← GovLove - A Podcast About Local Government
GovLove - A Podcast About Local Government
#718 State of Behavioral Science in Government with Joseph Sherlock, King's College London
00:57:56
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- [00:14]
A
Coming to you from Durham, North Carolina, this is Gov Love, a podcast about local government brought to you by Engaging Local government Leaders. I'm Tony Thompson, Director of Strategy for the North Carolina Department of Health and Human Services and and your GovLove host for today's episode. GovLove is produced by ELGL. Engaging local government leaders. You can support GovLove by becoming an EOGO member. EOGL is a national volunteer run membership based nonprofit organization with a mission to engage the brightest minds in local government. Check us out and learn more about our $50 annual membership@elgo.org and now on with our show. Today's guest is is Joseph Sherlock Jose. Joey is an assistant professor at King's College London and he's a behavioral scientist in a public policy school with 12 years experience running randomized controlled trials. As a behavioral scientist, he specializes in democracy and sustainability. With over a decade of experience designing and leading large scale randomized controlled field experiments in complex real world settings. Joey's work spans the usa, the United Kingdom, in multiple international contexts, partnering with major government bodies, civic organizations and multilateral institutions. Joey holds a PhD in Psychological and Behavioral science from the London School of Economics and Political Science and currently serves as assistant professor at the School of Government in King's College, London. Joey, thank you for being with me.
- [01:40]
B
Thank you. Great to be here.
- [01:42]
A
Yeah. Joey, we've known each other for a long time. I think I've had you on the podcast before, but I thought this would be a good time to bring you back because a lot of things have changed in the world, both personally and professionally and just in local government in general. So I just thought this would be a great time to just check back in with everything that's going on.
- [02:01]
B
Yeah, sounds great. Really exciting initiative you've got going here. So glad to be part of it. Thank you.
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A
All right, so as we start off with every episode, we do a lightning round to break the ice so our listeners can get to know you better. So I have a couple of questions for you. The first question is what is currently inspiring you in your professional work?
- [02:22]
B
So I think what I'll say here is that government, civic society, democracy, we're just not in a good place, right? You know, trust in institutions is really low. We are probably in lots of places really underfunded. It's, it's, it's tricky and we need to innovate. And so I see my role, part of my role in this is around trying to come up with sort of novel, creative ways to solve some of these issues. And run as many experiments as they'll let me run to try and crack some of that. So, yeah, I would say trying to drag us out of a bit of a hole here in the public sector.
- [02:59]
A
Yeah, I like that. I like how the current state of despair is. Is driving you to lead to better. So that's a good way of looking at it. Second question. What's a book, podcast or show you think every public servant should check out at least once?
- [03:19]
B
Yeah. Okay, so I'm going to give you a few weird answers here. One is a TV show that most of your American listeners won't have heard of called yes Minister. It comes from the 80s and it's about the British civil service. And it is very funny and very revealing. And if you're a government nerd or a civil servant, you'll. You'll get into that.
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A
Okay. Is it like your version of, like, Parks and Rec, like way back in the day?
- [03:44]
B
Yeah, no, that's a nice. That's a nice. Yeah, but like, maybe a decade or two before, but similar. Like just, you know, making fun of what it's like to be in government. Yeah, run with that.
- [03:53]
A
Yeah, I like that. What was the other one you had?
- [03:57]
B
I was going to go. It's a bit more nerdy, but there's a book that I love called Mindware, I think. Is it Richard Nisbet? Yeah, I think. Give that a run.
- [04:05]
A
Okay. Mine were. I like it. What is the most unique thing you can say about America during your time here?
- [04:15]
B
Yes. What a question. America. America the brave. Freedom. Chicken pickle biscuits.
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A
Chicken pickle biscuits.
- [04:28]
B
Chicky picky. Picky.
- [04:29]
A
Chicky picky picky. I've never heard that.
- [04:34]
B
I also will say, here's a little tidbit for you. I think it's the most unique thing, but we, we in the UK are brought up on a diet of, like, American movies, you know, like American Pie. Yeah, all that kind of thing. And. And prevalent in those movies is this idea that American beer is just really bad. You know, it's like, you know, drinking light piss. So I can say that on the phone card. And. And so when I went, I was expecting, like, you know, this is gonna be really bad. British beer is obviously so much better than American.
- [05:08]
A
Yeah.
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B
And it's just, like, not true. Right. Like, yeah, you can buy that terrible piss. But also they're like, there's a brewery on every street corner and every town has its own micro. What's it. What's it? You can get a chocolate cherry porter and like, you know, it's really cool and really innovative. So that's another one. I think American beer is good.
- [05:29]
A
I think you came during a time when there's just really been this explosion of like beer and you know, local breweries and you were, you were saved from going to college in America where, you know, a lot of that stuff comes from. Because I remember in college, like. Yeah, yeah, that's what it was.
- [05:52]
B
We were probably also like in a window there where we sort of had more money than sense. And so spending $9 on a like 12 ounce paw still seemed like a good idea at the time.
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A
Exactly. I had beer money. I still don't have wine money. So, you know, I had to elevate the game.
- [06:08]
B
I hope, I hope I never get to the point of having wine money. I'm happy.
- [06:13]
A
Chicken pickled biscuits though. I didn't think that was such an American, unique American thing, but now that you said it, I was like, it is.
