OK, everybody, thank you for coming this evening, welcome to the Oxford Martin School.
I'm Alan Robin. I oversee the research portfolio of programmes that we fund here at the school.
I'm delighted to host the Kerry Madsen to tonight.
So Jens is a classic example of an interdisciplinary researcher. His research focuses on how people form beliefs and why they act the way they do.
His main areas of interest are information integration, source credibility, dynamic decision making,
the dissemination of misinformation, political persuasion and complex environmental information systems management.
And he's taken those interests into a wide range of contexts. He's an Oxford Martin fellow in our Programme for Sustainable Oceans and a post-doctoral
fellow at the Complex Human Environmental Systems Simulations Lab in the School of Geography.
So he works on simulating the behaviour of fisheries that to best understand complex and dynamic models.
But he's here tonight to discuss his book on the psychology of micro-targeted election campaigns,
and this examines the capabilities of increasingly sophisticated models of persuasion in elections and its implications for democracy.
So again, it's going to speak for about 40 minutes, and we'll move straight into a question and answer session,
and we'll have a drinks reception in the cafe afterwards and you all warmly invited to it.
And lastly, just to note that the lecture is videoed, as is the Q&A, so it is going to be filmed and live webcast.
So bear that in mind. If you don't want to be filmed, don't answer it. Ask a question.
So without further ado, let me welcome you to the stage.
Yeah, hello, everyone, and thank you so much for coming on a Friday night instead of having a pint in the pub,
quite impressed that I actually managed to get so many people. So I'm absolutely delighted and I've massively misled everyone.
I am going to talk about the halibut. And it is going to be a long talk about sustainability of oceans as well.
And without further ado, the tuna now.
But all kidding aside, my job here at the University of Oxford and the Oxford Martin School is to understand what makes a
person form a particular viewpoint of the world and what makes that person act in a particular way.
And I've been doing this from a computational perspective, which means I develop mathematical models that I can test and try and refine in
order to explain how beliefs and behaviours or form both individually and socially.
And so while this is something that can be applied to fisheries, as we've been doing in my spare time,
I wrote this book on the psychology of microtargeting campaigns.
And so this is something that is near and dear to my heart because it's something fundamentally democratic and it's something
where I could apply the same kind of modelling principles that I've been working with in the fisheries and perspective,
but information theory and to do electoral decisions.
And this sort of ties back to my original and true sort of foundation of where my undergraduate degree comes from,
which is rhetorical theory, the art of persuasion.
So basically, I've always been interested in what makes someone tick and what makes someone believe what they believe.
So why did I write this book and all right.
So first of all, I think we've all heard a lot about micro-targeted election campaigns in the last couple of years,
in particular with Cambridge Analytica.
I'm going to talk about this a tiny bit, but I'm going to try and veer away from Cambridge Analytica as much as possible as possible.
Because while this is an interesting case, study might not tell the full story.
And so I was really interested in figuring out an investigation of the underlying microtargeting principles.
So basically, what is it that allows a model with data about specific people to build models of
those people to persuade them of something or to make them act in a particular way?
For me, that's a fundamental question to our democratic sustainability because if there
are models out there and companies out there who can target of innermost desires,
innermost beliefs and addresses in a way that is incredibly persuasive to us,
and it is worth knowing how that amount of power can be can be used and puts it potentially abused in a democratic system.
So in order to understand the degree to which our information systems are fragile and vulnerable to misinformation,
we need to understand the psychology of individuals, as well as how these models can be used to generate these individualised models.
So the intended use of the book is not academic per se,
although obviously it builds on academic work that I've been doing for the past decade and work that I've been doing with colleagues,
and I wanted to buy a book that was more geared as a broad perspective,
which can be used as a friend owes journalists,
students and even regulators and network providers to understand the type of modelling that they engage with,
in particular, if you want to regulate the use of faith and politics. We have to understand what these models can and cannot do.
And there's a lot of hyperbole around with a lot of newspaper articles saying data can predict everything or we,
the human mind, is so infinitely complex that mathematical models can say nothing.
Why do you like Nescafé coffee when Aly is better or whatever?
Like, there's always going to be hyperbole on both sides of that aisle.
And obviously, as with most things, the truth probably lies in the middle.
And where data can say something about your psychology, I probably can't say everything.
