Forum Posts

Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Apr 27, 2021
In Thoughts from Learners
After the interview with Stuart Firestein, I am left with several pressing questions I'd be interested in asking to keep the conversation going. First, I appreciated the discussion about the differences between how science is taught and how it is actually pursued. I would be interested in hearing Professor Firestein's thoughts on how to better explain to the public and to students that science is never as certain as we think it is . Does this reveal need to happen earlier than college, in even the most basic grade school science courses? When is the optimal time to explain how important ignorance is, and how fragile scientific truths are? Additionally, I liked his explanation of science as, historically, anti-authoritarian. I would ask: how can we balance that valuable characteristic with the fact that sometimes-- like in the case of a pandemic-- excessive skepticism towards scientific authority can be dangerous (see anti-maskers and anti-vaxxers)? Maybe the answer lies in an earlier point he made, about "Science" not necessarily being a brotherhood dedicated to the same kinds of questions; in seeking a health balance, the approach taken by astrophysics is probably going to differ from the approach taken by medical sciences.
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Apr 27, 2021
In Thoughts from Learners
I greatly enjoyed the entire discussion with Stuart Firestein, but was especially struck by his final point. He mentioned that throughout history, the mechanics of the human brain have often been compared to the most advanced technology of the day, such as hydraulics, clocks, and even neural networks (and that most of these metaphors have been wrong). This information was surprising to me, but I also think it illustrates his earlier point that few scientific truths hold for long. Whatever way we think about the human brain, or medicine, or "the heavens," is likely to shift, adapt, and respond to societal pressures in future paradigms. This seeming transience of science is slightly unsettling, but is quite the appropriate note to end on in this class on the history of prediction.
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Apr 21, 2021
In Health
Though the interview with Susan Murphy and Brendan Meade introduced me to a number of new ideas in the prediction of "earth" and "health," I was most surprised to learn about the existence of mobile health itself. I initially thought that this would mean something in the vein of mobile health clinics, but to find that Professor Murphy's work entails a collaboration between wearable sensors and mobile phones was very interesting to me. I also appreciated the discussion on the distinction between predictions on the level of the individual and on the level of society. Professor Murphy makes the point that convincing individuals, (and especially willing, goal-oriented participants utilizing mobile health) can be easier than convincing a society to consider the future or consider some prediction. This makes me wonder about the role of goals in prediction, and if having a goal makes it more likely for people to consider the future in a productive way. (On another, more rambling note- in my prediction journal, my "predictions" about how quickly I could get an assignment were based in data/past experiences, but there was also an element of goal-setting or even hope in there- as in, "I'm predicting it'll take me X hours to do all these readings, but I really hope it will take me that long or less so I can have more free time." Though this doesn't relate to the more quantitative and real-world predictions we're discussing in class, hearing from Professor Murphy reminded me that that assignment prompted me to wonder about potential blurring between predictions, goals, hope).
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Apr 21, 2021
In Earth
Watching an informative interview with Brendan Meade and Susan Murphy, several questions came to mind. First, I would be interested in asking Professor Meade to talk more about the Sendai "semi-prediction." Given that preparing for earthquake recovery is often more feasible than preparing for an earthquake itself, and given that the timeline for his predictions is so uncertain, how are those kinds of predictions handled? Who is the audience for his research?- Does his work reach policy-makers? I am also curious about the response to the paper predicting the Sendai area as a risky one, both before and after the earthquake happened. Second, I would want to hear him expand on what he thinks the future of earthquake prediction will look like, as I found it interesting about how much unknown there is in the world of earthquake prediction. A couple of times he mentioned that we may not be smart enough to fully understand the physics yet, or that we do not know how much data is truly needed to make earthquake predictions. Do he and his peers think this will be ever known? And if or once these unknowns are fully known, would we even know what to do with that new information? Finally, just because I had never heard this characterization before, I would ask him to explain what it means for probabilistic seismic hazard assessment to be "intellectually broken"- how does a field recover from that?
