Something that surprised me when I watched Professor Goodman’s conversation with Ned Hall was the discussion that they had about how we practically use models to promote understanding and make predictions. Dr. Hall brings up an example from his time as an undergraduate where he predicted different quantities using a model that combined elements of classical and quantum mechanics, despite the fact he knew the theory he was using was inconsistent and his model was therefore “wrong.” This reminded me of a phrase I’ve heard many times throughout my time at Harvard, which is that “all models are wrong, but some are useful.” The idea that a model’s value comes more from its utility than its accuracy is an interesting one in the context of the two professors’ opinions about machine learning. To what extent are we willing to use “incorrect” models if there is still something to gain from them? I really enjoyed the point Dr. Hall made about the reason humans do science: it is not just to make predictions, but also to understand the world. If I had conducted this interview, I would have additionally asked Dr. Hall about philosophical ideas related to causal inference. When we make predictions, we are trying to figure out something about the future (or some other unknown) given what we know now. When we investigate causality, we are attempting to create a direct link between two phenomena. I would be very interested in hearing how modern statistical ideas about causality have developed from philosophical underpinnings and the philosophical distinctions between creating a prediction and establishing a causal relationship. Why do so many people conflate or confuse the two in practice?

Something that surprised me in the conversation that Prof. Goodman had with Susan Murphy and Brendan Meade was the discussion of how humanity’s access to vast quantities of data and unparalleled computing power has changed the way “science” is done. It was fascinating to me how Dr. Meade described how earth scientists studying earthquakes try to remain “humble,” in the sense that they don’t claim to know all of the exact theoretical physics a priori. In past discussions of simulation in our course, we’ve emphasized the importance of the models on which these simulations are based, and how these models both directly reflect and parametrize the complex processes occurring in their settings. The idea that current science and future simulations could be based on recurring patterns before theoretical models and equations is an interesting one. If I could add a question to the conversation between the three scientists, I would ask what they see as the possible shortcoming of this new “data science” approach to prediction. The group spoke briefly about how some of the “features” identified in data science algorithms are not entirely interpretable/communicable on the human scale/with human language. Is it possible that phenomena such as this compound into more and more abstract representations of the world or setting at hand? How do they manage the risk of relying on these algorithms that possibly miss the forest for the trees? Is it the case that as long as the outcome is “accurate,” we trust these models and predictions even if we do not know their exact machinery?

I found the conversation with Dan Kammen very engaging. One part of the interview that stuck out to me was the comparison of the Earth’s climate to human health in the sense of focusing on prevention vs. a cure, and the related idea of performing “sensitivity analyses” to show just how important investment in resilience is in fighting the detrimental effects of climate change. One argument I’ve often heard against taking immediate action against climate change is one to the tune of “necessity is the mother of invention.” That is, once the effects of climate change grow more catastrophic, humans will find ways to innovate against these changes in a way that mitigates their consequences. I think it’s both interesting and important that proven methods for this mitigation already exist and are in practice, and are simultaneously giving us an idea about exactly how much change we may be able to “tolerate” in the future. If I had the opportunity to speak with Dr. Kammen, I would ask about his perception of the rise of “climate anxiety” or “climate doom-ism.” We often talk about climate extremists in the form of those who outright deny climate change, but rarely give attention to those on the opposite end of the spectrum. I would ask about how he thinks we can communicate a reasonable level of concern that inspires people to get involved in finding solutions without driving them into existential fear, hopelessness, and ultimately inaction. Specifically, I’d be interested in how the science of prediction plays into this communication. How can we use data, models, and simulations to show people that the climate does have a fighting chance?