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Aparna’s Week 2: BBC Podcast Response

“There’s more variation in males than there is in females. There’s fewer dolts among women but there are also fewer geniuses. This is a biological fact that you’ll find for almost any strain there is variation. And so it’s actually very unlikely that there’s ever going to be 50-50 Nobel Prize winners for example and to set that up for women as a goal, I think is a mistake” (Dr. Helena Cronin 18:27-18:53).

In the podcast “In Our Time,” Feminism with Melvyn Bragg, Dr. Helena Cronin argues for Darwinism, which is the theory that evolution has created innate biological differences between men and women. She believes that we need to change society to allow women to succeed given their biological limits. Meanwhile, Dr. Germaine Greer emphasizes that the difference between men and women is determined by society; not science. Both women agree that there are differences between men and women and treat gender as a binary. However, their main point of contention is where that difference comes about.

Throughout the podcast, Dr. Helena Cronin argues for her opinions on the differences between men and women under the guise of science. We can see this in action with the quote above. She offers her opinion that we cannot expect a 50-50 split between male and female Nobel Prizer winners, and defends it with a “biological fact.” First of all, such a fact has little merit because it is hard to compare intelligence. IQ tests and other standardized tests are now known to be flawed systems of judging intelligence, especially when there are so many different types of intelligence (ie. logical, emotional, etc.). Second, it is so difficult to ignore the societal factors that impact the number of female Nobel Prizer winners. Women in the past and present struggle to gain access to educational opportunities. In fact, women are banned from going to school in certain areas. That is clearly a societal constraint on the level of education that a woman can receive rather than a biological one. Additionally, the board that decides the winners of the Nobel Prize tend to be filled with men who have their own biases, which is another societal limit. Finally, there are general constraints within society that drive women away from working, such as a lack of maternity leave and support for working mothers.


Yesterday I sat on a bus and counted the people as they got on and off.

I drew a small map of the seats in my notebook, and when someone got on I made a marked that seat with a symbol of the bus stop at which they got on. When someone got off I crossed their mark off from the seat at which they were sitting, and then wrote down where they got off. I did this for one circuit along the TCAT 82 route, and then copied onto google docs.

Each link represents a passenger getting on and off the bus. Think of the links as traveling downwards; because the bus follows a linear path, a person cannot get on at at stop and then get off at a stop above it, so all these connections are arrows pointing downwards (one-way).

I tried rearranging the data to make it easier to read, with entries connecting to the right side of the boxes, and exits connecting to the left side of boxes. Frayed lines mean someone either got on the bus without me knowing where they got on, or got off without me knowing they did.

We can apply hub and authority scores to this like we did in class. Here they are after one update, and without being normalized.

Authority Score Stop Hub Score
1 Veterinary 1
1 Dairy 2
1 Brad 2
1 Kennedy 6
1 Uris 4
1 Rock 3
2 Balch 1
0 Jessup 0
2 RP 1
2 PG 0
1 A lot 0
0 A lot low 0
1 Hasb 1
1 Helen Newman 1
0 Balch higher? 0
1 Goldwin Smith 0
1 Uris 2 5
1 Bag 0
0 Dairy 2 0
0 Boyce 0
1 Veterinary 2 0
0 Hum 6
1 Mapl 0
3 End. 0

In this case, a score of zero means that nobody got on or off at this stop.

We could also apply multiple updates to make it more accurate, but for the general concept one should be enough.

The Great Bee Hoax

The article “Viewpoint: How a small group of scientists and pliable media created a ‘catastrophe narrative’ that hurts bees and farmers” by Henry Miller discusses the effects of pseudo-science and information cascades when it comes to the popular ‘bee-pocalypse’ trend. According to international statistics from 1995, the number of honeybee colonies has stayed seemingly constant (some graphs even show growth).

So why exactly has everyone been saying “save the bees”? Miller believes that it is the Pseudo-Scientific Method’s fault: experiments are botched on purpose to achieve desirable results, which are then published in predatory journals (that take on a fee to publish regardless of scientific validity). As soon as these articles are posted on social media, the falsified results will have already reached halfway around the world, trumpeted by Twitter retweets and Instagram stories. In relation to the bee story, the truth is that most of the studies were done on caged bees that were massively overdosed with the neonic pesticides originally in question.

This article is a prime example of the information cascades we learned about in class. Due to our massively interconnected society on the internet, news can spread more rapidly and dangerously than ever. Incorrect ideas can become widely accepted due to falsified literature and scientific evidence; two things that the general public does not conduct sufficient research into. Even worse, activists use the media as their research, which results in a mass repetition of fabricated crises in various articles and posts. In addition, social media creates increased social pressure to fuel the cascade, as users can see who is supporting the “movement” with hashtags and other similar features. As more and more people jump onto the bandwagon — especially celebrities and influencers — others soon follow.

