Self Satisfying Equilibria and Online Shopping!
Recently, while I was shopping for dresses, I noticed that there were many websites that seemingly sold the exact same dress. I would find a dress that I really liked, then after reverse image searching the pictures of the dress, would find that other websites listed the same dress in their catalogs. After attending the lecture related to good cars, bad cars, lemons, and self fulfilling equilibria, I found a similarity between the contents of that lecture and my dress conundrum.
To make things easier, let’s say that I found a dress being sold on two different websites, both for the same price. The photos used to advertise the dress were exactly the same (clearly, one of the dresses had been the original dress and the other one was a dupe), so it was impossible to know their actual quality. When I thought about whether or not to place an order, I would end up taking into account the expected value of the dress I would actually end up receiving. Let’s say that out of the two websites, I thought one of them would be good quality and the other would be bad quality. Each website had an equal chance of being one or the other. Personally, I value good quality dresses at $60 and bad quality dresses at $24. My expected value for buying a dress would be 60*1/2+24*1/2=42. This means that I would buy from one of the websites if they both listed the dress for $42 or below. From the lecture on self fulfilling equilibria, I know that if there is in fact a good quality dress on one of the websites, then the price listed must be at least the high quality dress seller’s value for the high quality dress. Let’s suppose the high quality dress seller values his dress for $40. In this case, both high and low quality dresses are sold and this constitutes a self fulfilling equilibrium. Then I can place an order.
Now, let’s say there were lemons in the dress market instead of bad quality dresses. In this case, lemons would be when the dresses are a complete scam and they are never delivered. I value good quality dresses at $60 and lemons for $0. My expected value for buying a dress would be 60*1/2+0*1/2=30. However, we know from before that the high quality dress seller values his for $40, which is more than $30. This means that this is not a self fulfilling equilibrium and I cannot order.
Information Cascades in Squid Games Reality TV!
Hi everyone,
I would like to discuss information cascades and their application to real life. Information Cascade is a phenomenon described in behavioral economics and network theory in which a number of people make the same decision in a sequential fashion. In class, we learned about this concept using marbles. The game marble game consisted of people picking a marble and making a guess about whether they picked out of the majority red or majority blue urn using their knowledge of the marble they picked and the knowledge of other people’s guesses. It is common for people to continue to choose the same guess as other people if at least two or more people say the same thing.
I noticed the appearance of information cascades in squid games reality TV on Netflix. It was crazy to watch! The players add to vote on at least three players to eliminate from the game. All of the voting was public. Each player voted one at a time during the voting device. The players could see the names that were nominated to be eliminated but did not see the amount of votes that each nominated player received. Once a person nominated a new name to vote on the screen. Many players just voted for that nominated person rather than nominating a new person. It is easier to follow the crowd than put up a new name on the screen and have everyone see that they put up that name. People do not want to put up a new name because they could potentially make new enemies.
Back to my point about the information cascade. The players voted the same as other players before them and made the same decision about eliminating. The show was super fun to watch, and it was great to see other phenomena about people’s behaviors! I recommend it to everyone! Thanks for reading 🙂
Cat
Social media Influencers and Adoption of Behavior
Understanding the conditions under which humans adopt certain behaviors can have an immeasurable impact on the likes of social networking sites, political campaigns, or even simple clothing trends and fads.
I’ve particularly been interested in how companies utilize the threshold model to maximize the number of individuals that adopt their targeted behavior, whether it’s buying a product, joining a social media site, or any other behavior that increases their profit. As someone who’s frequently on TikTok, a mobile application where you can post funny snippets for your following to view, I’ve noticed a sharp increase in “Influencer-based sponsorship videos where these influencers pitch a company’s product hoping their followers buy said product (influencers are almost like celebrities, they have a large following, but may not fit the traditional celebrity description/occupation).
These companies are capitalizing on the massive network of these influencers in a unique way. TikTok, being a primarily video-based platform, provides an especially engaging and interactive experience for users to get “closer” with the influencers they follow through comments, video replies, etc. These connections may not be considered strong, in the way we understand them in this course, but they are relatively stronger than those of other platforms like Instagram.
