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Anh-Thu Le
Harvard GenEd 2023
Apr 13, 2023
In Thoughts from Learners
Link: https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:5e3f229f?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction The most interesting concept brought up in the interview was the idea that humans do know their imminent future; however, they are willing to ignore it since the idea of the future is either too scary for people to understand or they believe the future will not affect them, against best judgment. The example that was given was a divination tent in which people were pushed to think about the future and guided into thinking about climate change. It was found that people did acknowledge that climate change will affect the future; however, their biases led them to believe that it wouldn't affect them, so they choose to ignore it. This phenomenon is fascinating to me because it shows how self-centered humans are. I would ask Gilbert about the psychology of self-fulfilling prophecies. Often, I am given fortunes that told me to avoid going out too often, or else something terrible would happen to me. Whilst on my journey to avoid this terrible prediction, how often is it that I end up fulfilling this prophecy as opposed to just going about in my normal life? In these predictions, is it considering the fact that I will continue my normal psychology or banking on the psychology that I would alter myself in order to avoid this?
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Anh-Thu Le
Harvard GenEd 2023
Apr 13, 2023
In Thoughts from Learners
Based on the discussion with Spieghalter, I found three main examples that give a good representation of the "theory to data" spectrum. On the far left of the spectrum of "pure theory", Cromwell's Law is a good representation of a pure theory. Cromwell's Law states that when dealing with probabilities unless it is a logical statement, humans must assign the unknown a probability. An example is that we believe the probability of the positioning of a bottle landing to be 1/3 upright, 1/3 downright, or 1/3 sideways. However, there is a really small chance that the bottle lands in a diagonal manner due to some force of the bottle or nature that can't be ignored despite how unlikely it might be. Therefore, Cromwell's Law states that you must assign some probability for those unlikely leaning towards improbable outcomes in the case that it does occur since it isn't impossible. Cromwell's Law is a pure theory because there is no feasible way to test his theory because it banks on the idea of unknown or improbable events occurring which is rare. Thus, his law is simply a way to think about probability. On the far right of the spectrum of "just Data", ancient star-tracking is a great example. In the past, there were people who noticed that stars tend to be in a certain location. With this, these people began to draw those stars and their relative location to other celestial bodies; however, aside from this data collection, no theories and hypotheses were explained on why these stars may be there. Thus, star-tracking was a pure data activity. Note that this doesn't mean people didn't use the data from these tracking to come up with laws and theories, but the act of writing down the geographical positioning of these stars, themselves, are pure data. In the middle of the spectrum with both aspects of data and theory is the Gaussian distribution. The Gaussian distribution is a statistical model that shows that many data will fall near the mean of the data instead of the extremes of the data, creating a bell-shaped model. The Gaussian distribution has aspects of theory because of the logic of the distribution itself. The distribution simply suggests that logically if there were 1000 outcomes in a situation, there would still be a 1/1000 possibility that it is a certain outcome despite the model showing that a vast majority of outcomes fall in the mean. The reason this occurred is the outcome themselves are independent of other outcomes; therefore, the likelihood of you having outcome #51 is the same as the likelihood of you having outcome #812 because you don't know where those outcomes fall in the model. Therefore, the Gaussian distribution is simply a way of thinking about probability; however, the distinction between the Gaussian distribution and Cromwell's law is that there is data to back up this model distribution of outcomes. There is a toy that holds 100 small beads and allows you to flip it around so that you can see where the small beads end up at. The vast majority of the time, the beads fall in a manner that creates the shape of a bell. While each iteration of this simulation creates a different variation of the bell, it still follows the logical manner that Gaussian predicted which shows that the Gaussian distribution has aspects of data collection within it.
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Anh-Thu Le
Harvard GenEd 2023
Apr 06, 2023
In Thoughts from Learners
I was very surprised at the idea of Cromwell's Law. More specifically, at the implication that Cromwell's Law suggests. Cromwell's Law tells us that we should always assign a certain probability for an unknown variable/outcome/etc. The implication of this law is that humans can never be 100% accurate or sure about anything. There is always some degree of uncertainty which then push me towards a philosophical conundrum about self-accuracy. If I were conducting the interview, I would ask Spiegehalter about his thoughts about news headlines falsifying or misguiding readers by providing unclear statistics about certain events. For example, some news articles may say, "Survey says 100% of all Harvard Students are Depressed". However, if they only sample 3 people and those students all answer, "yes", then while the statement isn't false, it is not representative or accurate of the true populations. I am interested in Spiegehalter's answer because, within the first few minutes of his interview, he discussed how statistics can be misconstrued depending on specific wording and understanding of the statement.
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Anh-Thu Le
Harvard GenEd 2023
Mar 27, 2023
In Thoughts from Learners
I found the attribution model that was discussed in the context of determining the economic impact of the Northwest Pacific bark beetle on tree stands to be very interesting to learn about. Initially, I thought the process of finding out the economic impact would be easy. However, when combined with the added factor of climate change and its effects on the environment and figuring out those environmental effects affected the bark beetle population, how the bark beetle population affects the quality of the tree, and how the quality of the tree affects the timber industry, and etc, it becomes much more complex. There seem to be a lot of running variables to consider and the attribution model does a good job connecting these different factors to find the wanted answer. If I were to conduct the interview, I would want to learn more about the quantitative data that was found about the economic impact of climate change. There were mentions of how fixing infrastructure to anticipate climate change will be more costly than ameliorating climate change, and other economic impacts of climate change. I would ask more specifically what is the quantitative data surrounding that. Is the economic impact significant enough to force politicians to spur action against climate change? This would be quite interesting to learn more about especially if the data was compelling enough.
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Anh-Thu Le

Harvard GenEd 2023
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