A Fascinating Intersection: The Rich Get Richer Phenomenon & the Music Industry
When observing the Rich get Richer Phenomenon, a fascinating and rather pervasive avenue which I sought to dig deeper into was the intersections with the music industry. Specifically, I critically examined how the music industry can – in fact – be understood through the lens of network effects and power law distributions. In some sense, the distribution of popularity in this often follows a power law rather than a normal distribution; This results in a few nodes (or artists) becoming extremely popular while the majority remain relatively obscure.
One particularly Bloomberg article written by Ashley Carman discussed Spotify’s planned changes to its royalty model, indicating a move that could exacerbate the rich-get-richer trend. By setting an annual stream threshold for royalty generation, the platform may inadvertently widen the gap between top-earning artists and smaller, independent acts. Carman further highlights Steve Stoute, a music industry executive, who criticizes this model for benefiting already successful artists at the expense of emerging talents, highlighting the fundamental issue of disproportionate gains within the industry. Essentially, by increasing the benefits given to the artists possessing the most traction, this quite simply produces a disincentivizing effect towards artists trying to make a name for themselves and or perhaps even further – make a living for themselves. For an industry where thousands of artists struggle to make ends meet, the ability to garner a suitable revenue stream for their work is imperative for a continuation of their pursuits. Without the sufficient means in order to continue their artistic livelihoods with one example being Spotify’s planned changes to its royalty model, this therefore contributes to further detriment to emerging artists as displayed by the rich-get-richer phenomenon.
I would now like to take perhaps an even wider lens to this phenomenon. From the music business newsletter, Penny Fractions, an article was published which delves into how major music labels, particularly Universal Music Group, have capitalized on industry transformations to consolidate power and wealth, illustrating the “rich-get-richer” phenomenon. Through strategic acquisitions and partnerships, UMG has not only expanded its market share but also its influence over the music distribution and production chain. This consolidation enhances its negotiating power with technology firms and artists, further entrenching its market dominance. The article particularly highlights the narrative of Edgar Bronfman Jr.’s, in which he had a strategic shift from the alcohol industry to entertainment industry – becoming extremely successful in the progress; this underscores broader trends where capital and influence enable large entities to adapt and thrive, often at the expense of smaller competitors and individual artists. This dynamic illustrates a broader economic principle where wealth brings about more wealth, particularly in industries undergoing rapid technological and structural changes.
Ultimately, an examination of the “rich-get-richer” phenomenon in the music industry highlights a critical concern regarding equity and sustainability. By analyzing the industry’s dynamics through network effects and power law distributions, it’s clear that the current system disproportionately benefits established artists, leaving newcomers at a significant disadvantage. The consolidation of power by entities like UMG, through strategic acquisitions and partnerships, not only amplifies this disparity but also challenges the industry’s capacity for innovation and inclusivity. All in all, these trends accentuate the need for equitable structures that support artist growth at all levels, ensuring a vibrant and diverse musical ecosystem.
Sources:
Source 1:
Source 2:
https://pennyfractions.ghost.io/a-history-of-universal-music-group/
From Individual Connections to Organizational Success
A recent article from Ben Rand of the Harvard Business Review explores the significance of networks in today’s economy. The paper highlights the findings of a new study that mapped LinkedIn connections among firms to examine the connection between organizational-level and individual-level networks. It reveals that companies at the center of professional communities, as indicated by their employee connections, tend to perform better. This finding underscores the value of human capital and knowledge-sharing, suggesting that professional social networks benefit not only individuals, but also companies.
In addition, the researchers’ analysis of the LinkedIn data revealed that highly connected companies tend to excel in innovation, with increased investments in research and development and more valuable patents. This correlation emphasizes the importance of forming tangible real-world connections for organizational success. While LinkedIn connections serve as proxies for real-world connections, they also offer unique opportunities to study network formation.
Based on the foundational role of network-building for success in the micro and macro, Rand offers that managers should strongly consider networking abilities alongside other qualifications when making hiring decisions, particularly for roles like sales or higher-level management that center around building organizational cohesion. Future research could delve deeper into understanding the quantitative impact of professional networking on company performance, including identifying which types of connections are most influential and in what contexts. Furthermore, the study found that middle- and lower-level employees contribute significantly to a company’s connectedness, highlighting the importance of networks across various organizational levels; an investigation into the factors at play (network size, network seniority, network location, etc.) in the context of hierarchy could provide valuable data to substantiate this finding.
The GameStop Saga: A Networked Revolution in Financial Markets
In the early months of 2021, the financial world was captivated by an unprecedented event that seemed to defy the traditional logic of the stock market. A group of individual investors, coordinating on reddit through a forum called r/wallstreetbets known for discussions on stock and options trading, started promoting GameStop stock and initiated a buying frenzy on shares of GameStop, a video game retailer. They believed the company was undervalued and saw an opportunity to profit by causing a short squeeze. A short squeeze occurs when the price of a heavily shorted stock starts to rise, and short sellers are forced to buy shares to cover their positions, further driving up the stock price. As more retail investors began buying into GameStop, its stock price skyrocketed, creating massive losses for hedge funds and other institutional investors who had shorted the stock. At the time GameStop was struggling immensely and this collective action led to a dramatic surge in GameStop’s stock price, causing substantial losses for hedge funds that had bet against the company through short selling. At its peak, GameStop’s stock, which was trading at around $20 at the beginning of January, reached an intraday high of nearly $500 by the end of the month. The GameStop story became a media sensation, highlighting the growing power of retail investors and the use of social media to mobilize and influence market movements. It sparked a broader discussion about wealth inequality, market manipulation, and the democratization of finance. This event is related to many concepts we have discussed in class, especially markets and strategic interactions in networks and bargaining and power in networks.
The GameStop phenomenon shows the network models of markets with intermediaries. Here, social media platforms and trading apps act as intermediaries, connecting individual investors in a way that has never been possible before. This connectivity enabled a large number of small investors to coordinate their actions and exert a significant impact on the market. This scenario illustrates the concept of Equilibria in Trading Networks, where the equilibrium of the market was shifted by the collective action of these individual traders. The traditional power dynamics of the market were challenged, showcasing how networked individuals can disrupt established market equilibria. The rise in GameStop’s stock is also a prime example of how trading networks can produce a domino effect across the market. The surge in GameStop’s stock price forced some hedge funds to close their short positions by purchasing shares at much higher prices, leading to a short squeeze. This situation not only affected the parties directly involved but also had broader implications for the market. It sparked a debate on market regulation, the role of social media in trading, and the power dynamics between retail investors and institutional investors. This incident reflects the discussions we had regarding Auctions and Ripple Effects by showing how interconnected the modern financial market is and how actions in one part of the network can lead to significant outcomes elsewhere. The GameStop event also shows the power in social networks and the impact of collective action. Traditionally, institutional investors have held significant power over market dynamics. However, the coordinated action of individual investors on platforms like Reddit and Twitter and individual forums showcases a shift in this power dynamic. This aligns with the concepts discussed in Bargaining and Power in Networks, where the power within networks, especially social media platforms, can lead to unexpected outcomes. The GameStop incident underlines the potential for networked individuals to not only participate in but also significantly influence the financial markets.