By targeting TikTok influencers, companies can start with a large sum of early adopters for their product, at this point the triadic closure property kicks in. For example, in my life, there is an influencer I find entertaining, and when I mentioned their name in a group of individuals, one of them also seemed to have a strong inclination towards said influencer, which helped us become friends. As these interactions occur more and more, individuals with no connection to the influencer who pitched the product will begin using the product, simply based on the number of friends they have also used it.
Utilizing large networks with relatively strong ties is a gold mine for companies; adoption of behavior begins in high numbers and spreads quickly as well.
How concepts of networks translate to real life applications, even space
This has been a great class in showing real life applications of material we learn at school. Through the semester, as we learned more concepts, I began thinking more consciously than before of the role of networks in our everyday settings and personal lives. I used what I learned to challenge myself to think of applications beyond social connections too, for example, space. In space exploration, communication networks between spacecraft and Earth are great examples of how concepts like graph theory can be applied.
These networks, which include spacecraft and satellites as nodes and communication links as edges, demonstrate complex patterns of information, command, and data flow. Analyzing these patterns with network theories helps in overcoming problems like signal delay and data transmission over vast distances. Furthermore, the future of space colonies brings game theory principles into play. Space agencies, private companies, and international coalitions are likely to form networks that combine cooperative and competitive interactions. Specifically, the way scientific data from space missions is transmitted to Earth parallels how information spreads in social networks. Both are constrained by factors such as bandwidth and efficiency. Even in the general business world, they face decisions similar to the Prisoner’s Dilemma: whether to cooperate for mutual benefit or withhold resources for personal gain. These decisions have a direct impact on the efficiency and success of joint space missions. Furthermore, allocating resources such as bandwidth in space communication networks is similar to managing scarce resources in economic networks. This demonstrates how economic principles are applied in the strategic management of space communication systems, balancing limited capacity against high demand.
In sharing my thought process on the real-world applications of network theory, particularly in the context of space exploration, I wanted to highlight the versatility and use cases of our course.
Corporate Chess: Business Prisoner’s Dilemma
The Prisoner’s Dilemma presents a strategic problem in the fast-paced world of business, where organizations must balance collaboration and self-interest in a complex game. Imagine two competing companies, each with a piece on the market chessboard, debating how to proceed in this crucial match.
Let’s delve into a scenario where two rival companies, Alpha as player 1 and Beta as player 2, navigate the complexities of cooperation and competition in the marketplace.
Business Prisoner’s Dilemma – Payoff Matrix:
Beta
Cooperate | Defect |
|————–|. ———— |. ——– |
Alpha | Cooperate | (100, 100) |. (0, 150) |
|. Defect | (150, 0) |. (50, 50). |
1. Cooperate-Cooperate (Cell 100, 100): If both Alpha and Beta choose to cooperate by maintaining stable prices, they each enjoy moderate profits. The combined outcome is better for both compared to defection.
2. Cooperate-Defect (Cell 0, 150): If Alpha cooperates, but Beta decides to defect by cutting prices, Beta gains substantial market share and profits, leaving Alpha with a significant loss.
3. Defect-Cooperate (Cell 150, 0): If Alpha defects by cutting prices, but Beta chooses to cooperate, Alpha gains substantial market share and profits, leaving Beta with significant loss.
4. Defect-Defect (Cell 50, 50): If both Alpha and Beta defect by cutting prices, they engage in a fierce price war, resulting in reduced profits for both companies.
The predicament arises when both businesses are faced with choosing between working together and competing. If both businesses decide to work together, they can prosper as a team, sharing resources and enjoying the advantages of a productive alliance. But in an attempt to obtain a short-term advantage in the competitive market, the attraction of obtaining a competitive edge frequently tempts every company to contemplate betrayal.
The consequences of this strategic technique are akin to a business checkmate. If one firm chooses cooperation while the other opts for betrayal, the latter may enjoy short-term gains, but the fallout could result in long-term damage to both parties. Conversely, if both firms betray each other, they risk mutual detriment, facing legal battles, damaged reputations, and potential market share loss.
