Author: Sofia Ilina

It is not hard to infer from the title of Pasquinelli’s article “Google’s PageRank Algorithm: A Diagram of the Cognitive Capitalism and the Rentier of the Common Intellect”, that he views the PageRank algorithm as a structure of Cognitive Capitalism. He explains that by mentioning a link in an article or even clicking on a hyperlink, each individual who uses the search engine contributes their knowledge and opinions to make the machine more intelligent, efficient and beneficial to others. Assuming that the majority of the people using Google have good intentions, we have a “wisdom-of-the-crowds” effect: the more a page is referenced, the more people consider it to be significant, the more value other people will potentially acquire from in it. As an effect, the pages which already have a high score keep growing it at a faster rate, because they are more likely to be visited and referenced in the future than pages with a lower PageRank.

It is quite simple to draw a parallel with how people become billionaires. According to Investopedia, the most common ways people become billionaires are through “inventing, investing, innovating and being an entrepreneur” (or a combination of such).

If we apply PageRank analysis to the dynamics of economic ranking in a capitalist society, we can see that the reason why people become billionaires in the first place is because they have generated something that was deemed valuable by many people who are willing to partake in transactions to acquire benefits from such innovations, as a result of which, the billionaire generates more revenue. “Cognitive Capitalism” also plays a role in placing billionaires in their positions of power: the more people engage in financial transactions with them, contributing to their wealth, the more they demonstrate their trust and approval of the billionaire’s endeavors, the more other people are willing to contribute their trust and resources, giving the billionaire a bigger platform.

As seen in lecture, some certain structures of a network could cause the Basic PageRank algorithm to unfairly accumulate PageRank in a cluster of nodes which have no edges going out, back into the rest of the graph. Although many other nodes could have lots of in-links and be valuable resources, they would end up with 0 PageRank score. This is why the Scaled PageRank algorithm introduces a scaling factor to redistribute the score among all nodes evenly, and this results in a more accurate score for all nodes.

Now imagine we lived in a pure capitalist society where no one paid taxes. We could easily run into the same issue as with the Basic PageRank algorithm, where all the wealth piles up in the wrong places. Therefore, in my opinion, it is unimaginable to live in a purely capitalistic society due to the issues with our socio-economic network structure. There needs to be a well balanced “scaling factor” applied to everyone (for PageRank, a stable scaling factor is considered to be between 80~90%). However, overtaxing would also be wrong. Imagine increasing the scaling factor in PageRank more than necessary: the greater the scaling factor, the more likely people will get the wrong sources mixed in with the right ones each time they search. Wisdom of Crowds is a legitimate phenomenon, from which everyone benefits, so some sense of hierarchy is essential to avoid giving irrelevant resources too much power.

However, the real economic network is not as simple in reality as can be depicted on a graph. I will end this blog with one such fact: according to the Business Insider, “The equivalent of 10% of world GDP is held in tax havens globally”. Above is a graphical representation of how much wealth in some countries is held in offshores to avoid paying taxes in proportion to the country’s GDP, which prevents a fair distribution of wealth in the world. (Taken from BusinessInsider, linked in references)

So, should billionaires be paying more taxes? Coming from a network analysis perspective, my answer to that is they should pay the taxes on all their wealth, including what is held in offshores, so that the wealth distribution would not be so skewed and each economic entity in our social network would have an accurate net worth, proportional to the benefit they bring to society.

References:

Mueller, Annie. “7 Real-Life Ways to Become a Billionaire”. Investopedia, updated Jun 25, 2019. Accessed Nov 12, 2019.
https://www.investopedia.com/financial-edge/0311/7-real-life-ways-to-become-a-billionaire.aspx

Pasquinelli, Matteo. “Google’s PageRank Algorithm: a diagram of cognitive capitalism and the rentier of the common intellect”. Published by Pankov Mar 16, 2010. Accessed Nov 12, 2019.
https://pankov.wordpress.com/2010/03/16/google%E2%80%99s-pagerank-algorithm-a-diagram-of-the-cognitive-capitalism-and-the-rentier-of-the-common-intellect

