I found his really interesting article on the evolution of decision analysis and how the Bayes’ Theorem played a major role in this. The article From “Economic Man” to Behavioral Economics explains how the experiment of pulling red or blue marbles from a bag and determining which color is more dominant in the bag actually proves that humans are conservative in their calculations. By this I mean that the Bayes’ Theorem says that given what was pulled from the bag humans say the probability of dominance is about 20% less than what the Bayes’ Theorem would suggest. Now given this, a lot of research has gone into why humans make such calculations and apply these calculations to decisions. A new field of Economics developed from these studies and this field is now called Behavioral Economics. This field takes into account the differences between pure statistics , the Bayes’ Theorem, and a human’s typical behavior. This typical behavior was deemed to be “irrational behavior” because it went against what statistics would typically recommend, and economists wanted to know why this occurred. The article explained that this “irrational behavior” occurred because humans either use a “rule of thumb” or a method that they previously learned that worked well. The article then went on to explain that true experience can cause humans to make decisions in ways that are more representative of the statistical evidence. But the caveat is that this experience comes from thousands of hours of repetition. This entire article is interesting in that it shows how the Bayes’ Theorem has a flaw when predicting the outcome of a normal humans decisions. But the Bayes’ Theorem does well with predicting the decision of an expert. The article had a lot more interesting information on how the field of Behavioral Economics was created, but how relevant the Bayes’ Theorem is is what was most interesting to me. Especially considering all we have learned about it and its application recently.
Hello class this is my blog post for an article on Six Degrees of Separation, an experiment through Facebook.
As discussed in class, six degrees or, “small-world phenomenon”, of separation is the idea by connecting with some sort of entity, whether its a business, person, town, etc., in six steps or under, without directly contacting that same entity, one must use their networks to successfully complete this experiment. In an article titled “Facebook Shrinks ‘Six Degrees Of Separation’ To Just 4.74” by the Huffington Post, the article states that the University of Milan, has “shrunk” this theory through the use of Facebook and by utilizing the social network within Facebook. The article states that “Ninety-two percent of individuals on Facebook are just “four degrees” from one another, while 99.6 percent are separated by 5 degrees”. The reason why the number is significantly smaller than six degrees is because it is much easier to connect with individuals on Facebook because of globalization. In a hyperlink in the article, it takes you to the Cornell University Library which they give additional information on the steps that they took in order to complete this test.
The idea of six degrees of separation is to find the most efficient and effective path to the destination that you are trying to get to. This test was matched perfectly on Facebook, which Cornell’s library states that it was the biggest social network ever analyzed,in that the more friends you have, the more likely you are to have more opportunities to find an efficient path that is less than six degrees. I thought this article was interesting because I was surprised to see that although I feel that getting to someone in a different country by six degrees was pushing the limitation, this test did it in only 4.74 degrees which goes to show how small the world is really getting.
Here are the links to the article and the hyperlink to Cornell’s Library:
In Bloomberg.com’s article ‘As the Rich get Richer, Unions are poised for a Comeback’, the ‘rich get richer’ phenomena is looked at from the perspective of those who are oppressed by unequal distribution of wealth (workers). The rich get richer phenomena is a result of power law where the “number or frequency of an object varies as a power of some attribute of that object”, to quote Murat Yuksel; in this case (wealth inequality) referring to the ‘frequency’ of one’s influence varying with the amount of wealth one already possesses. Essentially this leads to the occurrence of those holding influence/power in society or the labor system to be in the position to further their own influence and gain; this is the cause of wealth inequality- the rich get richer- those already in positions of power are the most ‘visible’ to the sources of power [opportunities, possessing appropriate resources/capital (social or economic), etc.] allowing them to keep others in positions where they are lacking power- invisible.
The Bloomberg article discusses how although workers’ unions have been declining in membership and influence since 1973 (between 1979 and 2014 memberships dropped from 21 million to 14.6), there appears to be a resurgence in union activity as the expanding wage disparity has become more and more apparent in recent years. With the discussion of power law in mind, the idea of a workers union appears to make much more intuitive sense than I had previously thought. The idea of banding together in solidarity allowing greater potential pressure towards labor management from the workers is a large part of the purpose of unions, but I think that more importantly unions are attempts to prevent power law effects from taking place by making the workers (previously invisible to the sources of social power) a visible entity, whose desires must be met or at least taken into account. Unionization seems to displace the effects of power law in the realm of management within the workplace by forcing those in possession of the ‘resources to further their own social power/influence’ to distribute it more equally in order to allow for the exponential growth patterns of the already successful to be brought down to more honest/less astronomical trajectories.
