Predict the Voting System with Game Theory

When people have different voices or opinions, voting seems to be the best method to make a decision. There are various types of electoral systems, and today, we will typically be interested in the First-past-the-post (FPTP) voting method and how game theory has an impact on it. Intuitively, we may think voting is the most trustworthy and civil way to pick a candidate. However, an article by Wesley Sheker has revealed that the recent elections have been disastrous due to the abuse of the game theory.

The major cause of these disastrous elections is gerrymandering, meaning the governing parties intent to take advantage of a party by manipulating the political map in their favour. However, gerrymandering alone is not accountable for this failure to reflect the democratic standard of honesty and integrity. Another assistance lies in the voting system itself, which is the FPTP method.

The power of prediction comes from game theory’s Nash Equilibrium, either it’s pure or mixed. People always tend to make their strategy according to other people in order to optimize their outcomes.

Suppose we have 5 voters and 5 candidates, each voter has a preferred candidate and the outcome of the election (i.e. winner) is measured as a utility (high: 16, low: 0, scales down with the preferred candidate). Now we have to consider whether Nash Equilibrium will bring an optimal outcome for the voting system. Consider the first case: all voters vote their preferred candidates and the winner is candidate 1

all voters vote their preferred candidates and candidate 1 as winner

The net utility for this election will be 48 (16 + 16 + 16). Now we change the winner from candidate 1 to 3, the table will look like this

all voters vote their preferred candidates and candidate 3 as winner

None of the voters are very happy as their preferred candidate is the winner. However, the net utility of this election is 60 (>48), meaning in an election with many candidates, voters and their preference, the outcome of the election will not be optimal as the voters just blindly vote for their preferred candidate. This simple mock election has demonstrated the philosophical result of an FPTP system which conveys the fact it fails to deliver an optimal outcome.

If game theory would bring us a negative impact, how should we avoid it? In fact, it is impossible to escape from game theory’s power since we are constantly in the game. Our life is affected by other people’s choices, changing our own behaviour will not be enough, as the philosophical rhetoric originating from game theory says: “We can create a better world by becoming better human beings ourselves”.

Reference

Das, Sangeet Moy. “Game Theory 101 for Dummies like Me.” Medium, Towards Data Science, 2 Oct. 2019, https://towardsdatascience.com/game-theory-101-for-dummies-like-me-2e9ab92749d4.

Sheker, Wesley. “Disastrous Elections: Predicted by Game Theory.” Penn Political Review, 13 Jan. 2018, https://pennpoliticalreview.org/2018/01/disastrous-elections-predicted-by-game-theory/.

Wines, Michael. “What Is Gerrymandering? And Why Did the Supreme Court Rule on It?” The New York Times, The New York Times, 27 June 2019, https://www.nytimes.com/2019/06/27/us/what-is-gerrymandering.html.

Cross the World within Six Steps

Friendship takes up a significant portion of our social network, a good friendship can enrich our lives as well as increase your chances of happiness as an adult. However, we are not taught much about how to form a friendship. Indeed, building a relationship with someone is difficult, there are lots of factors need to be taken account of, such as education level, distance, age etc. Nevertheless, there is at least one essential requirement: You have to share something in common with that person.

“People You May Know” just got Creepier

Have you ever noticed how Facebook’s “People You May Know” just got creepier? In fact, If two people in a social network have a mutual friend, there lies a reasonable probability that these two people might become friends at some point in the future, this is called the triadic closure.

Triadic Closure

The dashed line indicates that B and C might potentially build a relationship. What’s more interesting behind our social network is that if we randomly choose two people across the world, we only need six or fewer people (nodes) to construct a social connection between them. On April 24, 2019, a blog on exploring your mind called “The Six Degrees of Separation Theory” by Angélica interested me. This idea was originally set out by Frigyes Karinthy in 1929 and popularized in an eponymous 1990 play written by John Guare. It is sometimes generalized to the average social distance being logarithmic in the size of the population.

