Image: Chinese poster from the 1980s promoting science. Caption translates to "love science, learn science, use science". I loved hearing about how Prof Firestein came to create a course called “Ignorance”, regarding what we don’t know about science. It is fascinating to heard about his perception of gap between the way scientists pursue science and the way they teach it, quote: “we don’t care about we know, we care about what we don’t know.” What are the stakes of this with regards to prediction and uncertainty? Prof Goodman went on synthesised Firestein’s sentiment into a broader theme: (quote) ‘Most people just want to know what they can know about things they know nothing about”. This chain of ideas is undeniably important in terms of how science is communicated. In terms of public science, we might focus first on what information people want to know, before we were even thinking about how to communicate it. But the point that Prof Firestein makes is that some of the most interesting and rewarding pathways of scientific knowledge are embodied by the unknown. So what does it mean to disseminate scientific information that is contingently unknown? How can we truly embrace ignorance in a productive, educative sense? This dialectal project, which practically involves ‘producing’ ignorance in a way that is is generative. On some level our ‘innate sense of time’ and its relationship to progress is highly dictated by our sociocultural fabric, which leads me to think that an intellectual history or historiographical approach could be a fruitful way to explore our relationship to scientific futures. Studying techno-cultural events such as the Great Leap Forward and Cultural Revolution in China, for example, where scientific technology was fundamentally intertwined with conceptions of statehood, empowering individual action, and crucially scientific futures, can enrich our own conceptions of what could be done in terms of communicating science in a way that is not only informative, but also cultural. Seeing what went wrong with projects such as these is an invaluable opportunity when we’re answering these sorts of questions, but also witnessing the urgency and risk-taking that states will employ to disseminate scientific information when it is related to ideas of ‘progress’ or ‘prestige’. It leads me to pose these questions: What are the sociopolitical triggers that lead scientific knowledge to deeply permeate public consiousness? What would it take to re-invigorate the close allyship of scientific attainment and conceptions progress and prestige not just in the realm of policy-making, but also in the realm of the individual?

Picture: Cartoon of Washington saying "I cannot tell a lie". Young George Washington's truthfulness was worth more than 1,000 trees... but how much of what we perceive to be true is measured by its accuracy? In the talk with Susan Murphy, I was very interested to hear her talking about the goal of "Closing the loop to doing something about a prediction" with regards to stress reduction. In particular, I understood her to be saying that that a prediction "might not be the best"; but that doesn't mean it can't result in positive, (stress-reducing!), outcomes for the person receiving them. I really like the idea of 'truthiness' that she brings up, and how in terms of how we actually think; that in some cases it might seem more important to us than the actual quality of the prediction! If I were sitting across from Prof Murphy, I'd ask her to speak more on that idea of 'truthiness'. What are the markers of a prediction that might encourage action? Or does this psychological element of truth come through the surrounding materials or culture? I find it an interesting concept especially because I understand it intuitively, but putting a finer point on what that substance actually is, is more of a struggle.

Painting, "Storm below Mount Fuji" by Hokusai, from the Met. As a small, seismically active archipelago, more accurate earthquake predictions are crucial in Japan. It was fascinating to hear Brendan Meade speak about how predictions regarding earthquakes were substantially improved once he was able to *stop* thinking like a seismologist. Whereas seismologists study the aftermath of earthquakes, Meade explains, it is the time following the aftermath that the Earth is, in fact, preparing for its next earthquake—this is the time that is most valuable to study in terms of prediction. To me, this shift in approaches is illuminating especially when I think back to previous videos in this series regarding behavioural economics (Dan Gilbert's talk, for example), where it became clear that previous frameworks utilised in various fields, in fact, failed to integrated key elements or parameters which would allow a deeper understanding of actual conditions - the integration of which would produce higher-quality predictions. In various fields, both intellectual evolution and technological revolution have had deep impacts on our ability to make predictions more accurately. I wonder whether it will be the former or the latter that most impacts our predictions in the future: if only there was a way to know...!

Image: still from the 1982 film E.T. about an alien who becomes stranded on Earth. Firstly, listening to the talk with Jill Tarter I was completely enthralled by her accounts of the two occasions she believed she had come into contact with extraterrestrial intelligence. How incredible to hear her speak about staying up all night at the behest of the slightest palpitation of mechanical instruments, pointed towards the sky. While I have broadly been aware of the search for life in space I had never heard what that search from Earth consists of, particularly with regards to electromagnetic signals. To then extend this knowledge into the realm of predictive systems forged a link I hadn’t previously considered about what it means to approach uncertainty with a specific framework, in particular thinking of the example of hearing a signal which may be from an extraterrestrial source, but then contextualising that within a historically-informed framework of predictive systems as set out by Prof Goodman. Listen to the full talk here!

