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Confronting Uncertainty with Tamsin Edwards - Transcript May 21, 2020

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For leading climate scientist Dr Tamsin Edwards, probabilities and possible futures are part of her everyday work. She joins David to explore uncertainty in different aspects of her life: as a statistician and mathematical modeller, as a communicator about possible climate futures, but also as a cancer patient faced with a life-changing decision. *** David: In this episode, I’m speaking with Dr Tamsin Edwards all about confronting uncertainty - as a climate scientist as a communicator, but also as an individual in her personal life. This is a more intimate episode than usual and we weave together some different threads. We cover how Tamsin has dealt with communicating uncertainty in climate science and how she found listening to the perspective of the audience including those with deeply sceptical about climate change very important in learning how to communicate, but we also explore Tamsin's personal experience after a medical diagnosis forced to confront serious, but uncertain risks to her health. Tamsin welcome to Risky Talk Tamsin: Hi happy to be here! David: Now you and I are both ‘uncertainty nerds’! People often think the science delivers cold hard facts that everyone agrees with, but what place does uncertainty have in your work? Tamsin: I think the best way I've heard it put is that uncertainty is the engine of science. we couldn't do science without it. It's the idea that we are at the bounds of knowledge is both why we need to do the science and what's exciting about it. "Uncertainty is the engine of science" I got interested in uncertainty kind of right from the start of when I went into climate modelling, actually through joining a project which had a statistician involved - which was perhaps relatively unusual at that time – through a mutual friend of ours called Jonty Rougier, and I just got I got kind of bitten by the bug really. Of uncertainty, of exploring possibilities of being kind of open to not just thinking about the things that we didn't know, but trying to anticipate the things that might happen that we haven't yet, you know, the unknown unknowns and all of that, and the almost kind of philosophical aspect of trying to predict the future when we have all that uncertainty in how something behaves in something as complicated as the planet with a relatively limited amount of data to base it on and there's always going to be kind of limits to our knowledge. So I yeah, I just really kind of fell in love with the subject. David: You specialise in developing models for climate systems, but your blog is called All Models Are Wrong. Okay! Why did you call it that? Tamsin: Well, some people may know there's a famous quote 'all models are wrong, but some are useful' by the statistician George Box. David: It’s got its own Wikipedia page! "All models are wrong, but some are useful." Tamsin: Which has its own Wikipedia page! So I stole it from that. And it came about actually, I was always interested in that quote and I was already working in the idea of uncertainty, but it came about through some Twitter conversations about how one should communicate climate science, I guess to the public and climate prediction. There was this kind of split view around the idea of kind of trying to increase confidence in in climate predictions by emphasizing all the things we were really certain about and the stuff we were really sure about you know. There's all this kind of very hardcore well-known well-established physics at the at the core of these big climate models and emphasizing all the ways in which we think very carefully about the models and we test them and we you know, put lots of work into that versus the risks of doing that too comprehensively and saying we were sure to the point of absolute certainty and emphasizing scientific certainty and what dangers that can lead to in terms of when the evidence-base does change, when our understanding changes, when we have different predictions coming out in the media that might appear to be contradictory when we understand and discover something new. We then put ourselves at risk of people saying - but we thought you were sure about that but now you've changed your mind!? And actually as well as being interested in uncertainty for my research. I'm a firm believer in communicating the idea of uncertainty in cutting-edge research to the public to try and help resolve some of those difficulties around apparently contradicting and changing evidence - because evidence is apparently contradictory and changing because that's the nature of uncertain knowledge! David: In a previous podcast we did on climate change, one of our guests Anthony Leiserowitz said there's a double standard: that somehow people and politicians expect us to be so certain about things like climate change, and yet when you think about policies like Brexit or anything like that - actually there are huge uncertainties! And everyone seems to be able to accept those. What do you feel about that? Tamsin: Absolutely. I mean, obviously we're always making decisions under uncertainty, but I don't know how much I've seen people really saying you need to narrow your error bars on a prediction, something as explicit about uncertainty of that. It kind of runs much more deeply. I think that it's much more about the fundamental kind of tenets of what you're saying not about “oh, we'll wait until you can predict global mean temperature in the year 2100 to within 0.1 degree Celsius, as opposed to, you know, one point four degrees Celsius. It's much more about well the assumptions that go into that and the testing and the potential for groupthink and cognitive biases. So I don't normally see it as “reduce your uncertainty”. It's much more kind of fundamental than that. David: So it's not a matter of the width of the error bars, it’s more people thinking, “Do you even know what you're talking about?” Tamsin: Yeah. Do you even know what you're talking about, or are you lying with some agenda? Are you manipulating the results in some way? This kind of thing? And yeah, so and so are the kinds of questions I get asked I guess when I give a climate talk from people who kind of lean a bit skeptical, or get asked on Twitter ,isn't sort of oh, well, your prediction isn't credible until you narrow your error bars. It's about will have you forgotten that this other thing might drive climate or have you added some bias correction to the data that has made it, you know systematically wrong, that kind of thing. David: Yeah. So it's to do with systematic errors due to not including an important factor or just your model being wrong, which as you said, “all models are wrong!” Tamsin: Exactly, right? And that's all I really wanted With the blog name and the blog feed, you know, as well being about uncertainty to kind of confront that head-on. So it was partly about how do we come up with the error bars? Because that's kind of interesting and useful to talk about but also it was about how do we test those assumptions? How do we evaluate our models? How do we incorporate knowledge and check for systemic biases group think this kind of thing and really actually say look I want to talk about uncertainty and I want to examine the thought processes, and I want to lay it all out in the open so that we can have a conversation about you know to make it transparent about how these decisions are made about which things were more confident about and that have been sort of tested very thoroughly, and which things are more difficult because there isn't the data or we don't have such good understanding. David: Okay. Can I ask you more general question? What do you think at the moment