Author: Andrew Yue-Keung Leung

A business cartel is formed when more than one competing businesses in a market decide to coordinate with the goal of fixing prices to be higher than normal so that consumers must pay more for their products; this typically occurs in an oligopoly. A successful cartel is able to act almost essentially as a monopoly in the market.

Let me define a few terms first:
Monopoly – A market with pretty much only one producer and is thus able to dominate and set prices (think of the LCBO)
Oligopoly – A market with only a few, but big players (think of the Canadian banks)

As you can see just by this definition, a lot of game theory is involved in the process of forming a cartel. Do we compete? Do we collude? If we decide to collude, how do we know the other won’t cheat? Aside from regulations, this paranoia that the other business may decide to cheat is also what holds competing businesses back from colluding, even though they would both benefit much more by colluding.

Let’s take a look at an example, suppose we have business A and business B competing with each other. For this model, we will also have to assume that the product or service they are offering is exactly the same and that they are the only two businesses in the market. At the moment, these businesses are making a profit of 5 million dollars per year each. If they decide to collude, they can hike up the prices and will both make 10 million dollars per year each. However, any one of the businesses could decide to cheat and lower their price or produce more. Now, more consumers will be buying their product instead and they make 12 million dollars per year while the other business only makes 3 million dollars per year. The payoff matrix looks like this.

The model is simple and requires some unrealistic assumptions, but it also reflects what could happen in reality to some extent. If the businesses colluded, they would both make a total of 20 million dollars per year, making the businesses very happy. While if they both competed, they would only make a total of 10 million dollars per year, making the consumers happier. In the middle, we can see if one business cheats while the other collaborates, the one that cheated makes a huge amount of profit compared to the other. As you can see, this looks like the classic prisoner’s dilemma problem and both business’ best response is to cheat no matter what the other business decides to do. Thus, the single Nash equilibrium of this problem is for both businesses to cheat. Overall, game theory, which is part of the course content, gives us some insight as to why businesses would rather compete than collude (other than the fact that colluding is illegal).

Reference

Understanding a Cartel as a Prisoner’s Dilemma. (n.d.). Retrieved November 12, 2019, from http://college.cengage.com/economics/0538797274_mceachern/student/transcripts/8432.pdf.

The news article I will be discussing is headlined “Facebook Tests Hiding ‘Likes’ on Social Media Posts”. To summarize, Facebook began an experiment in Australia on September 26, 2019 where Likes, video view counts, and other similar metrics found on posts became hidden to users other than the original poster with the goal of reducing the significance of such measurements. In order to see if they are succeeding in improving users’ overall experience using their application, Facebook will be studying whether users would continue to comment and Like posts even when the numbers are not visible to them. An example of this can be seen in the picture provided below. Considering that the experiment is still ongoing, I will be considering three possible results that came to mind and how they relate to the material taught in the course CSCC46: Social and Information Networks. The cases that will be discussed are whether people will leave Likes and comments more often or less often, while we will not be going too far into detail about the trivial case, where there may simply be no significant changes to users’ behaviour.

As was mentioned in lectures several times, social media applications including Facebook have many different graphs in their backend that represent friendships, pages Liked, etc. These graphs are used to perform analysis to improve the application’s services such as friend recommendations and targeted advertisements.

Thus, as a result of the experiment, it is possible that users start to leave Likes and comments on posts that they otherwise would not have. For example, if a post already has thousands of comments on it, a user may decide not to leave a comment since they may think that their comment would not contribute anything to the discussion if they are too late to the post. However, if users do not know this quantity, then they may start leaving comments on posts whenever they feel like it without hesitation. This would likely lead to more connections being formed not only in real life, but in Facebook’s backend as well. Although there may be no significant immediate effect, if Facebook decides to keep the numbers hidden in the future, then there will definitely be a difference in their graphs than if they did not conduct the experiment at all. For example, although more connections may be found, a good chunk of these would likely be weak ties and some may even be negative edges, but communities would be larger in general. This change in their graphs may lead to the outputs of their machine learning and graph-related algorithms to be changed as well. For example, users may start to get more (possibly inaccurate) friend recommendations as well as advertisements for a wider variety of products.

Conversely, the experiment may lead to users to start limiting the amount of Likes and comments they leave on posts. For example, a user may start only wanting to leave Likes and comments on friends’ posts only. In this case, less connections would be formed, causing the communities found in the graphs to be more tightly-knit with more strong ties and positive edges. This may result in the previously mentioned algorithms to seem smarter since they would be more accurate in tracking our behaviours and interests. Also, let us not forget that it is possible that Facebook is selling our data, which could potentially lead to a difference in users’ experiences while using other applications as well.

In conclusion, although Facebook’s little experiment may seem trivial or insignificant, with further analysis, it has the potential to change users’ behaviour on their application. This could lead to a drastic change in the backend data as well as the services and content that people are exposed to not just on Facebook, but the other companies that they work with as well. However, it is possible that there could just be no real change in user behaviour; since this experiment is still in progress, we will see how this experiment goes but will not know the outcome until sometime in the future. Most of the ideas that were discussed here are hypothetical and are just my opinion, I happily welcome your thoughts and feedback. I also encourage you to read more into the articles referenced below if you are interested since all that was discussed here was a high-level summary and it also mentions how Instagram, another social networking service owned by Facebook, performed a similar experiment earlier this year with similar goals in mind.

Reference List:

Conger, K. (2019, September 26). Facebook Tests Hiding ‘Likes’ on Social Media Post – The New York Times. Retrieved September 27, 2019, from https://www.nytimes.com/2019/09/26/technology/facebook-hidden-likes.html

Hutchinson, A. (2019, September 27). Facebook Begins Hiding Total Like Counts on Facebook Posts in Australia | Social Media Today. Retrieved October 1, 2019, from https://www.socialmediatoday.com/news/facebook-begins-hiding-total-like-counts-on-facebook-posts-in-australia/563829/