Bite-sized interviews with top social scientists
Ellen Peters on Numeracy
“It’s been said there are three kinds of people in the world, those who can count and those who can’t count.” So reads a sentence in the book Innumeracy in the Wild: Misunderstanding and Misusing Numbers, published by Oxford University Press in 2020.
The author of Innumeracy in the Wild is Ellen Peters, Philip H. Knight Chair and director of the Center for Science Communications Research at the University of Oregon. In this Social Science Bites podcast, Peters – who started as an engineer and then became a psychologist – explains to interviewer David Edmonds that despite the light tone of the quote, innumeracy is a serious issue both in scale and in effect.
As to scale, she notes that a survey from the Organisation for Economic Co-operation and Development found 29 percent of the US adult population (and 24 percent in the UK) can only do simple number-based processes, things like counting, sorting, simple arithmetic and simple percentages. “What it means,” she adds, “is that they probably can’t do things like select a health plan; they probably can’t figure out credit card debt,” much less understand the figures swirling around vaccination or climate change.
Peters groups numeracy into three (a real three this time) categories: Objective numeracy, the ability to navigate numbers that can be measured with a math test; subjective numeracy, which is “not your actual ability, but your confidence in your ability to understand numbers and to use numeric kinds of concepts;” and intuitive or evolutionary numeracy, a human being’s natural ability to do things like quickly determine if a quantity is bigger or smaller than another quantity.
That middle type of numeracy, the subjective, is measured by self-reporting. “The original reasons for developing some of these subjective numeracy scales had to do with them just being a proxy for objective numeracy,” says Peters. “But what’s really interesting is that having numeric confidence seems to free people to be able to use their numeric ability.” While freedom is generally reckoned to be good – and objective results back this up – that’s not the case for those confident about their abilities but actually bad with numbers. Similarly, those who have high ability but are underconfident also do poorly compared to high ability and high confidence individuals.
“There are some very deep psychological habits that people who are very good with numbers have that people who are not as good with numbers don’t have,” Peters explains. “It is the case that people who are highly numerate are better at calculations, but they also just simply have a better, more developed set of habits with numbers.”
Less numerate people “are kind of stuck” with the numeric information as presented to them, rather than transforming the information into something that might better guide their decisions. Peters offered the example of a person with a serious disease being told that a life-saving treatment still has a 10 percent chance of killing them. Highly numerate people recognize that that means it has a 90 percent survival rate, but the less numerate might just fixate on the 10 percent chance of dying.
Closing out the podcast, Peters offers some tips for addressing societal innumeracy. This matters because, she notes, research shows that despite high rates of innumeracy, providing numbers helps people make better decisions, with benefits for both their health and their wealth.
Jonathan Haskel on Intangibles
The knowledge economy. Intellectual property. Software. Maybe even bitcoin. All pretty much intangible, and yet all clearly real and genuinely valuable. This is the realm where economist Jonathan Haskel of Imperial College London mints his own non-physical scholarship. “In the old days,” relates the co-author of Capitalism without Capital: The Rise of the Intangible Economy, “the assets of companies, the sort of secret sauce by which companies would generate their incomes and do their services for which they’re employed for, was very tangible-based. These would be companies with lots of machines, these would be companies with oil tankers, with buildings, with vehicles to transport things around. Nowadays, companies like Google, like Microsoft, like LinkedIn, just look very different.” And that difference, he explains to interviewer David Edmonds in this Social Science Bites podcast, is knowledge. “What they have is knowledge,” says Haskel, “and it’s knowledge assets, these intangible assets, which these companies are deploying.”
Intangible investments, as you might expect, have different properties than do tangible ones. Haskel dubbed them the four S’s:
Scale. Once you have a handle on a successful intangible, like software, that can generally scale up without more capital spending; Sunk Costs. These are invested costs you can’t get back, such as the costs of developing software; Spillovers. Aspects of your intangibles that others can copy or adopt for themselves; and Synergies. “If you put all these intangibles together,” he explains, “you get more than the sum of the parts.”
