I found myself largely agreeing the discussion of people tending to be creatures of habit. Most people follow a predictable schedule of events throughout the day: they wake up at 6AM, have their morning coffee, drive to work, drive home, watch their favorite TV show, then go to bed at 11PM. In aggregate, it is easy to predict if demographics will support a political stance or live in a certain part of town. That being said, I don't know if human behavior is predictable on an individual level. I might not know if my friend supports a certain piece of legislation or what part of the city my coworker lives in without asking them. Beyond schedule habits and vocabulary quirks, are people actually that predictable on an individual level? To what degree?

I liked that Professor Firestein framed scientific progression in terms of ignorance as well as terms of success. While it is tempting to believe that the scientific lexicon as we understand right now it is perfect, looking at the history of medical science suggests that we are probably doing some things wrong with the way that we view and treat certain ailments. We still have no idea why some people get type 2 diabetes and others don't despite similar risk profiles. We also don't really know why some people have what is sometimes described in the medical community as "poor protoplasm" or inexplicable frailty or sickliness. I think that the point to acknowledge the ignorance and to understand that sometimes ignorance can lead to brilliant conclusions- such as neuroscience having its foundation in the pseudoscience of phrenology. We are still learning about how the world works, and part of the learning process is recognizing that we might come to some wrong conclusions or observe inexplicable phenomena since we don't fully understand everything yet.

I was interested in the discussion about earthquake resistant buildings as a person who has never lived in a region that earthquake-prone. I did not know that there is lingering damage in Los Angeles from the 1989 and 1994 earthquakes that is still a problem today or that once earthquake proof buildings experience an earthquake they are no longer as resistant to subsequent earthquakes. In retrospect this makes sense because these buildings are made with breakable materials that do not heal over time, but I had never thought about it before seeing the interview. I can imagine that rebuilding buildings in large areas is an expensive and labor-intensive undertaking, so I wonder what practical and affordable steps can be taken in earthquake-prone areas to help rebuild buildings to their original earthquake-proof status after a major seismic event.

I was surprised that models of the Earth below the surface are still so difficult to make. While we obviously can't see structures below the ground with the naked eye or traditional cameras, I find it surprising that we can track surface movements down to the 10th of a millimeter but cannot observe movement below the surface. Is there emerging technology that may allow us to model what is beneath the surface? How could technology like this improve our ability to predict seismic activity?

I liked the grounding point in the Professor Loeb interview that it is important to be able to do more than just predict things accurately. Sometimes knowledge for knowledge's sake can have implications that are far reaching beyond the original curiosity. Professor Loeb cites Einstein's general theory of relativity's importance to GPS as an example of a non-obvious connection between two subjects that are seemingly disconnected: outer space and getting accurate directions from CVS to Cape Cod. Predictive models that are purely predictive are sometimes important and useful for applications where all we care about is accuracy, but deeper theoretical understanding allows for these models to be implemented in ways that allow for intra and interdisciplinary progress beyond the scope of the original research question. It is easy to forget that there may be many connections that we just have not discovered yet, and abandoning the underlying theory that supports our predictive models does a disservice to building upon that knowledge later.

Professor Laibson's suggestion that rational choice theory, behavioral economics, machine learning, and simple theory are all valuable and possible to combine in some way in designing models for and predicting economic behavior was very insightful. All of these ideas appear to point to a larger truth, but there are drawbacks to oversimplification, overcomplication, and lack of nuance. I am curious as to how these competing ideas are implemented in the behavioral economics field today. Professor Goodman's observation that perfect laboratory conditions are impossible when gathering economic data also serve to highlight that both oversimplified and overcomplicated models could lack some deeper insights about the phenomena being observed in real world markets where it is impossible to observe or include every variable and where oversimplification could prove disastrous when designing policy. It reminds me of the idea taught in Stat 110 that all models are wrong but some are useful. Combining these various and somewhat competing models helps us to get a fuller picture of the story behind why people make the economic choices that they make, and I appreciate stepping back and thinking about the merits and disadvantages of the lenses that we choose to view economic behavior through.

I particularly resonated with your discussion of creating plans to perform tasks that you are likely to procrastinate. Adding intentionality to performing tasks through an outside accountability source makes people more likely to follow through with their plans. What are the deeper psychological mechanisms that make people more likely to finish work on time or perform laborious tasks if they hold themselves accountable to an outside source even if the source will almost certainly never follow up later? I mean this in the context of both human accountability partners (think: telling a friend you plan to go to the gym later) and inanimate accountability devices (think: Apple or Google Calendars).

People are notoriously bad at understanding risk and statistical models intuitively. It is important for patients to be able to make informed choices about their healthcare plans, but a lack of deep understanding of probability and statistics may make information presented in a purely mathematical format difficult for the average person to understand. How can we present risk profiles to patients in a way that preserves medical transparency while also meeting patients at their personal ability to comprehend both statistical modeling and risk? A link to the interview can be found here.

The most interesting thing I heard in the Professor Shneiderman interview was the discussion of hubris surrounding AI in the context of the Google Flu fiasco. It appears that this is a classic example of computer algorithms being fallible in the AI world, but I was fascinated because I am so unfamiliar with anything to do with the computer science world. After a quick search, it appears that two of the major problems with Google Flu Trends were that spurious correlations found in the algorithmic training set threw off the results and that people who search for flu-related things online are en masse uneducated about medicine and therefore aren't very good at determining whether or not they have flu symptoms in the first place. This unreliable information is turned into inputs that are processed through a poorly-tuned computer algorithm that then generate unreliable results. The case study highlights some of the failings of relying purely on AI for prediction. It is dangerous for people to take the results of machine learning as gospel without further verification, and the implementation of computer "fairy dust" algorithms make it very easy to do just that. Instead of thinking of computers as magical machines we should think of them as tools that assist humans who are ultimately in charge of the predictive process. I wonder how that balance can be struck in a way that optimizes the power of computation without bulldozing the importance of some type of human understanding of the inputs and outputs of these complex equations.

Both my question and most surprising fact came from the Professor Rebecca Henderson interview. Professor Henderson highlighted how CEO and worker opinions about the importance of climate change were important in determining how actively a company would pursue clean energy. I wonder how from a public policy or consumer behavior standpoint we could provide additional incentives for corporations that are lagging behind to start considering the climate more when determining their energy policies. While many corporations are adopting greener policies, we will need to act at a much faster rate to mitigate the impending climate disaster. I would like to ask Professor Henderson about how we can push more reluctant CEOs to change the direction of their company's approach to clean energy. I would ask: Clearly major corporations need to change from their "business as usual" approach to climate-friendly energy policy. How can we incentivize reluctant CEOs to adopt climate-friendly policies? Why do you surmise that these incentives, if they exist and would be effective, have not yet been implemented? The most surprising fact from the interview was that people are likely to change their political stances on climate change based on their personal experience with climate events. After seeing some of the denial that fairly large swathes of the country have about climate change would have made me skeptical that people would respond to increased disaster frequency with a change of opinion. I think it is fascinating that people are more likely to change their voting behavior if they are struck with three climate change related natural disasters in a row. Professor Henderson used an example from the Carolinas to demonstrate that after three major natural disasters, voting patterns emerged that supported climate change mitigation efforts. I would like to know why three is the magic number that prompts a response. I am also curious about the human psychology behind why people sometimes only recognize pervasive issues if they are personally affected.