The Opioid Epidemic: information cascades gone wrong

The Cascade of Care framework was first proposed as a policy solution to the HIV/AIDS epidemic in the 1980s by public health officials. More recently, this framework has been revisited and reintroduced as a means of controlling the opioid epidemic that has decimated, in particular, rural white communities across the United States.

Unlike infectious diseases like HIV, the opioid epidemic is characterized by addiction, not contagion. Since the opioid epidemic is not contagious in the way that many epidemics are, but still fits the CDC’s definition of an epidemic, I wanted to explore whether or not the crisis fits the branching process model we discussed in class.

The branching process model defines a simple formula for seeing whether or not a disease or phenomenon will persist beyond fringe cases by multiplying the probability that you get sick upon contact with an infected individual and the amount of people they are exposed to. Logically, a drug addiction epidemic would not fit into this mold because there should be no positive correlation between exposed individuals and infection.

Curiously, we actually see that in the case of opioids, a higher k can actually contribute to better outcomes for affected individuals. This is because when others see the effect of opioid and the dependency that they cause, they are steered away from initially taking them. Furthermore, lack of information about using, particularly if an individual’s opioid usage escalates to drugs like heroine or other intravenous opioids, can expedite fatality due to secondary bacterial infections like blood-transferred Hepatitis.

We actually could observe that for initial outbreak of the opioid crisis, where information was largely concentrated amongst pharmaceutical companies, a more accurate model would be that of information cascades. Physicians and patients initially both did not have enough information to determine that opioid usage could spiral, so we saw pharmaceutical companies’ market false safety information through a sustained campaign of misinformation to medical professionals. Since such a large network of doctors did not have correct information and policy surrounding pharmaceuticals is relatively lax in the US, doctors and their patients were not able to form correct low and high signals. Their knowledge of the drug’s safety was a high signal, meaning that the expected value of the product was positive, despite the drug being bad. In this case, if purchasers computed the Bayes computation of what is the probability the product is Good given it’s high signal, they would get a probability higher than p or probability of being good because of manipulation by pharmaceutical marketing forces.



Information Cascades and the Rise of Sexual Assault Allegations

The article “Trump and the ‘information cascade’ created a cultural reckoning”, written by Gillian Tett, discusses how new technology connected more people then ever and caused an information cascade over sexual assault. Tett discusses that there probably aren’t anymore sexual assaults now than in past years, however the amount of allegations is at an all time high. Hett credits this to how quickly information can now travel. For instance, before social media, if someone wanted to report someone for sexual harassment, it was a long beuratic or legal process, but now it’s a simple tweet. The new ability to quickly communicate information to large masses of people all over the world exposed more people to sexual assault allegations. This new exposure to information caused an information cascade spawning more sexual assault allegations than ever.

This is relevant to what we’ve been learning in class because it very clearly discusses information cascades. What this article shows is that previously, people were not nearly as connected as they are now. So, if someone were to pursue a sexual assault allegation, not many people would see it and it would just die down. However, now because everyone is so connected, if one person pursues a sexual assault allegation, many people see it and may even pursue their own. As this continues, coming out and pursuing a sexual assault allegation doesn’t seem as difficult. This is due to it becoming more frequent among those we’re connected to.

Instagram’s Removal of Likes

Recently, Instagram has been experimenting with the removal of the likes counter for certain users in varying countries, and recently expanded to the United States.


There is an entire economy that goes on within Instagram for advertisements. Though Instagram itself has paid ads show up every few posts in your feed, individual accounts are also paid to post sponsorship pictures and videos that Instagram does not have influence over. The amount the individual accounts get paid vary by how many followers/likes the account gets.


The account likes to boast its large amount of likes to show they have a high ratio of followers to likes, to show that all their followers are real and not botted. This also applies to individual users that want are looking to follow a new account – they want to see user engagement in the posts, and now they will be unable to see if accounts have a ton of followers and no likes due to bot accounts.

This effect the “boom or bust” effect that displays the reactions of people to content. This makes the already popular accounts even more popular (to boom) and the not so popular accounts “Bust” as it is really hard for them to gain popularity.

In lecture, we learned about the “boom-or-bust” effect of the display of people’s reaction to certain content. This effect is the result of reactor’s decisions based on information.

Displaying the likes increases the number of likes on posts with an already high number of likes. Accounts with a lot of likes gain more views and are more credible and people are therefore more likely to follow. But as Instagram removes this metric of visible likes, the boom-or-bust effect may be thrown off as people will only have one metric to follow.


This will probably allow for more accounts to gain popularity. As you scroll through your feed, you will only see the account that posted, the posted content, the caption, and the comments. You will no longer be reminded how many likes a certain account gets, and therefore when scrolling through your feed, you become less biased towards looking for posts with millions of likes.


Instagram acknowledges that his hurts their business, but cares about people’s well beings as users were super concerned with how many likes they were getting in relation to other people.


Overall this will make Instagram less competitive as only the account owner will know how many likes they get.