This corporate chess game extends beyond hypothetical scenarios, resonating in real-world business partnerships, joint ventures, and industry collaborations. The delicate balance between fostering alliances and protecting one’s interests lies at the center of strategic decision-making.
Source:
Picardo, Elvis. (2022, May 22). Utilizing the Prisoner’s Dilemma in Business and the Economy. Investopedia.
Global Supply Chains and Pandemic Disruptions Through Networks Lens
The COVID-19 pandemic has served as a profound case study in the intricate world of global supply chains. When viewed through the lens of network theory, several insightful observations emerge, shedding light on the complexities and dynamics of this global web of interconnected entities. In theory, many companies sought to enhance their supply chain resilience by diversifying supply bases and localizing production networks. However, the pandemic revealed that, in practice, this often manifested as an increase in inventories rather than a true diversification of the network. This outcome is akin to nodes clustering around a few central hubs, with the difficulty in finding suitable suppliers and the time-intensive nature of capacity investments acting as barriers to broader diversification.
The pandemic also underscored the importance of robust supply-chain risk management. Companies established formal processes akin to nodes reinforcing their connections in response to a potential threat. Nevertheless, a significant lack of visibility persisted in deeper supply tiers, akin to a network’s limited knowledge of distant nodes. It is within these obscure tiers that critical shortages, such as the semiconductor crisis, emerged. This aligns with the growing role of digital technologies, particularly advanced analytics, in supply-chain management. Companies that navigated the crisis successfully possessed robust analytics capabilities, similar to nodes in a network boasting strong connections. The pandemic acted as a catalyst for digitization across supply chains, prioritizing visibility and planning tools. Yet, the scarcity of digital talent reflects the limitations of a network to connect with expertise.
These insights collectively suggest that global supply chains find themselves at a critical juncture, reminiscent of nodes in a network adapting to shifting circumstances. Some companies are seizing the pandemic-driven momentum to adapt their supply chains and technologies, mirroring nodes evolving their connections. Meanwhile, others risk reverting to old patterns, akin to a network node maintaining its existing connections despite changing conditions.
The pandemic’s impact exemplifies the intricate interplay of network theory in real-world scenarios. Companies’ responses mirror the adaptative strategies observed in evolving ecosystems. Prioritizing increased inventories in response to immediate threats reflects rapid evolutionary adaptation, much like nodes strengthening connections for short-term survival. The swift global spread of the virus resembles the rapid propagation of impacts in tightly interconnected supply chain networks, akin to a high R0 in epidemiology indicating quick spread. The complex links in supply chains led to widespread and fast-moving disruptions, akin to the impact of interconnected nodes in a network.
It also spotlighted the need for greater transparency and traceability in supply chains. Blockchain technology, renowned for secure and immutable records, emerges as a potential solution, enhancing visibility, especially in deeper supply tiers where current visibility is limited. This application parallels nodes in a network maintaining a transparent and traceable record of their interactions. Analogous to the PageRank algorithm’s assessment of web page importance, certain nodes (e.g., major manufacturing hubs) in supply chains disproportionately influence the entire network. The pandemic showcased how disturbances at these critical nodes could have far-reaching effects, reminiscent of the impact of highly influential nodes in a network.
The Ripple Effect: How Network Dynamics Propel E-commerce Marketplaces to Success
The impact of network effects on e-commerce marketplaces is incredibly huge especially in today’s digital age as we see this phenomenon shaping the success and growth of many platforms. The network effect refers to where the value of a product or service increases as a direct result of more people using it. This effect is particularly interesting in the context of e-commerce marketplaces since it manifests in the relationships between the number of buyers and sellers, aside from just one-dimensionally users. In essence, the more sellers there are, the more attractive the marketplace becomes for buyers, and vice versa.
