During the interview, Susan Murphy mentions this idea of personal vs societal prediction especially within the field of healthcare. For the Gened 1112 final project, I'm looking at predictive genetic testing as my predictive system and throughout my research on the topic I have found that it is relatively easy to detect the presence, absence, or mutation of genes. However, it is comparatively more difficult to look at the predictive possibility of the expression of these genes to lead to a disease. However, one prime reason of this predictive system is to use this data to be able to better predict the expression of the disease within at risk family members. So I would like to ask Susan Murphy about how to draw the line between personal and societal prediction within genetic testing and if it is even possible given the inherent nature of genes?

Something I found to be extremely interesting during the interview with Brendan Meade and Susan Murphy was their mention of short term behavior vs. long term consequence and the similarity of that concept in both fields. Meade mentions that currently seismologists are looking at data that is measured during an earthquake which surprisingly accounts for less 1% of time which is evident of short term behavior. While instead they should be focusing on measuring how they expect that plates to move to account for possible future earthquakes which describes long term consequence especially since he mentions that buildings are only survivable for a single earthquake leaving the resources susceptible to these earthquakes. Murphy mentions the similarity when looking at healthcare for short term disease control detection vs the ability to predict the effect or expressions of diseases in the long term.

Although this doesn't have too much of a connection with our predictive systems discussion, during the interview with Jill Tarter, what I found really surprising was the fact that they constantly protect a specific frequency to study the universe. The region of the radio spectrum that is relatively quiet called the terrestrial microwave window. I've never known about this and I found really surprising that they constantly maintained something such as that to look for possible signals that are too narrow for nature to produce but that they believe those with sufficient technology could reach. Especially since she's continues to talk about how the information on this is basically an "unknown unknown", but that doesn't let it stop them from beginning to create a predictive system of their own!

Something I found really interesting during the interview with Professor David Laibson was the idea of present bias. This stood out to me the most because it was something I could really relate to. I’ve constantly found myself, especially lately, pushing off almost everything I have to do until the very last minute. Even if I know that I would get more satisfaction from finishing it early with the free time I currently have, however I lack sufficient motivation. It is nice to be able to describe this feeling in such a tangible way and learn more about how consciously knowing I do has an affect on my future choices.

Last summer, I took Ec 10A and 10B, the introductory economics course taught by Professor David Laibson and like he said we mostly studied rules and theories based on a more deterministic and rational view of humans that allow us to creat steadfast explanations that we can follow to predict what may happen. However, I’d like to ask Professor Laibson if he thinks it’s possible to came up with a similar set of rules and theories if we use behavioral economics and base it on a more irrational and less logical population?

Something surprising I've learned during the interview with George Church is about the importance of using prediction to change the future. Throughout this course, we've talked a lot about the different methods to create predictions from oracles in the ancient times to simulations and models used today, but we haven't had as much discussion about the possible implications of those predictions on our decisions about the future. George Church mentions here the possible use of different environment management techniques or gene therapy to fight against your "genetic destiny" that is sometimes easily predictable and thus preventable. It would be very interesting to continue to study how different predictions that have been made throughout history, have literally rewritten their future as a result of this new information. As well as how much we believe in this prediction based on our perceived certainty of the method. For example, in regard to Phenylketonuria in infants, almost all parents seem to take the advice of changing the diet as an easy method to fight off this "genetic destiny, but how likely would they be to listen to this advice if the prediction method carries higher uncertainty or if the solution was of much greater cost.

During the interview, I found it surprising when Dan Kammen mentioned that despite the fact that we have more complex, evolved, less uncertain, and higher fidelity models. We usually forgo that in favor of models that use historic data and records to model climate change as they are more practical in regards to computational data and time. Despite the fact that climate is evolving so quickly that records need to keep up in order to maintain the accuracy of the models, the data series is still very powerful to tell us how the climate changes.

The question I would have asked Gina McCarthy in her interview is: How do you think changing the public's understanding of models and simulations would affect their behavior towards understanding and resolving climate change? I would have asked her this because it would have given insight to what she thought the correlation between two essential components of what we are discussing in our prediction framework are in regards to an important global issue . As well as perhaps give us a chance to provide new and more effective methods of climate change education.