In the Prediction and Philosophy video, a few immediate questions came to mind from the discussion between Agustin Rayo and Professor Goodman. After Rayo mentioned how he believed that humans were very predictable, Professor Goodman responded by asking if that means humans are well-behaved. Hearing that humans are very predictable was indeed intriguing, but I was more fascinated by the relationship between being predictable and being well-behaved. Even if humans are very predictable, does that mean they are well-behaved? If someone is unpredictable, does that necessarily mean that they are not well-behaved? How does one concretely define well-behaved in the context of predictions? https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:d0403012?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

In the video with Stuart Firestein, the most surprising bit of information I learned was in the section entitled “Humans’ innate sense of time and progress.” In this section, Firestein remarked how the beginning of the Scientific Revolution did not begin until the invention of timepieces. Back in high school, I remember learning that the Scientific Revolution began with the findings of Copenicus, and so hearing this from Stuart Firestein was definitely both surprising and interesting. https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:9041b2ca?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

In the video with Brendan Meade and Susan Murphy, Brendan Meade discussed how new technologies like GPS are allowing scientists to utilize new ways to predict earthquakes. I thought this was a very intriguing idea, as I never considered the role of GPS in predicting earthquakes before. However, one question that I do have regards the aftermath of Meade’s predictions. For example, Meade mentioned how the Sendai was only one of four regions he identified in his paper, highlighting that the other three regions ceased to have an earthquake like the one in Sendai. My question is this: what can then be done after his predictions are made? Because of the overall inaccuracy of predicting earthquakes, it seems unlikely that a government administration would implicitly trust Meade’s predictions, thus making his predictions not very useful in practice. How can he increase the accuracy of his predictions so that they can be trusted by people and governments worldwide? https://www.labxchange.org/library/pathway/lx-pathway:825945a0-367c-45dc-82b7-3d160c6e6f7a/items/lx-pb:825945a0-367c-45dc-82b7-3d160c6e6f7a:lx_simulation:5ad35586?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

At the end of the video with Brendan Meade and Susan Murphy, Professor Goodman explained the rarity of utilizing the scientific method in practice. Professor Goodman mentioned how current scientists generally do not follow the scientific method, something that was surprising to me. When originally learning about the scientific method in school, I thought that it was almost a necessity for scientists in their process of advancing science; yet, I was wrong, as a more trial-and-error based approach is the customary way science is pushed forward. https://www.labxchange.org/library/pathway/lx-pathway:825945a0-367c-45dc-82b7-3d160c6e6f7a/items/lx-pb:825945a0-367c-45dc-82b7-3d160c6e6f7a:lx_simulation:5ad35586?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

When Avi Loeb was discussing uncertainty and how current scientists deal with it, he stated that scientists must display their failures and their lack of certainty, as it can make them better scientists and it will also make the public more trusting of these scientists and their processes. Loeb went on to discuss that allowing oneself to make scientific mistakes is very important, because not doing so may limit one’s ability to make critical discoveries. I found this to be really compelling, as I have heard that many scientists do not feel the same way he does (due to their concern of awards, prestigious societies, social media, etc.). Consequently, my question to Avi Loeb is: how can a scientist avoid and not surrender to this social/academic pressure that exists in his/her respective fields, so that they can focus on what truly inspires them? https://www.labxchange.org/library/pathway/lx-pathway:34dd3b2c-3aec-460a-817f-da4af2ed1577/items/lx-pb:34dd3b2c-3aec-460a-817f-da4af2ed1577:lx_simulation:1a066234?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

In the video with Jill Tarter, the most fascinating segment to me was the public perception of science. I found the triviality (for lack of a better word) in which she discussed electromagnetic signals to be very intriguing, as she explained how utilizing electromagnetic signals might be entirely incorrect. In my own experiences and studies, I have found scientists to traditionally be steadfast in their beliefs and committed to their postulations; Tarter is the exact opposite. It seems like her field requires the characteristics of adaptability and versatility in which other scientific fields do not. I believe that this is due to the amount of unknown unknowns in her field, which forces conventional approaches (like electromagnetic signals) to be reimagined and reevaluated into new techniques (i.e. technosignatures). https://www.labxchange.org/library/pathway/lx-pathway:34dd3b2c-3aec-460a-817f-da4af2ed1577/items/lx-pb:34dd3b2c-3aec-460a-817f-da4af2ed1577:lx_simulation:e9099212?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

