Science
Creepy Parasite Stories; or, Bedtime for Daniel
I mentioned that parasite biology was one of my interests. It didn’t used to be.
When the children were smaller, we had bedtime rituals. The two oldest shared a room, so they would both get something at bedtime. Perhaps it would be a chapter from a book (Charlotte’s Web was a big hit, as was From The Mixed-Up Files Of Mrs. Basil E. Frankweiler). Or it might be a story. Stories could be about anything, but history and science were particularly popular.
So one night, they asked for a science story. About… bugs! *Creepy* bugs. Yeah!
Well. After a moment’s thought, I decided to tell them a little bit about wasps. I paused a moment, because wasps can get quite creepy… quite creepy indeed. But okay, they did ask, and I could avoid the most disturbing bits.
So I told them about how some sorts of wasps lay their eggs inside living hosts — most typically caterpillars and grubs, but potentially any sort of small creature. There are wasps that do this to ladybugs, cockroaches, spiders, ticks, you name it. There’s a wasp that goes after tarantulas. There are wasps that go after other wasps. The mother wasp usually stings the host with a venom that paralyzes it (though there are countless variations). And when the egg hatches, the wasp larva devours the helpless host from within.
I gave a few details: the larva eats its host selectively. It goes after fat deposits first, then muscle tissue. It saves the vital organs for last, because it wants the host to stay alive as long as possible, providing a steady flow of nutrients and oxygen. If the host is not paralyzed, the larva may manipulate the dying host to stop eating and seek shelter. When the unfortunate host does finally die, the larva goes on a final feeding frenzy, devouring most of the corpse from within before it can decompose. Then it pupates. Some days or weeks later, the adult wasp bursts out of the pupa and the corpse of the host, and flies off to complete the cycle.
(A bit macabre? Wasps do stuff that is much worse than this. Much creepier, and much stranger. Wasps get downright baroque.)
Anyway! When I was done, older child was mildly interested. But second child — son Daniel, six years old — looked at me with shining eyes and said, “Daddy… that was the best story /ever/!”
And then, “Do you have any more?”
And so was born the tradition of the Creepy Parasite Story. Daniel liked stories about nature and science generally. But he *loved* stories about parasites — the weirder and more creepy, the better.
So I had to learn about parasites. Over the next decade, I probably read several thousand pages about parasites, including at least a hundred scientific papers. I learned about leeches and mosquitoes, vampire bats and oxpeckers, mistletoe and athlete’s foot. I learned about the cordyceps fungus and the Sacculina barnacle. I discovered the mysterious superpower of ticks and the baffling ability of parasitic jellyfish to live without oxygen. I learned about nest parasites like the cuckoo and bully parasites like the skua. I read about the gross but harmless pinworms that cause millions of toddlers to scratch their bottoms every year, and the horrific Guinea Worm that is mercifully close to extinction. I learned about viruses… so many viruses. And I learned about wasps and all the amazing things they do.
Daniel loved them all. But if he had a favorite, it was the malaria parasite. The malaria parasite is so complicated that it requires three or four separate bedtime stories to get through the cycle. It does stuff that we’re still figuring out. It does stuff that we still simply don’t understand. Creepy Parasite Stories were a special treat for Daniel. He was a child unusually resistant to bribes and wheedling. But parasite stories were on the very short list of things that could motivate him — and malaria parasite stories were the very best of all.
Anyway: ten years later, Daniel was in his final year in Gymnasium, which is German high school for kids who are university-bound. And as part of his final grade, he had to research and present a 30 minute PowerPoint presentation on “a scientific topic of public interest”. He chose the malaria parasite.
Just as he was about to begin his presentation, the projector broke. After some minutes of fiddling, it became clear that it was not going to work today. Oh dear, said the teacher, what a pity; shall we re-schedule your presentation?
Oh no, said Daniel. I don’t need slides. Let me just tell you. Let me tell you about the malaria parasite.
And he spoke for 30 minutes without slides, and then took questions. And he got a perfect grade. And nobody ever knew that he was simply repeating his most favorite every bed-time story, that he’d been listening to eagerly since he was a small child.
(Should I blog about this sort of thing? I don’t think this is exactly the venue for parasite stories. Maybe a short post about wasps sometime. We’ll see.)
Art, Science and the Politics of Knowledge – review
In Art, Science and the Politics of Knowledge, Hannah Star Rogers challenges the traditional dichotomy between art and science, arguing that they share common approaches to knowledge-making. Drawing on Science and Technology Studies and using compelling examples, Star Rogers illuminates the overlapping characteristics – such as emphases on visualisation, enquiry and experimentation – of the two knowledge domains, writes Andrew Karvonen.
Art, Science and the Politics of Knowledge. Hannah Star Rogers. The MIT Press. 2022.
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Art and science are often described in oppositional terms. Artists engage in subjective, creative, right-brain activities to produce beautiful objects while scientists use their left-brain skills in objective and methodical ways to improve our collective understanding of the world. In Art, Science and the Politics of Knowledge (The MIT Press, 2022), Hannah Star Rogers challenges and disrupts these dichotomies through a detailed examination of how art and science intermingle and influence one another. She argues that we should set aside the long-standing assumptions about the differences between art and science, and instead recognise their common approaches to knowledge-making.
[Rogers] argues that we should set aside the long-standing assumptions about the differences between art and science, and instead recognise their common approaches to knowledge-making.