with the way that all the discussions going on about Covid 19, about which there's quite a lot of, you know, basic disagreement about what should be done. And is a highly skeptical movement as well. Do you think that, you know, the uncertainty about the science is being communicated well or not? Tamsin: I think It's been good that the UK government have sort of been placing chief scientific and medical officers in the briefings public-facing every day and been trying to sort of include some of that quantitative data on the predictions, and they've been trying to make it clear that the mortality rates don't include everything and have some time lag. So there's definitely probably better efforts to make things clearer than I feared there could be but at the same time there's a lot of things that I think are not very clear. Lots of people seem to be very surprised about the time scale likely for these lockdowns, this idea that we have a review every three weeks that people are kind of getting their hopes up about the lockdown stopping every three weeks. But when I had a look at the original Imperial report by Neil Ferguson team, it was very clear that, you know, the assumptions in those initial predictions that drove, as I understand it, that initial lockdown was several months for the first lockdown, followed by a lifting, followed by another lockdown, followed by a short lift, followed by another lockdown- and this would go in and out of lockdown based on the Intensive Care capacity for perhaps two years, with a majority of the time in lockdown! So that seems to me a really key piece of information that hasn't been communicated and it's not that that's a policy but it's obviously part of the evidence base that has gone into the policy. David: Exactly and I do feel that the you know, the critique and discussion about all these things has not been that great to be honest, and the journalists have almost seemed to be more much more concerned with blaming people rather than actually looking at examining what people are saying and critiquing the data and the assumptions being made and so on. Tamsin: Actually, there's a parallel with climate science that… well for a start the fact that predictions do get revised as we get more evidence and more data in, so that's not such a flip-flopping or some kind of weakness. That's a good thing. You know, we should be changing our predictions as new data comes in, but also that particularly it's sort of relevant to climate science the difference between I guess a scenario of a particular policy intervention, versus a sort of uncertainty range for a particular policy intervention or particular model or a particular study. So what I mean by that is that original Imperial study had the sort of predictions for no policy at all versus a sort of partial suppression, versus sort of full lockdown and those were very different numbers, but they would sometimes get communicated as “ They can't make up their mind if it's this number or that number”, or another study would come along with a different number and they say “well, you know, these people can't make up their minds” - but they were comparing two different scenarios! So I think there's a subtlety around that that idea of making predictions under particular scenarios. David: I agree that there's a difficulty in communicating some conditional predictions. As if that's your prediction of what's going to happen. No! That's the prediction were this to be the case. Something that within science, we’re so used to talking about these conditional scenarios, what-ifs and which when it turns into communication it becomes that's the prediction. It happened recently with the Office of Budget Responsibility making the dire, you know forecast- it wasn't a forecast it was a scenario for the possible economic harms of the lockdown and they explicitly said “this is not a prediction” and everybody said “that’s what they predict is going to happen.” Tamsin: Absolutely. It is about partitioning the uncertainty isn't it? I mean in climate change the equivalence is the scenarios of different greenhouse gas emissions in the future. So , you know, as climate scientists, we cannot predict what the future policies will be over the next century. So we make predictions for the high greenhouse gas emissions for the medium and the low conditional on those emissions - and that's because we can't, you know, we can't predict those. But what we can do is try and predict how the physical system of the planet and the atmosphere and the oceans will respond to a given scenario. But again people will say how can you how can you predict what people will do or emissions will be but it's a different thing. David: So what we're really concerned with here is the communication. You're one of the lead authors of the next intergovernmental panel on climate change - IPCC - report, which will receive extraordinary attention. What's the point of the report and how well do you think they have communicated their work - particularly in terms of the uncertainties the possible scenarios and so on? Tamsin: The IPCC assessment reports started in 1992, and they really gather the evidence base for the world's policymakers to think about climate change. Every sort of five to seven or eight years we've had new assessment reports, and the next one is going to be the sixth assessment report. And these things are now enormous dense, volumes of information - and they don't even capture every part of climate science! They're just really a sample of the key changes and they tried to explain and contextualize, you know, what we've learnt and what the impact of different policy interventions would be on climate change. The idea is to be ‘policy-relevant not policy prescriptive’ is the phrase, so they present the predicted climate change under different greenhouse gas emissions. There's been an interesting change in how we think about those predictions in recent years because of the Paris agreement setting things up in terms of a temperature target. So we often used to think about greenhouse gas emissions or concentrations particularly as a limit - We've got to keep to 550 parts per million of CO2, something like this and you may notice that we don't really think in those terms anymore. We think much more in terms of the probability of keeping to the 2 degrees warming, or the one and a half degrees of warming since the pre-industrial period. And, of course ,that's a different way of thinking about it. You have to map out all the possible ways in which you could limit greenhouse gas emissions in a way that would then lead to only two degrees of warming, or less or say a 2/3 probability of that - not even a hundred percent probability! So actually instead of thinking of a forward direction, of if we have high or low greenhouse gas emissions, what would the temperature be? The new reports the special report on one and a half degrees? Warming was a lot more about “what would greenhouse gas emissions need to be to maintain a warming limit of this”. David: Which I suppose does make it relevant to an outcome measure. That is the one that people are basically interested in. Tamsin: Well a very simplified metric. Obviously, I mean global mean temperature is a incredibly kind of coarse way of thinking about climate change, but it's a useful sort of ballpark metric of change. I guess. It's a summary of change that doesn't incorporate everything but it's a scale on which to kind of act on. David: So one of the things that we bring up in every one of these podcasts is what's the purpose of this communication of the science, and the uncertainty about the science. And to put it really quite crudely - are you trying to inform people or are you trying to persuade people of the importance of action over the threat we’re facing? And I…

