Rumors: Classic information cascades

 People deal with rumors everyday in their life. From a high school student to a company CEO, there is always some sort of a rumor circulating about in their social network. In a high school for example, a competitive student, say A, might have spite against another student, say B, who is getting better grades than him. If that is so, the kid A might want to get back at the kid B and start a rumor about him by saying that he was cheating. How would it be possible for him to do so? What is the tipping point that would lead other students to believe that? One way would be to go to an official and tell them about it. But if the lie is caught, then A would be in a lot of trouble. Another possibility is that A could tell his friends about this. But they could easily see through his lie and call him jealous and deceitful. Hence, A has to look for that one person in his class that is a source of all rumors, that person who is trusted by other students and is a center of gossip in the network. Acknowledging the information by that person would be the tipping point in the cascade of that rumor and would result in its spreading throughout the school. Even though other students might not know whether the student A is innocent or not, they would go with the crowd and side with the more influential person.

 According to the above explanation, 2 major factors can be identified leading to a successful rumor. The first major factor is the spread of the rumor. If the student in the above example tried to spread the rumor about student B in his family and relatives, then that would not have been successful because it was not in the right network. Spreading of the rumor in his family would not have affected the student B as it is not the correct network and the spread would not be efficient as the signals received by the nodes in this network would not have significance among them. The second most important factor is the type of node that receives the rumor. As I explained in the above example, if A tells the rumor to another student whose word is not as significant as some of the other important students, then that would not lead to an efficient spreading of the rumor. Hence, it is not only the spread but also the nodes that spread the rumors further. Such kind of strategy of spreading a rumor efficiently is evident in our daily lives. We see these everyday in financial markets and real estate networks.

 Let me explain about a real estate network. Suppose prices in an area for good houses or industrial plots are reasonable. If daily newspapers and television channels don’t talk about real estates, most people don’t consider investing their money on property and lands. However, a real estate salesperson can think of stirring up the activity in the market by spreading the rumor among people that land prices are going to rise rapidly very soon and it would be a very good idea to invest in them as it would lead to 100% profits. Spreading such a rumor through a couple of people would be inefficient and would possibly result in a failure. However, if that salesperson goes to a central node, a real estate enthusiast who actively deals with sale purchase of lands and who further spreads it around, then that rumor could result in a wildfire among people. Such a rumor would result in people actively thinking about buying real estate and this would increase the activity in the real estate market. People would ignore their own signals and intuition and would go with the crowd. The frequency of sale purchase of real estates would drastically increase leading to an increase in the demand of land, hence ultimately increasing the real estate prices. At this point, where the rumor actually comes out to be true, the information cascade comes crashing down because now everyone is aware of the fact that the prices are rising and would start investing in real estates. Hence, even though the rumor started by this salesperson was incorrect to begin with, it ultimately led to desired result that the person wanted and the rumor turned out to be true. A similar kind of pattern can be observed in stock markets where a company’s share price can increase if someone leaks a rumor to a magazine that the company is releasing a new product. People will think that the company’s share price would go up and will start buying their shares. As long as the company does not reveal its intentions and the rumor keeps on circulating, the information cascade will proceed and people would tend to go along with the crowd. However, as soon as the company reveals the truth about the rumor, the information cascade crashes as everyone will be aware of the outcome, whether the company really has a product or not.

 Another example that I would like to mention here is celebrity networks and company rumors. Celebrities and famous multi-billionaire companies deal with a ton of rumors everyday. People who might be against a company’s prosperity or a celebrity could try to spread rumors about them that might be untrue. I came across one such article about Google named:  Is the Google magic coming to an end?

This article talks about the fall of Google and an end to Google’s era. It says that Google share prices have been seen falling recently and a number of key members of the Google community, such as Ethan Beard and Sheryl Sandberg, have left Google to join Facebook, one of the largest growing social networks in the world. All this information about Google could be completely random and unrelated, that is, the above mentioned people could have left Google for their own reasons which could be completely unrelated to each other. However, if this information linking Google to Facebook were to gain stead and if people started believing in this, then it could pose a potential problem for Google. Its employees could think that if Google has started to fall, then it would be best for them to search for jobs elsewhere. They could ignore the hard facts that Google is rated the best employer of the year and is one of the best companies to work with and they might go with the crowd. This could lead to more employees leaving Google which could potentially make this rumor true. Hence, these examples perfectly depict how rumors are a classic form of information cascades and how the tipping point in the network could change the rumor into a truth.

Article link:

http://www.rediff.com/money/2008/mar/29forbes1.htm