Rogers draws upon Science and Technology Studies (STS) theories and methods to interrogate the overlapping knowledge communities of art and science. Just as STS has been used to destabilise scientific and technological knowledge practices since the 1970s, she argues that it can also be directed towards art and art-science practices. Her social constructivist lens draws upon well-known STS concepts such as Trevor Pinch and Wiebe Bijker’s notion of interpretive flexibility, Geoff Bowker and Susan Leigh Star’s emphasis on the power of classification, and Bruno Latour’s immutable mobiles to reveal the multiple ways that art and science are indelibly intertwined. She follows scientists and artists in their laboratories, studios and exhibition spaces to develop ethnographic evidence of the commonalities and synergies between their knowledge practices.
Just as STS has been used to destabilise scientific and technological knowledge practices since the 1970s, she argues that it can also be directed towards art and art-science practices.
Rogers’ first two case studies are based on archival studies of artists who contributed to scientific knowledge production. From the 1880s to the 1930s, the father and son team of Leopold and Rudolf Blaschka used their artisanal expertise in glassmaking to represent wonders of the natural world, notably sea creatures and flowers. Rogers argues that these models were not simply representations of the natural world but contributed to scientific knowledge in substantive ways. As she writes,
To create three-dimensional, detailed representational objects, the Blaschkas had to do their own studies and observations, and in doing so they were creating new ways of knowing sea creatures that would otherwise have been represented by flaccid specimens in jars or two-dimensional drawings. The knowledge that these artisans created was a method of displaying the salient features of marine life to the satisfaction of the scientific community (47). In other words, the Blaschkas positioned themselves as co-producers of scientific knowledge and their models provided new ways of seeing and knowing the field of natural history.
The Blaschkas positioned themselves as co-producers of scientific knowledge and their models provided new ways of seeing and knowing the field of natural history.
The power of visualisation is reinforced in Rogers’ second case study of the renowned 20th-century photographer Berenice Abbott. In the 1940s, Abbott developed a photo-realist technique that could accurately depict physical science laws and principles. She worked in close collaboration with scientists to stage images of soap bubbles, magnetic filings, light traveling through prisms, and falling objects such as balls and wrenches. These images were prominently displayed in science textbooks and were used to inform the scientific literacy of the general public. The realist photos of Abbott and the lifelike glass sculptures of the Blashckas extend earlier STS scholarship by Latour, Michael Lynch, Steve Woolgar, and others on the centrality of images and models to scientific knowledge making while also highlighting their aesthetic achievements. These artefacts are simultaneously works of science and works of art.
The fourth case study of tactical media is an outlier in the book. Tactical media is a social activist movement that emerged in the 1990s as subversive individuals began to employ the World Wide Web for political messaging. Rogers describes various performative, ephemeral interventions to critique capitalism and challenge authority through disinformation, humour, playfulness, and creativity. The case study provides fascinating insights about how technical artefacts can be used to promote alternative ways of knowing, but the work of tactical media practitioners has tenuous connections to the art-science thesis in the rest of the book.
Bioartists shared laboratory space, techniques, and materials with scientists to do science while also critiquing it.
Rogers’ fourth case study returns to the art-science knowledge nexus with an ethnographic study of SymbioticA, a laboratory for the biological arts at the University of Western Australia in Perth. She shadowed the activities of bioartists who collaborate with biotechnologists to develop interactional expertise and expand the knowledge domain of biotechnology. The bioartists shared laboratory space, techniques, and materials with scientists to do science while also critiquing it. As she notes, “Bioartists have seen themselves not as the mediators of scientific knowledge to the public but as the producers themselves” (145). The case study provides vivid examples of how artists and scientists contribute to the hybrid field of art-science in novel ways.
[Rogers] makes a compelling case for using exhibitions in art galleries and libraries to promote STS ways of knowing and to frame research activities as a collective intervention.
In her final case study, Rogers transforms from observer to action researcher by curating an art-science installation titled “Art’s Work in the Age of Biotechnology: Shaping Our Genetic Futures” at North Carolina State University in 2019 and 2020. The exhibition included objects with accompanying videos to create an open-ended, iterative, and interactive space where scientists, artists, and the general public could come together in a shared dialogue on biotechnology and society. She makes a compelling case for using exhibitions in art galleries and libraries to promote STS ways of knowing and to frame research activities as a collective intervention. As she notes, “Curators create new knowledge around objects by analyzing the layers of meaning added to them in different context[s]” (245).
While Rogers’ description of the curatorial process provides a titillating glimpse on how STS ideas can be mobilised in new ways, it also raises important questions about the role of the public in knowledge production processes. In the case study, she frames the public as critics rather than pupils of art-science knowledge production, but her description of the curated exhibit includes no evidence on how the public contributed to this shared dialogue. This omission highlights the long-standing challenge of transcending the boundary between experts and non-experts to co-produce knowledge through more democratic forms of engagement.
Rogers provides a wealth of compelling examples to reveal the networked production of art-science knowledge that enrols people, artefacts, and ideas in studios and laboratories through complementary modes of questioning and experimentation.
Overall, the case studies in this book illustrate how art and science are distinct yet overlapping knowledge domains with multiple commonalities. Rogers provides a wealth of compelling examples to reveal the networked production of art-science knowledge that enrols people, artefacts, and ideas in studios and laboratories through complementary modes of questioning and experimentation. The findings make a compelling case for how an STS perspective can be used to deconstruct and critique knowledge domains that extend far beyond scientific and technological development.
This post gives the views of the author, and not the position of the LSE Review of Books blog, or of the London School of Economics and Political Science. The LSE RB blog may receive a small commission if you choose to make a purchase through the above Amazon affiliate link. This is entirely independent of the coverage of the book on LSE Review of Books.
Image Credit: Museopedia on Wikimedia Commons.