In conclusion, the GameStop saga highlights the significant role that networks play in today’s financial markets and the power dynamics that can shift as a result of networked actions. It’s clear that the traditional models of market operation and power distribution are evolving. The GameStop phenomenon makes us reconsider our understanding of market equilibria, the impact of intermediaries, and the power dynamics at play in a networked world and provides us with invaluable insights into the complexities and opportunities within networked markets. It proves that the role of technology in financial markets and the potential for collective action through digital platforms is immense and can disrupt established norms.
Sources:
https://www.thestreet.com/investing/stocks/a-timeline-of-the-gamestop-short-squeeze
https://www.thetradenews.com/the-reddit-revolt-gamestop-and-the-impact-of-social-media-on-institutional-investors/
https://news.virginia.edu/content/qa-closer-look-social-medias-role-gamestop-stock-surge
Deciphering the Digital Labyrinth: Information Cascades, Fake News, and the dangers it poses for High School Students
Sources : https://journals.sagepub.com/doi/full/10.1177/1091142120960488 https://ieeexplore.ieee.org/document/9527415 https://news.stanford.edu/2019/11/18/high-school-students-unequipped-spot-fake-news/
In today’s digital age, information is abundant and easily accessible. From social media platforms to news websites, we are constantly bombarded with a ton of information. However, with this abundance comes the challenge of discerning fact from fiction, especially for high school students who are increasingly exposed to online content. Recent research by scholars at the Stanford SChool of Education sheds light on the alarming lack of digital media literacy among high school students and its implications for democratic processes. Let’s delve deeper into the intricate web of information cascades, fake news, and the role of high school students in perpetuating misinformation !
In an era where misinformation spreads with alarming speed, the importance of digital media literacy cannot be overstated. Stanford researchers discovered that despite concerted efforts to enhance digital literacy, high school students struggle to distinguish credible sources from unreliable ones. For instance, an astonishing 96% of students failed to consider the credibility of a website on climate change, even when informed about its ties to the fossil fuel industry. Similarly, over half of the students regarded a misleading video, depicting voter fraud in Russia, as “strong evidence” of voter fraud in the US.
To comprehend the dynaamics of misinformation propagation, we must dive into the realm of network theory and information cascades. Network theory explores the intricate connections between nodes (individuals or entities) within a network, while information cascades explainthe mechanisms driving the spread of information through these connections. In the context of fake news, information cascades occur when individuals share and amplify false information, leading to its widespread dissemination.
At the forefront of understanding information cascades lies Social Network Analysis (SNA), a powerful methodology used to model the relationships between nodes and edges within a network. SNA allows researchers to map and quantify the relationships among actors in a social network, providing valuable insights into the structure and dynamics of information dissemination. Key metrics such as node degree, which calculates the number of connections to a particular node, enable researchers to analyze the flow of information within the network.
In parallel to SNA, the Susceptible-Infected (SI) model offers a valuable framework for understanding the spread of information, paarticularly in the context of epidemics and contagion. Originating from epidemiology, the SI model classifies individuals within a population as either susceptible (S) or infected (I), with the transmission rate of the infection serving as a critical parameter. By employing nonlinear differential equations, the SI model captures the dynamics of information dissemination over time, shedding light on the progression of contagion within a network.
In Indonesia, researchers leveraged SNA and the SI model to dissect the information cascade of fake news. By analyzing retweet data and measuring the spread of fake news across various topics, researchers uncovered alarming trends. Fake news proliferated at an alarming rate, outpacing the dissemination of true news and reaching a wider audience. This highlights the pernicious impact of misinformation in online networks and underscores the urgent need for intervention.
The real question is why this information gains traction instead of being debunked – why so much more traction than real news ?
An article published in 2018 in the Guardian quotes a legal expert in online-harassment, Danielle Citron, “people forward on what others think, even if the information is false, misleading or incomplete, because they think they have learned something valuable.” Even a few people sharing a story, that they might believe to be true themselves, can cause an information cascade. When a large number of peers are sharing a particular story, there is an informational effect at work. The more people spreadit, the more reason you may have to believe it is true : it is easy to believe that the information you can infer from your peers’ choice to spread the story imply something stronger than your own private information source.
The implications of these findings for high school students are paramountt. As future participants in democratic processes, students must possess the critical thinking skills necessary to navigate the digital landscape effectively and without risk of falling prey to fake news. However, the prevalence of misinformation poses a significant risk, as students may inadvertently contribute to the spread of falsehoods, which could in turn enhance information cascades about wrong information, undermining trust in reliable sources and democratic institutions.
In conclusion, the intricate interplay between network theory, network modeling, and information cascades offers importanr insights into the propagation of fake news and its consequences for high school students. By empowering students with robust digital media literacy skills and fostering a culture of critical inquiry, we can mitigate the risks posed by misinformation and cultivate a more informed and resilient society. The is a capital issue for our democracies and futures as society and various frameworks are tested to stop the spread of fake news before they reach the stage f information cascades, which then poses ethical and political questions about liberty of speech.
The changing world of advertising — is VCG still sufficient?
In class a couple of weeks ago, we learned about the concept of sponsored search and the ads matching market. In this case, we used the Vickrey-Clarke-Groves (VCG) mechanism to set prices for ad spots by modeling the situation as an auction. When the advertisers’ valuations were not known, this mechanism was effective since it encouraged “truthful bidding” as the dominant strategy for buyers.
However, according to recent reports, the actual advertising market is not always as naive and simple as it was presented in class. Nowadays, social media companies are capturing much of the advertising market instead, soaking up billions of dollars in profits that once flowed to legacy media companies. This trend of social media advertising – through sponsored social media platform posts, dedicated company pages, and even influencer marketing – has continued to accelerate, simultaneously delivering massive blows to typical news organizations that once hosted those advertisements. In fact, The Messenger closed its doors just last month, BuzzFeed recently announced that it would cut an additional 16% of its already slimmed-down staff, and Vice News also recently announced drastic layoffs.
I find these news reports about the changing advertising market interesting since social media advertising differs from traditional search or media advertising in that there is a lot more flexibility and freedom. As aptly stated by Oliver Darcy of CNN, “Not only are these news organizations crucial to the communities — both local and national — that they serve, but they also diligently work to ensure that their platforms are grounded in facts and free of abuse.” And as such, it’s harder to model the complex, untamed world of social media advertising as a simple auction-based network, as we saw in lecture.
As I see the advertising landscape, social media advertising shares a conceptual connection with the VCG mechanism, a principle rooted in auction theory and mechanism design. At its core, the VCG mechanism aims to achieve efficient resource allocation among multiple participants with private valuations, ensuring that each participant’s payment reflects the externalities imposed on others. In the context of social media advertising, this framework can be applied to ad auctions where advertisers bid for ad space or user attention. However, another core component of social media advertising is influencer marketing, which necessitates a nuanced approach to pricing that transcends traditional advertising models. In the realm of influencer marketing, the value of an influencer’s endorsement is not merely based on audience reach but also on the influencer’s credibility, engagement rates, and the affinity of their audience with the advertised product. Prices for influencer marketing campaigns might be designed through a combination of performance-based metrics and the intrinsic qualities of the influencer’s platform. Factors such as conversion rates, engagement levels, and the relevance of the influencer’s audience to the brand could dictate the pricing structure.
As the advertising landscape continues to evolve and change, I’m interested in learning how the network theories behind advertising will also develop along with it. While the VCG mechanism is sufficient in modeling traditional sponsored search scenarios, I think it’s worth exploring more in-depth how effective VCG is in real-world social media advertising scenarios.
Echoes of Influence: Taylor Swift, Rumors, and the Dynamics of Information Cascades
The swirling rumors about Taylor Swift’s potential political endorsement of Joe Biden in the upcoming Presidential election provide an interesting example of information cascades and herding behavior in action. While Taylor made a statement of support on Instagram for Biden in the last election, she has not yet made a statement on the next one. Despite this, rumors of her political leanings have stirred lots of discussion and speculation. Biden even engaged with these rumors during an appearance on “Late Night with Seth Meyers,” where he humorously declared Swift’s endorsement for 2024 as “classified.” Interestingly, we see that information cascades and herding behavior might play a role in shaping public perception and influence, even in the absence of explicit actions or statements.
As we learned in class, information cascades occur when individuals, making decisions sequentially, rely heavily on the actions and decisions of those before them, often disregarding their private information or initial inclinations in favor of the perceived wisdom of the crowd. This phenomenon can be seen in various contexts, from choosing between two restaurants based on crowd size, as described in class, to the herding behavior observed in social media platforms. The speculation around Taylor’s political endorsement brings this concept to life in the political arena, showcasing how rumors and perceived actions can lead to a cascade of beliefs or behaviors among the public.
The herding experiment with urns and marbles, discussed in class, is an interesting way to think about the discussion on Taylor. Just as participants in the experiment might infer the majority color based on previous choices, the public may infer Taylor’s political stance based on rumors and past actions, leading to a cascade of assumptions and discussions. This process highlights the dual forces at play in information cascades: the informational effect, where the actions of others provide cues on which to base decisions, and the direct-benefit effect, where aligning with the majority offers perceived benefits.
In the case of Taylor Swift, the informational effect shows how the actions (or rumored actions) of a highly influential individual can lead to widespread assumptions, even in the absence of explicit statements. This scenario leads us to question the balance between information-based inference and social pressure for conformity, and whether the herding observed in such cases is entirely mindless or a rational response to the cues provided by those we perceive as influential or knowledgeable.
https://apnews.com/article/biden-seth-meyers-donors-2024-3ba258c294d0697f2f2963f3025dd529
Money Locking
The United States of America likes to consider itself a land of opportunity. After all, the notion of upward mobility is ingrained in its endeared belief of the American Dream— an ideal that anyone, regardless of where they were born or what class they were born to, can attain their own version of success. While it does seem tempting, beneath this mask lies a stark reality. In America, the rich get richer, while the poor get poorer. And this widening chasm between the rich and the poor is not merely a consequence of individual effort or merit. Rather, it is deeply influenced by systemic mechanisms that perpetuate the concentration of wealth and power among the elite.
At the heart of this phenomenon lies what can be defined as a “power lock” — a nexus where a distribution favors a small percentage of the population. In the context of this article, such small percentage of the population will be the rich and the distribution will be the wealth distribution.
To illustrate how severe this issue is, in 2020, the world’s five richest men have more than doubled their fortunes to $869 billion since 2020 while the world’s poorest 60%— almost 5 billion people—- have lost money. And while many families were struggling to feed themselves amid high levels of inflation, the world’s billionaires were $3.3 trillion richer than in 2020, and their wealth had grown three times faster than the rate of inflation. And for further emphasis, the top 1% (as shown below) saw their wages skyrocket 160% since 1979.
Yet, the question is why? Why are the rich getting richer while the poor are getting poorer?
One of the primary drivers of this power lock is the unequal distribution of resources and opportunities. Wealth begets wealth as those born into affluent families not only inherit material riches, but they also inherit social capital, access to quality education, healthcare, and networks that pave the path for success. Meanwhile, those born into poverty face many obstacles as they struggle to find their own path to success. This driver is known as preferential attachment in the sense that in a given copying model, the richer a person is, the more access to resources for further riches one has. That in turn will result in a positive feedback loop where the rich will end up becoming richer.
Another primary driver is the rigged economic and political systems that favor the wealthy. Tax policies, for example, benefit the rich through loopholes and preferential treatment. In turn, the rich are allowed to amass even greater fortunes while the poor are burdened with disproportionately high taxes or regressive policies. This would explain how the rich are able to stay rich and how the poor stay poor. They are forced into a treatment where any social mobility is heavily restricted. Likewise, rich individuals are able to support political candidates to help ensure that policies that favor the rich are enforced throughout the government. This helps the rich “stay in power” as well.
The American Dream will always remain one thing, a dream. It is something that has been made very unachievable by the myriad of rigged systems. And there is no set guide for how one becomes rich as well. The rich-get-richer effect is sensitive to unpredictable initial fluctuations as information cascades, the wealth of an individual, depends on the outcome of a small number of initial decisions in the population. It is entirely random and the belief that one can achieve something equivalent to how much work one puts in is just what it is. It will always be a belief, never a tangible outcome because of the many barriers that further the rich-get-richer effect.