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Apr 13, 2021
In Space
The discussion with Avi Loeb was an enjoyable one, and I appreciated the common thread between his interview and Jill Tarter's regarding the necessity of imagination in order for science to progress. However, the most thought-provoking moment for me occurred when Avi Loeb discussed restructuring, in some way, how science operates. Rather than researchers discussing solely among themselves and bringing that information to the public when there is certainty, science as an institution can gain some credibility (and can fight back against elitism) if its agents are honest about uncertainties and mistakes. First, his argument for engaging the public in scientific discourses made me wonder if it would be interesting for Professor Goodman to interview not only experts, but a few lay people, to bring them into the conversation. Second, I immediately wondered about times that conveying uncertainty, even if quantifiable, even if it doesn't mean we know knowing, may backfire. During the ongoing coronavirus pandemic, for example, if health scientists expressed that the efficiency of social distancing or mask wearing was in any way unknown, we would likely see even more health-endangering behavior (we see it already, even when scientists express their full confidence in these measures!) that puts others at risk. Discussing uncertainty about the safety or efficacy of vaccines, though both are high, could be even more risky. I think there may be less risk to societal well-being if, say, astrophysicists rather than doctors discussed the unknowns in their work, but this makes me wonder which sciences and which disciplines can afford to be openly uncertain.
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Apr 13, 2021
In Space
After watching a fairly mindblowing interview with Dr. Jill Tarter, I have a number of follow-up questions. First, I would ask her to expand on what was going on in her mind during the two false positives. What kinds of predictions was she making in her mind about what the result of those positives would be? What did her mind immediately leap to, and was it based in some scientific knowledge or more in some human instinct or gut feeling? Throughout the interview, I was trying to imagine how I would feel if I were on the receiving end of those signals, but I kept thinking about what seems to me to be a low likelihood that contact would happen to me or even in our lifetimes (bonus question: when does she think contact would happen? In the 21st century, as implied by her comment on this being a prime time for biology on Earth and elsewhere?). I understand that the history of science is filled with huge reaches, and scientists doing work without knowing if the results would be fruitful, but this skepticism that for some reason I could not shake relates to my next question: What motivates her to do the work she does? Does she ever lose hope or get discouraged, and what pushes her to keep going? Finally, I would also ask how the Drake equation ("it predicts nothing") relates to predicting the human response to aliens, which was a topic of discussion that piqued my interest in the interview. It seems to me that as much uncertainty there is in predicting the existence of extraterrestrial life forms, there would be that much more uncertainty in predicting how people on Earth would respond just because the incredible unpredictable aspect of human behavior is factored in. However, I would appreciate her expert perspective.
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Apr 06, 2021
In Thoughts from Learners
While watching the interview with Dan Gilbert, I was most surprised to hear him note that in the context of climate change, most people tend to recognize some sort of "confidence interval" (and this is why there is a general lack of urgency). I was initially surprised to hear this, though it ultimately makes sense: the general non-scientist public obviously recognize what uncertainty is, but don't generally use the language of confidence interval. This made me question if non-scientist people (myself included) know more about prediction, uncertainty, or models than we think, and they just do not understand it in the context of hard statistics. With this in mind, and given that Dan Gilbert argues people will be less motivated by statistics about model accuracy than by other campaigns (like behavioral based), I wonder how we can "meet people where they are" in educating about predictions, uncertainty, and models. Similarly, I was surprised to hear him use the word "simulation" to describe the personal predictions we make in our minds after considering past information. This has me thinking about potential ways to bridge how the public naturally understands models and uncertainty with the more academic (and more accurate, but harder to convey) explanations.
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Apr 06, 2021
In Thoughts from Learners
In the interview with Dan Gilbert, Professor Goodman makes the point that historians, unlike scientists, tend to disagree with the existence of some realistic, absolute, objective timeline of the human past. This makes me curious about where objectivity lies within Dan Gilbert's field of psychology, where individual perception seems to be such an important factor (at least, that's my perception). I would ask him which side he tends towards, that of the historian or that of the scientist? I also wonder how a belief in some objective truth, past or present, factors into his research, and into questions of uncertain in his research. He notes that even if we were able to perfectly predict everything that would happen, how we would feel about those events is more uncertain. It seems that even knowing about some absolute truth that will come to pass does not eliminate the presence of subjectivity, but I'd like to hear Dan Gilbert's discuss this further.