Six Degrees of Separation

The theory claimed that due to the ever-increasing network connectedness caused by technology, our world is “shrinking”. That is, even if the physical distance among two arbitrary individuals is great, the rapidly growing density of human networks made the actual social distance far smaller. If we consider a person as a node in the social network, and the number of friends they have as edges, it is not hard to imagine if a node has an extremely high node degree, then it is connected to many other nodes on the graph with a path.

The more degrees, the more connections?

In the context of CSCC46, we know that the probability that a random pair of nodes are connected on the graph is determined by the clustering coefficient, which is the quotient of the number of edges between the neighbours of a node and the degree of that node. And the average clustering coefficient is the sum of all nodes’ clustering coefficient divide by the total number of nodes. We can reveal that Karinthy’s hypothesis is true in today’s digital era. Every time when we like a post on Facebook, a video on YouTube or an image on Instagram, we somehow established a digital connection on the network, and we perform these actions constantly without realizing it. When there is more and more such digital connection on our social network, the clustering coefficient for each node increases and resulting in a higher chance that two random nodes are connected.

A social network is not only a matter of friendship, but it can also be applied to many other fields such as Theoretical Biology. José Carlos Santos & Sérgio Matos did an infodemiology study that evaluates the use of Twitter messages and searches engine query logs to estimate and predict the incidence rate of influenza-like illness in Portugal. And Vasileios Lampos proposed a method to track flu in the population using social networks. He first identified a set selected keywords to be looked for in Twitter posts, and collect a set of daily twitters, if any keyword is found in the tweet, it will be marked as 1 and 0 otherwise, and a set of equations turning statistical information into flu-score for different regions and areas.

There is still many myths about the social networks that we have not revealed its veil yet. For example, the friend paradox: people tend to have fewer friends than their friends, and homophily: the tendency for people to have (non-negative) ties with people who are similar to themselves in socially significant ways. In today’s era, it is not surprising to believe that the idea of six degrees of separation is true, and it has been several decades since this topic was popularized, who knows what number of degrees it will be now, maybe it is the same or smaller.

Reference:

Alessa, Ali, and Miad Faezipour. “A Review of Influenza Detection and Prediction through Social Networking Sites.” SpringerLink, BioMed Central, 1 Feb. 2018, https://link.springer.com/article/10.1186/s12976-017-0074-5.

Angélica. “The Six Degrees of Separation Theory and How It Works.” Exploring Your Mind, Exploring Your Mind, 23 Apr. 2019, https://exploringyourmind.com/the-six-degrees-of-separation-theory/.

Carlos, José, and Sérgio Matos1. “Analysing Twitter and Web Queries for Flu Trend Prediction.” Theoretical Biology and Medical Modelling, BioMed Central, 7 May 2014, https://tbiomed.biomedcentral.com/articles/10.1186/1742-4682-11-S1-S6.

Frei, Lukas. “Predicting Friendship.” Medium, Towards Data Science, 11 Feb. 2019, https://towardsdatascience.com/predicting-friendship-a82bc7bbdf11.

“Homophily.” Software, http://www.analytictech.com/mgt780/topics/homophily.htm.

Morse, Gardiner. “The Science Behind Six Degrees.” Harvard Business Review, 1 Aug. 2014, https://hbr.org/2003/02/the-science-behind-six-degrees.

Silver, Curtis. “How Facebook’s ‘People You May Know’ Section Just Got Creepier.” Forbes, Forbes Magazine, 11 Oct. 2017, https://www.forbes.com/sites/curtissilver/2016/06/28/how-facebooks-people-you-may-know-section-just-got-creepier/#47b743245f5a.

“Six Degrees of Separation.” Wikipedia, Wikimedia Foundation, 25 Sept. 2019, https://en.wikipedia.org/wiki/Six_degrees_of_separation.

“Tracking the Flu Pandemic by Monitoring the Social Web.” Tracking the Flu Pandemic by Monitoring the Social Web, Institute of Electrical and Electronics Engineers, 2019, https://ieeexplore.ieee.org/abstract/document/5604088.