Nash Equilibrium in Marriage Markets with Externalities

[2] The stable marriage problem is a problem of how to pair off two groups into stable matches.

In an article made available on-line the 13th of September 2019, titled “Incentives and implementation in marriage markets with externalities,” academics María Haydée Fonseca-Mairena and Matteo Triossi discuss the use of Nash Equilibrium to implement stable correspondences in marriage markets with externalities. As in CSCB36H3, the term “marriage market” refers to markets consisting of two non-empty, distinct, and mutually exclusive sets which need to paired off. Examples include: marriages, dance partners, and medical students and medical schools.

A quick review of CSCB36 material: in marriage market algorithms we assume that all parties are acting rationally, and that rational actions must include preferring any pairing over being on your own. Stable matches are matches that are not blocked by a pair. That is, no pair of participants in the market should prefer to be matched together over their assigned match. In CSCB36 we proved that in marriage markets without externalities, the dominant strategy is to be honest about your preferences and to reach out to members of the other group.

[3] In the stable marriage problem without externalities (where other people’s matches don’t matter to you) the dominant strategy is to be honest about your preferences.

Without externalities, honesty and initiative will secure an optimal match. In their article Fonseca-Mairena and Triossi examine the best strategy in marriage markets with externalities.

We all know that some of the algorithms and examples covered in many of our classes work only for simple or specific requirements that do not often occur organically in the real world. Often, in life, marriage markets include externalities – that is, the pairings effect more people than just the two members who have been matched. Consider the examples above; family members and friends likely care about who your spouse is, in dance competitions each pair is interested in who their competitor’s partner is, medical students are likely concerned not only with the school they are accepted to themselves, but also the school that accepts their friends and classmates. When taking externalities into account the concept of a stable matching changes. In marriage markets with externalities, a stable matching M is blocked by an individual if they prefer being alone to the pair they are assigned to in M. A stable matching can also be blocked by a pair if they prefer any matching in which they are paired together over their assigned matches in M.

Fonseca-Mairena and Triossi prove out that in a marriage market with externalities the honest and initiative-taking dominant strategy applied to marriage markets without externalities no longer works. In their article, Fonseca-Mairena and Triossi further prove that Nash equilibrium can be used to implement stable matches in marriage markets with externalities. As a CSCC46 student, I found this article interesting due to its potential applications. As mentioned above, in class we often learn about algorithms or theories that appear to have limited applications in the real world. With our lectures on game theory especially the examples seemed over-simplified and specific. I felt that it was unlikely you would come across such simple examples in real like where there are often many factors to consider. I understand, of course, that the examples were simplistic because the lectures were only introducing us the game theory, and simple examples made our professor’s points more clear. However, the use of Nash equilibrium to create stable matches in marriage markets with externalities suggests countless real world applications. There are the three examples in the introduction to this blog, but also countless others. Many people, from parents, teachers, managers, to administrators of various levels find themselves attempting to create a set of stable pairings for an activity or event. Fonseca-Mairena and Triossi ‘s article provides proof of a simple method for choosing.

RESOURCES:

[1] Fonseca-Mairena, M. H., & Triossi, M. (2019). Incentives and implementation in marriage markets with externalities. Economics Letters, 185 doi:10.1016/j.econlet.2019.108688

[2] Stable marriage problem. In Wikipedia. Retrieved November 15, 2019, from https://en.wikipedia.org/wiki/Stable_marriage_problem .

[3] How the internet has changed dating. In The Economist. Retrieved November 15, 2019, from https://www.economist.com/briefing/2018/08/18/how-the-internet-has-changed-dating .

Schizophrenic Brain Function Network Analysis

In class we learned about small worlds, clustering co-efficients, and path length. Throughout our class discussions we often used social media, and social networks as examples. But graph theory is a far-reaching field and the topics we covered in class can also be used in various other networks; including the brain function network.

In an article published in May 2019 titled “Brain Network Analysis of Schizophrenia Based on the Functional Connectivity” researchers outline how graph theory can be used to identify the effects of schizophrenia on the brain function network. In their study, the researchers analysed the Magnetoencephalography (MEG) of 9 patients with ‘normal’ brain activity and 9 patients who had been diagnosed with schizophrenia. MEG is a neuroimaging technique for mapping brain activity by recording the magnetic fields created by the electrical impulses within the brain [2]. In the introduction the authors note that, “High efficiency analysis of small world network topology has become the main way to analyze brain function network.” This is due to the high connectivity and efficiency of the human brain function network. The goal of the study was to compare the small world properties of the schizophrenic brain function network against that of the ‘normal’ brain network. This was achieved by first mapping the brain function connectivity network in a resting state with MEG. Since the brain function connectivity network can fluctuate over time, even in the resting state, the study used a sliding window technique to capture the left temporal and frontal MEG signals of the 18 patients in a resting state with their eyes closed.

[3]

The researchers then processed these signals in order to produce binary and weighted networks. From these networks they calculated the average shortest path length and the average clustering coefficient. The study found that the ‘normal’ human brain shows increased small world properties when compared to the schizophrenic brain. The healthy patients brain function networks had comparatively smaller shortest path lengths and higher clustering co-efficients. Graph theory analysis of the brain function network can produce significant results for better understanding schizophrenia, an often crippling disease. This study shows that patients with schizophrenia have decreased small world properties in their brain function networks, which may result in a slower information exchange rate and lower efficiency.

The study outlined above highlights how graph theory can be used not only to help us understand the overall structure of the social world, but also naturally occurring biological structures. The human brain is an extremely complicated organ that we do not fully understand. However, through the use of graph theory we can better understand the healthy structures and patterns that exist within our brains and how variations within those structures (such as decreased connectivity) can impact our health. Schizophrenia, among many other mental illnesses, is both poorly understood and potentially incapacitating. Knowing that graph theory can be used to provide better diagnosis and to possibly move us closer to providing assistance to those impacted by the disease highlights how deep of an impact graph theory can have, not just in understanding the world around us, but also in using that understanding to significantly help those in need.

RESOURCES:

[1] X. Zhang, L. Wang, Y. Ding, L. Huang and X. Cheng, “Brain Network Analysis of Schizophrenia Based on the Functional Connectivity,” in Chinese Journal of Electronics, vol. 28, no. 3, pp. 535-541, 5 2019.
doi: 10.1049/cje.2019.03.017
URL: http://ieeexplore.ieee.org.myaccess.library.utoronto.ca/stamp/stamp.jsp?tp=&arnumber=8812649&isnumber=8812608.

[2] Magnetoencephalography . In Wikipedia. Retrieved October 12, 2019, from https://en.wikipedia.org/wiki/Magnetoencephalography

[3] Brain Map: Temporal Lobes. In Queensland Health. Retrieved October 12, 2019, from https://www.health.qld.gov.au/abios/asp/btemporal_lobes