Full show notes at the publisher

Coronavirus: Understanding the Numbers Apr 02, 2020

Show notes

The unfolding coronavirus pandemic is a story driven by numbers. But how reliable are the numbers we have? What can the data really tell us – and what are the major areas of uncertainty?

In this special episode, recorded on 1st April, Michael Blastland and Professor David Spiegelhalter help us separate the signal from the noise.

Why the absolute numbers of confirmed cases and deaths from Covid 19 are dodgy, but the rate at which those numbers change is still a useful indicator.
Why Norway vs Sweden is an unfolding natural experiment: neighbouring countries with broadly similar populations, but Norway has gone into strict lockdown and Sweden is being relatively relaxed.
There are usually around 600,000 deaths per year in the UK. When we look back, will Covid 19 have caused many excess deaths – or could the figures for 2020 end up looking similar to a bad flu season?
How can we think about our personal risk of dying from Covid 19? David sets out how getting Covid 19 multiplies your existing level of risk depending your age: it provides a “pulse” of heightened risk over a short period.

Full transcript available at:

https://riskytalk.libsyn.com/transcript-of-coronavirus-understanding-the-numbers

***

Views to share? Get in touch on Twitter @RiskyTalkPod or email riskytalk@maths.cam.ac.uk

Risky Talk is produced by Ilan Goodman for the Winton Centre for Risk and Evidence Communication at the University of Cambridge.

