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Among Us & Herd Behaviour

The multiplayer game Among Us has become widely popular over the last few months. The game is set on a spaceship where individuals are required to complete some number of tasks to keep the ship moving toward its destination. However, among all of the crewmembers lies an impostor, whose goal is to kill everyone on board the ship. The impostor must attempt to fit in alongside the other crewmembers by pretending to complete tasks or stay hidden and slowly kill individuals one at a time. When an individual has been killed, if another individual finds the body, they can report the body or if they find someone acting suspicious, they can hold an emergency meeting, where the individual who started the meeting must explain what they believe happened.

The emergency meeting is a perfect example of a simple herding problem as discussed in class. The individual who hosted the meeting has some reason to suspect another member on board is the impostor and must explain to all the other individuals who the impostor is and why. After presenting their case, the group has the chance to discuss their findings and either vote someone to be kicked off the ship or to skip the round of voting if insufficient evidence is found. When the host of the meeting describes the situation, they immediately begin to influence the other individuals into believing the person being accused is an impostor.

The other individuals can take what has been presented to them into account as well as their own experiences as they attempt to make a decision. For example, let’s say Player A holds an emergency meeting, accusing Player B of being the impostor since he saw Player B standing around pretending to do tasks and following him around suspiciously. After Player A makes their statement, Player B begins to defend themselves, stating they were looking for their next task. Now, after both players have spoken, a third player, Player C, begins to describe how she agrees with Player A and did notice Player B act oddly. When it is your turn to speak you can evaluate whether Player B is the impostor by seeing whether your experiences AND what Player B and C are saying make sense or if what you experience does not align with their story, an example of Bayes Law as described in class. This is very similar to the example of the blue & red urns. When it your turn to declare which urn is which, you evaluate if your experience (the colour you saw) AND the experiences of the students before you, make sense.

If enough individuals begin to vote to kick Player B, even if your experiences lead you to believe otherwise, you will assume that everyone else is telling the truth and vote Player B out too. Similar to the sidewalk experiment conducted by Psychologists, Stanley Milgram, Leonard Bickman and Lawrence Berkowitz. They found that if a single person on a street corner began to look up at the sky, some pedestrians would look up too, to see what they were looking toward. If they put 5 people in the spot, this quadrupled the number of people who looked up at the sky and if they put 15 people at the corner, 45% of pedestrians looked up alongside them. This goes to show that as more people conform to a single idea, it is much more likely for an individual to follow suit so as to fit in with their peers and not seem like the odd one out. With respect to the game, if an individual doesn’t conform to the ideas presented if everyone else decided to vote a single person out, they may seem suspicious themselves and leave others questioning if they are the impostor.

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Analyzing Social Networks to Find New Cell Types

A recent study conducted at Uppsala University analyzed neural networks previously used to understand social networks and applied it to analyze mRNA production in human tissue.

Currently, the most common method to analyzing the presence of mRNA in a tissue at the microscopic level is using in situ sequencing. This method requires a lot of manual labelling and identifying of mRNA, cell types and tissue to allow for any substantial analysis to occur.

A good practice when building machine learning models is to ensure the model does not overfit to the data being presented to it. Overfitting refers to the concept whereby a neural network is trained with a certain set of data almost perfectly, but is unable to predict new data as it cannot generalize well. Keeping this in mind, many machine learning researchers will attempt to build models that can generalize to any data set.

Overfitting | DataRobot Artificial Intelligence Wiki

The researchers of the study used a model previously analyzing social networks. The model identified clusters of individuals based on similar followers on Twitter, similar Google Searches and many more similarities and differences on the internet.

When the model was provided with the cell data (images of human tissue with dots marking mRNA presence), the model was able to correctly cluster different tissues, cell types and identify the mRNA markers. Seeing success from models like this is a huge breakthrough in mapping cell types and mRNA functions.

Not only does this help scientists within this field better understand their research, it also depicts how networks can be generalized to help understand various different applications. A map of social network similarities is able to map cell tissue today, who knows what we will be able to map in the future.

Sources:

https://www.sciencedaily.com/releases/2020/10/201019111918.htm

https://techcrunch.com/2020/10/23/deep-science-alzheimers-screening-forest-mapping-drones-machine-learning-in-space-more/