Sources:
https://www.cnn.com/2023/01/15/business/top-1-wealth-oxfam-davos/index.html
Love at First Algorithm: Matching Markets in Online Dating
In the digital age, the quest for love and connection has intertwined with technology, leading us to the doorstep of online dating platforms. These platforms, powered by sophisticated algorithms, serve as modern-day Cupids, orchestrating romantic connections with a precision and efficiency that rivals traditional matchmaking methods. At their core, these platforms function as matching markets, a concept borrowed from economics, applied to the intricate dance of human relationships.
The concept of matching markets is not new, but its application in online dating has revolutionized how people find romantic partners. Users present their “goods” (profiles) in the market, hoping to find a match that meets their criteria. The algorithm’s role is to facilitate these matches efficiently, ensuring that users are paired with individuals who are most likely to result in a successful connection, based on the information available. The success of a matching market is measured by its outcomes. In the context of online dating, successful outcomes can vary from user to user, ranging from enjoyable conversations and casual meetings to long-term relationships and marriages. The ultimate goal of these platforms is to increase the probability of successful outcomes for their users, leveraging the power of algorithms to turn the chaotic world of dating into a more orderly, efficient matching market.
Looking at an online dating platform like Tinder that is used in over 190 countries with more than 75 million users monthly, we can see how Tinder’s algorithmic approach to matchmaking illustrates the principles of a matching market at scale. Users swipe left or right on potential matches, a simple yet effective mechanism that feeds the platform’s algorithms with data on user preferences and behaviors. This data-driven approach allows Tinder to continuously refine and personalize match suggestions, optimizing the user experience by aiming to connect people with mutually compatible partners. Enhancements such as ‘boosting’ to amplify visibility on the platform or ‘super liking’ to express keen interest in someone enrich the user experience and make it so that there are small things you can do to get ahead of the rest of the field.
Another interesting aspect of online dating platforms is that the relationship between the number of users and the number of matches an individual receives can be counterintuitive. One might assume that more users equate to a higher chance of finding a match. However, the dynamics are more nuanced due to the principles of supply and demand within these matching markets. When a platform has fewer users, each user’s profile is more likely to be viewed, and consequently, the chances of receiving a match increase. This is because, with a smaller user base, there’s less competition, and users are more inclined to consider each potential match more seriously. In this scenario, the scarcity of options can make users more open to engaging with those available, potentially leading to more matches per user.
Overall, the integration of technology and human desire within online dating platforms showcases a fascinating application of matching market principles in the digital realm. Platforms like Tinder, with their vast user base and sophisticated algorithms, exemplify how data-driven approaches can streamline the search for romantic connections, making the vast and often overwhelming world of dating more navigable and efficient. While the number of users presents a complex dynamic affecting individual success rates, features like ‘boosting’ and ‘super liking’ provide strategic tools to enhance visibility and express interest, subtly influencing the market’s supply and demand. Ultimately, online dating platforms, through their algorithmic intricacies and user-centric features, not only redefine how individuals find love and connection but also offer a window into the evolving landscape of human relationships in the digital age, where technology and emotion intersect to create new pathways to companionship.
Schwartz, Elaine. “How Online Dating Markets Affect Us.” Econlife, 5 Feb. 2023, econlife.com/2023/02/dating-markets/.
Algorithms and Instagram
In today’s technology based world, there is a lot of reliance on algorithms when it comes to social media platforms. One of the many social media platforms that uses algorithms is Instagram. An algorithm in this instance is basically calculated rules that determine what shows up on a users page. When specifically looking at Instagram’s algorithm, Decoding Instagram Following List Order: How it Works, states that “Instagram sorts any user’s following list order by default based on mutual interaction. It means the accounts that users interact with the most by liking their posts, tagging them in stories, and engaging via direct messages and comments will appear at the top of the following list” (Rabiaa Nawaz). Simply, this means that the order of names displayed in your following list is determined by the accounts you engage the most with, putting these accounts at the top of the list. The article also lists other factors that affect its algorithm and in turn, contribute to the sorting of the Instagram follower/ following list. Some of these factors include levels of interaction, accounts that directly engage with you, and mutual followers.
I think that social media algorithms are interesting and connect with the basics of the class because of the Bow-Tie Structure we learned about in class. The Bow-Tie Structure basically states that nodes are part of a larger strongly connected network. So basically, every node within the Giant Strongly Connected Component can reach each other. In this same way, every node (or follow) within a person’s following list follows this Bow-Tie Structure since they can all reach each other- they would all at least be mutuals with the original person. So in term, networks can be compared to social media algorithms and many similarities can be found within both of them.
Works Cited
Nawaz, Rabiaa. “Decoding Instagram Following List Order: How It Works: Socialbu.” SocialBu
\Blog, 15 Feb. 2024, socialbu.com/blog/instagram-following-list-order/.
The Influence of Big Tech on the Global Economy
In recent years, the dominance of Big Tech companies like Amazon, Google, and Facebook has become increasingly apparent, not just within their respective industries but also in shaping the global economy. A recent article by The New York Times, titled “The Rise of Big Tech May Just Be Starting,” delves into the implications of this phenomenon. The piece discusses how these companies’ vast resources and data have enabled them to expand into various sectors, disrupting traditional industries and creating unprecedented wealth and power.
One particularly intriguing aspect highlighted in the article is the growing concern over the influence of these tech giants on market competition and innovation. As they continue to acquire smaller companies and control key platforms, questions arise about fair competition and the potential slowdown of innovation. Additionally, their immense wealth enables them to wield significant political influence, raising concerns about their impact on regulatory policies and societal norms.
This article resonates with our course theme of “Rich Get Richer” as it explains how these tech companies, already immensely wealthy and powerful, continuously expand their influence and wealth, creating a feedback loop where their dominance further solidifies. As we discuss the dynamics of wealth accumulation and distribution, it’s crucial to consider the role of these tech giants and the broader implications of their dominance on the global economy and society.
Link to article: https://www.nytimes.com/2022/02/16/opinion/big-tech-stock-market.html
Information Cascades in Schools of Fish
https://www.princeton.edu/news/2019/09/30/get-moving-mystery-animal-group-behavior
Nature has a lot of bewildering phenomena, and near-instantaneous decisions of animal crowds are one of them. These “behavioral cascades” are not too different from the information cascades found in human behavior. One animal being skittish in response to a threat can lead to another and yet another responding in the same way. However, the behavior of one or two animals is not nearly guaranteed to set off a behavioral cascade.
