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The Information Cascade of Sexual Allegations

Unfortunately in this day and age, especially this year, sexual harassment allegations are something way too familiar. We see it on the news and social media happening everywhere from work settings to college campuses. Although it seems like the rate of sexual assault has dramatically increased, this number hasn’t changed as drastic as one may think. This is because the rate of sexual harassment was most likely always high while the amount of people who reported sexual harassment was and still is significantly low. This can be due to many reasons such as the victim getting blamed, getting fired, or further harassed. So why the sudden shift in people speaking out about sexual harassment? According to US managing editor, Gillian Tett from Financial Times, in “Trump and the ‘information cascade’ created a cultural reckoning” one reason to explain the phenomenon is Information Cascades. More specifically, the information cascades formed by social networks.


An Information cascade is a situation where each person makes a decision/choice based on the observations or choices of others while ignoring his own personal information (Investopedia). We learned about this concept in class when explaining the example of the two urns filled with a number of red and blue marbles. One urn has a majority of blue marbles while the other has a majority of red marbles and each student would have to guess which urn is which based off the marble they chose as well as other people guesses. This example showed that people started to guess the popular answers that they heard before them regardless of what marble they chose. This same concept can be exemplified in the situation with sexual harassment too. Decades ago if women was to claim she was sexually harassed there was a very slow bureaucratic legal process and many months of research before it would get reported. Now, according to Tett, with the help of social networks, information can spread very rapidly beyond the control of lawyers and traditional authority figures. Isolated victims can suddenly congregate into a crowd to support each other. Also, people can repost or share someone’s story without needing to do anything other than clicking of a button. Thus, once a story is shared thousands of times it gets a perception that it is true then more people begin to share the story. Conclusive, due to this information cascade more victims feel comfortable speaking out.

Social Contagion and Campaign Donations

Traag, V. A. (2016). Complex Contagion of Campaign Donations. PLoS ONE, 11(4).                                              dio:101371/journal.pone.0153539.

Campaign Contributions have been at the forefront of every major political election, ever. This past election brought to light the many issues around campaign contributions, specifically the ethics behind disclosing how much money flows through Super Political Action Committees (PACs) and how income inequality affects political influence. While many campaign donations don’t come from the numbered elite in this country, a larger sum of money does. “Complex Contagion of Campaign Donations” by Traag (2016), looks at 50,000 elite and finds that campaign donations are socially contagious by creating network models.

The main finding of the study confirms independent reinforcement (pg 10), suggesting that exposure to donors, especially from many donors in one’s network who all don’t know each other, increases the likelihood of donating as well. This study also found that the “viability” of a candidate also affects their amount of money they’re able to raise. Indeed, people – wealthy or not – do care about the electability of a candidate. If a candidate seems like they will not win, people will not donate to their campaign. Further, this study showed that “Independent reinforcement is especially relevant for campaign donations to assess viability.” Because of this, it’s incredibly important to diversify the communities campaigns reach out to. Diversity of supporting communities significantly affects amount of money raised by a campaign due to independent reinforcement. This study also showed that different aiding communities can predict the success of a campaign more so than the number of donors. One really cool finding was that memes found on social networks also affect campaign contributions.

This past election brought to light many of the issues of both major parties, Democrats and Republicans. Division among party lines is not uncommon. It’s also not a surprise that many close friends in a network are likely to have the same or similar political ideologies. Traag (2016) found that exposure to people who support one particular party can trigger a rise in donations to another. This proves just how much animosity between political parties and one’s network can also affect campaign donations.

Bitcoin Block Chain Network

I found this article very interesting:

It described how the IRS was using a startup called Chainalysis to track the flow of bitcoins through the block chain. The IRS, being interested in tracking individuals’ hidden assets, clearly would like to know if someone has bitcoins but hasn’t disclosed that. Law enforcement have been attempting to do the same thing to track silk road purchases and payments made to ransom-ware. A piece of ransom-ware might distribute itself and only ask individuals to send bitcoins to a particular address, which would simply appear as 1HyasSC2VifTZo7YkUNn33udnWXw3Ffq7T.

The problem is that addresses are free to make, and so people trying to hide their identity have no reason to use the same wallet twice, or not use many intermediary wallets. On the other hand, every transaction is publicly available for inspection, so many straight forwards tricks such as an intermediary wallet would be pointless. What is less pointless is sending bitcoins to a large, “hub”, node and receiving them out to another address or to multiple addresses. Because of the branching factor near such hubs it becomes very difficult to track the true identities behind these transactions.

This presentation showed off some of the techniques used to catch scammers and track bitcoins:

Some of the techniques are as simple as noticing patterns, such as a set of transactions always of a certain size and always divisible by certain values. Other tell tail signs involve tracking how bitcoins are divided by certain wallets. For example, in one incident the author of a malicious piece of code split the profits in a 20-80 manner with a partner. This split quickly became a tell-tale sign of malicious activity. In other circumstances, the original creators of wallets could be deduced by checking who originally put bitcoins into it. In another circumstance, related nodes were found by calculating how often they participated in similar transactions.

The anatomy of information cascades in the classroom

This observational study analyzes how information cascades appear and evolve and what factors are relevant for the formation of cascades within a classroom through online learning platforms. This study found that students don’t prefer to share the content given to them by professors, rather they prefer to share the content they find themselves. It was also found that high-performing students shared documents with more information, or high information. The study defined an interaction as a communication between two students sharing some documents or messages. They recorded interactions such as conversations in the course Facebook Chat Canvas, documents shared on online platforms, and files shared as URLs by students in their course specific Facebook accounts. There were informal resources, such as blogs, Q&A sites, or online tutorials, as well as formal resources, which were manuals, peer-reviewed papers, and presentation slides. Results found that only a fraction of the documents from the educational portals, user accounts and social platforms were propagated to the students and then never accessed again. Although student content was re-shared more frequently than professor content, students did reference the professor content. Overall, longer information cascades contained content suggested by students, while shorter cascades contained content shared by professors. Some factors that led to a cease in the information cascade were information density, length of the content, and whether the student was high-performing or low-performing.

This relates to the concept of information cascades in lecture, in which the spread of technology, products, social movements, or opinions can be analyzed. This study revealed a few factors behind information cascade within the classroom. Content suggested by students tended to be shared more, with longer information cascades. It was also found that high-performing students shared content faster, in more complex cascades, and more regularly than mid- and low- performing students. More content shared led to longer cascades. We can also look into the idea of low threshold compared to high threshold. As learned in lecture with thresholds and what may lead to a cease in the cascade, this study touches upon characteristics of the content that may lead to a cease in the cascade. Such factors include information density or information of documents. So, this information cascade is a bit different from lecture in that it is not that a cascades stops because there needs to be a certain number of your neighbors using the new technology for you to switch, but rather if the content that is being shared would be beneficial or valuable to a student based on the length, or content. A student may stop the information cascade if they don’t find that the content is valuable enough to pass on. Overall, this study emphasizes that information cascades exist within classrooms and online platforms, as students are able to pass along information and content they find helpful to other students, and then the other students will continue to pass along the information if they find it helpful.



The rise of sexual harassment cases and information cascades



The article linked above, written by Gillian Tett, discusses the election of Donald Trump and the recent wave of sexual harassment cases. In this article, Tett, explores two factors that she believes contributed to the new surge of sexual harassment cases. These two factors, she states, are Donald Trump and information cascades facilitated by the prevalence of social media. According to Tett, Donald Trump and his election to the Presidency contributed to the rise of sexual harassment cases by empowering feminist. She states that, “When [Donald Trump] was elected, shattering hopes that Hillary Clinton might be America’s first female president, most observers presumed that his victory was bad for women. However, a year later, it has become clear that Mr. Trump has unexpectedly empowered feminists. One early sign of this was the women’s’ marches.” Tett’s claim is that Donald Trump’s election, empowered women giving them a figure to stand against. Someone who actively strives to damage any social progress made in this country.