Meanwhile, intangibles help keep modern economies humming – we think. “Accountants and statistical agencies are quite reluctant to measure intangibles because it’s -- intangible. It’s a rather difficult thing to get at; these are often goods that aren’t traded from one person to another …”
Part of Haskel’s research effort is to quantify how much investment in intangibles is going on “behind the scenes,” which fits in with other interests of his such as re-engineering how gross domestic product gets measured. Businesses are now spending more on intangibles then on tangibles: Haskel’s work reveals that for every monetary unit companies spend on tangible assets, they spend 1.15 on intangible ones.
In addition to serving as a professor at the Imperial College Business School, Haskel is director of the Doctoral Programme at the Imperial. He is an elected member of the Conference on Research in Income and Wealth and a research associate of the Centre for Economic Policy Research, the Centre for Economic Performance, LSE, and the IZA, Bonn.
Haskel has been a non-executive director of the UK Statistics Authority since 2016 and an external member of the Bank of England’s Monetary Policy Committee since 2019.
Sheila Jasanoff on Science and Technology Studies
Sheila Jasanoff, the Pforzheimer Professor of Science and Technology Studies at Harvard University’s John F. Kennedy School of Government, is a pioneer in the field of STS. That acronym can be unpacked as either ‘science and technology studies’ or ‘science, technology and society.’ Jasanoff -- who describes herself as a sociologist of knowledge and a constructivist, trained in law, working in the tradition of the interpretive social sciences – is content with either use. “I think that represents two phases of the same field,” she tells interviewer David Edmonds in this Social Science Bites podcast. “First of all, it’s the field that looks in detail at the institutions of science and technology and asks, ‘What are they like?’ ‘What does it feel like to be doing them?’ ‘What do they operate like as social institutions, as cultures, as formations in society?’ The other face of STS – science, technology and society – is more about how science and technology function when they get out into the world at large.”
Amid that expansive view, some areas, of course, particularly interest Jasanoff. “The more interesting turn,” she details, “was the turn that tried to occupy the territory previously given to philosophy of science, and started asking sociological and political questions about it.”
One such question is the eternal “What is truth?” STS, a brash newcomer, took on the inquiry with gusto.
“It took a kind of arrogance, if you will, certainly a bravery, in the 1970s, to say that, ‘Hey, truth isn’t just out there. It’s not just a Platonic thing and we try to approximate it. We can actually study truth as if it was a social production.’ That,” she explains, “was the heartland of science and technology studies.”
In the interview, Jasanoff outlines how science is often presented as a capital-T repository of Truth even in an age where the ‘death of the expert’ has become a common trope.
Citing the pandemic and how scientific advice changed on mask wearing, Jasanoff argues that “people should not be surprised that in crisis mode the way we know things changes and therefore the advice may change. Science has been sold as a bill of goods for so long that it is the Truth, it is reliable, a fact is always fact the moment we assert it, that these sorts of commonsensical things that we ought to understand have become difficult for people to grasp.” (Jasanoff’s own research often looks at cross-national differences in her research, and after looking at mask-wearing in 16 nations she reports that “only in America has it become an article of faith – are you for science or against science” – based on your mask usage.)
Remember, she continues, “The expert is not an embodiment of scientific fact. An expert is a particular kind of person who is qualified in particular ways, and every time we say ‘qualification,’ something about the English language or about language in general, forces us to look at the skills that allow one to be considered qualified.
“In fact, we should look at the external periphery of the qualification; a qualification sets boundaries on what you know, but it also sets boundaries on what you don’t know.” Expertise is this double edged-thing.”
Jasanoff is the founder and director of Harvard’s Program on Science, Technology and Society. She’s the author of several books aimed at both the academy and the public, such as 1990’s The Fifth Branch: Science Advisers as Policymakers, 2012’s Science and Public Reason, and Can Science Make Sense of Life? in 2019.