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Mar 31, 2021
In The Future of the Future
The discussion with Ben Shneiderman was surprising in general: I wasn't expecting, but came to appreciate, the importance he placed on specificity of language when discussing AI, its existence as a tool and not a partner, and "the future of the future." However, the most surprising thing I heard was about algorithmic hubris and the often undue trust placed in algortithms to "get it right." I can see this hubris contributing to a problem that Shneiderman later mentioned, or technological tools presenting racism, anti-semitism, sexism, etc (see example below, where Black natural hairstyles come up in response to the search for 'unprofessional hair for work'). Source Link Those prejudiced or stereotypes responses may be reified if we take the algorithm to be objective, right, or true, when in reality there are ultimately humans with their human biases behind it all. To stave of this hubris and dangerous unquestioning attitude towards AI, Shneiderman suggests studying past failures and how they came to be. This approach seems to me to match the important "Evaluate Accuracy" and "Make Changes" cycle on the Predicitive Systems Framework of modern times, if on a slightly more meta level.
PredictionX Simulations: Surprising Information with Ben Shneiderman content media
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Mar 31, 2021
In Health
I enjoyed watching the discussion with Megan Murray and have several questions that I'd be interested in asking, given the chance. I am curious about the utilization of the data visualization tool that is an atlas of genes associated with certain diseases. I would want to know what happens after a correlation has been determined. How can that information be used to make a meaningful prediction or lessen rates of disease to better a population's health? Murray mentions that those correlations may have genetic or environmental origins. What might the next public health steps be if a correlation is shown to be driven by genetics? By the environment? As someone interested in the history of medicine and public health, I am curious as to what she believes the role of history to be in modern public health. Why study John Snow- and does it matter if he was really the father of epidemiology? Additionally, are most epidemiologists really familiar with the reality of the story?
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Mar 30, 2021
In Earth
I also found the conversation with Rebecca Henderson to be particularly fascinating, and given the chance, I would ask her a number of additional questions. First, when I saw her start drawing the 2x2 matrix, I thought that she was going to describe climate change as some sort of variation on Pascal's Wager. I would ask her if she would find it useful to express this wager to corporations, or if the tendency towards business-as-usual is too insurmountable an obstacle for that argument. Second, I would spend more time exploring what she calls techno-fanaticism. I understand why people fall into this trap, given the historical record of seemingly-miraculous technological remedies, but would want to hear what the term really means for her. Did discourse about climate change inspire the term, or did it emerge from some other area of her research? Does she see techno-fanaticism reflected in corporate policy and action, or is it more like a belief harbored by individuals? Compared to other reasons for climate change denial or inaction, how big of an obstacle is techno-fanaticism? Finally, though she works mainly with corporations, and with her comment on employees sometimes inspiring change in mind, I would ask what power she thinks individuals have against climate change.
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Grace Yeboah-Kodie
Harvard GenEd 2021
Harvard GenEd 2021
Mar 30, 2021
In Earth
The most surprising thing I learned, or perhaps the most thought-provoking idea I was introduced to, came from the LabXchange PredictionX discussion with Gina McCarthy. Having worked in both environmental science and public health, she remarked that people don't necessarily question claims made about something like toxicity, but they tend to question claims made about climate change. (Similarly, she later noted that people tend to trust doctors more than scientists). This was surprising because it makes sense, but it was a very clear and revealing comparison that I hadn't considered before. Accepting predictions about our immediate health seems easier: there is expert consensus about the prediction that ingesting some toxic substance will lead to negative effects in an individual's health. There is also expert consensus about climate change, which has been predicted to lead to negative effects to entire populations, their health, and their general livelihood. Though likely inevitable, as poisoning is, the harms of climate change are projected to come some time in the future, and thus this prediction about harm is stretched past a point that people will care about it. As McCarthy was discussing this, I found significant her quote that "people don't accept problems there are no solutions to." This is reflected in the toxic substance/climate change comparison, but I am struck by the paradoxical nature of this truth. Not accepting a problem because of its perceived lack of a solution makes it no less a problem, and in fact likely lessens the likelihood that there will be any solution.
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Grace Yeboah-Kodie

Harvard GenEd 2021
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