In middle of Gaza genocide, 12 UK universities sign new research partnerships with Israel
Deaf ear to the suffering of Palestinian civilians as UK government gives away cash to incentivise partnering with Israel
Twelve universities across the UK have applied for and accepted government grants to undertake ‘a range of ‘mobility projects focussed on innovation and entrepreneurial skills development’ in partnership with Israeli universities.
The Department for Science, Innovation and Technology is handing out the cash to pay for researchers to ‘hone their expertise via international collaboration’ with a focus on ‘entrepreneurship and Technology Readiness Levels’ (TRLs) ‘, according to the Universities UK website. The cash will also fund researchers to travel to Israel and will ‘further links with the Israeli ecosystem through existing research and innovation collaborations and open the door to new opportunities’.
At a time when students and activists around the world are demanding a boycott of Israeli products, services and institutions, the universities below have taken the cash – some of them twice:
- Aston University – Weizzman Institute of Science and Bar-Ilan University
- Edge Hill University – Tel Aviv University
- Queen Mary University of London – The Hebrew University of Jerusalem and Tel Aviv University
- Royal Veterinary College – Hebrew University of Jerusalem
- Teesside University – Tel Aviv University
- UCL – Tel Aviv University
- University of Exeter – Tel Aviv University
- University of Greenwich – Hebrew University of Jerusalem
- University of Kent – Technion
- University of Leeds – Tel Hai College
- University of Plymouth – Technion
- University of Surrey – Bar-Ilan University
The Boycott Divestment and Sanctions movement has described Israeli universities as working closely with the Israeli state to develop weapons and systems that can be used to oppress and kill Palestinians:
Israeli universities are major, willing and persistent accomplices in Israel’s regime of occupation, settler-colonialism and apartheid.
They are involved in developing weapon systems and military doctrines deployed in Israel’s recent war crimes in Lebanon and Gaza, justifying the ongoing colonization of Palestinian land, rationalizing gradual ethnic cleansing of indigenous Palestinians, providing moral justification for extra-judicial killings, systematically discriminating against “non-Jewish” students, and other implicit and explicit violations of human rights and international law.
To end this complicity in Israel’s violations of international law, Palestinian civil society has called for an academic boycott of complicit Israeli academic institutions. Refusing to normalize oppression, many academic associations, student governments and unions as well as thousands of international academics now support the academic boycott of Israel.
As Skwawkbox revealed yesterday, Shadow Health Secretary Wes Streeting last week promoted – and visited in 2022 – an Israeli data company with close links to the Israeli military that is processing NHS test results. The founder of the firm is a ‘tech entrepreneur’ who has spoken and written about the importance of technology in fighting ‘terrorism’. Yet another occasion where the ‘Labour’ front bench is completely aligned with the views and behaviour of the Tories they are supposed to be opposing.
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More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech – review
In More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech, Meredith Broussard scrutinises bias encoded into a range of technologies and argues that their eradication should be prioritised as governments develop AI regulation policy. Broussard’s rigorous analysis spotlights the far-reaching impacts of invisible biases on citizens globally and offers practical policy measures to tackle the problem, writes Fabian Lütz.
More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech. Meredith Broussard. MIT Press. 2023.
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As the world witnesses advancements in the use of Artificial Intelligence (AI) and new technologies, governments around the world such as the UK and US the EU and international organisations are slowly starting to propose concrete measures, regulation and AI bodies to mitigate any potential negative effects of AI on humans. Against this background, More than a Glitch offers a timely and relevant contribution to the current AI regulatory debate. It provides a balanced look at biases and discriminatory outcomes of technologies, focusing on race, gender and ability bias, topics that tend to receive less attention in public policy discussions. The author’s academic and computer sciences background as well as her previous book Artificial Unintelligence – How Computers Misunderstand the World make her an ideal author to delve into this important societal topic. The book addresses algorithmic biases and algorithmic discrimination which not only receives increasing attention in academic circles but is of practical relevance due to its potential impacts on citizens and considering the choice of regulation in the coming months and years.
[More than a Glitch] provides a balanced look at biases and discriminatory outcomes of technologies, focusing on race, gender and ability bias, topics that tend to receive less attention in public policy discussions
The book’s cornerstone is that technology is not neutral, and therefore racism, sexism and ableism are not mere glitches, but are coded into AI systems.
Broussard argues that “social fairness and mathematical fairness are different. Computers can only calculate mathematical fairness” (2). This paves the way to understand that biases and discriminatory potential are encoded in algorithmic systems, notably by those who have the power to define the models, write the underlying code and decide which datasets to use. She argues that rather than just making technology companies more inclusive, the exclusion of some demographics in the conceptualisation and design of frameworks needs to stop. The main themes of the book, which spans eleven short chapters, are machine bias, facial recognition, fairness and justice systems, student grading by algorithms, ability bias, gender, racism, medical algorithms, the creation of public interest technology and options to “reboot” the system and society.
Biases and discriminatory potential are encoded in algorithmic systems, notably by those who have the power to define the models, write the underlying code and decide which datasets to use.
Two chapters stand out in Broussard’s attempt to make sense of the problems at hand: Chapter Two, “Understanding Machine Bias” and more specifically Chapter Seven “Gender Rights and Databases”. Both illustrate the author’s compelling storytelling skills and her ability to explain complex problems and decipher the key issues surrounding biases and discrimination.
Chapter Two describes one of the major applications of AI: machine learning which Broussard defines as to take
“..a bunch of historical data and instruct a computer to make a model. The model is a mathematical construct that allows us to predict patterns in the data based on what already exists. Because the model describes the mathematical patterns in the data, patterns that humans can’t easily see, you can use that model to predict or recommend something similar” (12).