The Network Effects of a Currency Revolution

Facebook is the trigger for a currency revolution that’s long overdue — like it, or not


In the next few years, we may be seeing a transformation in the way we use currency. Recently, Facebook CEO Mark Zuckerberg proposed to lead a group of tech companies in creating a new currency know as ‘Libra’. Zuckerberg explained that the current public system of currency, banks, agencies, and financial regulators are costly and inefficient and by introducing this new private money network system, we can have a much more efficient way of transferring money that is “as easy and secure as sending a text message”. The article also discusses some of the powerful network effects from the introduction of this new technology. As people and companies start adopting this new technology, this will quickly spread to new people and areas. The more people that use this new technology, the higher the network benefits there will be as well, which has great potential in eliminating the high costs of transferring money including banking fees, foreign exchange costs, telecommunications costs, and interchange fees. Additionally, Zuckerberg claimed that that China has already been beginning to move towards similar currency systems and that if the US doesn’t make this movement as well, we will be further behind in the trend of adopting network technologies.



This topic relates to some of the concepts that we have covered in class. First the idea that the widespread use of this new currency network will have direct network benefits to everyone using them. The more people that use this new technology, the easier it will be to transfer money using Libra and the lower the transfer costs of money for everyone using it. Next, the quick spread of this technology relates to network cascades. As people start adopting this new technology, they will affect the people around them to switch to this new currency. Similarly, we can extend this model to much larger groups. If China adopts a similar system of currency, the US will be pressured to make this change as well.

The Prevalence of Power Laws in Society

Tauberg, Michael. “Power Law in Popular Media.” Medium, Medium, 9 July 2018,

In this article, Michael Tauberg dives into different industries and looks at graphs of data regarding their popularity, whether that’s number of sales, box office numbers, weeks on the New York Times Bestsellers list, or other such measurements. What Tauberg observes is that so many industries and spaces in popular media follow the power law, which is a concept we spent several lectures on. In simplified terms, the power law says that a small portion of the participants in an industry will hold a large portion of the popularity, however that’s measured. For example, Tauberg provides a table in the article, and one of the data points shows that for game publishers, 97.69% of the success was held by the top 20%. Thus, a relatively small portion of the participants, 20% of them, hold an astronomically high amount of the success.

The bulk of the article is a display of graphs for different industries and fields, where the x-axis contains participants and the y-axis contains the measurement of success. As we learned in class, a power law graph will seem to have a “long tail,” which represents the large portion of participants that each have a relatively small amount of the success, while the head of the graph will show a spike which represents the small portion of participants that each have a relatively large amount of the success. The visuals in the article do a great job of illustrating this concept, as they show relatively similarly looking graphs across a wide variety of industries, showcasing the prevalence of this phenomenon in society. I was fascinated reading this article because it set in stone how the concepts that we learn in class, such as the power law and the rich-get-richer idea, are clearly relevant in real life. Tauberg does an excellent job of driving this point home, as the industries he looks at in this article, such as music, movies, games, and books, are some of the most influential and known fields in our society. As we learned in class, the rich getting richer phenomenon often occurs because people in society make decisions that are at least partially based on what others in society do. The article articulates that notion and further explains that “as our lives become more and more connected, we should expect that power law curves will become even more common. Moreover, winners in this new world will become even more dominant.” This makes sense because our lives becoming more connected means that we have more information based on other people’s opinions and actions, which furthers the network effect of popularity.

The spread of non-credible information

The information that we are exposed to can often change our perceptions of the world around us, and an accurate perception of the world requires the ability to discern fact from fiction.  With the vast amount of information in the world today, being exposed to reliable information can help ensure that we make informed decisions.  The article addresses how misinformation can spread and emphasizes the importance of fact-checking as a way to combat the spread of misinformation.

Simply knowing that a piece of information is false and choosing to not share can have varying degrees of effect on how misinformation spreads.  Someone who is connected to a large number of nodes in the network would have a larger influence than someone who is only connected to a few nodes.  Therefore, news sources, which hold significant influence on the spread of information, are responsible for fact-checking prior to the release of new articles.

The Spread of Disease and Transmission Awareness

When we think of the spread of widespread disease there is usually a focus on how to stop the initial susceptibility to the specific virus. But once the disease is out into the world and difficult to quarantine, it is usually much more of an involved task to get the strain of disease treated or eventually die out. This involves high amounts of communication between various networks and groups of people to develop methods that control the possible devastating effects of the disease. The changes in behavior that people have in response to the outbreak of disease can determine the outcome of the progression of the viral agent. Too many times has there been an outbreak of some contagious disease and the general population has not taken the necessary precautions to keep themselves safe from contracting and spreading the agent.

There have been several ways of clustering people together to protect the spread of disease. This has decreased the overall devastation of potentially viral diseases and the mortality rates through the years alongside the progression of medicine. The network that advises people what to do in epidemic situations and develops methods to transport medicine and advice to people in need has saved many lives. This is just one of the ways networks work in epidemics and our daily lives.

The Spread of Awareness:

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