As someone who has never taken a rigorous stem course at Harvard nor considered themselves a quantitatively inclined reader, this essay was surprisingly helpful and clear for me when it came to understanding the difference between risk and uncertainty. Even though the risk/uncertainty matrix professor Goodman supplied was a qualitative representation of the issue, the quantitative aspects of things like covid modeling help us understand why mitigation is important in cases of high uncertainty particularly due to the human factors at play. I thought the article blended these two perspectives really well through the numerical examples provided in the covid examples in particular.

Hi Zev, I though this was a really interesting question especially as we think about the question of uncertainty in our models for astrophysics and the fundamental forces that govern our universe. I was wondering how we might go about determining the uncertainty and impact of forces that we haven't even accounted for in our models. How does testing the extremes about what we know about material behavior such as the experiments that take place in the CERN Large Hadron Collider play into pushing the limits of these models? It seemed that Professor Loeb was optimistic for our future prospects.

I was really interested in this point too Andrew. With more and more people discussing the proliferative impacts of notifications, pushes, and nudges, I would have loved to understand how our devices maintain our (un)conscious attention through the pervasive threat of a notification. In terms of explanations to describe these behaviors it seems that the bottom line in terms of profit is what drives many of these digital models in the attention economy.

I am really interested in Ben's question about biomimicry as well and would love to understand how contemporary AI and computer science researchers are leveraging emerging understandings from fields of biology and neuroscience to gain inspiration for new computing structures (and vice versa as big data applies to medicine of course!). Does our biological understanding of the human brain inform how computer scientists go about networking? How do intersections of biology and computer science embodied in projects like the Connectome change how we view the digital and natural worlds alike?

The complex network that social media affords us is incredibly useful for our day to day lives, but as Dan Kammen and Oswaldo indicate, it also has a dangerous potential to spread inaccurate information to mass amounts of people. It can be difficult to discern authority on social media platforms and as such I do think that the social media platforms themselves can do a better job at verification. However, I believe the paramount issue at hand in addressing issues of fake news and misinformation is education to empower the individual. Endeavors to educate citizens about the role of data and coming to scientific conclusions, such as this class, allow people to better discern the robustness and fit of a model being applied in a given prediction or conclusion they are reading online. When the individual has a greater ability to discern accuracy for themselves then the importance of platform oversight diminishes; replaced by a pursuit of wider societal education.