During the conversation between Prof. Goodman and Rayo, they honed in on the problems of public perceptions of prediction and, relatedly, prediction accuracy and trust. Prof. Rayo made a fascinating point about trusting predictions, arguing that people should often trust a prediction even if they are fairly certain that the prediction is wrong. More specifically, Rayo contends that people should trust predictions when they are fairly certain that the prediction is more reliable than the other methods available. This raises an important point—how do we get people to trust new, better predictive methods, especially when they have uncertainty. People are naturally biased towards the status quo—this is a key tenet of behavioral science. Thus, people are likely to irrationally distrust new predictive methods.

I found Prof. Goodman and Rayo's discussion on the philosophy of prediction incredibly fascinating. In particular, the final section on determinism and free will piqued my interest in the intersection between science and the philosophy of free will. Prof. Goodman raised the idea that—one day—we might understand the brain at the level we understand astrophysics: that we might be able to predict human behavior with near absolute certainty, down to the next word we say. If I could add a question to this specific conversation, I would ask if pure deterministic understandings are even possible due to the inherent randomness of the physical world? In particular, I would ask about quantum indeterminacy and its implications for determinism.

I found the discussion between Prof. Goodman, Susan Murphy, and Brendan Meade extremely engaging. In particular, Susan Murphy's explanation of her mobile health studies in Chicago piqued my interest. Murphy detailed the incredible specificity with which they examine personal health data—down to an individual's craving levels when in contact with different personal relations. This type of data—on how the brain reacts to specific inputs, stimuli, and situations—is also commonly found in behavioral science. Murphy also outlined how her studies test recommendations to individuals, like recommending mindfulness each day and measuring the effect on stress levels. This, too, runs parallel to behavioral science by altering how people interact with choices to change their behavior. Even the challenges and concerns seem adjacent: Murphy notes that there are often privacy concerns involved with measuring and employing this hyper-personal data. Similarly, critics of "nudges" in behavioral science often argue they violate key rights to autonomy and privacy. It is fascinating to see how these two fields intertwine.

I am personally fascinated by behavioral economics and the topic of nudges in particular. Nudges are when an outside actor changes how an individual interacts with a choice by playing on their subconscious psychological biases. However, as Prof. Goodman and Laibson discussed, we do not have a perfect deterministic model of the brain and human psychology. I would have loved to ask Prof. Laibson whether AI can help behavioral economists develop a more comprehensive understanding of psychological biases by picking out patterns in the data and exposing new subconscious, systematic human behaviors. Is the story complicated further by our inability to discern explanations from AI? How close could we get to a deterministic model of the human brain? https://www.labxchange.org/library/pathway/lx-pathway:b5121779-9f49-49db-93d9-80d5d67dadb3/items/lx-pb:b5121779-9f49-49db-93d9-80d5d67dadb3:lx_simulation:f10b9110?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

Throughout Professor Goodman's interview with George Church, they discussed how individuals might interact with information about their own genetic data. While Church discussed the psychology and ethics of informing people of their genetic data, I would have liked to ask him questions about the delivery method of genetic data. Genetic testing kits are increasingly accessible and can tell you anything from your ancestry to your susceptibility to certain diseases. However, the data are not always easy to interpret and their conclusions may be uncertain in ways that are not immediately obvious to average consumers. I would ask Church what ways he thinks we ought to regulate the flow of genetic data from commercial testing kits? Should we use genetic counselors?

I found Professor Goodman's interview with George Church on Personal Genomics to be among the most compelling videos in the module. In particular, I thought Church raised important, eye-opening concerns about CRISPR and Uncertainty. In the interview, he notes that CRISPR (and related technologies) are unique in that people have to decide whether or not they want to participate. As such, potential participants must weigh the enormous capability of CRISPR with the uncertainties. This discussion led me to ponder how different types of people might interpret these considerations differently. Economists often divide people into categories based on their level of risk tolerance, such as risk-seeking or risk-averse. When confronted by CRISPR, risk-seeking individuals might be more inclined to try it, while risk-averse individuals might be more reticent. Thus, uncertainty means different things to different people. And because people's risk tolerance is not simply random, those differences could have significant implications for technologies like CRISPR. For example, recent research has shown that white Americans are systematically more risk-seeking than black Americans. If CRISPR has massive benefits, will those benefits be spread in a racially unequal manner? These are key considerations in thinking about public communication surrounding CRISPR and the future of genomics in general.

The interview with Daniel Kammen was comprehensive and fascinating, but I would have asked follow up questions on his point about "silver bullet" solutions to climate change. Kammen says that even important breakthroughs in climate technology have little to no effect in the long run. This may make sense up to this point, but could future breakthroughs disrupt this trend? For example, could an historic leap in battery technology enable significant increases in energy efficiency? Or could a revolution in carbon capture technology allow us to go carbon-negative in the near future? Part of the problem is that we are limited in our prediction of future innovation by the framework of current technology. While human history has seen slow and steady progressions, many of the most important moments have been revolutionary breakthroughs. The digital age, which could not have been predicted even decades before it, has completely altered the fabric of society. Could such "black swan" innovations happen in climate science?

Of the three talks we watched, I found Daniel Kammen's talk, "The Future of Energy and Earth," the most compelling. In particular, I found two related points most interesting. First, I found the discussion of how the study of climate change has become dominated by the STEM fields, like physicists, chemists, and engineers. Understandably, most efforts to study climate change have focused on the hard science—discerning the processes and dynamics of the earth's climate and the potential for green technologies. Kammen points out that, while this focus is an important part of the field, the study of climate change has underemphasized the humanities and social sciences. To address the global crisis of climate change, we need a comprehensive climate strategy that pervades society. As such, an interdisciplinary understanding of climate change is crucial. Relatedly, I found the discussion of the psychology of climate change fascinating. The social sciences are key to understanding how society can and will respond to climate change. In particular, the interview mentioned how the developing field of behavioral economics will contribute significantly to the study of climate change. Specifically, behavioral economics will uncover cognitive biases which can be exploited to "nudge" individuals toward climate-friendly behavior. Such nudges can be incredibly effective and may be a central component of a comprehensive, interdisciplinary climate change response plan.