As stated in the attached article, a fish suddenly darting one direction does not automatically lead the school to hold the same response. This differentiation in responses has been somewhat of a mystery–while it is intuitive that there will be some tipping point that leads to group behavior, it is less intuitive to pin down how this tipping point is reached. Golden shiners are a particularly skittish schooling fish. An individual fish will sometimes exhibit darting behavior without any actual threat. It would not be too beneficial for a school to suddenly change directions for every singular fluke.
Golden shiners rely on their own kind of “private information” to determine how credible a threat is based on the behaviors of other fish. There is a chemical mixture called “schreckstoff” (this translates literally into “scary stuff” from German) that is released into water through injuries in the fish’s skin. The presence of such a compound generally causes fear responses in fish by signaling there is a potential threat that harmed a nearby fish. Golden shiners tend to bunch closer together when there is schreckstoff present. This response indicates to individual fish that the fright responses of other fish are more credible. Thus, even if a fish cannot see what is making one or two other fish dart away quickly, it is much likelier that they will follow the same behavior. This information travels incredibly quickly to move away from a threat, making it seem as if schools of fish are moving simultaneously.
On the other hand, the lack of schreckstoff prevents shiners from bunching together. This leads to each individual fish’s “private information” indicating that there is no threat present even if one or two other fish are exhibiting frightened behavior. Under these conditions, it is far more unlikely for a behavioral cascade to form.
Information Cascades and The Bachelor: A Dive into Viral Dynamics
Never would I have imagined myself getting hooked on reality TV. Not really my scene. I was that person at watch parties, half-joking, “You know this is all scripted, right?” And yet, here I was, dedicating over an hour every Monday to watch over 30 women chase after one man’s heart. Why the change of heart? Simple: FOMO. My living room turned into the epicenter of “Bachelor” fandom every week, and I didn’t want to be left out.
“The Bachelor” isn’t just a show; it’s a cultural phenomenon. Women go on individual and group dates with the Bachelor, and each episode ends with some getting a rose to stay, while others leave. Watching it unfold became more than a pastime; it sparked endless debates among my friends about who’d be the next to leave or who was stirring the pot this season.
But the real game-changer was TikTok. Post-episode, my feed would explode with theories and reactions. “The content of this page is generated through algorithms which take into account the videos you have previously seen, liked or shared. It is the ultimate time killer and very addictive because it never runs out of content” (Middleton, 2019). Overnight, Daisy became the crowd favorite, and Maria, initially the one everyone loved to hate, started winning hearts with the support of this algorithm. This wasn’t just happening in my circle; it was everywhere.
Here’s where it gets interesting: This shift in opinions and allegiances? It’s a classic example of information cascades in action. People lean heavily on others’ views, especially on platforms like TikTok, where opinions are shared, stitched, and spread rapidly. “TikTok turns anyone with a phone into a reality TV producer,” as Hewitt puts it. Suddenly, you’re not just watching the show; you’re part of a massive, interactive narrative. Here is an example of this happening on TikTok: https://www.tiktok.com/@sydneybernier27/video/7335321275751091498?_r=1&_t=8kKjqKGWqUM
As the season wore on, the narrative built around certain contestants showed how powerful these cascades can be. It wasn’t just about what happened on the show anymore; it was about the story being spun online. Daisy’s surge in popularity and Maria’s redemption arc were as much about the social media buzz as their actions on screen.
This phenomenon isn’t limited to picking favorites. It shapes the entire narrative arc of the show, influencing everything from fan theories to the contestants’ off-show popularity. It’s a vivid illustration of how interconnected our opinions are and how digital platforms can amplify specific narratives, potentially swaying the show’s direction itself.
Now that the show is coming to an end and only four girls remain, predictions for who will take it home are circulating the internet. It’s interesting to observe how, even before the next episode airs, the tide of public opinion begins to shift, influenced by a myriad of TikTok videos, Instagram stories, and Twitter threads dissecting every detail of the previous episodes. The speculation surrounding the outcome not only fuels engagement but also highlights the intricate dance between media production and audience participation in the digital age. The “Bachelor” phenomenon, therefore, is not just about the quest for love on screen; it’s a mirror reflecting our society’s engagement with media, the power of collective opinion, and the increasingly blurred lines between reality and the reality we construct online. As we await the final rose ceremony, one thing is clear: the journey there will be as much about the conversations happening off-screen as those happening on it.
Here is one of the Tik Toks of the many sharing their predictions for the finals two:
https://www.tiktok.com/t/ZTLeFP3U2/
Sources:
https://hedgehogreview.com/issues/theological-variations/articles/tiktok-extends-the-wasteland
Beyond the Rich Get Richer: Exploring the Paradox of Economic Power
The study “Do the Rich Get Richer and the Poor Poorer? Experimental Tests of a Model of Power” by Yvonne Durham, Jack Hirshleifer, and Vernon L. Smith takes a deep dive into the intriguing “Paradox of Power,” challenging the all-too-familiar narrative of the “rich-get-richer.” Grounding their investigation in the influential theories of scholars like Hirshleifer (1991) and Bourdieu (1986), the authors explore scenarios where, unexpectedly, the economically disadvantaged manage to gain an upper hand over their wealthier rivals. This research not only expands our understanding of the dynamics behind wealth disparities but also highlights specific conditions under which the common trend of wealth accumulation by the affluent can be reversed.
By conducting experiments where participants are tasked with deciding how to allocate their resources between productive efforts and redistributive actions, this study sheds light on the strategic thinking that shapes economic outcomes. It questions the straightforward link between initial economic status and wealth accumulation, unveiling a complex interplay of strategy, power dynamics, and initial conditions. In challenging the “rich-get-richer” concept, the research advocates for a comprehensive approach that looks beyond economic policies to include social dynamics and the psychological factors driving socioeconomic divisions. This nuanced perspective not only helps illuminate the root causes of economic inequality but also points to potential strategies for breaking the cycle of wealth concentration, offering new insights into the structural foundations of economic disparities. This nuanced exploration invites us to rethink the entrenched “rich-get-richer” paradigm, suggesting that under certain conditions, the economic playing field may offer unexpected opportunities for reshaping wealth distribution.
https://www.jstor.org/stable/117014?seq=3
“Rich-Get-Richer” Dynamics of the Professional Tennis Tour
The professional tennis tour is all about player matchups and how players intersect with one another within the expansive network of professional players and the extensive year-round tournament calendar. The “six degrees of separation” and “rich-get-richer” principles underscore the intricate interconnectedness of players on the tennis tour—evidenced in players stats, rankings, and rivalries.