During the discussion between David Laibson and Professor Goodman, Laibson was explaining the connections between behavioral economics and climate change. He stated that people have a very hard time understanding uncertainty, and that the predictions of scientists deal with bell curves that have larger tails than what the public expect (meaning more extreme events like extreme global warming and extreme global cooling). Because of this, Laibson posited that we have to understand climate change both on the point estimate (i.e. what the general public sees) but also on these extreme possibilities that we have to plan for. I thought that the previous arguments were really interesting, and I would love to have been able to ask David Laibson this follow-up question: how do modern-day scientists (or those making predictions) wrestle between reporting/analyzing the uncertainty that the general populace sees and understands as well as the uncertainty that academics and scientists focus on? https://www.labxchange.org/library/pathway/lx-pathway:b5121779-9f49-49db-93d9-80d5d67dadb3/items/lx-pb:b5121779-9f49-49db-93d9-80d5d67dadb3:lx_simulation:f10b9110?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

I found the conversation between Professor Goodman and Dan Gilbert to be really engaging, as I was very intrigued by the links psychology possesses to prediction. The most surprising information I heard from this talk was around using the past to predict the future. Gilbert mentioned how people often do not correctly utilize the past in order to make predictions about the future. Instead, what they do is take unrepresentative instances from the past and use them to simulate the future (which is incorrect), and therefore their simulations turn out to be wrong. I think that this was definitely more true a long time ago, as humans did not have the predictive technologies that we do today (i.e. haruspicy, Roman auguries, etc.). Yet nowadays, with the incredible power that modern technology has, we are (ideally) able to make much better predictions than in the past. This point of Gilbert’s was surprising to me because, while I still consider his argument true today, I believe that this was much more relevant to previous times and not as relevant to today’s statistically advanced prediction models. 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

In the video with Ben Shneiderman, he discussed Google Flu Trends, which was something I had never heard of before. In 2009 Google began to predict the way the flu breaks out in different cities around the country by analyzing Google search data from individuals (i.e. if people search for tissues, flu symptoms, etc.). This seemed to be a decent early predictor of flu breakouts; unfortunately, over time, it began to lead to incorrect predictions that then led to a poor allocation of resources from public health officials. Four years after its inception, Google removed the Flu Trends website. In this example, Schneiderman illustrated the algorithmic hubris of those at Google (and elsewhere) who assumed that Google Flu Trends would be a fantastic, long-term predictor of flu outbreaks. This was a really fascinating segment of the video, and I am very glad that I got to learn about the failure of Google Flu Trends from Ben Shneiderman. https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:997b23d6?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction In the segment discussing John Snow and his role in epidemiology, Professor Goodman remarked how some scholars do not consider Snow to be the father of epidemiology, due to the fact that his study design failed to contain a control. In response to this, Megan Murray stated that she judges studies on how effective they are (thus illustrating her admiration for Snow). Clearly, in the case of John Snow, his now-famous study certainly was effective and did work. However, when I heard this response, a question immediately came to mind that I would want to ask Megan Murray: would you be critical of the lack of a control in John Snow’s cholera study if the study was not wholly effective? It seemed like she was following a very results-based approach in the video, and I am interested to hear more about that. https://www.labxchange.org/library/pathway/lx-pathway:0b417a9e-6227-44a8-a887-dbfdf44e37e3/items/lx-pb:0b417a9e-6227-44a8-a887-dbfdf44e37e3:lx_simulation:7f50189c?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

In a question asked by Professor Goodman about giving people positive news regarding the future of climate and energy, Dan Kammen explained how he is a large proponent of electric vehicles because, if we make our electricity greener, the equivalent mile per gallon goes up. He went on to describe that the exact same electric vehicle being operated in a coal-intensive state such as Wyoming has around 45 miles per gallon, but that same vehicle in California (where there is very green electricity) has 120 miles per gallon. This was very surprising to me, as I did not realize the difference in miles per gallon across states. Kammen also stated another benefit of electric cars was less asthma in children, which is something I never really considered before. He explained that if you drive more electric vehicles, there are (obviously) fewer tailpipe emissions, and this translates to less pollution, especially in cities, which leads to healthier individuals and less asthma (especially among children). The above information was both very informative and very surprising to hear. https://www.labxchange.org/library/pathway/lx-pathway:825945a0-367c-45dc-82b7-3d160c6e6f7a/items/lx-pb:825945a0-367c-45dc-82b7-3d160c6e6f7a:lx_simulation:fa741ca2?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction After doing a bit of research on Gina McCarthy, I found that she had been selected to serve as the first White House National Climate Advisor under President Biden. Although this interview was filmed before her confirmation, I would have loved to ask about how her experience at Harvard would impact this new role: How do you think that your time at the T.H. Chan School of Public Health will impact the decisions you make in the Biden administration? https://www.labxchange.org/library/pathway/lx-pathway:825945a0-367c-45dc-82b7-3d160c6e6f7a/items/lx-pb:825945a0-367c-45dc-82b7-3d160c6e6f7a:lx_simulation:6113c65e?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction