Political economist and journalist Will Hutton, author of the influential 1995 book The State We’re In, offers a state of the field report on the social sciences in this Social Science Bites podcast. Hutton, who was appointed in 2021 to a six-year term as president of Britain’s Academy of Social Sciences, addresses various critiques of modern social science – especially in its British incarnations -- from host David Edmonds. As defined by the academy that he now heads, “social science is the understanding of society in all its dimensions,” and encompasses the societal, economic, behavioral and geospatial sciences. Despite that broad remit, the first question posed is whether social and behavioral sciences take a back seat to the natural sciences in the public imagination. Hutton, for his part, says no – although he does see them not always getting their due. He notes that in combatting the COVID-19 pandemic, yeoman’s work was conducted by social and behavioral science. “It wasn’t called social science, but it was driven by social science.” The same, he continues, is happening as Britain confronts its economic demons. “Academic prowess is a kind of team,” he details. “You need your humanities, you need your physical scientists, your natural scientists, your medical scientists and your social scientists on the pitch. Sometime the ball falls to their feet and you look to them to make the killer pass.” One thing that might help in achieving that overdue recognition, he explains later, would be if the social sciences themselves shared their commonality as opposed to denying it. “[T]he Academy of Social Science was established 40 years ago, because we felt that good as the British Academy is, it couldn’t represent humanities and social science co-equally. Social science needed its own voice. Four decades on, I would say that social science’s standing in the world is higher than it was 40 years ago. But if [a score of] 100 is what you want to get to, we probably haven’t gotten beyond 20 or 30.” Impacting society, meanwhile, is how the sciences must improve their score (although Hutton acknowledges the vagaries of what impact looks like by saying “I’m not willing to castigate people if it looks as if what they are immediately doing is not impactful or having an impact.”) Asked what he sees as the “most fundamental issue” social science should tackle straightaway, Hutton offers four broad avenues to move down: Economics, governance, change behavior to keep the planet in good shape, and constructing a civil society of institutions that serve both individual and community needs.  Among those, he concludes, “I think combining ‘the we and the I’ is the most important thing that social science can do.” Hutton’s wide-ranging answers follow from a wide-ranging career. He served as editor-in-chief of The Observer newspaper, was chief executive of the then Industrial Society, was principal of Hertford College, Oxford from 2011 to 2020, and has authored a number of bestsellers since The State We’re In: Why Britain Is in Crisis and How to Overcome It. Those books include 2008’s The Writing on the Wall: China and the West in the 21st Century, 2011’s Them and Us, 2015’s How Good We Can Be, and 2018’s Saving Britain: How We Can Prosper in a New European Future (written with Andrew Adonis).

Batja Mesquita, social psychologist at Belgium’s University of Leuven, challenges the notion of universal emotions. She explains how emotions are culturally determined, highlighting the differences in anger and parental love across cultures. The podcast explores the implications of cultural differences in emotional interactions and discusses the speaker's inspirations, influences, and personal background in psychological research.

In the West we routinely witness instances of intergenerational sniping – Boomers taking potshots at over-privileged and under-motivated Millennials, and Millennials responding with a curt, “OK, Boomer.” What do we make of this, and is it anything new? These are questions Bobby Duffy, professor of public policy and director of the Policy Institute at Kings College London, addresses in his latest book, Generations – Does when you’re born shape who you are? (published as The Generation Myth in the United States). In this Social Science Bites podcast, Duffy offers some key takeaways from the book and his research into the myths and stereotypes that have anchored themselves on generational trends. “My one-sentence overview of the book,” Duffy tells interviewer David Edmonds, “is that generational thinking is a really big idea throughout the history of sociology and philosophy, but it’s been horribly corrupted by a whole slew of terrible stereotypes, myths and cliches that we get fed from media and social media about these various differences between generations. My task is not to say whether it’s all nonsense or it’s all true; it’s really to separate the myth from reality so we don’t throw out the baby with the bathwater.” One thing he’s learned is that the template for generational conflict is fairly standard over time, even if the specifics of what’s being contested are not. “The issues change,” he explains, “but the gap between young and old at any one point in time is actually pretty constant. … We’re not living through a time of particularly ‘snowflake,’ ‘social justice warrior’ young people vs. a very reactionary older group – it’s just the issues have changed. The pattern is the same, but the issues have changed.” Taking a look at climate change, for example, he notes that there’s a narrative that caring young people are fighting a careless cadre of oldsters unwilling to sacrifice for the future good. Not so fast, Duffy says: “The myth that only young people care about climate is a myth. We are unthinkingly encouraging an ageism within climate campaigning that is not only incorrect, but it is self-destructive.” That example, he notes, adds evidence to his contention that “the fake generational battles we have set up between the generations are just that – they are fake.” In the podcast, Duffy outlines the breakdowns his book (and in general larger society) uses to identify cohorts of living generations: Pre-war generation, those born before the end of World War II in 1945. Duffy says this could be broken down further – the so-called Silent Generation or the Greatest Generation, for example – but for 2022 purposes the larger grouping serves well. Baby Boomers, born from 1945 to 1965 Generation X, 1966 to 1979 (This is Duffy’s own generation, and so, with tongue in cheek, he calls it “the best generation”!) Millennials, 1980 to around 1995 And Gen Z, ending around 2012 He notes that people are already talking about Generation Alpha, but given that generation’s youth it’s hard to make good generalizations about them. These generation-based groupings are identity groups that only some people freely adopt. “We’re not as clearly defined by these types of groupings as we are by, say, our age or educational status or our gender or our ethnicity.” His research finds between a third and half of people do identify with their generation, and the only one with “a real demographic reality” (as opposed to a solely cultural one) is the Baby Boomers, who in two blasts really did create a demographic bulge. Duffy, in addition to his work at King’s College London, is currently the chair of the Campaign for Social Science, the advocacy arm of Britain’s Academy of Social Sciences. Over a 30-year career in policy research and evaluation, he has worked across most public policy areas, including being seconded to the Prime Minister’s Strategy Unit. Before joining KCL he was global director of the Ipsos Social Research Institute. His first book, 2018’s The Perils of Perception – Why we’re wrong about nearly everything, draws on Ipsos’s own Perils of Perception studies to examine how people misperceive key social realities.

