled “swarming” in the contexts of political protests but can also be observed in contexts as diverse as celebrity watching or article editing on wikipedia. As a result of the role peer influence plays in a smart mob, the network structure of a smart mob, and the high connectivity that characterizes it, smart mobs can be highly adaptive and unpredictable.

Try logging on to New York Times. you may realize that there are not any links to other news articles other than articles that originate from New York Times. Try another news source, such as CNN. Again, you won’t be able to find an article that links to New York Times or other News sources. People subscribe to different sources for News, but as these news sources are owned by a group of people who share the same interest, it is highly likely that the news articles covering the same story will be biased in different ways. For the common reader, the best way to attain the accurate information is to get a collection of articles from various sources. However, as I’ve explained above, it is difficult to come up with a big list of news providers off the top of your head.

That’s where search engines come in. Search engines normally have the news search function. For google, it’s http://news.google.com, and for Yahoo! it’s http://news.search.yahoo.com/news. Let’s search for anything arbitrary, such as cookie. Yahoo! gives us articles about cookie starting from The Philadelphia Inquirer, CBS News to The New Orleans Times and RTE News. you can even get international articles: search for Networks in Google News: You get articles from Atomic, Australia and Biology News Net from Canada.

Search engines can be utilized as a great hub for hypersearching news articles. Since it points to many different sources of news, the hub score for search engines will be extremely high. However, the authority score will be very low compared to the hub score since none of the News sources tend to link back to Google News or Yahoo! News. The usage of multiple search engines to get a collaborative picture of an event will be most certainly helpful in getting the most accurate summary of events.

I am extremely interested in ways that we can represent online social networks that are both informative and visually pleasing. I am currently writing an annotated bibliography of social visualization research as a reference for my own future research. Visualizations of online social activitiy is important for business, researchers, and technology experts. It helps to spot trends in networks and where trends are coming from next. I believe that as the web becomes ever present in our lives, we must continue to develop innovative ways to visualize how the web is affecting our lives.

Judith Donath is “an Associate Professor at the MIT Media Lab, where she directs the Sociable Media research group. Her work focuses on the social side of computing, synthesizing knowledge from fields such as graphic design, urban studies and cognitive science to build innovative interfaces for online communities and virtual identities” (http://smg.media.mit.edu/people/Judith/Bio.html).

Annotated Bibliography of Selected Work in Online Social Visualizations

Visualizing Conversations

Judith Donath, Karrie Karahalios, and Fernanda Viegas

Proceedings of HICSS-32, Maui, HI, January 5-8, 1999. (Also in .pdf)

Abstract

Although the archive of text generated by a persistent conversation (i.e. newsgroup, mailing list, recorded chat, etc.) is searchable, it is not very expressive of the underlying social patterns. In this paper we will discuss the design of graphical interfaces that reveal the social structure of the conversation by visualizing patterns such as bursts of activity, the arrival of new members, or the evolution of conversational topics. Our focus is on two projects: Chat Circles, a graphical interface for synchronous conversation and Loom, a visualization of threaded discussion. Through these examples we will explore key issues in the generation, design and use of graphical interfaces for persistent conversations.

My Comments:

I have always thought we need more innovative ways to visualize chat networks. When I switched to Mac OSX, I was welcomed by Ichat’s “Speach bubble” style instant messenger client. Why arnt there faster, easier, ways to express emotions accross chat networks? Further, how can we create visualizations of chat networks over merely making a standard node and edge graph? THese type of technologies will take web chatting from a web 1.0 concept to a web 2.0 concept.

Visual Who

J. Donath

Proceedings of ACM Multimedia ‘95 Nov 5-9, San Francisco, CA. (Also available in postscript)

Abstract

Visual Who is a tool for visualizing an electronic community. Using data such as mailing list subscriptions, it creates a spring based model of the patterns of affiliation within the community. By varying the groups chosen as anchor points, the user can interactively explore the underlying social and organizational structure. The resulting image of the community can be used as the basis for visualizing other data, such as the patterns of activity found in the record of login and idle times.

The goal of Visual Who is to make the social patterns of an electronic community visible. This paper discusses Visual Who within the context both of social interface design and of data visualization.