This article is a little dated however I thought it was fascinating how actually preventing an information cascade was on the European Union’s agenda and it does provide great case studies where cascades were most likely in effect, Netflix valuations, financial crisis, Arab spring, occupy wall st,and EU debt crisis.
The author seems unimpressed with the “information cascade phenomena” calling it “people imitating other people’s behavior” but maybe the conversation should be harnessing the effect of information cascades that could prevent irrational (or rationally irrational) decision making.
A group of researchers from the MIT technology review are attempting to pin down network behavior with respect to information cascades within the confined network space of Facebook. In order to conduct their experiment the researchers (Cheng and his team) took to analyzing the way users shared photographs on Facebook over the course of a 28 day period. They were able to collect data from over 150,000 photos that were shared about 9 million times over their 28 days period, and with that data they were able to see which users shared each photo at what time – all important information for constructing a graph of this behavior.
Cheng and his team note that the typical route to this type of research involves viewing large cascade behavior and attempting to apply some sort of algorithm to predict future cascades. Cheng suggests that their unusual method of watching the cascades grow (compared to looking at end results and working backwards) has allowed his team to better predict cascade behavior. They begin their procedure by observing a single image to be shared with previous shares denoted as K. Then their team predicts how likely that picture is to double its current shares. What they observed in that cascade behavior in this network follow a power law, that half of cascades with a given size will more than double while the others do not.
What they ultimately found given this experiment is that there are given predictors to whether or not an information cascade will occur. They report something called “temporal performance” how quickly the photo spreads and they claim it is the best indicator of all. Cheng and his team explain that “if something is spreading quickly it’s likely to continue to spread”.
And the end of the article they claim, that this experiment although only conducted using Facebook as their subject of experiment, has applications that reach far beyond Facebook and social networking. While I can see how this could make some sense in the real world given their findings, I feel as though there are far too many externalities in the real world for this to work the same way.
In recent years the Blackberry company has been in steady decline. With new smart phone manufactures eating up the market Blackberry has been losing subscribers at a rate of 3 million per quarter. The CEO of BlackBerry however believes that the BlackBerry company can make a turn around. CEO John Chen believes the key to BlackBerry’s turn around will be “Network Externalities”. Network externalities are the changes in benefits a user of a product receives, when the number of people using it increases. BlackBerry hopes to reach these positive network externalities with the introduction of a partnership with Samsung and the release of BlackBerry’s newest product BES 12 EMM. BlackBerry’s product BES 12 EMM is an operating system that will improve security for smartphone users and allow a phone to have two numbers, which makes it easier for companies to pin point how much to pay employees for work related phone calls. Studies suggest that in years to come, many employers will have a bring your own phone to work policies. BlackBerry hopes to fit into this niche with their BES 12 EMM product, which claims to have the best security system. The network externalities come in when more people start to use the BES 12 EMM. With more people using BES 12 EMM it would make companies more secure, improve ease of use of the network, and create a stronger network. The positive network externalities do not end with users and the BlackBerry company. With the use of the BES 12 EMM system partner Samsung would benefit, as well as Google with the increases in network security and revenue. If BlackBerry stays ahead in the market, positive network externalities just might put them back on top.
Last year when a Malaysian airliner (with 239 people aboard) went missing on its way to Beijing, there were difficulties in locating exactly where it went down; this caused researchers to look at similar cases in the past and see if there was anything to be learned. It turns out that in 2009 an Air France flight went missing on its way from Rio de Janeiro to Paris, what followed was an exhaustive 2 year search for the plane that could only locate the plane (by locating its black box) within an area the size of Switzerland; but after scientific consultants applied Bayes Theorem, they found the black box in five days. The main areas that Sharon Bertsch McGrayne (author of “The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy”) says Bayes theorem is ‘a short simple equation that says you can start out with a hypothesis about something- and it doesn’t matter how good it is’. This is an interesting take on Bayes Theorem that I did not take full notice of when initially learning about it class; the adaptability of Bayesian hypothesis, ‘the hypothesis can change and improve and still be used with the theorem’, is its strong point, as the theory begins with an extremely subjective hypothesis that, through exposure to new information, becomes more accurate via modification to that hypothesis. Even though in the case of last year’s downed Malaysian plane, Bayes Theorem was not formally applied, the Bayesian treatment of hypothesis as a fundamental starting point that is adjusted throughout with the introduction of new information appears to be a helpful model for decision making in general; consciously focusing on conditional probability as a path to more practical answers (if x is true, and I know y is true, then what else must be true?).