- [06:21]
B
Oh yeah. Oh no, absolutely. It's like everything from like, we, we're going to bicker about what a biscuit is, right? And like, we can get into that if you want to, but you guys are wrong. But you're also right. And I'm so pleased that you're right.
- [06:35]
A
We would, we're not going to that because I was about to say something. Okay, next, next lightning round question. What fictional world would you most like to live in?
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B
So I went to a boarding school in, in the UK and as part of that was in the Harry Potter movies just as an extra. So I'm gonna, I'm gonna pick Harry Potter.
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A
Wait, wait, you were an extra in a Harry Potter movie?
- [06:59]
B
Yeah, just like it was filmed at my school and we got to, we got to be in it. We got to, you know, walk past in the corridor scene. So if you, if you go on the sixth, sixth movie, you can technically see me in the sort of like background walking past.
- [07:11]
A
I'm going to do that. I'm absolutely going to do that. Why did I not know this? Why did I not know this?
- [07:15]
B
I'll send you the clip. You can edit it in. Maybe, maybe they'll sue you if you
- [07:19]
A
do send me the clip. In the clip. I'll just do that myself. Okay, last one. What's the best professional advice that you've ever received?
- [07:26]
B
Yeah, okay, this, this bit of advice is something that was given to me recently and I'm really aspiring to follow it. But it's also tricky and it's, it's that you should always try. You should try and not be the bottleneck. What is the thing that you can do in the next, you know, five minutes, half an hour in the next day that unlocks someone else? I like that. And, and that's challenging. I find myself being the bottleneck on lots of things. And so, so aspiring. Aspiring not to be a long neck, long bottleneck. That's.
- [08:05]
A
That's a good one. And the fact that you said that has forced me to reflect on a lot of my recent professional experiences and I need to take that one to heart. Yeah, that's a good one. Okay, thanks for the lightning round. We can get started in our conversation, but I want to give our listeners more of an opportunity to learn about your, your professional journey. So can you tell our listeners more about your journey, how you got to where you are today and what you're currently doing at a high level?
- [08:33]
B
Yeah. So how far back? I'll go all the way back. I was born in Ireland, so I would consider myself Irish. Although, fun little tidbit here. I recently did a DNA test and I'm not at all Irish, which is really Sad. I'm like 89% English, which I'm gutted about. I was born in Ireland, mum's English, dad's Australian, and my parents were hippies and I was raised on like an organic farm in the middle of nowhere in Ireland and went from there and ended up going to Australia for a year to play cricket and went from that and got a place at a scholarship at a boarding school in England to play cricket and never made it as a cricketer. Broke my finger at university, but went on and studied psychology at university and then worked for MNC Saatchi in branding and advertising for a year, which was really cool. Very implicitly psychological, but also methodologically hollow and, and sort of at best ethically neutral. And yeah, it went from there. And then did a master's in in policy, Behavioral Public Policy at the London School of Economics and then got a job in the behavioral insights team in public health. So started running big field experiments in health, trying to get people to take up NHS health checks and things like that. And then went from there and got a role in the behavioral science team in hmrc. So that's the irs. They're like big tax, tax agency, tax department and had a lot of fun running big experiments, trying to get people to take up digital government services and get people to pay their taxes on time and thinking about how can we use predictive analytics to identify who are the people who are going to self clear versus not Clear then how do we use behavioral solutions to focus on the ones that aren't going to clear things like that and then convince them to let me go to duke University for six months in, and in 2016. So the idea was go, go on secondment, which is like kind of like a load, you know, like go and work for this other place, learn some things, bring it back to government and flew over, landed at Duke and got stuck into chicky picky bickies and chocolate porters. And then you know, we started playing games and like running experiments in municipal food waste and transportation and you know, you say it, you name it. So we had a lot of fun and my, my six month got pushed and pushed and pushed till ended up breaking at about four years and they were like look bro, you ever coming back? So, so I, I stayed, you know, had a good bit of funding spent and so, so was, was having fun out at duke in the US and then ended up staying for nine years and in that time did a PhD at the London School of Economics. I sort of did it kind of remotely kind of like rode a wave in there where like you, during COVID you could kind of be where you needed to be to get stuff done. So and, and then as I was finishing that was sort of went on the job market, thought I'd roll the dice as an academic. And, and there's this, you know, there's a theme here for me, which I'm sure we'll pick up a few times, where I want to want my career to be about sitting in this intersection between academic creation of evidence and knowledge, but also the impact that comes with working in a fast paced policy environment with people who are pulling the strings on government services, government operations, government policy. And so I want to have a foot in both of those camps and at different points in my career I've had lent more or less on each foot and I'm currently leaning more on the academic foot, but sort of determined not to turn into one of these stuff the academic losers that are, you know, theoretically precise but practically irrelevant. So, so I'm trying to, I'm trying to, I'm trying to, trying to make that dance work, Professor. Yeah, a little bit of that, but
- [12:54]
A
yeah, yeah, yeah, I love that. That's a fascinating journey. And as I was listening to you, I was like, man, you've really took some really cool things from your marketing, your time in marketing to, to convince some people to just be abscond away in the United States for a decade.