The author distinguishes between different forms of training a model and discusses the so called “black box problem” – the fact that AI systems are very often opaque – and explainability of machine decisions. Starting from discriminatory treatment of bank loan applications, for example credit score assessment on the basis of length of employment, income or debt, the author explains with illustrative graphs how algorithms find correlations in datasets which could lead to certain discriminatory outcomes. She explains that contrary to humans, machines have the capacity to analyse huge amounts of datasets with data points which enable for example banks to make predictions on the probability of loan repayment. The mathematics underlying such predictions are based on what similar groups of people with similar variables have done in the past. The complex process often hides underlying biases and potential for discriminations. As Broussard points out,
“Black applicants are turned away more frequently than white applicants [and] are offered mortgages at higher rates than white counterparts with the same data […]” (25).
The book also demonstrates convincingly that the owners or designers of the model wield a powerful tool to shape decisions for society. Broussard sums up the chapter and provides crucial advice for AI developers when she states, advice for AI developers when she states,
“If training data is produced out of a system of inequality, don’t use it to build models that make important social decisions unless you ensure the model doesn’t perpetuate inequality” (28).
Chapter Seven looks at how databases impact gender rights, starting with the example of gender transition which is registered in Official Registers. This example illustrates the limitations of algorithmic systems as compared to humans, not only in light of the traditional binary system for assigning gender as male and female, but more generally the binary system that lies at the heart of computing. Both in the gender binary and computer binary framework, choices need to be made between one or the other leaving no flexibility. Broussard describes the binary system as follows:
“Computers are powered by electricity, and the way they work is that there is a transistor, a kind of gate, through which electricity flows. If the gate is closed, electricity flows through, and that is represented by a 1. If the gate is open, there is no electricity, and that is represented by a 0” (107).
When programmers design an algorithm, they “superimpose human social values onto a mathematical system.” Broussard urges us to ask ourselves, “Whose values are encoded in the system?” (109).
The resulting choices that need to be made within AI systems or forms used in administration often do not adequately represent reality. For people who do not feel represented by the options of male and female, such as gender non-conforming people, they are asked to make the choice in which category they fall even though this would not reflect their gender identity. Here again, Broussard reminds us of the importance of design choices and assumptions of coders which impact people’s everyday life. When programmers design an algorithm, they “superimpose human social values onto a mathematical system.” Broussard urges us to ask ourselves, “Whose values are encoded in the system?” (109). The chapter concludes with the challenge of making “technological systems more inclusive” (116) and argues that computers constitute not only mathematical but sociotechnical systems that need to be updated regularly in order to reflect societal change.
Computers constitute not only mathematical but sociotechnical systems that need to be updated regularly in order to reflect societal change.
The book successfully describes the invisible dangers and impacts of these rapidly advancing technologies in terms of race, gender and ability bias, making these ideas accessible through concrete examples. Ability bias is discussed in Chapter Seven, “Ability and Technology”, where she gives several examples, how technology companies try to provide technology to serve the disabled community in their daily jobs or lives. She gives the example of Apple shops where either sign language interpreters are available or where Apple equips employees with an iPad to communicate with customers. For consumers, she also highlights Voiceover screen reader software, auto-captioning and transcripts of audio or read-aloud functions of newspaper sites. Broussard points both to the advantages and the limitations of those technological solutions.
She also introduces the idea of tackling biases and discrimination with the help of audit systems
Readers are invited to reflect on concrete policy proposals and suggestions, on the basis of some ideas sketched out in last chapter, “Potential Reboot” where she shows her enthusiasm for the EU’s proposed AI Act and the US Algorithmic Accountability Act. She also introduces the idea of tackling biases and discrimination with the help of audit systems and presents a project for one such system based on the regulatory sandbox idea, which is a “safe space for testing algorithms or policies before unleashing them on the world” (175). The reader might wish that Broussard‘s knowledge of technology and awareness of discrimination issues could have informed the ongoing policy debate even further.
In sum, the book will be of interest and use to a wide range of readers, from students, specialised academics, policy makers and AI experts to those new to the field who want to learn more about the impacts of AI on society.
This post gives the views of the author, and not the position of the LSE Review of Books blog, or of the London School of Economics and Political Science. The LSE RB blog may receive a small commission if you choose to make a purchase through the above Amazon affiliate link. This is entirely independent of the coverage of the book on LSE Review of Books.
Image Credit: Vintage Tone on Shutterstock.
Michael Polanyi in 1960 on Teilhard de Chardin on evolution
Michael Polanyi was highly suspicious of the hyper-reductionism of neo-Darwinism. It’s reduction of the evolution of a thing so vast as life into a single causal mechanism. And it was a good call.
Darwin himself had proposed that natural selection was a major mechanism of evolution, but not the only one. He was good with the existence of Lamarckian mechanisms, which was a pretty good call given that they keep turning up. But neo-Darwinism held that there was just one mechanism behind evolution — genetic variation — and that this was driven exclusively by random mutation. It’s worth pondering the hankering for closure this claim embodies. Why the enthusiasm to shuffle such mechanisms off the scientific stage.
Neatness is one reason. Arrogance another. Laying down the law on the grounds that you’re uniquely qualified to pontificate about them is inherently satisfying to many. There’s also a doubling down on driving purpose out of evolution. And that’s something science had been doing since the scientific revolution — driving our Aristotelian notions of telos from biology. And that was also driving God out of biology. All good if God is seen as some imposition — some being intervening in the universe whenever he wants to vote someone off the island.
The thing is, immanent purpose is an obvious fact of biology. The heart has the purpose of pumping blood. It’s designed to pump blood. That doesn’t mean it has an intelligent designer watching on, occasionally reaching for their remote. But it does mean that it was designed. It was designed immanently. We’ve known for a long time that the immune system works this way — it creates a randomising process of experimentation and then puts its thumb on the scales by amplifying the more promising experiments. (This is the way social media is driving our species to conflict — only where the immune system is part of a healthy emergentism (at least from our point of view, and depending on your values, from the universe’s) the immanent design in social media is, at least in the first instance regressive, leading us down the brainstem towards lower levels of capability and organisation. Perhaps over time we will evolve ways of using its potential positively.
In any event the idea of systems of “directed chance” and the ‘emergentism’ that naturally arises from it fascinated Polanyi and lay as one of the core elements of his philosophy of science and of humanity.
Which meant that I was fascinated and impressed by this brief review.
An Epic Theory of Evolution
THE SUCCESS of “The Phenomenon of Man,” by Pierre Teilhard de Chardin is a mystery and a portent. … I have seen a dozen reviews highly praising it and have noticed no adverse criticism. I, myself, had readily turned to Teilhard, since I reject the current genetical theory of evolution and had no doubt that Teilhard rejects it too.
But what about all those who so eagerly read and praise the book? Does their acclaim mark the rise of a vast underground movement, sweeping aside the writers and readers who had shortly before accepted the worldwide pronouncements made on the occasion of the Darwin centenary? Where was the public now applauding Teilhard when Sir Gavin de Beer declared that modern genetical research has “established as firmly as Newton’s laws of motion that hereditary resemblances are determined by discreet particles, the genes, situated in the chromosomes of the cells. . . “? Can this be reconciled with Teilhard’s teachings?
Puzzled by these questions, I kept fingering my copy of Teilhard’s English’ translation and finally looked through Julian Huxley’s introduction. In it he praises Teilhard’s work highly and, indeed, claims to have largely anticipated it. Yet it was Sir Julian’ Hpdey who prefaced one of thentost authoritative statements of current selectionist theory (“Evolution as a process”) with the words: “A single basic mechanism underlies the whale organic evolution—Darwinian selection acting upon the genetic. mechanism.”
Teilhard declares: “We do not yet know how characters are formed, acumulated and transmitted in the secret recesses of the germ cell.” In his view, “the blind determinism of the genes” plays but a subordinate part: “We are dealing with only one event,” he says, “the grand orthogenesis of everything living towards a higher degree of immanent spontaneity.” “The progressive leaps of life” must be interpreted “in an active and finalistic way.”
This active striving towards ever higher, more vividly conscious forms of existence, which eventually achieves responsible human personhood and establishes through man a realm of impersonal thought, is the dominant theme of “The Phenomenon of. Man.” . . ‘This image is.very different from that of the repeated failures of precision in the self-copying of Mendelian genes, to which Huxley and the rulihg orthodoxy attribute evolution. It is precisely the kind of theory violently condemned by this orthodoxy for trying to explain evolution by some inherent bias, guiding the direction mutations take. Admittedly, Teilhard’s wording is vague.…
Teilhard’s way r of shrugging aside any question concerning the mechanism of heredity also casts a veil of obscurity tn the foundations of his position. And this is how he avoids an explicit attack on genetical selectionism and also feels entitled to use, without more than the most cursory acknowledgment, the ideas of Samuel Butler, Bergson, and others who have previously interpreted evolution in his way.
And yet in these shortcomings we discover the secret of Teilhard’s achievement and success. He is a naturalist and a poet, endowed with contemplative genius. He refuses to look upon evolution like a detached observer who reduces experience to the exemplification of a theory. Instead he stages a dramatic action of which ‘man is both a product and a responsible participant. His purpose is to rewrite the Book of Genesis in terms of evolution. The thousand million years of evolution are seen here as one single act of cre-ative power, like that revealed by Genesis.
This creative act is inherent in the universe. By producing sentient beings the universe illuminates itself, and through human thought it gradually achieves communion with God. Teilhard uses scientific knowledge merely as a factual imager* in which to expound his vision. His work is an epic poem that keeps closely to the facts. Gaps in the factual imagery of a poem can be safely left- open. So there is no need for the author to argue with selectionism.
As a poet, Teilhard stands powerfully apart and commands assent from many who continue to hold views that are incompatible with his vision; and this is how his work is startlingly novel though it contains few new ideas. . But would Teilhard’s poetry have received such warm response fifty years ago? No, its contemporary success, is a portent. There is a tide of dissatisfaction mounting up against scientific obscurantism. Book after book comes out aiming against the scientific denaturation of some human subject.
Teilhard owes his present success to this movement. But, unfortunately, this has made his success a little too easy. I do not believe that the origin and- destiny of-man can be defined in such vague terms. A text that is so ambiguous that people whose views on its subject matter are diametrically opposed can read it with equal enthusiasm cannot be wholly satisfying. And I suppose that this is why, in spite of its many insuring and luminous passages, it is tedious to read ‘the book from cover to cover. Having avoided so many decisive issues, it can serve only as a new and powerful pointer towards problems that it leaves as unsolved as before.
Read the full review here.
What Boris Johnson’s Testimony at the Covid Inquiry Reveals About his Inability to Engage with Scientific Evidence
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Boris Johnson spent two full days under cross-examination at the Covid Inquiry this week. Although much of the evidence presented about the government’s chaotic handling of the pandemic served to reinforce what had been revealed in previous testimony, there were some new insights to be had direct from the horse’s mouth.
For example, Johnson claimed that “Eat out To Help out was not presented to me as something which would add to the budget of risk.” Of course, as we heard earlier in the inquiry, this was probably because scientists were not consulted about Eat Out to Help Out before it launched. Johnson claimed in his testimony that he had assumed they [scientists] had been consulted and was subsequently surprised to learn this was not the case.
In a separate exchange, Johnson was shown a document, presented previously at the inquiry, describing the serious ongoing symptoms associated with long Covid and outlining the need for greater awareness of the condition. Atop the document he had scribbled “Bollocks” and “This is Gulf War Syndrome stuff” – comments which KC Hugo Keith suggested meant that Johnson questioned whether the disease even existed. In response Johnson apologised saying “I regret very much using that language…” although his follow up comments “…and I should have thought of the possibility of a future publication,” make it sound more like he was more sorry that his callous remarks had come to light than for making them in the first place.
‘The Lack of Transparency about COVID Science Will have Cost Lives During the Pandemic’
The public – and scientists – were not able to scrutinise the Government’s interpretation of the scientific evidence with which it was being supplied, argues Independent SAGE member Kit Yates
Kit Yates
This perfunctory dismissal of a scientific report presented to him seems typical of Johnson’s approach to much of the evidence that was so crucial during the pandemic. We were aware, even at the time, that, as his former adviser Lee Cain put it “this was the wrong crisis for Boris Johnson’s skill set”. However, was what less clear initially was exactly how little of the science Johnson was actually taking in.
Conversations revealed in Matt Hancock’s leaked WhatsApp messages earlier this year made it clear exactly how poor was Johnson’s grasp of some of the crucial scientific concepts required to understand the pandemic. Johnson himself admitted in his testimony that the rise of the Omicron variant in December 2021 was one of the “possibly rare” occasions when he felt he had “got a pretty good handle on the data”. “Maybe I was flattering myself,” he quips. For me, his unguarded, even light-hearted, admission that he did not have a good understanding of the data for most of 2020 and 2021 is one of the most shocking moments of the inquiry so far.
Why did Johnson Fail to Grasp the Science?
Last month the Inquiry was shown the contemporaneous notes of the former Chief Scientific Adviser Sir Patrick Vallance, which suggested that Johnson was bamboozled by scientific data. In his testimony Vallance suggested that Johnson having given up science at 15 meant that he struggled with some of the concepts. Some have suggested that his scientific misunderstandings are perhaps, in part, the result of his having taken a humanities degree.
But this seems like too reductionist a theory. Having undertaken a humanities degree does not preclude one from engaging with scientific evidence. Others have argued, it was Johnson’s “laissez-faire ideology” rather than his want of scientific training that explains his lack of engagement and his desire to overrule or ignore the scientific evidence he was presented with. Whatever else Johnson may or may not be, he is not stupid. Concepts like exponential growth and infection fatality ratios should not have been beyond him.
In some ways it could have been an advantage to have someone scientifically naive who robustly questioned the scientific evidence as Johnson claimed he was doing when he is reported to have said "There will be more casualties, but so be it. They've had a good innings" and "We should let it rip a bit". However, the evidence seems to bear out that he was not fulfilling this inquisitive role, but instead consistently failed to properly engage with the scientific data in a meaningful way.
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As a case in point, in his testimony on Wednesday he admitted “If we had actually stopped to think about the mathematical implications of the forecasts, and we'd believed them, we might have operated differently.” What is so difficult to understand about this statement is why he didn’t believe the forecasts he was presented with. What basis did he have for disbelieving them? What was the alternative data from which he was reasoning – if he wasn’t using the official data – that led him to a different conclusion to the official reports?
Johnson’s failures during the pandemic (and there are many) are not just about his scientific illiteracy, but about his outright unquestioning dismissal and failure to engage with the evidence that was being placed before him. It’s extraordinarily unfortunate that, at a time when we most needed a leader who would make the effort to understand the scientific evidence in this overwhelmingly science-dominated crisis, we were lumbered with Johnson.
Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do about It – review
In Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do about It, Erica Thompson explores how mathematical models are used in contexts that affect our everyday lives – from finance to climate change to health policy – and what can happen when they are malformed or misinterpreted. Rather than abandoning these models, Thompson presents a compelling case for why we should revise how we understand and work with them, writes Connor Chung.
Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do about It. Erica Thompson. Basic Books. 2022 (Hardback; 2023 paperback).
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“World is on track for 2.5°C of global warming by end of the century.” “US recession odds are falling fast.” “New wave of Covid predicted as UK’s return to school and social mixing hit.” Amidst the challenges of recent years, mathematical modelling has become an ever-more-important tool for understanding our world. Done right, this can empower us. Distilling complexity into bite-size pieces, after all, can be a key step towards changing things for the better.
Embedded within every model are certain assumptions about how the world works. Sometimes, they do the job. Yet, other times, our visits to model land go awry. Thompson fears that modern society never learned to tell the difference
Yet modernity’s faith in modelling has come with a dark side, suggests statistician Erica Thompson in Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do about It (Basic Books: 2022). Embedded within every model are certain assumptions about how the world works. Sometimes, they do the job. Yet, other times, our visits to model land go awry. Thompson fears that modern society never learned to tell the difference, and that as a result, we’re becoming trapped in a mirror-world of our own making.
The core problem? That it’s all too easy to approach models as sources of objective scientific fact. Yet “[s]uch naive Model Land realism,” Thompson warns, “can have catastrophic effects because it invariably results in an underestimation of uncertainties and exposure to greater-than-expected risk.” “Data, that is, measured quantities, do not speak for themselves,” and at nearly every stage of finding the story, the world finds ways of seeping in.
It’s all too easy to approach models as sources of objective scientific fact.
Let’s say, for example, you want to know how climate change will impact GDP. A preeminent tool for doing so is the DICE model family. As recently as 2018, its factory settings concluded that global warming of 4˚C by 2100 would reduce global economic output by only around 4%. The Intergovernmental Panel on Climate Change, meanwhile, has warned that such warming would bring about “high to very high” planetary risks “in all reasons for concern.” So how does one conclude that a world of cataclysmic weather, of cities swallowed up, of climate-driven refugee and food crises would barely register in the economic metrics?
First, there’s what’s fed into the model: since costs and benefits of building a solar farm or passing a clean energy regulation don’t play out all at once, one must instruct a model how much to value the present versus the future. This variable (one of many dials to which DICE is highly sensitive) is called a “discount rate,” and no amount of math can hide the fact that it’s ultimately a moral judgment. As its main creator, Yale economist and Nobel laureate William Nordhaus, has himself written, “[t]he choice of discount rates is central to the results” – DICE can be made to say just about anything depending on what inputs are chosen. Relatedly, there’s what’s not fed into a model: models are informed by pre-existing knowledge. As a consequence of history, less economic and climactic data are readily available from the developing world, for instance.
Models are informed by pre-existing knowledge. As a consequence of history, less economic and climactic data are readily available from the developing world
Then follows the construction of the model itself. As economist Nicholas Stern and co-authors point out in a recent paper, certain presumptions of rational actors, of market efficiency, and of exogenous technological progress are embedded into DICE’s fundamental wiring. More broadly, Thompson notes, DICE takes as granted that “the burden of allowing climate change can be quantitatively set against the costs of action to avoid it, even though they do not fall upon the same shoulders or with the same impact.
Models are by nature parsimonious: their utility derives from reducing complex phenomena to a much smaller set of parameters. Yet the real world is full of higher-order impacts (good and bad) beyond what gets specified in the math
Then, there’s how results are generalised to the world at large. Models are by nature parsimonious: their utility derives from reducing complex phenomena to a much smaller set of parameters. Yet the real world is full of higher-order impacts (good and bad) beyond what gets specified in the math. And when models set the bounds of what’s possible, viable, or optimal (DICE, Thompson points out, is enshrined in policy analysis pipelines at some governmental and intergovernmental agencies), nuance risks being lost in translation: “The whole concept of predicting the future can sometimes end up reducing the possibility of actively creating a better one.”
None of this is to say that DICE is useless. Assumptions, even simplistic ones, are necessary for making decisions about complex phenomena. But at the same time, they indelibly embed the modeller in the modelled, and we get nowhere by ignoring this reality.
Thompson isn’t the first to point out that model-making is a deeply human endeavour. But it is in these case studies of present-day debates in the modelling community, as informed by first-hand expertise, that her work really shines. Alongside DICE, Thompson deftly pries open black box after black box in cases ranging from financial markets to public health to atmospheric dynamics, finding in each case that turning morality into a math problem doesn’t purge the human touch. It only buries it just below the surface.
Models emerge as ‘tools of social persuasion and vehicles for political debate’ as much as they are quantitative processes
Models emerge as “tools of social persuasion and vehicles for political debate” as much as they are quantitative processes. And since “we are all affected by the way mathematical modelling is done, by the way it informs decision-making and the way it shapes daily public campaigns about the world around us,” it becomes a real challenge for modern democratic society when models are insulated from understanding or accountability.
The easiest response at this point might be to surrender – to declare that the ineffability and complexity of the world makes mathematical modelling inadequate. And yet… there’s also the pragmatic reality that, amidst compounding crises, models have quite simply proven useful. The empirical record has largely vindicated scientists’ (and, for that matter, literal fossil fuel companies’) climate predictions. Energy system simulations from Princeton played a key role in passing the Inflation Reduction Act, one of the most globally significant pieces of climate legislation to date. And modelled pathways from the International Energy Agency are playing key roles in guiding a rapid buildout of clean energy – and in challenging fossil fuel expansion.
How does one ensure that, in grappling with the social nature of modelling, the baby isn’t thrown out with the bathwater?
History, after all, is full of seemingly progressive (and indeed radical) critiques of objectivity, scientific consensus, and expert practice that end up merely reinforcing the status quo: just take the long history of social constructivist scholarship being used by allies of the tobacco and fossil fuel industries to support and justify their misinformation campaigns. Meanwhile, the climate denialist camp has long had the reliability of climate modelling in their sights. So how does one ensure that, in grappling with the social nature of modelling, the baby isn’t thrown out with the bathwater? It’s a tough needle to thread, yet something Thompson manages to do with grace. Just as there is “a problem in trusting models too much,” she writes, “there is equally a problem in trusting models too little.” Although “failing to account for the gap between Model Land and the real world is a recipe for underestimating risk and suffering the consequences of hubris,” she counters that “throwing models away completely… lose us a lot of clearly valuable information.”
More transparency and intentionality about the role of expert judgement, Thompson suggests, might help close the ‘accountability gap’ between the models and the humans acting on them
This may be the book’s most valuable contribution: it’s ultimately a call not to abandon model land altogether but instead to become better travellers. This begins with seeing the social nature of models as a feature, not a bug. More transparency and intentionality about the role of expert judgement, Thompson suggests, might help close the “accountability gap” between the models and the humans acting on them. Similarly (echoing a rich literature in the philosophy of science), she notes that greater institutionalised diversity of methods and standpoints might result in fewer unseen biases and blind spots.
Ultimately, this book is a plea for humility. It’s wrong, Thompson tells readers, to presume that we’ve somehow created the capacity to transcend the limits of human rationality. Instead we must realise that “taking a model literally is not taking a model seriously,” as Peter Diamond noted in his Nobel acceptance speech – that only by cultivating an ethos of responsibility can we truly treat our creations with the care they deserve.
Such a conclusion may be uncomfortable, but it’s also deeply pragmatic advice for better modelling, better truth-seeking, and better public reason in an empirical age. Modellers, scientists, policymakers, and more would do well to take it to heart.