Recently, the disparities and unfair structure of the tennis tour, specifically with regard to how it allinates those outside of the world top 100 rankings, has been a topic of discussion in the tennis world. Lower ranked players struggle to earn a living, traveling to tournament after tournament and often coming out at a financial loss. In this regard, the harsh reality of the tennis tour is a perfect example of the “rich-get-richer” dynamic of networks: “a page’s popularity grows at a rate proportional to its current value, and hence exponentially with time.”
As players compete for rankings, prize money, and recognition, the structure of the tournaments itself mirrors the interconnected nature of social networks. Higher-ranked players receive automatic entry into more prestigious events based on their ranking, while lower-ranked players must navigate through qualifying draws and lower-level tournaments in order to earn spots in the main draw. This hierarchical structure perpetuates the disparity, reinforcing the dominance of the top players and the challenges faced by newcomers seeking to break through to the top.
Higher ranked players are able to qualify for higher-level tournaments, thus gaining the opportunity to earn more ranking points, more prize money, play against higher ranked players, and play on bigger world stages. Lower ranked players, conversely, are forced to play only the tournaments they can qualify due to their lower rankings. Unsurprisingly, these tournaments award less prize money, less ranking points, and less overall opportunities.
Another aspect of rich-get-richer dynamics comes with player’s success: a victory over a higher-ranked player elevates an underdog’s status and ranking, opening doors for them to climb the rankings, illustrating how success breeds success within the tennis network.
Yet another way that tennis exemplifies the “rich-get-richer” effect, is how top players often have extensive “networks” (which are called “teams”) with whom they travel (consisting of coaches, agents, trainers, and sponsors). As players ascend the ranks, they accumulate victories which afford them greater access to resources and endorsements, thus amplifying the “rich-get-richer” effect wherein stronger players are awarded a disproportionate share of opportunities and rewards.
How can tennis afford up-and-coming players more opportunities? How can the tennis tour be remedied to make it more financially feasible for lower-ranked players as they begin their journeys as tennis professionals? These questions are at the forefront of the discussion in the tennis community and profound changes are imminent.
https://theathletic.com/5147362/2023/12/28/how-to-fix-tennis/
The Unseen Forces of the Power Law: Navigating the Educational Divide
Imagine walking down a bustling city street, where skyscrapers tower above and every building represents a level of educational achievement. In this cityscape of opportunity, you’d expect a diverse range of structures—some tall, some short, representing the spectrum of success. Yet, as you peer closer, a startling pattern emerges: a few towering giants casting long shadows over countless smaller buildings, barely scraping the sky. This is not just a city of dreams but a vivid illustration of the power law distribution in education, a phenomenon where the rich in resources and opportunities reach dizzying heights, leaving the rest in their shadow.
The power law distribution is akin to discovering a secret passage in the labyrinth of societal structures, revealing how a few accumulate the lion’s share of resources, popularity, or, in our case, educational achievements. It’s a statistical narrative that defies the average, focusing instead on the outliers and the extremes. In this distribution, the reality is stark: success breeds success, and the initial advantages held by the affluent amplify over time, creating a self-reinforcing cycle of accumulation.
Delving deeper into the educational divide, Sean F. Reardon’s analysis offers a sobering view of today’s reality. He points out that “the achievement gap between affluent and low-income students has grown by about 40 percent since the 1960s,” highlighting not just the expansion of this gap but also how the mechanisms of the power law exacerbate it. In this scenario, students from wealthier backgrounds gain an increasingly larger edge through access to superior educational resources, pulling ahead in the academic race. Meanwhile, students from less affluent families find themselves further behind, their futures constrained by a lack of similar opportunities. This situation starkly illustrates the “rich get richer” dynamic within the educational sector, showcasing a troubling trend where socioeconomic status dictates educational success, sidelining talent and hard work.
The potential impact of this growing educational divide is profound, stretching far beyond the classroom walls. It not only cements socioeconomic inequalities but also stifles social mobility, creating a cycle where the socioeconomic status of one generation becomes the likely destiny of the next. This widening gap threatens to fracture the very foundation of meritocracy, suggesting that opportunities for success are increasingly reserved for those born into privilege. As this divide grows, so does the risk of creating a society segmented not by talent, ambition, or hard work but by access to resources. The long-term consequences could be a decrease in innovation, as diverse perspectives and untapped talents are marginalized, and an erosion of social cohesion, as the shared belief in fair opportunity and upward mobility is undermined. Addressing this divide is not just an educational imperative but a societal one, essential for ensuring a future where success is determined by potential and effort, not the circumstances of one’s birth.
So, where do we go from here? The path to bridging this divide demands creativity, courage, and a collective will. It calls for policies that don’t just level the playing field but actively uplift those in the shadows of our educational skyscrapers. Imagine a world where community centers become hubs of learning and innovation, where schools in underserved areas receive the support and resources to nurture the next generation of leaders, scientists, and artists. By investing in early education programs and ensuring that every child has access to quality learning environments, we can begin to dismantle the towering barriers erected by the power law.
In this journey toward educational equity, we’re not just redistributing resources but reshaping futures. The power law distribution, with its stark portrayal of inequality, serves not as a sentence but as a challenge—a call to action for educators, policymakers, and communities to come together and rewrite the narrative. Together, we can construct a new cityscape of opportunity, where every building reaches proudly into the sky, and the shadows of disparity are dispelled by the light of potential realized.
The “Taylor Effect”: Exploring Collective Behavior Through the Lens of Taylor Swift’s Influence
Being a recent fan of football, and after watching my first-ever superbowl, it was hard not to notice the full 54 seconds of screen time popstar Taylor Swift received during the 3-hour game. It was clear to me that there was a buzz about the “Taylor Effect”. This term has been coined to depict the remarkable influence that comes from the power of Taylor Swift on collective behavior.
Swift has been spotted, as of this past season, at Kansas City Chiefs games supporting her boyfriend and Chiefs tight end Travis Kelce. Since Swift started turning up at Chiefs games, the National Public Radio article reported that Kelce jersey sales went up by a whopping 400%. Another report from Fox Sports highlights that as of this past season, Taylor Swift is drawing a record-breaking rate of NFL viewership with female viewers aged 12-49.
The dynamics of the social influence and collective behavior, which occurs as part of the “Taylor Effect” can be better understood by drawing from the principles of herding and information cascades.