Transcript - Coronavirus: Understanding the Numbers Apr 02, 2020

Show notes

The unfolding coronavirus pandemic is a story driven by numbers. But how reliable are the numbers we have? What can the data really tell us – and what are the major areas of uncertainty? This is the transcript of a special episode of Risky Talk, recorded on 1st April. Michael Blastland and Professor David Spiegelhalter help us separate the signal from the noise. *** Michael Hello, and welcome to a special edition of a Risky Talk with Professor David Spiegelhalter. Except that many of you will know, I'm not David Spiegelhalter, and not because he's isolated with Covid 19. No, David is here or rather, at one end of a line with me, Michael Blastland, at the other. Plenty of distance between us. David, welcome to your own show! David Grand to be here. Michael We're doing it like this this week simply because the team who made the podcast decided we wanted to hear more from David on Risky Talk, where he's usually asking the questions. This time, he'll be answering them. And what we want to hear about especially at the moment, guess what, given that David's a world leading statistician, are his thoughts about coronavirus or covid 19: Where we are now, what the data we have can tell us and what it can't and how we should think about the risks. Now, we're having this conversation based on data available up to March 31st. So do bear that in mind if you're listening far into the future - new evidence is coming in all the time. David, a more loaded question these days than it usually is. How are you? David Well, I'm actually feeling very fit because this confinement is making me pay a bit more attention to my exercise! Michael Do you have a sense of your personal risk? How risky does it feel for you? David Oh, yeah, not too bad. I wouldn't actually be too worried about getting it - I think it could be very nasty - but we'll come back later to how we can put our personal risks into perspective. Michael Alright, let's begin with how we should approach the problem because there are a lot of numbers kicking around. We've got numbers of deaths, we've got death rates, we've got numbers of cases, hospitalization rates, doubling periods, all of that. Which data would you focus on? David I think this is the first time in an international crisis like this, where there's been immediate access to huge amounts of data. But you know, the data that's most easily available is on cases and on deaths. And there's a problem with both of them. It's one of these very basic things that, before getting down and doing any fancy analysis, we should ask: what are we counting in the first place? And with confirmed cases - those who've actually had a test and been positive - we know that's a very limited view of the actual number of cases out there. People say maybe there's 10 times as many. But of course, it depends on the testing regime. Some countries, like Germany, have done a huge amount of testing and so the confirmed cases there might get closer to the number of people that have actually got it. But other places, like the UK - we've only been testing people in hospital, not even NHS workers really yet, and so actually the number of people that are in this country with the virus is a deeply unknown quantity. With a common testing regime - as long as that's constant - you can use the confirmed cases to monitor what's going on, the trends up and down - provide some information. If the testing regime changes, of course, that all changes as well. I guess you would think what would be a good measure, to put it very crudely, is counting the bodies. But we found out increasingly what a tenuous measure that is of the fatal harm of Covid 19. Each country may be different in what it counts as a death. Some countries are counting deaths as where you can actually say ‘this is what caused the death’. In the UK, we were only just getting clarity from the Department of Health - they made this public just today - of what these deaths actually mean: that the deaths being daily reported are in fact, just deaths in hospital that have occurred for people who have tested positive. So, strictly speaking, if someone tests positive in hospital but actually dies of something else, they'll still be recorded as a Covid 19 death. But those figures don't include any deaths in care homes outside of hospitals. And also the deaths figures are delayed. They're not the deaths that happened today or yesterday - some of them are coming in from two weeks ago. Hospitals have to wait until the family have been informed and given permission. And it seems clear also that they save them up over the weekend and send them all in on Monday. So these numbers are very erratic from day to day. Michael So we're seeing a bit of a spike on Mondays are we? David Oh, yeah, the Monday spike. In fact on Saturday and Sunday, the figures went right down. So we thought this can't be true: this is an artifact. And then suddenly they spiked - in a day they went from 180 to 380. This is completely impossible from a standard statistical perspective. Assuming there’s some sort of smooth process going on - we know what the variability should be - and what we see is vastly more than that. So we've got to be very careful in drawing conclusions about what's going on underneath. Michael Now given all these uncertainties, and they sound pretty large on all the elementary bits of data that we'd expect to have, can you draw any conclusions about the seriousness of the problem? David Both cases and deaths in the early part of the epidemic in almost every country increase by 30% per day. So you get a feeling that at least the rate of increase is representing what's going on, even if the absolute numbers are undercounted or whatever. From the rate of increase we can really get a feeling of how fast something is spreading throughout the population. Of course, the epidemiologists have built mathematical models that are being used to make predictions, and they have been using this kind of data to better inform the models. Michael So, I mean, I've seen one statistician, John Ioannidis, call it a “data fiasco”. Despite that, are there things we can say with some confidence? David Yes, by looking at the rate of change. We can get a lot of information from rates of change. Rates of change, are unaffected by systematic underreporting. They're unaffected by whether it's done per million or just in the whole country, and so on. So all these different ways of measuring make no difference when we monitor the rate of change. And that is the thing everyone is focusing on, because we're so interested in whether that rate is declining. In other words, what we'd love to see both in cases and in deaths is that this rapid increase smooths off into plateaus, into a constant rate, and then falls down as it did in China. And everyone's trying to work out what is going to happen in the Western countries. Michael So that's one good solid bit of evidence. What about the one that I think a lot of people are concerned with - the absolute rate of fatality. I've seen numbers around 0.1% or 0.2%. The Imperial estimate, I believe, is about 0.9%. Is there any way of adjudicating between them? David It's very difficult. It's highly contested. This is the infection mortality rate. The case fatality rate, which is how many people who we know got the virus actually end up dying, that can be estimated a bit more firmly. But the infection mortality rate, which is the percentage of people who get the infection and then die, depends crucially on of course, how many people have the infection - and that's the one thing we do not know! We have to just guess it until we have some population measures of who's had it. So eventually we will know (rather late…). The Imperial group are working on 0.9% as an average, they released some analysis of China a couple of days ago where they lowered that to about 0.6%. But other sources are saying much lower than that - 0.1% and so on - which is more like seasonal flu. Those studies are influenced by places like Iceland, who measured, you know, a lot of people, did a big lot of testing, - they managed to test around testing 3% of the population - and found a very small number of deaths. But that's Iceland! We're assuming that this is some constant number that applies just as much in Iceland, as it would in India or somewhere like that. And that is so deeply unlikely because whether you die or not, depends crucially on the health services available. It will also depend on how serious the illness is that people get. For example, their viral load, how much have they been exposed to? We're getting multiple deaths of healthcare workers and it's really appallingly tragic, and that is sometimes in young healthy people, presumably because they've had a high exposure to the virus. And, in places where people live more distantly, less crowded in, might get very low exposure to the virus, and so actually get a much less severe form of the disease. So I think the idea that there is a fixed infection fatality rate, is pretty inappropriate. There will be big heterogeneity from place to place. Michael So would you feel confident about making a general comparison with things like normal flu or SARS or the 1918 influenza - to say simply ‘is this better or worse?’ David Well, the standard thing that people are saying about it, compared to normal flu, is that it's twice as infectious and ten times as dangerous. And I think there is still reasonably strong evidence for that. It's different to SARS - SARS was far more lethal but actually much less infectious. And so we were actually able to keep it clamped down and controlled. I'm not sure about Spanish flu, I can't remember the numbers for that. But the crucial thing about that, actually, is that it hugely affected younger people. And that is not the case with this disease. Michael When we look at the various efforts by governments around the world to deal with this, there are a lot of lockdowns, but not everybody's locking down, and we're not all locking down in the same way. Can we work out which policies are making a difference yet? David Everyone's interested in this. And I don't want to make any grand claims at all. The plots I find most useful- and I'm looking at Our World in Data at the moment because I like their presentations a lot - is one of a log scale of cumulative deaths by country. And there, the gradient gives you a very powerful idea of the increasing number of deaths. Lines that run in parallel, are running at similar rates, the epidemic is taking a similar form. And we see for example, by that the United Kingdom at the moment is running very similar to Italy was at the same stage. People have been observing that Germany has got a very low mortality rate, but actually, although it's lower at the moment, its track is exactly the same as the UK's. The very interesting controlled experiment in a sense is Sweden vs Norway. They're next door to each other and they've got very similar population structure, very similar climate, but they've taken extremely different policy views. Norway has implemented quite stringent criteria but Sweden, is still really quite relaxed, people going out for meals and no lockdown at all, schools still open and so on. Sweden's progress, although it's lower at the moment, seems to be following the trail of everybody else, of United Kingdom in Germany. Whereas Norway, actually - although it's still really barely started in Norway - is tracking quite low at the moment. And of course, we've got Belarus who have volunteered to be the ultimate control in that they're taking no notice of the virus whatsoever. Michael I assume it will take a little while before we get a clear idea of how those policies have panned out? David Yes, exactly. Michael Okay. Well, we've talked about some of the limitations of these numbers. But let's add a few more complications (as if there weren't enough) and some more uncertainties. So, there's a lot of discussion about whether we're not really preventing many deaths, we're just delaying them and maybe not by very much, maybe because a lot of people are going to die quite soon anyway, if you're already elderly or very ill. What's your view about that? David Yeah, it's an interesting argument, and it's recognized by epidemiologists, they call it "mortality displacement". And it's often said that hot weather will do this. For someone who's frail and vulnerable, hot weather might bring their death forward a little bit. That's known as mortality displacement, or I've got to admit it, the technical term also used is ‘harvesting’! Michael Oh, gosh! David Yeah, exactly! I mean, I suppose it brings up images of the grim reaper, but that is the technical term. So people have said - in fact, Neil Ferguson, the head of the Imperial University modeling team, said in evidence to the parliamentary committee last week that he thought maybe two thirds of deaths were people who would probably have died reasonably soon anyway! So, there's a couple of issues about that. First of all it means, perhaps, that when we come out with this, we'll find that the number of excess deaths in the country over the year might very well not be excessive. It might look just look like even an average flu season in terms of the extra deaths. Michael Really!? Within the kind of normal range that we might expect to see? David Well it does vary very much - and that's to do with the flu. An average flu season will bring it up 17,000 or so extra deaths. A bad flu season would take it up to 25,000 extra deaths over the winter. Many of whom are, to use the technical term, ‘harvested’ from people who would die soon after anyway. Michael And that is assuming lockdown, in saying that it's within what we might expect? David Oh, yes, this is assuming lockdown! This is the crucial thing, this is what I dread at the end of this. If these lockdown measures are effective, and the aim is, as has been made explicit, is to try to keep the deaths below 20,000. If that happens, and people may look back at the end of the year and say, "oh, well, not many extra people died anyway", then the accusation will be made that therefore, we didn't need to do the lockdown, which is not a logical consequence! It's only as low as 20,000 because we have taken these measures. The Imperial group estimated that, even with the less stringent mitigation measures which were in place before the lockdown - there would still have been 200,000 deaths. Now that really would make a dent in the population! We have around 600,000 deaths a year in this country, so we'll use that as the norm. So actually, you know, 20,000 extra deaths is within the margins what can happen in a flu year. Michael But even if we took even if we took two thirds off that 200,000 figure, we’re going to see something that's outside the normal kind of variation? David Yeah, and the crucial thing - and people do forget this, even though it's been emphasized again and again, by the Imperial modellers and everybody - is that in the end it’s not a matter of saving the lives of some old vulnerable people who are going to die soon anyway. It's the fact that if you didn't do that, if you didn't bring in these measures, you know, the NHS would be just totally overwhelmed, particularly as it’s short staffed. It's going to be touch and go, I think, anyway, whether it can deal with it. I mean, amazing measures being taken at the m…

