Understanding opinions with Bayes’ Theorem

Understanding opinions with Bayes’ Theorem

Remember that one nasty math equation that was presented in class? Well, here is it again and how it is used in the case of people who are dead set on a certain opinion despite overwhelming evidence. You know, that one friend on social media that post article like “our sun is not a star”In this article, the idea behind Bayes’ Theorem is described as seeing how a set of “prior beliefs… change in the face of given evidence.” An example on a supposed cricket match used first to demonstrate. The question of whether a team is using a batting pitch’ or a ‘bowling pitch’ is asked. Given that ‘batting pitch’ has probability 0.61% while a ‘bowling pitch’ only has 0.11%, it can seem that a ‘batting pitch’ is in motion. But Baye’s Theorem says we can not jump to conclusion with out considering ‘prior beliefs’. Consider spectators R and S, where R believes both pitches are 50/50 , while S believes that a ‘bowling pitch’ has a probability of 90%. Both R and S observe the same round of the same match and use Baye’s Theorem, but end up with different probabilities on whether a batting or bowling pitch is in play. Despite the same observations (evidence), both spectators have different conclusions (opinion). Apply this to that one friend on social media, we can partially see why they are against evidence against their opinion. Their prior beliefs greatly influence their current belief. This friend’s opinion could have been the result of some information cascade (maybe they heard it from another friend on social media), so information superior to their current and previous information is needed if you wanted to change their opinion. Read the article below to get all the math details.