- [13:15]
B
It's exactly right. Who doesn't like freedom? You know, you know, here's the big thing, here's the big thing I took away from that year at Saatchi is that everything is a story, right? Like a brand, it's just a narrative, right? Like your CV is a story. You know, if you have, if you're on an interview, you're just telling stories about yourself, an academic paper, you're just, you know, you, you're, you're using data and, and theory to tell a story about the world and to, you know, assert your confidence about, about how about this story. And humans, you know, read Harari. Like humans have evolved to work, work with narratives. We don't work so well with data, we work well with narratives. And so whatever you're doing, you need to be a good, a good storyteller. And, you know, we can, we can, we can look at people around us who are really good at that.
- [14:01]
A
I love that. I have another semi lightning round question for you, but I think it kind of leads into what we're talking about. In your experience, what is the biggest difference in government between the United States and the United Kingdom and how government shows up in people's lives?
- [14:22]
B
That's a good question. It's a tricky one to answer because I in the US worked more with sort of local and maybe as high up as state, but, you know, never really worked at a federal level. I guess that's, you know, difficult to do that. Whereas in the uk, my, my links, my contacts, my experience have more been at a federal, not like a central government, national government level. And so I can compare US uk but I think the comparison I'm really making is sort of like local granular government and central government. So thinking about that comparison more cleanly, I actually would really encourage innovation at the local level. Like if you think about, sort of visualize this, there's lots of different local government offices all around the country trying out different things. And you have people often sort of earlier in their career with cool and interesting ideas working on problems that are sort of local, hyper local, and have a good understanding of it. And there's a bit more flexibility. You know, one of the, one of my big themes in my research and my interest is in this, like, how do we innovate as government? How do we bring novel solutions to complex problems? You know, this is something that the market does incredibly well. You know, incentivizes innovation and novelty and rewards what succeeds. And, you know, government doesn't really do that, so well, it's not the same incentive Structure, but in, to draw it back to this, this sort of local, central contrast, local government. There's much more of an opportunity, much more wiggle room, much more leeway to try things out, run experiments. You know, there are fewer people you have to convince. There are fewer, fewer things that could go wrong. And if they go wrong, the consequences are less bad. And what that means is that we can try things and then scale what works. And so I would, I would sort of push that agenda, agenda around. Like, hey, hey, you all us all in local government, like that's where the innovation is, right? Like by the time it gets to central, we want to be pretty confident that something's working.
- [16:27]
A
Yeah, I like that. I like that a lot. Okay. You've been doing behavioral science in government settings for a long time. And when we first met, it was as you mentioned, running behavioral science experiments in a local government setting. And behavioral science was kind of this new thing for local government. And how can we integrate behavioral science in government? It's been a decade now and I wanted to bring you on to talk about how things have evolved in this last decade or so as we've tried to integrate behavioral science into local government to have positive impact in people's lives. But I want you to kind of explain for people who may not know, you know, what is behavioral science? And can you provide some clear examples of how it has been applied in the public sector?
- [17:20]
B
Yeah, yeah. So there's lots of ways to answer this question. And you're completely right that about, you know, early 2010s, there was this real buzz around, you know, the book nudge came out thinking fast and slow. You know, there's lots of really cool examples that the UK government had, the behavioral insights team that was going from strength to strength. Ideas 42 were harming Basara are up and up and running. So there's like, lots of, there's lots of like a big groundswell around around behavioral science, applied behavioral science. And there were some really cool examples in that. So you have, you know, things like if we, if we default people into contributing to their pension plan rather than than the default is their app is they're not contributing and they have to choose to opt in. You know, then naturally people are much more likely to, to contribute and stay in a pension plan. You know, things, things like this telling people that nine out of 10 people pay their taxes on time means that, you know, you're more likely to pay your taxes on time. That's the social norm. The previous example was default and friction. So you know, lots of these, these examples came out and many others. And it was a really exciting era and it was sort of like one of those, you know, you can imagine the hype cycle of ideas. You know, everything all of a sudden was like, how can we use behavioral ideas? You know, like counterterrorism, how do we use behavioral ideas? You know, like obesity? How do we use behavioral ideas? You know what. And in the subsequent decade. Oh, and sorry, and one other theme on that is a lot of the ideas that were coming out around that, that era were sort of came from, from lab research. You know, they came from experiments that were run with maybe a few hundred undergrads in a business school somewhere. And so fast forward, we, we have found that lots of the like flashy, exciting, pop sciencey ideas actually sort of don't really hold up, right? Some of them do. You know, some of them are pretty reliable friction social norms do, the ones I mentioned. But others, others aren't. You know, there's lots of other things where it's like, you know, actually it turns out this sort of buzzy, buzzy, you know, counterintuitive idea is counterintuitive because it's wrong. Like, you know, and, and, and the, as we move into trying to do more robust science and stronger innovation, these ideas don't stand the test of time. So, so to sort of like fast forward, where are we now really? Where, where we're at I think is, is, you know, how I think about this is let's take, take a little bit of a step back and say what are we, what are we trying to do? Well, as government and, and sort of government adjacent actors, we're trying to solve problems and we're trying to do so in a way that is, is evidence based. So is if we can say this is effective and it's working and behavioral science in the, you know, we can think about that a few different ways. You can think about it as like nudge, you know, like small changes to context, to decision architecture that sort of pushes someone in a particular direction behaviorally without limiting their, their options, their choice. But you can also think about behavioral science as, as an approach, as a, as a vehicle for solving problems. It's sort of like a lens, if you will. And one of the ways that we sort of teach this is to think about yourself, you know, as a behavioral scientist, as an innovator with a toolbox. And in that toolbox there's lots of different skills, lots of different methodologies, lots of different ideas and your skill, your role as an innovator, as a civil servant or an actor in civic society is to try and use that toolbox skillfully to crack the problem. And in there you might have, you know, how do we reduce friction, how do we change the default? But you also might have, you know, is this something more complicated that needs systems thinking or, you know, what is the, you know, what stage in the innovation process are we, are we trying to diagnose, are we trying to prototype, are we trying to run an experiment? And so I think where we're getting to with applied behavioral science is that it is embedding into and giving us more nuance to our innovation function in civic society and in government. And I think that that is a really exciting agenda and gives us the ability to also bring in things like how is AI helpful here? And let's recognize that it's definitely not going to be a panacea, but it might be a useful tool. System thinking, as mentioned, pulling in these more multidisciplinary as well.
- [21:53]
A
I like that. If I were to give you a very a simple equation of behavioral science plus the public sector equals X, what would you say the common answer might be to this equation from a behavioral scientist perspective? And do you think this answer has changed over the years?
- [22:17]
B
Behavioral science plus the public sector plus
- [22:20]
A
the public sector equals some kind of outcome? Yeah,
- [22:26]
B
I would say evidence based government. Innovative and evidence based government.
- [22:34]
A
Okay.
- [22:37]
B
And I think that you would. I think that's the sort of reality of it. I think what you might get is like people saying, well, behavioral science plus government is manipulation.
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A
Right, right.
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B
Or behavioral science plus government is coercion. And I think that those people have been reading too many sci fi novels.
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A
Interesting.
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B
Do you.
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A
Okay, I want, I want to, I want to dig into that a little bit. Do you think as behavioral science has expanded and been allowed to be integrated into the public sector, into governments more, that a need for a base of ethics has developed for behavioral science to make sure that you might be conscious of any harms that might be done in behavioral science. Do you think?
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B
I guess, yeah.
- [23:39]
A
Do you think that a code of ethics has developed or evolved as behavioral science has integrated into government?
- [23:52]
B
Yeah. So let's pass these out. Let's start with like, should we be innovating ethically? And like, the short answer is like, obviously yes. You know, we should be taking strong ethical principles into account as we are looking to influence, hopefully improve people's lives. Linked to this is this question around paternalism, like how, how paternalistic do we want to be and you, and this, this cuts across all government services, right? So you can imagine a, a whole spectrum here between, you know, very libertarian all the way through to very paternalistic. And for each, each one of us, we sort of fit somewhere on that spectrum. And for each policy or each intervention is, is also on that spectrum, you know, it can be more or less paternalistic. And that's, you know, that's an ongoing struggle that happens every day. Is, you know, where, where are we on this continuum? Where is this solution on, on the continuum? Let's sort of tie that into behavioral science. So behavioral science generally is in this like, soft paternalism space. It's in the space where we're saying we, we're looking to influence people with information or with changes to decision making environments or incentives, but we are rarely mandating or requiring. Which would be harder paternalism, right? Like, here is a ban, here is a regulation, here is a, you have to wear your helmet or else, you know, this is harder, harder paternalism. So behavioral science is typically soft. It's also typically asymmetric. And what I mean by that is that it influences the people who don't have strong preferences while maintaining freedom for people who do have strong preferences. So like a classic example of a nudge is this, you know, we default you into saving for your pension. Now that's, that's just a default. So if you, if you don't really know much about pensions and you're, you know, not financially savvy, the default is that you'll save. And that's a good thing for you and for everyone else. But if you feel strongly about pensions, right, if you have, you know, you come from a really rich family and you're like, you know what? I don't need a pension. Like, thanks, but no thanks, I don't need to pay for this, or you have a strong opinion about it, you just opt out. Right? And so in that way, it's asymmetric, right? It influences those that we think need influencing, but without controlling them. Now I think sort of. So, you know, we can think about different government interventions in this way. And I would argue that lots of the behavioral ones are softer than the alternatives. And this is often why, why behavioral science is looked to in the first instance is that it's cheaper and less, less coercive. Now one, one debate that often comes down is this idea around, like, are we taking ideas that we've learned from psychology to, to manipulate people in ways that, you know, they're not aware of or, or they're, you know, subverting their unconscious thinking. And like, sure, you know, you can level that critique and you can say, look, what we're trying to do is innovate with an understanding of psychology, understanding of how humans work. I don't buy the argument that it's like, if people only knew that they were being manipulated, they wouldn't, they wouldn't fall for it. That's actually a really interesting study called warning, you're about to be nudged, which kind of shows that if you tell people, hey, we're about to default you in a particular direction, that often they go with it and that it can be more effective than not telling them. And what that hinges on, and sort of this next point is it hinges on who is the choice architect, who is designing that default, who is putting the default there, and how much they trust them. And this sort of ties back to think about who do we want to influence us. Well, you bet your bottom dollar that any company worth its salt, anything in the FTSE 500, any of these big FMCGs or Amazon or, you know, like Netflix, you know, these people are investing a lot of money to influence and a lot of data work to influence you and push you in a particular direction. And that direction is explicitly to make money for shareholders. And so I would argue that government is, at least in theory, and I would say genuinely trying to, to maximize utility for its citizens across, across the, across the piece. And so if anyone should be looking to shape behavior, it is this more benign force that is trying to improve people's lives.
- [28:35]
A
I hear that and I think that's really, that's a really good answer. Have you seen any impact on behavioral science in government? When public trust is low in government, is there a modifying factor about how people perceive government to be that might influence the effectiveness of behavioral science tools being applied?
- [29:11]
B
I don't know if this is quite on your question, but I want to tell you about a paper I'm working on, I work a lot on, on democracy, you know, and to give the sort of the opening sentence on that, it's, you know, like our democracies are struggling, you know, like across the board that we are, we're struggling. Who knows, you know, America might have a third term president soon. We'll see. That was a dick. So I think, I think broadly speaking, look, our elections, our democracy needs to innovate. The world is changing really quickly. Speed of, speed of change, speed of information, AI, you know, we need, Democratic institutions need to keep up to be able to match this change. One of the Issues is trust. Right. To narrow in on your question there, which is like, you know, as it relates to our elections, as it relates to our democracies, people are increasingly skeptical. Don't, don't trust. So if we zoom in on trust and why, why people don't trust one of the mechanisms. Right. There are many. Right. And so you can, you can think about trust in a few different ways. Benevolence, competence, integrity, typically are the three big pillars. But one of, one of the reasons I think we, we mistrust things is that we don't, when we mistrust things, it's because we don't understand exactly how they work. So if something is opaque and unclear, we, we don't trust it. Right? It's, we're not, we're not sure what's happening. And so if we apply this to elections, I think, you know, elections are really complex, particularly sort of mail in voting. Right. Take an example, that part of the US system that people don't trust mail in voting. And that in part I think is because mail in voting is kind of ambiguous. Right? Like you get something in the mail. How did this get here? You know, the, the media, parts of the media are telling you that like, you know, they're digging up dead people and sending ballots out to people who don't exist. And then other parts of the media,
- [31:11]
A
yeah, you get to election day and there's a candidate leading and it's like,
- [31:14]
B
oh, here's a certain, yeah, they just made them up. And like, you know, there's lots of this noise. And because it's ambiguous, because it's opaque, you don't understand what's happening. Well then, and you have fears about, you know, you know, the elections getting stolen. And because you understand it, you know, it's, you can, it's believable that like the, the ballot had bamboo paper. And so one of the, one of the reasons that, so one of the ways that I think we can overcome this is with this idea called operational transparency. This idea that if we can to increase people's trust in things, we increase the transparency of it and how it works and that increases their, under their understanding of it and as a result they trust it more and use it more. So I basically, long story short, click read the paper if you're interested. It's, it's a. Working on my website. But you know, what me and a colleague look to show is over a few experiments that if we can just do just this, give people a stronger understanding of how elections work, they'll Understand it more, they'll trust it more, and they'll vote more as a result.
- [32:20]
A
Yeah, I like that. You mentioned nudging people and having trust and government to do that and even saying, hey, we're nudging in this direction. What would you say about nudging when there are areas that there are still debates about whether that's the right thing to do? I think about this, maybe not the best example, but AI, Right. If you were running a behavioral science experiment where government is nudging people to use AI, and I think there's still a lot of debate about whether that's good or not, what does behavioral science have to say about that from a, from an ethical standpoint? Like, do you have to concretely know that you're nudging someone to, to an action that universally is considered the right path, or can there be ambiguity about is this the right thing to do or not?
- [33:29]
B
Yeah, look, I think there's a few themes to that question. I'll start with that last one, which is like, in what direction should we be looking to influence people? That's a really, really Nazi problem. Right. It's, you know, it sort of ultimately links to like, what is the meaning of life? Like in what direction are any of us focusing our effort? Right. And so you can, you can come at this from a few directions. You can say it's the role of government to help its citizens flourish. It's the role of government to keep people healthy. It's the role of government to maximize utility, financial utility of its citizens and of, of the economy. It's the role of government to maintain stability. I think if you boil it down, and it does get a little philosophical, the role of government, the, anything we're all striving for is to, is to maximize happiness, maximize well being and minimize misery. And so we sometimes get a little disconnected to that. But ultimately we should be nudging, we should be angling public service, public, you know, activity towards the greatest happiness, for the greatest numbers, very utilitarian perspective. And so as a result, you know, if we're looking to nudge people, we should be nudging people in that direction and for strong proxies in that direction. So that might be increased health because we know health is a big predictor of happiness. It might also be increased wealth because we know wealth and stability is a big predictor of happiness. But if we take this angle as well, we might think about looking to actually reduce misery. Right. And so then we get into this redistributive argument where we're saying, look, we, we. There are sort of diminishing marginal returns for people's happiness, well, being from, from increased wealth. And so we should be designing government services, designing nudges to redistribute utility from, from those who are doing really well to those who are, who are really struggling. Okay, So I think that's sort of toyed with your, with part of your question which was like, in what direction should we be going? Another part of your question was, was AI, Right? And like, how do we deal with the ethics of this? And look, this is a whole button.
- [35:38]
A
Yeah.
- [35:39]
B
And I'm sure you will and are getting AI experts on. But there's, there's definitely a lot of, they're throwing up a lot of ethical questions. Right. You know, the obvious one is the sustainability of this. Right. And you know, we are burning through energy to create coffee recipes and should we be doing that? I think there's also this point. I mean, education and people are increasingly using AI as a replacement for, for learning. Right. And so, yeah, you know, like, writing is, is, is thinking, reading is learning. If you're using AI to do your writing and your reading, what are you doing? You're not being educated. So, so there's, so there's that point too. Maybe, maybe matters a little bit less in other contexts. But, but, but possibly not. But at the same time as, well, look, AI is, is coming, right? And it's, you know, I'll be already here. And it's, it's, it's also doing some really positive things. I think my personal opinion is that the, the ethics are still fraught. We're still figuring it out and coming as a, as you know, on that paternalism spectrum, someone who is slightly more on the paternalistic end. I think we need to do a better job of figuring out the global governance, the regulation of AI. I don't know that, you know, we can't just not regulate this thing. Right. That's just not an option. And it feels like we haven't really got a good solution yet. But again, I'm not a, I'm not an expert.
- [37:06]
A
I want to, I want to go back a little bit because we've been talking about nudges for a while. Why nudge people? Are, Are we nudging people? Because, hey, there's just so much information in the world that it's just hard to make the right choice every time for things that we know are the right choice. You're trying to combat negative externalities or. Yeah, the second one is you're trying to combat negative externalities, even if you know what's the right thing to do. There's just so much in the world, so much in your environment that forces you down a path that you shouldn't go on. And so we're trying to put you back on the right path. Like why fundamentally do we want to nudge people? People?
- [37:50]
B
Yeah, there's a few different ways you might, you might come at this one. Is, is that thing that they answered that you're, you're alluding to there around. You know, the world is, is complex and messy and getting increasingly complex and messy and humans haven't really changed. You know, humans are, you know, we are highly sophisticated and highly dumb monkeys. You know, like we've evolved to this position of species superiority, but we're also incredibly flawed. Right? We rely on sort of fast and frugal heuristics, rules of thumb, to help us make decisions in complex environments. And those heuristics, and sometimes the resulting biases can, you know, they can be helpful, but they can also lead us astray. And so sort of one of the big angles around using behavioral science, applied behavioral science nudging, if you will, is to say, look, humans are flawed and as a result of us being sort of cognitive misers, we are trying to go through day to day life using the least amount of energy as we can while still making good and effective decisions to maximize utility. As a result, we make mistakes and we are very influenced by the environment in which we make decisions, the sort of context, the decision context, that choice architecture, if you will. And so why nudge? Why use behavioral science? Well, fundamentally you can say it's to simplify and it's to help take an understanding of humans and understanding of their psychology and design environments to make it easier for us to, to navigate effectively, less likely to fall into the big pitfalls. And this is where you can come back to the paternalism debate around where should we be nudging. But ultimately you could boil this down to we're doing this because we recognize humans are flawed and we're trying to design environments to help those flawed humans. I say those like, I'm not one of them. I absolutely help us flawed humans make better decisions in a complex world.
- [40:09]
A
Yeah, I got you going quickly back to the AI conversation. How are you using AI in your behavioral science work? And how do you see AI influencing behavioral science?
- [40:21]
B
Yeah, okay, I've got a few different ways to answer this. I'll give you one sort of benign one, but then I'll give you more of a sharper one. So the benign one is I'm getting a lot out of AI by talking to it literally. Like I, I narrate my ideas and I, and I get it to not create ideas for me because I think you get very middle of the road slush if you do that. But, but take my thought process and organize it so that it saves me time anyway. That's the one throwaway that I'm, I'm finding very useful. But to sort of think about how AI is influencing the field. I do have an angle here that I think is curious, which is one of the things that AI is doing as it relates to sort of research, behavioral research is, it's giving us. So go back a few steps and say one of the, you know, I mentioned earlier on the podcast that in the sort of early 2010s, a lot of our ideas, a lot of our thinking was influenced by these sort of lab studies, right? There were studies done in sort of psychology departments or business departments all over, all over the world. And as we've moved forward, they've sort of, those experiments have moved online and they've sort of moved into survey instruments. And so you would remember from our work that we did a lot of work playing with surveys, getting people, you know, asking people their opinions and their perceptions and whatnot. That's, that's, that's fine. It's, you know, that's evolved into this big industry where you pay people online to take surveys. And I'm just really skeptical of these surveys as instruments for running experiments, as instruments for gathering knowledge. I think people at best are answering quickly and, you know, not giving it much attention and at worst are actively lying or are bots. And so what we're, where we're getting to with AI is a position where large language models can take these surveys, these responses, and can sort of sort of siphon through different perspectives to create sort of synthetic answers. And these synthetic answers to surveys are reasonably predictive of real humans about sort of 80, 80% the same. 90% the same. I think depending on the context. And I think that's really interesting. Right. I think it is. Get. So what it means is that, you know, ethical concerns about overuse of AI to one side. It means that we as researchers can very quickly or will soon be in a position to very quickly develop answers to some of these questions we have, you know, like, why do people mistrust elections? Well, you know, we can, let's run a thousand simulations and 1000 synthetic responses and See, see what we get back. And it might not be exactly what people think, but it's going to be about 80, 90% as good as if we had paid, you know, paid people to, you know, paid people $3 each, $5 each, $8 each, whatever it was for that survey to answer that question. So it's a lot cheaper, a lot quicker and still pretty good. So what impact does this have on the research field? Well, I think that if you think about this in very simple supply and demand principles, it will hugely increase the supply of this kind of answer. An answer to a stated preference survey question. What it won't really influence or it will influence at least in different ways, is the answer to what do real people do in a messy environment. The sort of like behavioral question. I don't think we get that from this, from this AI innovation and AI solution. And so again thinking about it in supply and demand, I think that we will see increased demand for the hard thing in research, which is the field experiment. Right. I think we'll see increased demand for what happened when we changed the policy from A to B over a three year period. What happens when people use ballot A versus ballot B? What happens when people are, when we introduce a congestion price, congestion charge? What happens when insert here policy issue depending on what your listeners are interested in. But that sort of difficult field experiment, I think that's where the value will be. And so I would push people and I think government innovators have a lot to contribute to the world because they hold the keys to these policy environments, which means they hold the keys to innovating in them and to experimenting in them. And I think that that is going to be an increasingly valuable contribution to the world is learning the knowledge that can be created from those, from those frontline positions of power.
- [45:02]
A
Yeah, wow, that's, wow. Yeah, that's a little. Essentially using AI to create user profiles and then testing those user profiles in a, in a survey setting. Yeah, virtual setting.
- [45:20]
B
And it's worth saying that like I kind of went through that, I guess reasonably quickly and it's just a rant at this point. You know, this is just my opinion, but it's worth saying that I think there's a lot of value that can come out of that. Right. So you know, if you are in a fast paced policy environment and you need to make a decision and you're like, well what are people going to do? Well, use the synthetic response and use it as a guide. Don't use it as the answer, use it as like, well if we're not sure about which one of these three to go for or which one of these five to go for, use the synthetic responses to narrow it to three, and then run experiments on the three. Do you see what I mean? I think it's a useful tool in the innovation, but I just don't think we should, we should be relying on it as the answer.
- [46:04]
A
I got you. Okay. Is there enough data to confidently say that there are areas in government where behavioral science has demonstrated consistent positive impact, that we should always just do this thing, right? Like, I think a lot about universal basic income, right? Like, there's been enough about that. Like, we know the effectiveness of it, we know you should just do it. Is there a similar thing in behavioral science where it's like, we've done this enough time in government to know that you should just always default to doing this as a universal practice? And on the other side of the coin are there things where we know, hey, this just doesn't work in government, we should just not even try again?
- [46:48]
B
Yeah, I mean, I think I would bicker with the premise. I don't think you can ever be that certain. I don't think you should be. I think gravity is a theory, you know, like I, yeah, you know, I've got my little trusty mechanical pencil, you know, pretty strong priors that if I drop this, it's going to fall. But it's a theory and, you know, we can argue that changing the default to, you know, sorry, where people are defaulted, will be where they will follow is a pretty strong prediction. We've got lots of evidence that that holds out. You know, people are more sensitive to losses than they are to gains, loss, aversion. Again, pretty strong, pretty strong prediction. You know, people are influenced by, by what those around them are doing, like a social norm. You know, again, pretty strong prediction. But I don't think we should take these predictions and just say, you know what, this is always going to work. You know, context to context, things change and these ideas play out differently. You know, a lot of what we're drawing on here is sort of social psychology and culture and, and, and that is different from context to context. And, and also, even if even in the same context, time is changing, right? And over time things are different. And you know, the economy changes and AI has brought stuff in and, you know, it's now summer and not winter. And so I, you know, I think it's, it's actually winter, not summer, but that's fair. And the point being that, like, I don't I think, I think one of the big lessons, you know, you kind of the core of your question is what does behavioral science teach us? And I would sort of spin that and say one of the big things that behavioral science teaches us is to stay humble, right? That you don't know the answer and that you, if you think you know the answer, then you're probably some arrogant white male and you should be taken down a peg or two. Like, no, like you don't, you shouldn't be that confident in much around the world. And so, and so what I would push as an agenda is this idea of, let's think about confidence and certainty as a continuum. And, you know, how confident can we be about a given solution in a given environment? And given previous evidence, we might be more or less confident in different solutions. You know, you mentioned universal basic income, right. Like, I would say it's a really, really interesting but really complex idea that will work differently in different contexts and for different problems and to different ends. And so I would, you know, I would start by saying I don't, you know, initially say I'm very progressive liberal, but even still, I'm very evidence orientated. And I would say I don't think we have enough evidence. I don't think we should be rolling that out as a policy without, without good learning mechanisms around it to figure out if it's working, if it's working over time, how is it working, who for, et cetera, et cetera. So what I think I would push is this, like, hey, you, government innovator. It's great that you have good ideas and big ideas and a strong agenda, but like, tone your confidence down a level and start running experiments and figuring out if your ideas work and if they work over a long period.
- [50:02]
A
All right, so I'm somebody listening to this podcast. I hear about the default setting of pension plans, right. I shouldn't just say, oh, that's great, you've already done the work, Joey. You know, it works. I need to test that out in my own local context first before just changing the defaults on my pension plan savings.
- [50:23]
B
Yeah, when you put it like that. Yeah. Okay. And like, if you want to get into the sort of, like, technicals of it, I think what I would suggest there is as best you can, try and run an experiment or run an evaluation. And it might be that that evaluation is not just this, like, off the shelf randomized control trial where half of the people get it and half of the people don't. You might do a thing where you Say, look, we're pretty confident this is a good idea, so we're going to give it to 90% of the people and we're going to just hold back a 10% control group and we're going to watch it over time. Or you might do a thing where you say, you know what, we're going to roll out this idea, but we're going to stagger it. You know, we're going to roll it out to, to one department and then, you know, three months later or next year, we're going to roll it out to the next one and on and on. And so what you're doing is creating this sort of like quasi experimental learning environment where you're able to establish, you think reasonably confidently that it's working, but, but, but do so in a controlled way. And sort of. One other note that I'll add, there is, is to, as best you can, try and get a reasonably rounded picture of, of the outcomes. Right. So we drop a pebble in the pond and we look to like we're aiming that pebble at an intervention, increased savings for retirement, and we hopefully hit that. But it also is the case that that splash creates ripples and that those ripples might change behavior in other ways. And so it's important to try and.