A prime example of this are the huge platforms such as Etsy, eBay and in particular Amazon. these platforms all thrive as a result of the network effect which has allowed them to grow their user bases exponentially. Looking at Amazon in particular, we see this platform exemplifying the two-sided marketplace, bringing together huge amounts of buyers and sellers. In the case of Amazon, we can view buyers and sellers as nodes. However, what is interesting is that the links between these buyers and sellers are indirect as they each rely on Amazon as an intermediary. This results in a highly dense network with many links which benefits from the network effect to an even larger degree than those of low-density networks.
A business model like Amazon’s is incredibly appealing for the reason that it can generate profit as a result of facilitating transactions between buyers and sellers without directly having to handle the products themselves. This can allow for incredibly high profits of up to 70% especially if platforms are home to a large number of buyers and sellers as sellers are willing to pay a premium to access a larger consumer base, and on the flip side, consumers are also willing to pay a premium in order to access a more diverse array of options. Hence, the amount of buyers and sellers a platform has, and by extension how much it benefits from network effects, can directly affect the profitability of a platform.
That being said, obviously, there is a catch with how able marketplaces can create and hence benefit from these network effects and that is overcoming the initial hurdle of attracting a sufficient number of users. Interestingly, this brings into discussion what some see as a chicken-and-egg problem as a marketplace first needs a large base of sellers to attract buyers, however, to have a large base of sellers, a marketplace also needs a large base of buyers. The point at which platforms can experience rapid growth from network effects is called critical mass, which can be thought of as when the “value produced by the network exceeds the value of the product itself and competing products”. That being said, once critical mass is reached, a marketplace will grow exponentially quickly. New marketplaces employ various strategies such as discount pricing in order to quickly increase their customer base to the critical point. However, this is often non-sustainable as companies are often operating at a loss when this happens.
That being said, sometimes excessive network effects can also lead to challenges and be counterproductive. When too many sellers join a platform, we see this resulting in buyers and infrastructure being overwhelmed, diminishing the overall user experience. This overcrowding also intensifies competition among sellers, leading to price competition and driving down profits as a result. More sellers can also introduce the problem of quality control as the chance of encountering a low quality seller and risking the marketplace’s credibility increases. These issues can also be applied to buyers and the amount of them for example the case of Ticketmaster during the Taylor Swift “Eras” tour ticket presales in November 2022. The immense demand for tickets in addition to a number of bot attacks overwhelmed the platform leading to delays, payment issues and crashes causing widespread frustration among fans and significant negative media coverage.
Overall, network effects play a crucial role in the e-commerce industry, dictating the rise and dominance of marketplaces and shaping the strategies new entrants must employ to compete effectively.
Sources:
https://www.shopify.com/blog/network-effects
https://www.sitecore.com/blog/commerce/online-marketplaces-network-effects
Relating Animal Behavior to Learning Algorithms Through Bayesian Inferences
Introduction:
Bayesian inference is at the center of many sophisticated machine learning algorithms. In this paper, we try to understand how the outcome of reinforcement learning algorithms is improved over time by analyzing animal behavior and decision making which is based upon the probabilistic models. Animals rely on these models in order to make decisions based on incomplete information and uncertainties in their surroundings. Animals learn from their experience similar to how the reinforcement learning algorithms which interact with an environment, and are improved upon receiving feedback in form of rewards and penalties.
Following is the Bayes Theorem: P(A∣B)=P(B∣A)⋅P(A)/P(B) where
P(A∣B) represents the posterior probability,
P(B∣A) is the likelihood of the observed data given the hypothesis,
P(A) is the prior probability of the hypothesis,
P(B) is the probability of the observed data.
In simplest terms, Bayes theorem involves combining prior knowledge or beliefs (prior probability) with new data (likelihood) to arrive at an updated or posterior probability.
Animals construct a posterior opinion grounded in sampled data, such as the quality of food patches or the average qualities of potential mates. Their prior knowledge stems from personal experience or the adaptive wisdom passed down through generations.