My Comments:

As mentioned above I am very interested in new ways of visualizing social networks. I am particularly interested in creating a model like this or like the pop sketch project ( http://structuredsound.net/popsketchseries/index.html) . I think it would help us enjoy visual complexity and learn how to translate visual data into meaningfulness.

http://www.slate.com/id/2095993/

The article entitled, “The Kerry Cascade,” explains how the unpopular John Kerry was able to win the Democratic presidential nomination.  However, this article takes a psychological approach at analyzing what occurred in the 2004 presidential nomination.  Psychologist Solomon Asch performed an experiment on conformity. This experiment had a group of people stare at a set of 4 lines, 2 of which were clearly the same length, while the other 2 varied greatly. The subjects were all instructed, excepting 1, to give the same incorrect answer. When the uninstructed subject was reached, s/he gave the same incorrect answer 1/3 of the time.  This social decision making experiment shows how people will conform to fit in and not be an outcast. This was used to explain how once Kerry took 1 states, the next state would obcerve the choice and the people would think that the other state knew something they did not.  This can work with politics because many people are able to vote but are not as informed as they should be, so they rely on others to persuade them.

This idea of conformity can in a way be perceived as a cascade.  One state at a time Kerry was able to form a voting cascade.  This mirrors the restaurant example, in that at first informed individuals vote.  After this, the next person makes a decision based on their signals. Now once an entire state has gone to Kerry, it seems pretty obvious that a majority of people in some area know something and other people should ignore their own signals.  The next factor is that not only do individuals form a cascade, but then states begin to cascade.  The cascade may be interrupted once in hits a predominantly red state, or a state that has such strong signals that it will not vote for Kerry no matter what.  Obviously not everyone is politically uninformed, and cascades within a state may lead to other outcomes.  But depending on which candidate a state decides on first, a few extra states may be swung over to agree than would have originally; once that happens, a few more might be captured, and then a few more…

Recently we’ve been talking about the cascades of information and influence. Communications networks quickly come to mind. We think of mobile phone services and chat clients. Users want to choose the service that allows them to talk to the most people with the least cost. With cell phone service providers create incentives for prospective customers by giving free calls to people in the network. It seems like a great sacrifice on the provider’s part to do this (they’re loosing out on all the money they could charge for those calls) but it really is the market share that matters.

Last year MSN and Yahoo officially connected their chat networks, allowing MSN user to talk to Yahoo users right from their MSN account and vice versa for Yahoo users. Why? Some reasons are obvious. Yahoo and MSN can be considered the underdog when it comes to the chat market. AOL, older, weightier, has a larger user base. AOL dominates ~50 of the market while Yahoo and MSN put together take up the other ~50 percent. Microsoft and Yahoo are by no means small fish in the information industry, but even large corporations such as these need humble themselves when they are playing catch up.

Let look at some of the possible outcomes from a network theory point of view, namely what can Microsoft and Yahoo hope to gain when they considered the new network bridges they’ve formed.

Unlike fax machine or cell phones, chat is an inherently free service (providers make money using advertisements). In class we looked at an economy without networks effects, where cost and demand are the only factors. There we had a price-# users curve that was straight and where users increased with decreasing price. Then we looked at the introduction of network effects where in fact people would be willing to much more than their reservation price in the non-networks-effects price because many other people were also using the same service. With chat we cannot speak of price in that same sense.

From an intuitive point of view it seems that the more people there are using a chat service the more likely someone will switch or adopt that and this would grow without bound: there is no price and payoff increase as more and more people join the same network. This is our simple model. So it makes sense then that Yahoo and MSN are teaming up. They want to beat out AOL. Right? Yes of course. On a macro level, our simple model would say that the eventual equilibrium is that AOL will one day have 100% of the market and everyone else will be drive out. There are a lot of mitigating circumstances that push this the other way, but the larger that gap between AIM’s market share and the nearest competitor the more likely and quickly the market will move in that direction. So by joining their networks, MSN and Yahoo nearly wipe out this macro level effect. Good Move!

But analysts say there is another concern that Yahoo and MSN are trying to address, and that is the emergence of small but quickly growing networks such as Skype G-talk. These smaller, somewhat more intimate, networks are surprisingly robust in the face of the heavy-weight chat clients. Here we consider the micro effect that most people only talk to a small group of people anyway. As long as all of my friends are one the same network, I have no incentive to leave and join the bigger network. G-talk especially takes advantage of the fact that people are already using G-mail to email those who we are likely to talk to in a chat environment. Why do Yahoo and Microsoft want to drive out these smaller networks? I think it’s because these small networks will more likely eat out of Yahoo and Microsoft’s market share than out of AIM’s. Since AIM is old, traditional client, people who try MSN or Yahoo tend to be more willing to try new things, and if they’re going try MSN or Yahoo, they’ll probably give these smaller networks a try too.