The second key factor that Tett touches on is that of information cascades contribution to increase in sexual harassment cases. According to Tett, information cascades contributed to the upsurge of the sexual harassment cases and experiences being revealed because with the commonness of social media in our lives, information about these cases can be spread easily and accessed by large amounts of people effortlessly. This information can then be broadcasted by one victim and spread to another, who, might be inspired to share their story and thus continue the cascade. Tett states that:

If a woman wanted to complain about sexual harassment allegations two decades ago, there was a slow-moving bureaucratic and legal process. And if a reporter wanted to corroborate a story, this entailed months of painstaking research. But in cyber networks, information can spread at lightning speed, beyond the control of lawyers or traditional authority figures. Journalists can appeal for tips and be inundated within minutes. Once-powerless victims have a megaphone. Isolated victims can suddenly congregate into a crowd. Informational cascades, in other words, overturn power structures.

Information cascades in conjunction with the catalyst Donald Trump is, provided the optimal conditions for sexual harassment occurrences to finally be brought into the light.

The Weinstein Effect as a Tipping Point

The article linked below is a conversation between NPR host Noel King and NPR writers Mary Schmich, Elizabeth Blair and Alexandra Schwartz about the recent revelations surrounding sexual assault by powerful men not necessarily in Hollywood, but across industries. “The Weinstein Effect”, as the writers have dubbed the phenomenon, refers to the growing list of women coming forth with their stories of sexual harassment and assault by powerful men after Harvey Weinstein was exposed earlier this year. Allegations against Weinstein, an American film producer and former top film executive, were brought to light through an expose done by the New York Times detailing decades of allegations of sexual misconduct by several women. Since The Times’s expose, however, several women have come forth with allegations surrounding other men as well.


Similar to the tipping point in cascade models we discussed in class, the NPR writers engaging in this conversation argue that the Weinstein scandal was a tipping point in the cascade that occurred after. Although there were several factors that contributed to the increase in women coming forth with their stories of sexual harassment/assault – President Trump and the Access Hollywood tape is one of the factors mentioned – the Weinstein scandal was the tipping point for the trend that occurred after. As we covered in class, a tipping point refers to the point in a situation at which a seemingly minor development instigates a cascade of behavior. In this case, the Weinstein scandal was the tipping point for many of the movements that followed – the Me Too Movement, the spike in reports of harassments and assaults by women in Hollywood, the accusations against Kevin Spacey, Matt Lauer, Roy Price, Charlie Rose. An interesting and relevant application of tipping points, the Weinstein Effect refers to the cascade of women coming forth with their stories of powerful men engaging in sexual assault and/or harassment with the Weinstein scandal being the tipping point for the cascade itself.



How social media affects tipping point

We have learned the definition of the tipping point and how marketers use that to build their strategies for success marketing. Tipping point is an important information that marketers need to see what is wrong with the current strategy and guides them to look for solutions. Any points right below tipping point will lead the business to failure and any points above tipping point can attract many more customers. In the past, advertisement methods were the only solutions to improve business situations and attract more customers, so marketers used advertisements to raise the point to be above the tipping point.

With the rise of social media, it has become much easier for both individuals and organizations to reach their possible audience. This article examines how social media have viral superpower and compares old period where traditional advertisement methods were used and current period where social media controls who sees what. According to the article, Lady Gaga sold 305,000 copies in 2 weeks by spending millions on bus advertising, billboards, 2 pop up stores and performing countless interviews. On the other hand, Beyonce posted her new post about her album on her social media and was able to sell 828,733 copies in three days.

Both results prove how powerful social media is and spending on traditional advertisement methods may be a waste. It is so much easier to raise the point above the tipping point and more people will be convinced to follow the herd. Although social media sounds like an answer in marketing but businesses can lose customers as easy as it convinced them. If any person or organization loses reputation and lose many customers, just posting something on social media will not bring the point to tipping point.

Is this the Social Media Marketing Tipping Point?

North Korea and Evolutionary Game Theory

North Korea claims missile puts all of US in range, Financial Times

Despite the fact that the United States and several neighbour countries kept making strong oppositions and warning North Korea of its possible aftermath if it continued its missile test, North Korea didn’t stop its nuclear development plan. According to the recent missile test of North Korea, North Korea has already gained the capability to put all of the United States territory inside of its missile range. Based on traditional international politics logic, strong opposition signal and possible harsh punishment can help deter countries from taking certain actions. However, such logic seems to be not applicable in the North Korean nuclear crisis case.