And so if you really want to understand how we can improve political discourse
and which I seem to sort of feel this kind of at an all time low at the moment,
unfortunately, but like, let's hope not.
Well, let's hope it isn't. An all time low is bouncing back, I should say.
But if you really want to understand how we can improve political discourse and the kind of democratic institutions that we have,
we have to understand the fundamental nature of the psychology of the citizens and of the electorate,
and also understand a how they can be modelled and persuaded by the people in power.
And B, how we can set up institutions and protections to make sure that those are not being abused in an unlawful manner.
And finally, I think it bears saying that if we don't do this at a university where all of our information is disseminated publicly,
it will be done behind closed doors like what was done.
Cambridge Analytica, for instance, was not publicly available and the whole principle behind doing a thorough academic study of persuasion,
a thorough academic study of micro-targeting is that we can get the results into the hands of people who might
want to consider who to vote for in an election and why we want to structure society in the way that we want to.
So obviously the most famous case of micro-targeting in the last couple of years, I've been Cambridge Analytica.
I'm sure that you will know it was the 2016 so-called hacking of the US presidential election,
in which a London based company used personalised data to draw up individual
profiles of citizens in the US to optimise their persuasion attempts to solve.
Yeah, basically do a shrewd marketing campaign. So that's a really interesting case study, and it got a lot of attention.
But I think it's an inadequate example and it's a good historical case,
but it's inadequate to understand the fundamental principles that underpin
these models and these ways of using data to to generate persuasion campaigns.
Why? Well, first, because case studies can't be generalised.
They represent a single data point, and the social cultural context and the candidates of any given election will differ.
And they're necessarily unique to the situation.
If you think that you can retrofit a model of what Cambridge Analytica did in 2016 or even 2014 when they were involved in the midterm elections,
you are sadly mistaken because the situation is going to be different.
It's not going to be Hillary Clinton versus Donald Trump, presumably, although some rumours has it that she will run.
But let's see. And so you can't necessarily rely on that individual case study.
Furthermore, you don't know exactly if Cambridge Analytica really influence the situation.
Would Trump have won, regardless of the involvement of Cambridge Analytica is literally impossible to say,
given the amount of and confounding variables that are in that situation.
Secondly, the methods are very specific to the situation.
So, for instance, the API that they use to generate personalities of individual citizens is no longer available.
So if you're going to go into a big discussion about like, Oh, we should protect the API of the citizens.
Done and done. Problem solved. But that doesn't mean that the intention of getting that data is not out there.
So they appeared as well to have relied on some pop psychology. So the effectiveness of it,
which is unknown and further journalists and people who have been involved in Cambridge
Analytica may have an interest in beefing up the impact of Cambridge Analytica.
So maybe a bit of sensationalism. So then further, in future campaigns,
elections may have better with data because the type of data that we have available for any given person will constantly shift.
Like, for instance, in 2016, no one was talking about Tik. That's a really important app right now.
And for anyone who doesn't know what tick tock is, it's a massive app.
Social media app that is out of China, which is collecting a lot of data on people.
So if we don't understand what TikTok does in a fundamental principle about gathering data from individual citizens,
we risk running into a retro sort of prospective way of doing regulation and analysis.
So in that case, like, we need to understand the micro-targeting in principle rather than in cases.
And that's a really key point.
So while I applaud all the journalism and it is a vital piece of journalistic work that's been done on Cambridge Analytica,
it would be a bit like saying, OK, how do we design the best football team to beat Real Madrid?
Well, I will. I will show you because I am Danish.
I am from a tiny little town called on site and in a glorious full day.
Or maybe actually sorry, spring day of 1994,
all into Bullet Club went to the mighty Bernabeu and beat Real Madrid two nil, knocking them out of the UEFA Cup Shimbun.
So what does that mean?
Clearly, if we just extrapolate from a single case study, it means that clearly we have to wear yellow if we want to beat Real Madrid.
That's a really good explanation, and we probably have to have a lot of things.
That's a really good explanation as well. But unfortunately, if any of you have ever followed the subsequent endeavours of my beloved hometown team,
you will have realised that they are not at the pinnacle of the European scene lifting the Champions League trophy year after year.
And so extrapolating from one case, maybe a slightly dicey thing to do.
So what I want to do today is basically talk about three aspects of microtargeting,
and I want to talk a bit about how you can model subjectivity because that seems like a nebulous thing, but it has been done and can be done.
Then I will try and make this as a transition into what I call analytic micro-targeting.
So this is reportedly what Cambridge Analytica did.
And these are great models about segmentation of peoples.
But we'll see some of the limitations as we go into the third section, which is dynamic microtargeting, and then we'll end with a bit of Q&A.
So part one is models of subjectivity. So how does it capture your beliefs?
Because obviously, we know that individual differences are absolutely critical. And obviously, everyone's political leanings differ.
You can have a wide range and wide range of beliefs that barrier within the population,
both in terms of what they think is important and what they think is credible or not.
So, for instance, some people might think like, Oh, what really matters to me when I choose a candidate is the economy.
And it is their stance on jobs and labour market policy.
Whilst another person might think like what really matters to me is people who want to ensure women's rights or who want
to understand socio economic consequences of poverty or who want to safeguard our society against environmental disaster.
So. Those people will have different proclivities towards what they really value in terms of what's important in this election.
But they will also have different personal beliefs about the likelihood of outcomes.
So for instance, if you talk to a climate change denier and if you talk to someone who has looked at the data and is a climate change believer,
you might have very different subjective perspectives on the likelihood of particular outcomes.
Like, for instance,
what's the likelihood that Miami will suffer from severe flooding and if weather conditions in the next decade or 20 years or 30 years?
If you ask a climate change denier, they might say like, Well, do you know?
I don't think that's very likely because like even though there's been a couple of like storms or whatever
like weather comes and goes like there was an Ice Age once there was like a solar flare or whatever,
and they're entitled to their opinions and they're entitled to their subjective perception of the reality.
Similarly of comparatively. If you talk to someone who believes in climate change, they might say like, Yeah,
I expect those kind of extreme weather patterns to increase with time because we're getting into a worse situation in terms of climate change.
So one so a perspective of this is like all like human beings are too messy will subjective.
We're all just like bulldozing down the road of of our own personal beliefs.
And it's kind of impossible to to conceptualise and model.
But there's been some strides in cognitive psychology to address this.
So I'm going to simplify this by four pathways to personal beliefs.
And so I should note that in all of these pathways and I along with colleagues,
some of which are in the room today, Toby and we have been doing work on what is known as Bayesian modelling,
and I'll explain what that is in a bid to try and account for how people use their subjective probability
estimations of the world to predict how they will change their belief given new information.
But there's no two ways you can get new information about the world. So, for instance, you can have an personal experience.
So if you just fired from a job and you're asked to evaluate the volatility of the labour market, you might say like, it's pretty damn volatile.
I am not feeling good and I'm not feeling good about the prospects of my future.
That's an entirely sensible thing to say if you like she has been canned walking away as you do with your P45.
So there's obviously personal experiences that will drive a subjective perception of how the world looks.
But it's not the only way that we're getting information about the world.
Probably most of the information we get about the world is from other people.
So we read news reports and we read meteorological reports about whether or not it's going to rain
in Oxford this afternoon and which I believed it was going to be because I looked at the BBC.
And so I consulted a source who could give me some report about something that I was interested in some hypothesis.
Now I can have a greater or lesser extent to which I believe in that source.
So for instance, if a drunk person stops me in the street and goes like the economy is going to crash tomorrow, I'm going to go like Hamlet.
That's that's that's a really solid prediction, man. And like pop back to the pub, please.
And I might not revise my beliefs about it.
Like significantly, comparatively, if the Bank of England comes out with a report saying the economy is going to crash tomorrow,
and I'll probably take that a bit more seriously. Why? Because I trust they are going to disseminate information to the best of their knowledge,
and I trust that they have some expertise in that particular area.
So it makes sense that I would skew my belief or adjust my belief provision differently,
depending on whether or not I trust that particular source to be a credible source of information for that particular domain.
So trustworthiness, for instance, is something that cuts across domains,
and I would trust my brother in almost any situation, like following maybe playing a game with him.
But would I have a high rate of expertise for everything with my brother?
No, that's how I dependent on some things I know that he knows or not.
And on some things, I know that he doesn't know that much.
So if he advises me on something where I know like, OK, he's got a lot of expertise in this, he's got my best interests at heart.
So I trust what he's saying. I'm going to listen to him.
But conversely, if I consult him on something where I know he knows nothing, then even though I trust him,
I'm not really going to trust my body so much because my subjective perception of his expertise will differ in that case.
So you can see that the subjective assignments of probability that we can give to
people can start influencing how are we going to treat information from that source?
So this is just to sort of say in mostly kind of just words rather than in maths, that we have our subjective estimation,
both of the particular case that we're considering like the hypothesis in question.
How likely do I think this is? Then you've got some information coming in which you can think of as more or less strong.
And so like you can think like, Oh, if I'm going to if every billionaire like I report came out saying every billionaire has sold the house in London,
like this week, you go like, Oh, there's something going on here, like something.
Something is afoot in terms of London's housing market.
And you would probably adjust your belief in the crack in the volatility of that housing market accordingly.
But that's because the strength of the relationship between the evidence and the hypothesis is quite strong, comparatively.
If I see a bunch of semi random stuff that has little to no relationship to that hypothesis,
I can see a lot of information like butterflies floating the wrong wings or something of that.
But I'm not really going to adjust my belief in that hypothesis because I don't think there's any relationship between those things.
Now the key difference here is I may think that there's a strong causal relationship
between two pieces of information or a piece of information and an hypothesis. But another person might think that that is a spurious causality.
So we may disagree subjectively on the prior belief as in like, how likely do I think this is before I listen to any information?
Yes, then we can disagree on the degree to which this evidence is strong or weak, or positively or negatively correlated with that hypothesis.
But then we can also disagree on whether or not that source is trustworthy.
So for instance, let's imagine that you have a climate change denier who thinks like, you know what?
Climate scientists are paid by universities to tow a particular company line,
much in the same way that people from Exxon and show paid to tow a particular party line or company line.
They may then think like, Well,
there's no operational difference between the credibility of a scientist and someone from Shell or of other petrol companies,
in which case they should operationally adjust their beliefs in the exact same manner.
Given reports from scientists and reports from oil company executives, and those can be subjective again.
Thirdly, you may think that people are dependent on each other as in life.
I think these guys have talked to each other before giving this report, or you can have different perceptions of causality.
So all of this, these kind of different perspectives on how evidence links to hypotheses, how sources are related to each other, like, for instance,
if you see three people and they all say the economy is going to boom after Brexit or something or the economy is going to crash after Brexit,
then it makes a [INAUDIBLE] of a lot of difference. If you learn that those three people are drawing the same,
drawing their conclusions from the exact same report because they're basically just all saying the exact same thing from the exact same report,
in which case the real thing that you should worry about is what's the credibility of that report?
Not the amount of people who independently say what they say or independently in this case.
So the computational impact to people's been subjective beliefs has been fleshed out by this wonderful priest, Thomas Bayes.
There's also a French mathematician called Laplace, and he actually formalised it a bit better.
So. But I mean, seeing that face is buried in London, I'm going to go with bass.
So what this basically tells you is that you can conceptualise people's subjective beliefs as a spectrum between zero and one on
hypotheses prior to any kind of evidence as related to the strength of the diagnostic city of the evidence as related to that hypothesis.
And you can integrate those two to figure out, given your prior belief.
And this information, what should you now believe?
Depending on your subjective understanding of that relationship, so to give you an example of this, his model that I've been working with for a while,
which tries to conceptualise exactly what I was talking about earlier and the probability of trustworthiness,
probability of expertise, PVH is the probability of the hypothesis before you hear a report from that source.
And h given is what's the likelihood of this hypothesis, given that I'm that this source is giving me a positive report about this?
And we've done loads of testing on this.
And just to give you a couple of examples, this is us trying to predict and posterior degrees of belief after someone gets a report from a source.
As you can see, the dotted line and the solid line are almost identical, and those are the predictions versus observations.
Similarly, I did a study in 2016 on on political candidates,
and we could we could predict for Republicans and Democrats alike exactly how they would change their beliefs,
given reports from candidates given their subjective perceptions of the credibility of that person.
So it is possible to do mathematical modelling of people subjective beliefs.
But the question is how do I start to populate those models? Like, how can I start doing my analytic microtargeting?
So I have segmentation of that population? And there's loads of ways that you can do this.
So one thing is traditional segmentation. So for instance, if you know that certain demographic information or certain information about
the population is correlated with a particular thing like so for instance,
these are income, education, gender shopping habits or hobbies.
So for instance, if you are an American and you buy a foreign beer, you are eighty three percent likely to be a Democrat.
So if I know if I get access to your target loyalty card, which may be sold and I go through your shopping list,
I can see like, Oh, this person's buying a lot of organic products.
This person is buying a lot of foreign beer, like slightly troubling amount of foreign beer,
but you get my drift that you can start building up models of.
What is this person like?
What is this person probably going to value in terms of priorities, beliefs like is this person conscious about environment or not?
And like, if you just have like a slab of red meat, you're probably not like the massive environmentalist that you would otherwise proposed to be.
Same thing you can do digital segmentation. So like, for instance, what kind of apps do you like?
How how do you use your phone and where do you move in the city, if I can to you locate you, I can see like, Oh, you went to the extinction.
Extinction Rebellion every time they were in town, you probably care about the environment and we can do word clouds.
So, for instance, this is a cloud that has been segmented in between Democrats and Republicans.
So it turns out that in this analysis, if you say Democrats, a lot of the tweets, you're probably a Republican.
But if you say Democratic, you're probably a Democrat.
So imagine that I'm going to scrape your Twitter account for all the words that you've ever posted in your thousands and thousands of tweets,
and I can start doing analysis of what you probably think about things.
What do you care about? How do you write about it? And you can start correlating those with subjective perspectives on the likelihood of these things.
Like, for instance, if you like, we're all going to die after, like in 10 years.
Hashtag environment. The likelihood is that you probably have a pretty strong perception on the prior on on climate change.
Yes. So you can train models to exactly do this same thing with your social position in the network.
And you can also use psychometrics such as personality measures and the wonderful media.
It is also from the University of Cambridge. Feel free to boo, though.
And from Cambridge, he's done a lot of stellar work on personality pressures, but equally,
this moral foundations, heuristics and biases like confirmation bias and the need for closure.
As in like, how willing are you to accept that something is open ended and not sort of settled?
So you can imagine that you can start drawing a lot of data from people,
either by asking a representative sample and then correlating those responses with other people in the population who exhibit the same kind of traits.
So if you learn that like, for instance, to be an example,
that people who buy foreign beer are more likely to be Democrats than if you can get hands your hands on a lot of data about people's shopping habits,
you're going to have one data point that pushes them towards the Democrats.
But then they also own SUV, which maybe puts them in a different direction.
So you can get that you can start building up these individualised models where you build up a perception of that person,
which you can then later use to segment the population.
And importantly as well, you can separate your campaign into what is like beliefs and behaviour phases.
So like the beliefs is how do you change your belief, which is dependent on how you see the credibility of the source,
the likelihood of that evidence, your prior priorities for policies?
But it's also important to know that some people need to go out and vote for your
candidate so you want to have a campaign phase as well and get out the vote phase,
which basically says, I know that you're in my base is I'm not going to contact you through the persuasion phase,
but I am damn sure going to sound like a person to your car, to your house to remind you, is Election Day because I want you to turn up.
So this requires persuasion. This requires get out the vote.
Finally, you can in the constituency based elections like the UK and the US, you can start segmenting people along the lines of Do you even matter?
So for instance, if I find a Republican in California or a Democrat for that matter,
it doesn't really matter because that state is going to be democratic no matter what.
So you're not really going to you're going to waste your money if you're going to go and campaign in California.
So in in the states and the UK as well,
we have a first past the post constituency based system where you can weed out the people
who don't really matter either people in the same states or the safe seats in America.
This has the added wrinkle. A wrinkle of not every state is equal in the eyes of the founders.
And so I took the liberty of calculating how many people you need per electoral vote in in the various states.
And that looks like this. And it turns out that you need like, I think it's like a hundred and eighty thousand people per electoral vote in Wyoming,
but you need to convince seven hundred forty four thousand Texans to get a single electoral vote.
So that basically says some states are more important than others. So armed with this information, you can start doing analytic voter segmentation.
So if I really want to figure out who am I going to talk to, what am I going to talk to them?
And what am I going to say to them? You're going to throw all your data into this and model generation machine, which basically, as I said,
just trying to draw correlations between what a person is like to believe in what a person is likely to find important.