So what is the future chat? Google will take over the world…

http://www.technewsworld.com/story/54380.html

            In his article, “Information Asymmetry: Shattered by Technology”, Theodore F. di Stefano gives some general information about how you, as a buyer, can avoid getting ripped off by instances of asymmetry of information. In terms of the auto market, healthcare, trading stocks and getting a mortgage, his central argument is that the best way to correct this asymmetry of information do this is to do research by the internet. For purchasing a car, he says that certain websites, such as vehix.com and edmunds.com, provide absolutely all the information that one could need about any type of car that you could want. These sites can be used to find information about both the new car and the used car market. Next, in relation to healthcare, doing some research beforehand on the internet can help make you a more informed patient and can help you and your doctor come to a rational and safe solution to your health problems. Mr. di Stefano brought in the results of a study concerning a condition that he has, a study that his doctor had read, and so the two were able to work out a good treatment for Mr. di Stefano based on this study. Third, there is an excellent example of information asymmetry in terms of the stock market. If you have money in the stock market, a stock broker will sometimes tell you to keep your money in the market when it would actually be wiser to take it out. The broker might also lie about the quality of certain stocks, telling you to invest in some companies that just aren’t worth your money. Doing research online about stocks is easy and can help make you much more informed when you talk to your stock broker, and can prevent you from losing your money. In Mr. di Stefano’s last example about mortgages, he says that banks often try to take advantage of mortgage purchasers. He gives a description of a site called lendingtree.com offers the mortgage prices of various competing banks to the purchaser so that the purchaser can make a more informed decision.

In class, we discussed information asymmetry in relation to the used car market. In this example, the buyers’ personal values for purchasing a good car versus a lemon in relation to the sellers’ minimum prices that they would be willing to sell their car would determine whether the car was sold. The problem was that the buyers would not know whether the car was a lemon or not but the sellers would, leading to an asymmetry of information. With the boom of the internet in the past decade, anybody can find the information that they are looking for in just a few clicks. This means that the opportunities to have the upper hand in a transaction because of information asymmetry are diminished because the buyer has a lot more available information. In addition, the “sellers” know that more information is available, so this forces them to play more honestly, which is good for the consumer as well. Of course, none of this will mean anything if you are not willing to do a little bit of research beforehand. If you don’t, you could be on the short end of such an asymmetry which could cost you a lot of money later.

http://users2.wsj.com/lmda/do/checkLogin?mg=evo-wsj&url=http%3A%2F%2Fonline.wsj.com%2Farticle%2FSB110244506243393408.html

Carl Bialik’s article on billionaire Mark Cuban’s idea for a hedge fund related to point spreads is very interesting in terms of arbitrage and online gambling. Just recently, the US outlawed online gambling, forcing many poker and betting sites to close. As someone who has been to Las Vegas and made many bets, I never tried to “hedge” my bets by looking for small discrepancies from sports book to sports book. Now with the internet, Cuban can just click on the countless of sports gambling websites to find any bets and then decide what he wants to bet on. Unlike regular sports bettors, Cuban will just bet lots on games where he finds two points spreads for the same game that have different numbers. He would bet on the two different teams and hope for the games actual outcome to be between the two spreads so he wins both bets, but if it does not, then he wins one bet and looses one. While he wouldn’t break even because of commission’s fees, I think this idea is a good one. If you have billions of dollars to spear, then this type of strategy can net someone a lot of money. While you would take small losses(commission) all the time from bets that break even, hitting one bet would bank a lot of money. The main problem for small time internet users though is that they can not bet, because they don’t have offshore bank accounts like Cuban.

So the question is it fair that rich people can use the internet differently then the average US citizen? I believe there should be laws outlawing people like Cuban to still be able to bet on sports. While social network sites like Facebook and MySpace make millions, the government is taking away the common citizens chance to make money. Unless the US government brings back online gambling for everyone, then people like Cuban should not be exempt from the law.