- [51:49]
A
All right, a couple more questions for you. I think government, this is just my opinion. I think government in general, we're really bad at experimenting. And you're talking about we need to experiment more to learn. How would you tell a city manager or a government leader, how should they go about building more experimentation in their organization?
- [52:15]
B
Yeah. Okay, so let's start by saying that experiments come with this, you know, this. I don't know if taboo is too strong a word, but like, oh, isn't it scary? Isn't it complex? Isn't it ethically daunting? Like, no, it's not.
- [52:29]
A
Right.
- [52:29]
B
Like, what we're trying to do is we're saying, we've got an idea. We're going to randomly give it to some people and randomly not give it to other people. We're going to see what happens. Right. It's very, very straightforward really, when it comes to it. And we can make it more complex. And if you need to make it more complex, then I would say one of a few things. One is like, reach out to, to some. The local school. We've got some nerdy policy wonks who's
- [52:51]
A
absconding from the United.
- [52:52]
B
Yeah. Who's like hiding, you know, ran away from the queen and you know, they Put, they put cream instead of jam. And so he left and, and see if you can get someone to, to help, to help with that. I would also say that, you know, you, you government city manager should be, and I'll start, I'll die on this hill, should be investing in your, your staff and your workforce to learn these skills. Right? Like as we're saying, like, experiments aren't hard and, and, and we need people in positions of innovative power to be able to be at least good consumers of evidence and experimentation. They don't necessarily need to be the technical expert that like runs the regression or does the power calculations, but they need to sort of have good rules of thumb and good, good hygiene around this stuff and know when, you know when they are limited and when they should bring in an expert. And then so I would, I would, I would say, look, none of this needs to be really that complex. You can rely on the existing administrative data that governments collect all the time for good reason. Design and learning environments. Simply invest lightly in training, pull in masters, PhDs, you know, young academics to help. But really what you're trying to do is create this culture. You know, you, government manager, create and endorse this culture around. Look, it's, we're here to test things, we're here to try things. We don't know necessarily what's working and we should be accepting that things aren't going to work and we should be know, looking to scale the things that are.
- [54:20]
A
All right, people want to learn about behavioral science. We want to build this culture and government. Where would you tell them to start?
- [54:28]
B
Well, I wish I was cool enough to say read my book, but I haven't got one.
- [54:33]
A
You've got a lot of papers out there, you know.
- [54:35]
B
Yeah, working on it. So, yeah, look, I think, I think I would say there's a good, you know, read, thinking fast and slow. Read, nudge, get into those readings I mentioned mind where earlier. I think that's a good one. I would generally say like get a, get my one Bezos plug with it but get an audible account like, and listen to as many audiobooks as you can a year. Read as much as you can and you educate yourself like there's no limit there. And then, and then I would say, you know, convince your government department to let you do a short course and if you are interested in that, then hit me up. But also look at like, you know, you might, you might, you might convince them or you might take a few years out and go and do a master's degree particularly in this window when it's more difficult to operate in government. So look, I would say, look, you know, educate yourself broadly and maybe one of, one of the thread there is. The best way to learn something is to do it right. And so what I would really do is push people to like in collaboration, like run an experiment in their job. You know, just like figure out a way work overtime if you need to do something cool and learn while doing in that way, I think that's the best way to get new skills.
- [55:54]
A
Cool. Thank you, Joey. Okay, if you could be the Gov Love dj, we asked everyone this. What song would you pick as your exit music for this episode?
- [56:03]
B
You know, when I, when I moved to the U.S. it was in what, like 2016 and it was peak, peak Hamilton, right?
- [56:12]
A
Oh yeah.
- [56:12]
B
Everyone and I, and I and I, we, we don't get taught. At least I grew up in Ireland because as discussed at the top of the podcast, I'm heavily Irish. We don't get taught American history. Really. So I learned history through Hamilton. So I'm gonna say like, you know, I wrote my way out or like non stop, like one of those two.
- [56:35]
A
Okay, I like that. I love it. All right, Joy, thank you so much. That ends our govlove episode for today. Thanks again for coming on and talking with me. Govlove is brought to you by elgo and the best way to support govlove is to become an ELGO member. You can reach us online@elgo.org govlove or ovlovepodcast on LinkedIn, Instagram or X support. Subscribe to GOV Love on your favorite podcast app. New episode drops every Friday. If you are already subscribed to Gov Love, go tell a friend or colleague about this podcast or share on social media. Have us spread the word that govlove is the go to place for local government stories. Thanks again for listening to us. This has been Gov Love, a podcast about local government.
- [57:21]
B
Clicking in the mice crawl all night long and 87 Reagan is the many pages I'm written on Writing songs about rights and wrongs and bells bonds master bedroom bigger than the crib that I was raised at I'm the architect like I wrote the code, the ways at I'm driven like Elohim from the streets of Queens the definition of what it was written means know what I mean? When the world turned his back on me I was against the wall I had no foundation, no friends and no family to catch my fall running on.