This post gives the views of the author, and not the position of the LSE Review of Books blog, or of the London School of Economics and Political Science. The LSE RB blog may receive a small commission if you choose to make a purchase through the above Amazon affiliate link. This is entirely independent of the coverage of the book on LSE Review of Books.
Image Credit: Mingwei Lim on Unsplash
‘The Lack of Transparency about COVID Science Will have Cost Lives During the Pandemic’
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The Government's Chief Scientific Advisor during the pandemic, Sir Patrick Vallance, gave evidence at the COVID Inquiry last week. The headline-grabbing story was probably his testimony that SAGE was not consulted about the now infamous 'Eat Out to Help Out' scheme. Chris Whitty, England's Chief Medical Officer, corroborated his testimony in his own appearance later in the week, revealing that “there was no consultation. Neither Patrick nor I can recall it and I think we would have done”.
Other insights were offered by Sir Patrick's testimony, perhaps most tellingly through his diary entries. These include suggestions that Downing Street wanted science “altered”; that then Chancellor Rishi Sunak suggested “it is all about handling the scientists, not handling the virus”; and that then Prime Minister Boris Johnson described the Coronavirus graphs as a “mirage” suggesting, in direct contradiction of evidence to the contrary, that "the curves just follow natural patterns despite what you do".
In under-reported parts of his testimony, Sir Patrick was asked about members of SPI-B (the behavioural committee of SAGE – the Government's Scientific Advisory Group for Emergencies) joining the Independent SAGE group. He replied: “I'm second to none in my belief in academic freedom, but if you join a government committee it’s slightly odd to the be on a committee that’s set up to challenge the government committee.”
His testimony demonstrates a misunderstanding of the reasons for the formation and the purpose of Independent SAGE, of which I am a member.
The alternative scientific advisory group was not set up to challenge SAGE advice but to communicate science transparently and directly. Indeed, the advice shared by Independent SAGE in its weekly public briefings and regular reports was largely in line with SAGE. We drew frequently on the advice and evidence of the excellent scientists working for SAGE, and the group has always put transparency in communication – which many felt was not being offered by the official government committee.
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Transparency is a vital part of the scientific process. This is especially true for scientific advice, which has such significant ramifications.
If you are asking people to undergo restrictions on their liberties and livelihoods based on scientific advice, scientists owe it to the public to explain the science and the modelling behind those decisions. It doesn’t have to be the scientists who are doing the work who share it with the public – understandably many scientists working on the UK’s pandemic response already felt overwhelmed – but someone capable should be keeping the public up-to-date.
Too often during the pandemic, science was either poorly communicated or left entirely uncommunicated – which left a vacuum for potentially bad actors to step in and manipulate the situation to their own ends.
In his testimony, Sir Patrick Vallance went on to describe “a chilling effect, where people didn’t want to bring things to either SAGE or sub-committees as a result of either this [members of SPI-B joining Independent SAGE] or indeed the transparency of publishing all of our minutes”.
Understandably, some of the documents discussed at SAGE meetings may have been sensitive or confidential, but the logical consequence of that sensitivity is not that all SAGE minutes should be secret. This is especially true given that the type of minutes that SAGE was publishing were more akin to a high-level consensus statement than a detailed transcription of everything that was said, and all the documents reviewed during meetings. Many would have liked to have seen more detailed minutes that captured the nuances of the debates that were had.
Increased transparency also leads to increased accountability.
Being able to scrutinise the minutes of scientific committees such as SAGE means that outsiders can verify the assertions and check the results for themselves. Reproducibility and replicability through transparency lies at the heart of science and it should be no different in the case of emergencies.
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Indeed, early on in the pandemic, there were scientific mistakes in the SAGE minutes – for example the doubling time of the pandemic was overestimated. The scientific advice which stemmed from this mistake potentially led to UK decision-makers assuming Britain was further behind Italy’s – which in March 2020, was the worst affected country outside of China – pandemic trajectory. This misunderstanding may have induced the UK to pursue a mitigation strategy – as opposed to a suppression strategy – for too long, at the cost of many lives.
SAGE minutes were not made public until May 2020 (at almost the same time that Independent SAGE held its first briefing).
But if these minutes had been available for public scrutiny sooner, it’s likely that interested non-SAGE scientists would have been able to highlight the mistake.
The motto of the Royal Society is “nullius in verba” – take no one’s word for it. What we were asked to do all too often in the acute phase of the pandemic is exactly that: to accept the Government’s interpretation of the scientific evidence with which it was being supplied, without the ability to subject it to scrutiny.
That was not a model for good science – and one that should have been replaced in favour of greater transparency and openness.
Kit Yates is a a senior lecturer in the Department of Mathematical Sciences and co-director of the Centre for Mathematical Biology at the University of Bath. He is a member of Independent SAGE
First Issue of Philosophy of Physics Published
The inaugural issue of the journal Philosophy of Physics (PoP) has been published.
The open access journal, announced last year, is published by LSE Press on behalf of the Philosophy of Physics Society. According its website, PoP “aims to be a flagship journal for the field and to span the various different axes of philosophy of physics: metaphysical, historical, mathematical, practice-oriented (and more). It is intended for all researchers in philosophy of physics and for interested readers in cognate disciplines, including outside philosophy.” It’s editor in chief is David Wallace (Pittsburgh).
The inaugural issue, published today, includes articles by Emily Adlam and Carlo Rovelli, Samuel C. Fletcher and James Owen Weatherall, Álvaro Mozota Frauca, Klaas Landsman, Wayne C. Myrvold and John D. Norton, Laura Ruetsche, and Ward Struyve. You can read it here.
The post First Issue of Philosophy of Physics Published first appeared on Daily Nous.
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