For instance, Swift’s attendance at a Kansas City Chiefs game may cause individuals to align their behaviors with that of Swift or her fans due to both direct-benefit effects and informational effects. The direct benefit may include the desire to adopt Swift’s fashion sense or donate to her favored charities. Furthermore, her actions and that of her fans serve as signals that convey information about what is considered socially desirable or culturally significant. This is something companies and sponsorships can take advantage as information cascades are formed of the Swift fans who only buy the products, not by their own knowledge or necessarily for the quality of the product, but due to Swift herself being seen with the product or a trend that is created online, which introduces the idea of herding.
Following on from the above, a herding experiment in which participants are given a bucket full of balls—each one blue or red—is one that illuminates how people react to social cues. The first participant selects a ball without revealing its color and announces their choice. Subsequent participants then face the decision of whether to trust their own observation or conform to the announcements of those who came before them. This experiment highlights people’s propensity to follow the herd, even when their own intuition tells them otherwise. Similar to the experiment, those who are affected by the “Taylor Effect” might seek advice from Swift and her fan base regarding topics like entertainment, fashion, and consumer preferences. In the same way that subjects in a herding experiment might make judgements based on the opinions of the majority, people who are affected by the “Taylor Effect” might also adopt the attitudes and actions that Swift and her supporters have.
To sum up, the “Taylor Effect” is a prime example of how information cascades, herding behavior, and celebrity impact interact. We can learn a great deal about the workings of societal trends and cultural phenomena by analyzing these ideas in light of Swift’s influence on group behavior.
Links:
https://www.standard.co.uk/showbiz/taylor-swift-screen-time-super-bowl-revealed-b1138857.html
https://www.npr.org/2023/09/27/1201992668/taylor-swifts-travis-kelce-jersey-sales
A “Rich Get Richer” Case Study: HPV Vaccination & the Pharmaceutical Patent System
When the COVID-19 vaccine was first released, I remember being confused about what happened to the dozens of other companies that had announced continuous research in the field. There were two options (Pfizer and Moderna) at the time, but it seemed like not a lot of companies continued to make vaccines afterward.
After the “rich get richer” lecture, I began to research the patent system that impacts companies and the products they create. As with the patent system in all industries, IP law encouraged development and progress in the field of biotechnology. The system encourages researchers to further explore the implications of their patented method or product without the fear that competitors will copy their invention. Especially in a field such as biology where results are rarely immediate and clinical trials must be carried out with caution, this system has been beneficial.
However, the “rich get richer” phenomenon can also be observed from the patent system. As an overall statistic, in 2020, the top 1% of patentees received more than 50% of the new patents. In the Emory Law Journal, Chien explains that there are often “clusters” formed where those with the resources to develop benefit from this system. Additionally, equality in opportunity is often hard to measure with the patent system that already protects prior inventions and methods created (Chien).
One notable example is the development of the HPV vaccine, which is now recommended for all children ages 11-12 (CDC). A pharmaceutical company, Merck & Co., released the first FDA-approved HPV vaccine called Gardasil in 2006. Since then, Merck has been continuing to develop newer versions, including Gardasil 9, which accounts for even more strains of HPV and a higher percentage of cervical cancers associated with HPV (FDA). On the other hand, a similar drug called Cervarix by GSK was also approved in 2006, but the company left the market in 2016 due to low demand (in 2015, GSK made $107 million off of the revenue while Merck made $1.9 billion). With Merck’s initial success with the patent and its collaboration with government groups (including the CDC) towards the HPV campaign that would encourage vaccination, it was difficult for Cervarix (and other companies who may have attempted to address HPV concerns) to compete.
The following is a table created from Merck’s revenue with its Gardasil product from 2021 up until now. Merck is still succeeding in its contributions to this field of HPV.
| Year | Revenue | % Increase |
| 2021 | 5,700,000,000 | 44% |
| 2022 | 6,900,000,000 | 22% |
| 2023 | 8,900,000,000 | 29% |
While companies technically have equal opportunity and access until a product is FDA-approved and patented, the patent system still resembles a monopoly at times. With such high barriers to entry, it becomes difficult for companies to compete; yet, with so many new sectors of interest and need in the medical industry, a question should be raised about whether it is a necessity that each drug has a competitor. Competition with each product may decrease the high prices of medication, but there could also be a potential loss in the research that could have been conducted in other fields. Thus, the patent system within the biotech industry remains a complex issue as with other applications of IP law.
Sources:
Emory Law Journal (Author: Chien)
An Analysis on Game Theory Concepts from Squid Game’s Glass Bridge Game
Green tracksuits. Debt. Prize Money. Death. These four things should point towards a very familiar Netflix show: Squid Game. Squid Game is a survival game where 456 debt ridden players compete for 45.6 Billion Won prize money by playing sadistic versions of Korean children games. While children’s games sound simple, each game consists of unique game theory techniques, allowing us to understand what strategies would put players at a higher advantage to win each game and proceed to the following rounds. Winning each game would require both luck and game theory strategies.
There are six games in Squid Game, but I want to put an emphasis on the second to last game: Glass Bridge. The players choosing their order of embarking the glass bridge involved information cascade and herding behaviors, and the game itself involves game theory and dominant strategies.
https://criticalcommons.org/view?m=UKbzDpzSN
(Link to the clip)
Quick summary of the clip above. The players were told to choose a vest numbered from 1 through 16. Before hearing about the rules of the game, two risk-loving players ran forward to get a vest, a number 6 and number 7 vest consecutively. After that, six other players followed and ran towards the mannequins to get numbered vests for numbers in the middle of 1 through 16. This is an interesting phenomenon of information cascade and herding behavior. The first two people ran to get a vest first because they are risk-loving. The six players right after followed just because they thought that the first two people were right. In fact, they also believed that choosing a number in the middle would be safe because the people in front of them were all choosing numbers in the middle. I thought this was an interesting reference to the information cascade concept we learnt in chapter 16. The reference is that we only need two people in the beginning to have the same decision, and once the third person and people after sees that, they are likely to follow what the two people’s choices are. This was exactly how it played out in the scene. Once the first eight players choose their vests, the announcer announced that the vests determine the order they play their next game, which is the glass bridge (shown on the clip below).
https://criticalcommons.org/view?m=KlZ58e5OJ
(Trigger Warning: Death, Violence, Blood)
The optimal choice should be listening to what the rules are first before choosing a vest to determine their order. However, due to herding behaviors that the six players committed to, they ended up at a disadvantage compared to the last eight players, who at least had a chance to consider what numbered vest to choose after knowing the rules. In fact, choosing the end numbers would be the best. There are 18 total steps to reach the end of the bridge, and each step there are two panels, one panel that is sturdy, while the other would break when you step on it. The chance of the first player making it across the bridge would be (0.5)^18, which is 1 in 262,144. The second player would have a (0.5)^17 chance, third would have a (0.5)^16 chance and so on, so being the last few players is definitely an advantage. To make the game even harder, there is a time limit to getting across the bridge.