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Transcript - Communicating About Climate Change Mar 05, 2020

Show notes

How can we communicate about climate change in the most engaging, informative and even persuasive ways? What are the different audiences we need to reach, and how can we craft effective communications for each of them? Joining David to guide us through the psychology, politics and science of climate change communication we have: Dr Anthony Leiserowitz, Director of the Yale Program on Climate Change Communication Dr Emily Shuckburgh OBE, Climate scientist and Director of Cambridge Zero Dr Sander van der Linden, Director of the Cambridge Social Decision-Making Laboratory https://riskytalk.libsyn.com/communicating-about-climate-change David 0:03 Hello, and welcome to Risky Talk, the podcast where we talk about risk and how to talk about risk. I'm your host, David Spiegelhalter, and in this episode we're tackling possibly the most urgent and contentious issue of the day climate change. Anthony 0:21 Four out of 10 adults on the planet have never heard of climate change. Never heard of it. That's almost 2 billion people. Emily 0:27 It's about telling stories as much as it is about showing people facts and figures around the climate science. Sander 0:32 A lot of the framing is about loss. Nobody likes losing, right, you can get different responses to people just by changing the frame. David 0:46 What are the most effective ways to communicate about climate change? A few years ago, I had a crack at it myself, and I co presented a BBC documentary called climate change by numbers. We tried to find entertaining ways to make some of the numbers around climate change come alive. I remember firing a paintball gun in a forest to illustrate Monte Carlo modeling. We were hoping to entertain and inform. But of course, there are lots of groups both individuals and governments who originally want much, much more. They want to achieve major transformational change, changing the way we behave as individuals and changing the way whole industries and economies operate. Now, communication is going to be vital if any of this is going to happen. And my guests today all have skin in the game as climate change communicators themselves and as behavioral scientists studying climate communications, Dr. Emily Shuckburgh is an accomplished mathematician and climate scientist, and also the Director of Cambridge zero. Emily, welcome to Risky Talk. Emily 1:49 Thank you, David. David 1:50 Before I introduce the others, can you give us a quick sense of what your mission is with Cambridge Zero? Emily 1:54 So Cambridge Zero is a new initiative of the University of Cambridge which aims to bring all the resources of the university together to help support the delivery of a zero carbon world. So to focus specifically on the solutions. David 2:08 We're so pleased to have you here. We also have Dr. Sander van der Linden with us. Sander is the director of the Cambridge Social Decision Making Laboratory. Welcome Sander. Sander 2:17 Pleasure to be on the show. David 2:18 Sander, you're a social psychologist. So what kind of topics do you work on that are relevant to climate change? Sander 2:24 So I study how people think, feel and act on the issue of climate change. And so that entails how people assess evidence, how they perceive the risk, the emotions that they experience, as well as how people decide whether or not to act on the issue. David 2:38 And finally, I'm delighted that we've got Dr. Anthony Leiserowitz on the line from the USA. Anthony is the director of the Yale Program on Climate Change Communication. Welcome, Anthony. Anthony 2:49 Hi, David. Great to be with you. David 2:50 So Anthony, what does your work focus on at the Yale Program on Climate Change Communication? Anthony 2:56 Well, two broad things. One is research and the other is public outreach space. On the research side, much like Sander, we study how Americans and other people around the world respond to this issue. So what do they know? And misunderstand? How do they perceive the risks? What kinds of policies they support? What kinds of behaviors are they engaged in? And then what are the psychological, cultural and political reasons why some people get really engaged, others are kind of apathetic, and some are downright dismissive and hostile. And then based on all that research, we also have our own effort to engage the at least the American public with a national radio program we call Climate Connections. And these are 90 second stories - brand new every day Monday through Friday - that play on about 525 stations across the country. And they feature the voices of everyday people from every walk of life, who are either experiencing the impacts of climate change right here right now. Or even more importantly, rolling up their sleeves and taking action to actually address the issue within their own domain or sphere of influence. So great to be with you. David 3:59 Well, we've got a fantastic panel of guests to discuss these issues. So let's get started. Can I just stand back a moment and ask: what are you all actually trying to do? I'm chair of the Winton Center in Cambridge and our mantra, which we drone on about the whole time is that we're seeking to inform people, rather than persuade them. We want to empower people to make informed choices. We work in health and all sorts of others areas on informed consent. But, you know, climate change communication is to do with getting people to change their behavior. I mean, am I right in this? And, you know, does this mean it's very different from a lot of the other risk communication that we that we do and we talk about? Emily 4:48 If what we're trying to achieve through climate science communications is essentially better informing people so that they make whatever decisions they want to make, then that's one perfectly laudable aim. And there are certain things you might want to try and do in order to make sure that that communication happens as effectively as possible. If what we're really wanting to do is to get people to take a particular course of action and change that behavior in certain ways, then then you might question whether actually telling them about the climate science is the right thing to do in the first place. And so being really clear about what the goal of those communications are... David 5:25 But do you think people are generally clear? Emily 5:28 I think it's often completely mixed up. And I think that's understandable because if you think about the people who are communicating, let's take climate scientists just because I am one. We have been studying, you know, the changing climate, we've seen the changes. I'm a polar scientist, I've seen the changes and the scale of the changes, and it's horrifying and so it's very difficult not to get personally involved and and to want certain outcomes from your communication. Trying to dissociate and step back from that and be as dispassionate as possible is challenging David 6:08 Sander, is it okay to use these behavioral science techniques have been developed for behavior change in the context where you're perhaps more trying to change attitudes? Sander 6:17 Yeah, I have two big thoughts on that. The first thing I tell all my undergraduate students in in my influence class, is that we're constantly influencing each other, whether we like it or not. With every word that you say, there are unintended consequences that you could not have anticipated on somebody else. Every time somebody comes to class, they hear things and they're influenced one way or another, either because I intend to by showing them evidence or talking about studies or psychology, or by accident, and every time they tell a story to somebody else, they're either intentionally or unintentionally influencing people all the time. So I think the influence process is part of being human. But what we want to do is, is do that in an ethical way and I think that's the key issue. People think stuff all the time and people don't know what other people are thinking, if I reveal to you what other people are thinking, and that influences your behavior, or you change your behavior, that has nothing to do with the information that I gave you! I just told you objectively what other people are doing. And I think that's an interesting distinction to draw. Now, the kind of explicit behavior change stuff like Do we want people to change? I think it's a moral question of what what as a society we decide is normative. We don't want people to steal stuff. And we do change people's behavior when they steal stuff. And we all think that's okay. If we decide we want to do the same for climate change, and maybe that's an ethical thing to do. David 7:36 Yeah. And I think we have to talk about ethics and in this area. In what situations is it reasonable to try to persuade somebody of a fact? That's correcting a misunderstanding, perhaps that seems fairly reasonable. But actually, there's a reason for doing that. And I'm sure when you do your studies, your your some of your outcome measures might include things like intentioan and attitude and things like that. Which then actually reveal perhaps the aim of what you're doing. Tony, how do you feel on this business of informing versus persuading? Anthony 8:10 Yeah. So I think it's important to recognize the messenger as well as the role that they're in because of the context they're in. So context is always king. If a woman goes into see her doctor, and the doctor runs a bunch of tests, and the doctor comes back and says, I'm sorry, Mrs. Smith, but I have to inform you that you have cancer. That's a fact. But that's a fact that comes with an enormous amount of weight, right, a enormous amount of emotional reaction to your concern, and perhaps motivation to do something about that. And then the doctor can follow up with a subsequent set of facts. And here are three different choices of you know, a prescription, it could be chemotherapy, could be something else that we want, that you need to know to are the options and that's informing the people of what those choices are. That seems to me a really nice example of what we think of as the prototypical scientists job in this issue is to inform the public inform policymakers, informed decision makers, here is the threat. Here's the danger that we face now with this thing called climate change. And moreover, let's not stop there, here are the range of potential solutions that can be done. That seems entirely appropriate. But the other thing to say here is that scientists are also citizens. And it is entirely okay and appropriate for a scientist to say, Okay, I'm going to wear a different hat now. And I'm going to speak as a citizen who happens to be a particularly informed citizen because I happen to study this issue, David 9:44 but they hide you know, whether Do they know which hat he's wearing, because again, you know, when you hear somebody talking all day, which hat are they wearing, when they're being a scientist that isn't there some issue about, you know, the scientific view with that all that all authority, just carrying over into advocacy so easily. Anthony 10:03 Yes. And that's I think back to Emily's point is where it's actually incumbent upon scientists to try to make that clear to their audiences when they're speaking as a scientist that is trying to be true to the data and the evidence as best they can, even if that's inconvenient, versus what their ambitions are as a citizen that happens to care about the future that we all are going to live in together. David 10:26 Okay, so on to say, Tell me how a company might use psychology in order to change their customers behavior. Sander 10:31 Yeah. So this is what Robert Cialdini, who's a social psychologist called ethical influence. And and the way they do this is so, O Power was a famous example of an energy company. And you might ask, why would an energy company actively encourage their customers to consume less energy? But there were startup and they were, you know, more inclined to do something about about climate change. And so they basically gave customers a bill and on that bill, there was comparative information about the level of energy that their close neighbors were consuming. So people saw their own consumption relative to that of their neighbors. And they found that people would adjust their own energy consumption to the level of their neighbors, which is kind of conforming with the local norm. And again, they weren't telling people what they should be doing with their lives. But that is just a behavior that emerged. And later they found that sometimes this doesn't really work. When if somebody is doing well, and they're consuming less energy, and they found out to do much better than their neighbors, they start to backslide and conform to the opposite. But they find out if they put a smiley face on the building, you're doing good, that reinforces the behavior and just a small incentive like that actually had an enormous effect. Well, the effect actually was small was about a 1% to 2% increase. But when you scale that across all of the hundreds of thousands of customers, it really added up. And they think that's ethical because we're just revealing actual information to people. And that's why I'm always confused when people say that knowledge doesn't matter, because what we're doing in those experiments is just exposing people to information they hadn't considered before - that is what other people are actually doing. And then, you know, to the last point, David, your point about about wearing different hats. When I give a talk I always ask the audience, because I'm generally interested in this is whether they want scientists to advocate for evidence based policy. Because ultimately, you know, scientists are paid often by tax dollars. And if the public wants us to advocate for science and evidence based policy, if that norm is changing, then maybe that's legitimate for scientists to start considering doing that. But if the public doesn't want it, that's another story. Right? We can have a conversation, but it's kind of interesting that scientists talk about it, but we less often ask the public about what they want from scientists. David 12:43 But there's a difference between advocating for evidence to be used in Policymaking, which I think no one would disagree with, and advocating for a particular policy, which I think is not you know, many might consider is going beyond the role of many of scientists. Sander 12:56 Okay, that's a fair that's a fair point. I think most scientists would advocate a range of, as Tony was saying plausible solutions to climate change. And then we have to communicate the benefits and harms and debate each of these solutions to see which one is going to be best. But you know, what does best means you know, it's good for some people and bad for other people. That's a very complex political question. David 13:21 Winners and losers? Sander 13:22 Winners and losers. That's right. David 13:28 Okay, so we agree that communicating about climate change involves informing different audiences, both about the science and the potential options available, but can also involve ethical persuasion, at least to get people to take notice of the issue and not be misled by false claims. Now, the next step is to learn about the different audiences so we can tailor different communications. So first, we need to shut up and listen! What do we know about the attitudes of different groups of people towards climate change? What do they know already? What…

Full show notes at the publisher

Communicating about Climate Change Mar 01, 2020

Show notes

Joining David to guide us through the psychology, politics and science of climate change communication we have:

Dr Anthony Leiserowitz, Director of the Yale Program on Climate Change Communication
Dr Emily Shuckburgh OBE, Climate scientist and Director of Cambridge Zero
Dr Sander van der Linden, Director of the Cambridge Social Decision-Making Laboratory

Full transcript available here

Views to share? Get in touch on Twitter @RiskyTalkPod or email riskytalk@maths.cam.ac.uk

Risky Talk is produced by Ilan Goodman for the Winton Centre for Risk and Evidence Communication at the University of Cambridge.

Stats in the Headlines: Crime, Immigration and Political Polls Mar 01, 2020

Show notes

Stats about crime, immigration, unemployment and political polls fuel political debate and drive headlines. But how are these ‘soft’ numbers really calculated? How uncertain should we be about them? And how can they be reported honestly and usefully - without losing trust?

Joining David to make sense of the stats and how to communicate them are:

Dr Robert Cuffe, Head of Statistics at the BBC
Ed Humpherson, Director General for Regulation at the UK Statistics Authority
Dr Sarah Dryhurst, a psychologist at the University of Cambridge

Views to share? Get in touch on Twitter @RiskyTalkPod or email riskytalk@maths.cam.ac.uk

Risky Talk is produced by Ilan Goodman for the Winton Centre for Risk and Evidence Communication at the University of Cambridge.

Communicating Genetic Risk Mar 01, 2020

Show notes

Personal genetic risk information is set to be become part of everyday healthcare. But is some unhelpful, even dangerous? What psychological impact does it have on patients? And do doctors know enough about genetics to effectively guide patients and inform treatment decisions?

Joining David to discussing what genetic risks should be communicated and how, we have:

Professor Robert Green, Director of the Genomes to People group at Harvard Medical School.
Dr Gemma Chandratillake, Course director of the Genomic Medicine Programme at the University of Cambridge
Dr Saskia Sanderson, a UCL psychologist working on the impact of genetic information

With a special contribution from Jillian Hastings Ward, Chair of the participant panel for the 100,000 Genomes Project.

Views to share? Get in touch on Twitter @RiskyTalkPod or email riskytalk@maths.cam.ac.uk.

Risky Talk is produced by Ilan Goodman for the Winton Centre for Risk and Evidence Communication at the University of Cambridge.

Introducing Risky Talk Feb 05, 2020

Show notes

Risky Talk features conversations with the world’s top experts in risk and evidence communication addressing urgent, practical challenges: How can doctors communicate the risks and benefits of medical treatment? How should scientists communicate evidence about climate change? How can journalists make numbers meaningful to readers? How should government institutions convey important statistics?

Hosted by David Spiegelhalter (@d_spiegel)

Get in touch with us on Twitter (@RiskyTalkPod) or email (riskytalk@maths.cam.ac.uk)

Produced by Ilan Goodman for the Winton Centre for Risk and Evidence Communication at the University of Cambridge.

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