Consider a squirrel that has acquired knowledge about the likelihood of finding nuts in different areas of the forest based on past experiences or ancestral wisdom. While foraging, the squirrel keenly observes nut availability in various forest regions, combining this information with its prior knowledge to estimate the probability of finding nuts in a particular area. This estimation guides the squirrel in deciding where to concentrate its foraging efforts. Additionally, in response to seasonal changes affecting nut availability, the squirrel adapts its strategy accordingly. With each foraging experience, the squirrel continuously refines its understanding of where nuts are most likely to be found.
Above principles used by animals underlie reinforcement learning principles. In reinforcement learning, continuous learning is also based on integration of prior knowledge with new experiences that allows for adaptability over time as conditions change, hence leading to improved decision-making.
Source: Youssef, Yasmin. (2022). Bayes Theorem and Real-life Applications.
Exploring Braess’s Paradox in Air Traffic Congestion: A Counter-Intuitive Approach
Air travel—the epitome of modern connectivity—is soaring to new heights, quite literally. But with this upward trajectory comes a pressing issue: congestion. As demand skyrockets and airspaces reach their operational limits, the solution isn’t just about adding more airways or links; it’s about challenging our assumptions.
Enter Braess’s Paradox—a concept that might sound counterintuitive but holds the key to mitigating air traffic congestion by removing links rather than adding them.
In a recent study exploring this paradox in airway networks, researchers uncovered a remarkable phenomenon. They discovered that by strategically removing certain airways, the overall travel time for flights reduced, leading to a substantial 3.8% saving in travel time. That’s not just a minor tweak; it’s a game-changer in the aviation industry.
Let’s break down this counter-intuitive approach. Imagine an intricate web of airways and waypoints forming a network in the sky. When demand surges and congestion looms, conventional wisdom might dictate adding more routes. But here’s where the paradox unfolds: by removing specific links within this network, the flow of air traffic can be better distributed, reducing congestion and flight duration.
This paradox isn’t a mere mathematical curiosity; it has real-world implications for air traffic management. The traditional mindset of continually expanding airway networks to accommodate growth might not always be the optimal solution. Instead, a more strategic, counter-intuitive approach—inspired by Braess’s Paradox—could hold the key to smoother, more efficient skies.
This study, conducted over six months using ADS-B data in the South-East Asia airspace, demonstrated the existence of this paradox in airway networks. By developing a method to detect these paradoxical links, researchers were able to pinpoint specific airways whose removal led to a significant reduction in overall travel time.
This isn’t just about shaving a few minutes off flight durations; it’s about reshaping the industry of air traffic management. It challenges the status quo and offers a fresh perspective—one that emphasizes optimization over expansion, efficiency over sprawl.
In essence, this paradox echoes a fundamental truth: sometimes, less is more. By strategically removing certain airways, the flow of air traffic can be optimized, easing congestion and enhancing the efficiency of our skies. Braess’s Paradox might just be the unexpected twist that transforms how we navigate the boundless skies.
Sources: https://www.sciencedirect.com/science/article/pii/S0968090X19302621
https://www.sciencedirect.com/science/article/pii/S0968090X19302621#b0145
Diving into Information Cascades: How Media Connections Shape Advertisements
In a world inundated with information, the art of persuasion has evolved into a sophisticated science. One of its most potent tools? Information cascades. These cascades are the invisible currents shaping our decisions, influencing what we buy, what we believe, and even how we perceive the world around us. It’s a fascinating phenomenon that goes beyond the realms we have explored, and when it comes to advertising, it’s a force to be reckoned with.
Picture how advertising works today—it’s not just about flashy billboards or catchy TV commercials anymore. Sure, those classic approaches still pack a punch, but the real magic happens in the intricate web of social networks and online communities.
Nowadays, a company launches a new product and rather than putting up a few ads and waiting for new customers they dive into the depths of information cascades. They target specific groups—communities of interest, clusters of like-minded individuals—knowing that these groups are ripe for influence. Online social networks has become playgrounds for advertisers and marketers. Facebook, Twitter, Instagram—the platforms where billions converge to share thoughts, ideas, and recommendations— have become a space to influence a new audience within a cascade.