This game ends in a fairly complicated payoff matrix with no nash equilibrium or dominant strategy. It seems that shoving the player in front can be the dominant strategy, but the player can shove back and cause both players to be eliminated. With rational back and forth strategies, it would not be surprising if no one ends up making it to the end.
In theory, the expected number of players to die would be 9, calculated from 18*0.5, which means that 7 players on average would survive if everyone behaves rationally and follows the rules of the game by jumping on the panels. Surprisingly, 3 players ended up surviving. This is the result of the lack of cooperation between the players, human greed, and fear of dying. Player #017, a glass worker who can decipher which panel is sturdy glass, only agreed to help after 12 players in front of the game were eliminated. Player #101 decides to stall and not move forward as he didn’t want to take the risk, costing time for the players to figure out the path.
Although a terrifying example to look at about game theory, Squid Game’s has other children’s games like “Red Light, Green Light” that also portray interesting game theory concepts. Definitely an interesting show to look at in terms of understanding game theory!
https://www.economicsofsquidgame.com/post/risk-profile-in-the-glass-stepping-stone-bridge-game
CFB Realignment: The Rich Get Richer
College football (CFB) conference realignment is a dynamic process that continually reshapes the landscape of collegiate athletics in the United States. It involves the strategic restructuring of conferences, where universities decide to join, leave, or switch allegiances to different athletic conferences. These changes are often driven by factors such as financial considerations, geographic alignment, competitive balance, and media rights agreements. Conference realignment not only impacts the competitive dynamics of college football but also influences institutional identities, fan loyalties, and the broader economic landscape of intercollegiate sports. However, over the past year, things might have gotten out of hand.
The Pac-12, a Power 5 conference, dissolved in what seemed like a week. Starting with USC and UCLA leaving the conference over financial disputes to join the Big Ten from 2025, the conference dispersed rapidly. Following their departure, Oregon and Washington followed them to the Big Ten and Colorado, Utah, Arizona, and Arizona State all signed contracts with the Big 12. With the conference in a feeble state, Cal and Stanford departed for the ACC, leaving only Washington State and Oregon State in the Pac-12 conference. Now this seems abrupt and rash, but there may be more validity to their moves than you might think.
The main motivation behind conference realignment in general, and in this case especially, comes down to money. The schools want to make the most financial gain from their athletic successes and this can be achieved by joining a more profitable conference. The reason all of these teams left an otherwise healthy and competitive conference was due to their TV deal running out after 2023. The Pac-12 was not able to provide a competitive deal being on the west coast and attracting less viewership than other conferences around the country. For example, in 2023 Pac-12 teams are receiving $37 million in revenue distribution while the Big Ten is closing in on their new deal to give each team $80-100 million per year. The Pac-12 fell behind and wasn’t making significant strides to get back on level footing with the other conferences in the country, so teams decided to take their talents elsewhere.
What this comes down to, and what the departing teams recognized, is that CFB is a networks problem. In order to make the most money, you have to be connected to the most popular and profitable teams and conferences (SEC, Big Ten, Big 12, Georgia, Alabama, Michigan, etc.). These entities have the most fans which set them up for success in conference negotiations. The more games you have against popular teams, the more viewership you will have for your games. Each conference is just a network of teams working together to achieve the most utility. As a member of each network, these schools are searching for the strongest, most profitable networks available. USC and UCLA realized that they were too profitable to remain in the Pac-12; they were able to leave and take their large fan bases with them under a new, more profitable TV deal to be appropriately compensated for their popularity. Being connected and associated with more popular teams can only boost their viewership and, in turn, their financial compensation.
Although this benefits these teams in the short run, there is one problem with this style of hopping ship to the most popular conferences. This problem is called the rich-get-richer phenomenon. As the best teams stay in the best conferences and converge into fewer and fewer “elite” conferences, everyone else is left out to dry. As these teams at the top get all of the viewership and money, teams outside of this threshold are not able to get comparable revenue distributions, facilities, and recruits and thus are unable to catch up to these top teams, following the power law distribution. As the best teams make all the money and become better, the other teams are unable to compete. If this behavior continues, there could just be one large “Power 5” conference for all of the best teams. These teams would benefit from their high viewership and high revenue distribution, but all of the other teams would struggle. The rich or “elite” would get even richer and the poor or “average” would get poorer. If we go down this route, there could be devastating consequences for all of the teams who sit below this threshold.
Sources:
https://www.latimes.com/sports/story/2023-09-01/pac-12-obituary
Upvotes and downvotes on Reddit and information cascades
In 2014, a scientist got banned on Reddit for having 5 accounts he used to downvote others’ posts/comments and upvote his own. While five votes doesn’t seem like much when posts/comments can get hundreds or even thousands of votes, we know from learning about information cascades and herding behavior that five early votes can and does make a difference.
We’ve learned that if just the first two people making a choice make the same decision, it can start an information cascade where everyone after also makes the same decision. In this scenario, upvotes/downvotes are visible to everyone and act as a public signal to Redditors of whether a post/comment is good or bad. This is at odds with their private signal, their own opinion of whether a post/comment is good or bad. A Redditor may think a comment is bad, but if the comment has 5 upvotes minutes after it’s posted, the Redditor may start to rethink their belief. Maybe those first five voters were more informed on the topic and had information this Redditor doesn’t. Maybe if everyone else thinks the comment is good, it is rational for this Redditor to also upvote it.
Due to herding behavior, the early responses to a Reddit post/comment can be a good indicator of its popularity down the line. Thus, this scientist is artificially controlling public opinion and affecting the integrity of the social network. If the scientist uses all his accounts to upvote his comment and downvote someone else’s, that’s already a ten-vote differential between the two comments. If people use these votes to influence their own behavior, the scientist is able to control the broader outcome of these comments by simply logging in to his multiple accounts and spending a few minutes voting.
https://www.vice.com/en/article/8qx57x/reddits-favorite-scientist-just-got-banned-for-cheating-the-site