Questions based on a talk with Professor Jill Tarter. I was interested in the brief discussion of interdisciplinary subjects in the conversation with Jill Tarter about the Search for Extraterrestrial Intelligence, and the role that artists, or non-scientists in general has had in impacting research. While I naturally understand the value of artistic projects, is there truly an interplay between empirical and non-empirical research that is constructive toward’s the former’s project? To me it seems like artistic output exemplifies the very thing that is disruptive towards the path towards accuracy, i.e. human’s erratic, random and chaotic tendencies. However, I am constantly quite depurate to be proven wrong and understand all of these fields of research as potentially integrated and somewhat cumulative. To answer my own question to some extent, that Prof Tarter describes Sci-Fi as a ‘good vehicle for helping us imagine life’, I think immediately of H.G. Wells’ work in “The World Set Free”, which although written in 1913, remarkably predicted the interaction between humanity and the atomic bomb, exploring before anyone else did the moral questions that would come to dominate decades of nuclear fear over fifty years later. I can understand in this way the value of such work in providing creative foresight that is unrestrained by absolute truth, yet it is only a narrow realm of art that is so literal in its explication of possible futures or alternatives. Is there room in this interdisciplinary collaboration for the more abstract? Or does this gap between the sciences and arts in fact, speak to the arts themselves going astray from a productive project?

These questions were inspired by this interview between Prof Goodman and behavioural economist Prof David Laibson. Watch it here! Image: Shang Yang, "Remaining Water No. 6", 2015. A work by Chinese artist Shang Yang, using traditional landscape portraiture imagery to interrogate water’s role in landscapes undergoing environmental degradation, often caused by hyperdevelopment. I found Prof Goodman’s regarding free will vs determinism fascinating in the context of behavioural economics; Prof Goodman points out that in science, complete determinism has been determined to be impossible. We can never be entirely certain of our predictions. It was interesting, then, that the question could still remain that determinism could still exist in the field of economics. There is some level where, as people, we are striving for perfection in prediction; and yet, scientifically, we know that our ideal form doesn’t exist. How do scholars square this circle in various disciplines? Must we acknowledge the ultimate unknowability of the future in our pursuit of it? Or, like how the Path to Newton ended with Newton, will we reach a breakthrough once we reach the horizon of predictability which will open up a whole new era of human knowledge?

Find a link to the interview here! Watching the interview between Professor Goodman and behavioural economist Professor David Laibson was a really fascinating introduction to behavioural economics for me! I have heard of behavioural economics before, but never understood it as a reaction to previous shortcomings in the field, and in relation to improving social wellbeing in relationship to improving the accuracy of our predictive systems. It’s especially interesting to understand this work as part of the broader path we’re exploring in class, as humanity endeavours to consistently improve our predictive frameworks and achieve greater accuracy in our predictions. As someone who can sometimes get caught up in political nihilism, it actually brings me a lot of hope to see that there are ways we continue to transform our core understanding of the social sciences. I had never heard of ‘present bias’ before and it makes me think of an assignment we did for this class, where I tried to predict how long it would take to do my readings for class every day; my behavioural patterns totally fell into the trap of present bias that Prof. Laibson brings up about our sometimes dysfunctional psychological approaches towards future time. Finally, I loved this phrase that Professor Goodman used too (around 9 minutes in): “The future of humanity is the sum total of our individual predictions”. I think this sentence really captures the scale and influence of predictive methods, but also how individual choices and behaviours play a crucial role within that larger framework.

Image: Historical illustration of the Great Plague of London in 1665. Image depicts carts used to transport the dead. Source: WikiCommons I would be interested in asking the professors of epidemiology Peter Kraft and Immaculata de Vivo: on a structural level, what would it take for society to better tackle public health problems when not in a moment of crisis (such as a global pandemic)? To explain my question slightly; during the COVID pandemic we saw the massive mobilisation of resources to produce a vaccine extremely expediently, and bring about a massive rollout in many countries. Outside of times of crisis, however, it's rare to see this level of functionality and resource distribution to public health problems, especially when these problems disproportionally impact those generally left-behind in society; folk who are disabled, low-income, living in non-urban areas, immigrants, ethnic minorities, just to name a few. Even with this pandemic and the high level of recognition and attention it has been accorded by all governments around the world , international aid has become an ethical issue, with regards to developing countries being clearly left behind in the vaccine roll out. Do our existing political and institutional structures adequately look out for most of society and prioritise public health? I know this is a policy issue more than one that is specifically science related, but if not, would reforms be adequate to existing structures or do we need new institutions? What is the relevance of international relations institutions in terms of public health, particularly in terms of data-sharing and global health collaboration? Link to the interview

Image: A piece of "data art" by Refik Anadol, which transforms the 'movements' that take place in the act of remembering in the brain into algorithms via a sensor developed at the neurology laboratories at University of California. Read more While interviewing Immaculata De Vivio & Peter Kraft, I was interested to hear Prof Goodman speak on the 'unanticipated combinations' of data being a key focus of concern with regards to privacy and security. I hadn't thought before about the specific relevance of data intersections that are 'unanticipated', and the inherent difficulty that would arise in terms of data-sharing consents and maintaining privacy. This is particularly interesting given the huge amount of data that is collected by private companies regarding intimate details of our health and lifestyle choices; and particularly poignant given the use of such technologies in countries without strong data protection laws to restrict the spread of COVID-19 (i.e. China), at the expense of privacy. (To really escalate this idea into a slightly unrealistic proposal, I guess the best data-sets and highest privacy level might potentially be achieved by a supranational, 'apolitical' public health body dedicated to collecting and sharing data on a large scale while maintaining state-secret level of privacy???) Link to the interview