The University of Bergen, acting for the Norwegian Ministry of Education and Research, awarded her the Holberg Prize in March. That was the latest in a slew of honors for her research, including the University of Ghent Sarton Chair and the Reimar Lüst Award from the Alexander von Humboldt and Fritz Thyssen Foundations, a Guggenheim
John List on Economic Field Experiments
Any work in social and behavioral science presumably – but not necessarily immediately - tells us something about humans in the real world. To come up with those insights, research usually occurs in laboratory settings, where the researchers control the independent variables and which, in essence, rules out research ‘in the wild.’
Enter John List.
“For years,” he tells interviewer David Edmonds in this Social Science Bites podcast, “economists thought that the world is so ‘dirty’ that you can’t do field experiments. They had the mentality of a test tube in a chemistry lab, and what they had learned was that if there was a speck of dirt in that tube, you’re in trouble because you can’t control exactly what is happening.”
Since this complex real world isn’t getting any cleaner, you could conclusively rule out field experiments, and that’s what the ‘giants’ of economics did for years. Or you could learn to work around the ‘dirt,’ which is what List started doing around the turn of the millennium. “I actually use the world as my lab,” the Kenneth C. Griffin Distinguished Service Professor of Economics at the University of Chicago says.
Since an early start centering on sports trading cards and manure-fertilized crop land (real field work, a self-described “bucolic” List happily acknowledges), his university homepage details a raft of field experiments:
“I have made use of several different markets, including using hospitals, pre-K, grammar, and high schools for educational field experiments, countless charitable fundraising field experiments to learn about the science of philanthropy, the Chicago Board of Trade, Costa Rican CEOs, the new automobile market, coin markets, auto repair markets, open air markets located throughout the globe, various venues on the internet, several auction settings, shopping malls, various labor markets, and partnered with various governmental agencies. More recently, I have been engaged in a series of field experiments with various publicly traded corporations—from car manufacturers to travel companies to ride-share.”
In the podcast, List explains, “I don’t anticipate or assume that I have a ‘clean test tube,’ but what I do is I randomly place people into a treatment condition or a control condition, and then what I look at is their outcomes, and I take the difference between those outcomes. That differences out the ‘dirt.’
“I can go to really dirty settings where other empirical approaches really take dramatic assumptions. All I need is really randomization and a few other things in place and then if I just take the simple difference, I can get an average treatment effect from that setting.”
His work – in journal articles, popular books like The Voltage Effect and The Why Axis, in findings applied immediately outside of academe – has earned him widespread praise (Gary Becker terms his output as “revolutionary”), a huge list of honors, and a recurring spot on Nobel shortlists.
For this podcast, List focuses on two of the many areas in which he’s conducted field experiments: charitable giving and the gig economy.
He describes one finding from working with different charities around the world over the last 25 years on what works best to raise money. For example, appeals to potential donors announcing their money would be matched when they gave, doubling or tripling a contribution’s impact. When he started, it was presumed that the greater the leverage offered by a match, the more someone would give, since their total gift would be that much greater.
“There was no science around it … it was art, or gut feeling.” It was also wrong.
List tested the assumption, offering four different appeals to four different groups: one with just an appeal for funding, one with a 1:1 match, one with a 2:1 match, and the last a 3:1 match. And the results bore out that matching a contribution
Kathelijne Koops on Chimps and Tools
Kathelijne Koops, a biological anthropologist at the University of Zurich, works to determine what makes us human. And she approaches this quest by intensely studying the use of tools by other species across sub-Saharan Africa.
“Look at us now …” she tells interviewer David Edmonds in this Social Science Bites podcast. “We are really the ultimate technological species. And the question is, ‘How did we get to where we are now?’ If we want to know why we are so technological, and how do we acquire tool-use skills, etc., it’s really interesting to look at our closest living relatives, chimpanzees and also bonobos.
“Why do, or don’t they use tools, and what do they use tools for, and what environmental pressures might influence their tool use.”
So Koops has been studying, first as a grad student and now as director of her own lab, the Ape Behaviour & Ecology Group at the University of Zurich, several groups of wild apes. (Chimps and bonobos, along with orangutans and gorillas, are labelled as great apes, and with humans, are members of the family Hominidae.) She also directs the Swiss National Science Foundation-funded Comparative Human and Ape Technology Project, which looks at ecological, social and cognitive factors on the development of tool use.
