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Social networks to bias votes in the Election process

As the presidential election process is approaching us, this post will expand upon the election process and the influences of the social network that results in a biased voting system. More specifically, connections between components, and decision making based on game theory, which is supported by a study conducted at University of Houston. The article mentions the concept of gerrymandering, and how connection or edges in social networks can easily be altered if someone were to put in random bots into the network. If there were two parties, the random bots would affect the decisions made or not made by any individual which would affect an entire group. These random “bots” are not only physical, like humans supporting the cause, but also digital. The nodes in the social networks can be changed to alter the outcome of the elections by influencing people to join the favored side. This network can be setup such that all the nodes are voters or added “bots”, and the edges represent connections between the voters or “bots”. Consider a “bot” that is placed into the network, and is connected to another node, a voter that was undecided until influenced by the “bot”. According to the Triadic Cosure Principle, it would be likely for the “bot” to also influence a friend of that voter since the the voter has provided the “bot” with an opportunity to meet with other nodes in its component.

This is also true in terms of the structural balance property. The voter is undecided, therefore it most likely has a friendly relationship with a node on the opposite side. However, if the “bot” has a friendly relationship with the voter, then the friend of the voter would either join the bot’s side or change the status of its friendship with the voter to unfriendly, to balance the network. The case study conducted at University of Houston consisted of a game, created to test the digital influence in the voting process. There were two teams in the game, and whichever team has the most number of people would earn the most points. The team that would have less number of people would also earn some points, but the only way to lose is if the outcome was a draw. This can be modeled as a game theory problem, where each side has a certain payoff. The probability of a side being chosen would depend on the current standing of the teams, however the best response would be the case where both teams would earn points. This analysis can prove that the added “bots” can change the current standing of the teams, therefore changing their side to be the most favored since adding to the opposite side with the lower number may result in a draw where no points will be given.



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