Nicolaci da Costa, Pedro. “The ultrawealthy have 10% of global GDP stashed in tax havens — and it’s making inequality worse than it appears”. BusinessInsider, Sep 13, 2017. Accessed on Nov 12, 2019.
https://www.businessinsider.com/wealthy-money-offshore-makes-inequality-look-even-worse

Diffusion of innovation is the phenomenon where a new idea/innovation is introduced into the observed social network, and initially, a few people adopt this idea, and then either the idea dies down, or more and more people adopt it over time. Different people adopt to new innovations at different rates, and many papers including Rong and Mei’s “Diffusion of Innovations Revisited: From Social Network to Innovation Network” classify these people into 5 categories: innovators, early adopters, early majority, late majority, and laggards (7).

The diffusion of information through a social network is a very important field of study because it affects everyone. Being able to model a network of social relations and roles within a community, identifying leaders of clusters within that network and discovering thresholds that impede the diffusion of information gives us the power to create drastic changes in a community. Most articles I came across when researching this topic studied methods to improve the diffusion of information. In many cases this is desirable: when companies introduce innovative products into the community, they try to market them in a way that as many people as possible adopt their new products. They use the tactics of identifying social leaders and influencers who can help more people gain awareness and trust in this innovation, also they identify competitors in the market and try to outperform them, and they collaborate with other companies to gain greater influential power over the community. However, in some cases diffusion of information is undesirable, so we also need to study ways to control how information spreads through a network, including intervention of the diffusion.

When I found the article by ResearchFeatures which discusses Professor Thomas Valente’s studies of how social networks affect an individual’s health-related behaviors, I realized that since unhealthy habits and misinformation often result from the influence of an individual’s environment, they can be changed on a large scale by using our knowledge of how to control diffusion of information in a social network.

First, let’s analyze how the behaviors (both positive and negative) spread through a social network. Valente describes the network structure of a community as several dense clusters with limited connections between the clusters (see image below). This correlates accurately with the social network architecture presented in class. Examining these clusters more closely, we will be able to see that each cluster has a group leader, who has a much higher “out” degree of edges leading to the people they influence. If these leaders are the innovators, then the ideas will diffuse very quickly across the community. Considering the 5 categories mentioned earlier which represent people with varying aptitudes for adapting to new ideas, this article introduces node weights representing individual thresholds to the diffusion of information. So, the quicker an individual can adapt to a new idea, the faster the idea can spread to their connections and so on.

To battle the spread of behaviors that negatively affect individuals’ health, Valente proposed four intervention strategies: identification of influential change agents, group segmentation, induction, and network alteration. The first strategy – individual interventions – involves targeting opinion leaders to influence their behavior, and then depend on these individuals to propagate these good habits on their followers (or, conversely, to intervene with their negative influence to stop its propagation). The second strategy involves creating segmentation interventions – helping small clusters of individuals overcome a negative habit or to embrace positive ideas. This is also effective because the probability and speed of the average person adapting an idea in a community are directly proportional to the percentage of their surrounding connections who have already embraced the idea. Induction intervention is a strategy to raise exposure to positive behaviors by cascading them via word-of-mouth, commonly known as “going viral”. Finally, alternation interventions differentiate from the previous 3 strategies which take advantage of existing network strategies. In this strategy, the network is altered in order to facilitate optimal behavioral adoption by influencing social connections of individuals.

These strategies have been employed with varying degrees of efficiency even before the study of network analysis gave us more knowledge to do so more efficiently. With modern tools such as computer network simulations and the plethora of available research, we are able to influence more people at greater speeds than ever before. We need to use this power to create healthier, more educated communities.

References:

[1] ResearchFeatures. “Diffusion of innovations within social networks“, May 31, 2018 . Study done by Professor Thomas Valente from the University of Southern California.
( https://researchfeatures.com/2018/05/31/diffusion-innovations-within-social-networks/ )

[2] Rong, Xin; Mei, Qiaozhu. “Diffusion of Innovations Revisited: From Social Network to Innovation Network” [499-508]. ACM, November 1, 2013. Taken from:
( http://www-personal.umich.edu/~qmei/pub/cikm2013-rong.pdf )