In this interview, Koops focuses on two decades of work she and her team conducts, along with Guinean collaborators from the Institut de Recherche Environnementale de Bossou, in the Nimba Mountains in the southeastern portion of the West African country of Guinea. The field site is remote, and work takes place in 10-day shifts at one of two camps. Researchers gather data on the chimps during daylight hours – if the chimps cooperate. “If the chimpanzees want to get away they can,” Koops details, “so even though we’ve worked there a long time you cannot follow them all day like you can at some other study sites.” The researchers also use motion-triggered cameras near well-trod areas – the humans dubbed them “chimpanzee highways” – where the chimps frequent.
Among the tool-using behaviors Koops has seen in the study group is seeing these chimps use long sticks to dig up ants for a snack without being devoured themselves, and using stones and branches to open up fruit casings. What this group doesn’t do, she continued, is use “percussive techniques” to open up edible nuts, even though another population of chimps a few kilometers away does exactly that.
To see if it is opportunity or is it necessity that spurred tool use and tool evolution, Koops’ team “cranked opportunity up by a million” by scattering lots of nuts that were otherwise less common in the primary forest habitat of the Nimba residents alongside lots of handy stones good for nut-cracking. The result was … not much innovation by the chimps.
“It really seems difficult to innovate on your own,” she comments. “… They really need to see from another chimpanzee how to crack these nuts.” In general, she notes, there’s not much ‘active teaching’ among her subjects but a lot of observation of older individuals.
She cites other experimenters’ similar work on 4- and 5-year-old humans, which in turn saw similar low instances of innovation. While being careful not to overclaim, Koops says “it looks like some of the building blocks of our culture are really already there in chimps.”
George Loewenstein on Hot and Cold Affect
The idea of walking a mile in someone else’s shoes is often trotted out as a metaphor for understanding empathy. The act of imagining someone else’s reactions may be hard, but based on the body of work by George Loewenstein, predicting how -- under varying circumstances -- we might walk in our own shoes may not be all that easier.
Loewenstein is the Herbert A. Simon University Professor of Economics and Psychology at Carnegie Mellon University in Pittsburgh, Pennsylvania. His enormous range of research interests can be boiled down, after a lot of boiling, to applying psychology to economics and, more recently, economics to psychology.
His career as a founder of both behavioral economics and neuro-economics has seen him delve deeply into how we react when our “affective state” is cold – when are emotions are absent and our physical needs are currently met – compared to when our affective state is hot. The latter is when out emotions are active or when our passions, as the old philosophers might term things like things hunger, thirst, pain, sexual desire, are pulling us.
It turns out, as he explains to interview David Edmonds in this Social Science Bites podcast, “when we are in one affective state it’s difficult for us to imagine how we would behave if we were in a different affective state. … The worst mistakes we make are when we are in a cold state, because we just can’t imagine how we would behave if we were in a hot state.”
While this may seem like something we know intuitively (or after years of high-profile experiments by Lowenstein, his frequent collaborator Leaf VanBoven, and others have conducted, several described in this podcast), it’s not something we act on intuitively. “No matter how many times we experience fluctuations in affective states,” Loewenstein says, “it just seems we don’t learn about this. We are always going to mis-predict how we’re going to behave when we’re in a hot state if we’re making the prediction when we’re in a cold state.”
This, in turn, affects the products of people who make predictions (or if you prefer, policy prescriptions) as a profession, he adds, such as economists.
“According to conventional economics, when we make decisions about the future we should be thing about what it is will we want in the future. What all of these results show is that your current state influences your prediction about what you’re going to want in the future; it influences these decisions that we make for the future in unproductive, self-destructive ways.”
Interesting and concise content
Though the audio filter used for correcting the interviewee’s voices is usually turned up too high, leading to a choppy sound.
Informative and easily digestible. Particularly the Danny Dorling interview is worth checking out. Perfect delve into a critical part of academia.
Blue church theology
Orthodox blue church theology. Borderline insane. So stupid only an intellectual would believe it. Wouldn’t recommend.