Quite often the ideas of ‘risk’ and of ‘uncertainty’ get bandied about interchangeably, but there’s a world of difference between them and it matters greatly when that distinction gets lost. That’s a key message from psychologist Gerd Gigerenzer, who has created an impressive case for both understanding the distinction and then acting appropriately based on the distinction. “A situation with risk,” he tells interviewer David Edmonds in this Social Science Bites podcast, “is one where you basically know everything. More precisely, you know everything that can happen in the future … you know the consequences and you know the probabilities.” It is, as Bayesian decision theorist Jimmie Savage called it, “a small world.” As an example, Gigerenzer takes us a spin on a roulette wheel – you may lose your money on a low-probability bet, but all the possible options were known in advance. Uncertainty, on the other hand, means that all future possible events aren’t known, nor are their probabilities or their consequences. Rounding back to the roulette wheel, under risk all possibilities are constrained to the ball landing on a number between 1 and 36. “Under uncertainty, 37 can happen,” he jokes. “Most situations in which we make decisions,” says Gigerenzer, “involve some sort of uncertainty.” Dealing with risk versus dealing with uncertainty requires different approaches. With risk, all you need is calculation. With uncertainty, “calculation may help you to some degree, but there is no way to calculate the optimal situation.” Humans nonetheless have tools to address uncertainty. Four he identifies are heuristics, intuition, finding people to trust, and adopting narratives to sustain you. In this podcast, he focuses on heuristics, those mental shortcuts and rules of thumb that often get a bad rap. “Social science,” he says, “should take uncertainty seriously, and heuristics seriously, and then we have a key to the real world.” When asked, Gigerenzer lauds Daniel Kahneman and Amos Tversky for putting “the concept of heuristics back on the table.” But he disagrees with their fast-slow thinking model that gives quick, so-called System 1 thinking less primacy than more deliberative thinking. “We have in the social sciences a kind of rhetoric that heuristics are always second best and maximizing would be always better. That’s wrong. It is only true in a world of risk; it is not correct in a world of uncertainty, where by definition you can’t find the best solution simply because you don’t know the future.” Researchers, he concludes, should “take uncertainty seriously and ask the question, ‘In what situations do these heuristics that people use (and experts use) actually work?’ and not just say, ‘They must be wrong because they are a heuristic.’” Gigerenzer is the director of the Harding Center for Risk Literacy at the University of Potsdam and partner at Simply Rational – The Institute for Decisions. Before that he directed the Center for Adaptive Behavior and Cognition at the Max Planck Institute for Human Development and at the Max Planck Institute for Psychological Research. His books include general titles like Calculated Risks, Gut Feelings: The Intelligence of the Unconscious, and Risk Savvy: How to Make Good Decisions, as well as academic books such as Simple Heuristics That Make Us Smart, Rationality for Mortals, Simply Rational, and Bounded Rationality. Awards for his work include the American Association for the Advancement of Science Prize for Behavioral Science Research for the best article in the behavioral sciences in 1991, the Association of American Publishers Prize for the best book in the social and behavioral sciences for The probabilistic revolution, the German Psychology Award, and the Communicator Award of the German Research Foundation. He was a 2014 fellow at the SAGE Center for the Study of the Mind University of California, Santa Barbara (SAGE Publishing is the parent of Social Science Space) and a fellow of the Association for Psychological Science in 2008.

“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.

Economist Jonathan Haskel discusses the rise of intangible assets in the knowledge economy, emphasizing the shift from tangible to intangible investments in companies like Google and Microsoft. He highlights the unique properties of intangible assets, such as scalability and sunk costs, and explores the challenges of measuring intangible productivity. The podcast delves into the impact of intangibles on economic growth, the need for policy adjustments, and the transition to an intangible economy.

Sheila Jasanoff discusses the dual focus of Science and Technology Studies, evolution of the discipline, and the societal impact of scientific knowledge dissemination. The podcast explores the complexity of scientific knowledge, challenges in scientific communication, and the relationship between technology and society. It also highlights the importance of proactive governance mechanisms in adapting to technological advancements.

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 amped up the results – but the leverage didn’t matter. “Just having the match matters, but the rate of the match does not matter.” List was later the chief economist with ride-share behemoth Uber – and then with its competitor, Lyft. He coined the term Ubernomics for his ability to manipulate the tsunami of data the company generated. “It’s not only that you have access to a lot of data,” he says, “it’s also that you have access to generating a lot of new data. As a field economist, this is a playground that is very, very difficult to beat.”

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.”

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.”