Thinking from an Evolutionary Game Theory angle might help explain why nuclear weapons are so attractive for North Korea. Although a shared agreement on nuclear nonproliferation was formed in the past one or two decades, most of the major players in the current world have nuclear capacity. Consider the situation that most of the players have nuclear weapons and only a small factions of players don’t, in which it is possible that the payoff of countries that have nuclear weapons is at least equal to if not larger than the payoff of countries that do not have nuclear weapons. Moreover, because of the lack of mutual understanding, countries don’t have an actual table of payoff as the one we get in problem sets. Then, North Korea’s anticipation of payoff may be generalized from previous history cases. The fact that most of the countries that tried to acquire nuclear weapons didn’t suffer from harsh punishment and that some of them even gained a stronger leverage and higher international status also give North Korea the strong incentive of acquiring nuclear weapons, despite the possible punishment of the United States.

Artificial Intelligence and The Kentucky Derby


Artificial intelligence has come to the Kentucky Derby.   According to a recent article in Forbes, the Kentucky Derby is partnering with an AI company to apply the science behind artificial intelligence to “handicap the race.” According the Forbes, the AI company correctly predicted the Superfecta outcome of last year’s Kentucky Derby, which means that the technology could predict the first, second, third and fourth horse correctly in the correct finishing order.  “To put it all in perspective, getting the first four horses correct is such a tough task that a $20 Superfecta bet on last year’s race would have returned $11,000.” For this year’s Kentucky Derby, the same AI will attempt to predict the results and then release the predictions so that the bettors will be more informed.  In theory, this informed universe of bettors will result in odds that more accurately reflect the risk adjusted potential outcomes of the race.   The AI company, Unanimous A.I utilized technology it created called Artificial Swarm Intelligence. Dr. Louis Rosenberg, founder of Unanimous AI, emphasized that “[W]hile predicting sports always involve a large element of chance, Unanimous A.I. taps the intelligence of groups and evokes the best possible prediction based on the available information.”  I was curious so I did some additional research on Swarm A.I and learned that this technology attempts to combine the ideas of many participants to create the outcome of one brain, which is similar to how flock of birds use their combined intelligence to for navigation. The Swarm AI uses the human input of many to create datasets that can be used to create predictions of future events or conditions. The article explains that the predictions being made concerning the Kentucky Derby will be made with the help of Swarm AI in combination with data from top handicappers and other horseracing experts.


As discussed in class, handicapping horse racing involves betters distributing their wealth across a wide range of races. Our conclusion was that the bettor always bets his belief about the potential outcomes. In effect, the proportion of wealth that one puts on a horse should be based on the probability the bettor places on his belief that any one horse will win the race. This fact, combined with the new variable created by the use of Swarm AI in this year’s Kentucky Derby  raises new questions for the bettors at the race. Most likely, the odds this year will most likely be more spread out because of the input from Swarm AI, which will create more viable choices.  This may create more incentives to bet on the underdog. In my mind, even though this may appear like an appealing strategy based on AI, the science is untested and a bettor may still do better by just betting his beliefs.

Artificial “Swarm Intelligence” Says Vladimir Putin Will be TIME’s Person of the Year



Preparedness for a Diseasae Outbreak

How Ready Are We for a Pandemic? We Asked an Infectious Disease Expert


This article discussed the probability of an upcoming disease outbreak. Despite countless advances in technology, we are still incredibly susceptible to diseases. Furthermore,  some technology, such as antibiotics actutally increases the probability of a disease outbreak when it is used incorrectly. Currently, antibiotic resistance is at a crisis level, meaning there is a high risk of a disease outbreak. As it stands, the current administration is not adequately prepared for a disease outbreak. This is particularly troublesome because the world is long overdue for a major disease outbreak. It is estimated that the next viral disease outbreak will happen by 2070. With this approaching probability, it is important that policies are in place that is able to rapidly reduce the spread of a disease, each by decreasing the contact that sick individuals have with the healthy population, such as with quarantine, or by reducing the probability that an individual contacts a disease, such as vaccination.

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