And this can be done by trawling through your digital data, trawling through your shopping lists, your Google searches,
your activities on on apps like how many TikToks of little means you post today,
for instance, and maybe inversely correlated with your likelihood of voting.
I would imagine. And so you can start drawing up these segmentation lists.
So let's say that you want to figure out like what is the subjective belief of any kind of person, according to the model that I've been drawing up?
And bear in mind, you can have a really bad model in which case you're going to be really bad at predicting people.
And this is why I'm interested in the principles underpinning micro-targeting rather than the specific case of, say, Cambridge Analytica,
because it's really difficult to tell how good their models were, partly because it's a single case study,
but also partly because they probably haven't published exactly how they did everything.
So there's a lot of hearsay, there's a lot of sort of rumours and stuff going around.
So fundamentally, what this kind of a perspective means is that you can start drawing up these Bayesian belief networks,
which here I'm just going to take focus on a single subjective belief.
So like, for instance, what's the likelihood that I'm going to vote for this guy or that guy or this candidate or that candidate?
So in this case, I have segmented first to the left.
Anyone who has a very strong preference for my candidate, so above point seven, according to where my model deems that person to be.
Or in other words, that's my base. And like, those guys are pretty safe in my own modelling perspective.
Conversely, to the far right, no pun intended and you've got people who are definitely not in my base,
but who are definitely, definitely in the other person's base in the opposing candidate's base.
They're not really that attractive either, because it's going to take a [INAUDIBLE] of a lot of effort to draw them to my side.
But then you've got all the delicious people in the middle and all the swing voters,
the people who haven't really decided, am I going to vote for a candidate, a candidate B?
And then you can start winnowing away what your model thinks they they think about your candidate.
Credibility wise. So for instance, on the left hand side, do they think positively of my candidate?
Will they respond positively to reports from my candidate or negatively about my candidate?
In which case you probably shouldn't contact them even though they're swing voters,
because you might just annoy them, like Ted Cruz going door to door like an unlikeable guy?
So in the middle, for instance, you can see that you can segment it into a more positive versus less positive,
and in the base that is adversely position to what your candidate,
you may want to segment it not as a 50 50 as in like, oh, just mildly positive, but like wildly positive, like above point seventy five.
So this may be something where say you want to persuade someone that climate change is happening and you know that they strongly disbelieve it.
But you know that they really respect you as a as a person or a scientist or as a source,
then you may want to actually contact those guys, even though they are already pretty far on the other side.
So, for instance, you can ask yourself to eye contact this person the base.
Not so much because they're already in your pocket.
And here you should probably forgo the people who hate your candidate candidate or who doesn't really like your candidate,
and you should only contact the people who kind of like your candidate. Same thing here,
where I have beefed up the proportion to which the degree to which they really should like your candidate before you should contact them.
So now you can see, like we segment of the population into six bins, two of which are now going to be contacted.
Um, do they have an influence on the election? Well, yes or no.
So like, let's say that this guy in the middle who actually likes my candidate, but he lives in California.
Who cares? And in that case, I'm going to leave him.
Same thing, obviously, for the people who strongly like your candidate, but who was marginally towards the other person's base.
And what's it like to have a voting fun? You can buy that in America.
You can't buy what they voted because obviously that's secret.
But there is records as to how many times you participated in elections that you were eligible to participate in.
So I can get a pretty decent estimate of that.
So let's say that anyone who is more than 75 percent likely to vote, I can segment my population into that as well.
And anyone who is less than that. So now I think, OK, who should I try contacting to get out the vote for us?
So in the last couple of days before the election, who should I really try and motivate to get out of the House to vote?
Well, you definitely like reach out to my base now because they're the ones that I really want to make sure turns out on Election Day.
So, for instance, in in 2008, and one of the strategies that Obama invoked was a pretty clever one.
So there was some communities of African-American citizens in not Illinois that was a safe state.
I think it was Iowa, one of the neighbouring states. And obviously, they've done the legwork.
So they knew that if they get a thousand African-American people to turn out for them to vote for in the election,
probably nine hundred and ninety seven of them are going to vote for Obama and like three of them, are going to vote for John McCain.
And so what do they do? They didn't contact them in the persuasion phase, but they just drove in buses after buses,
and they didn't bother bother persuading people or like and convincing people like, vote for Obama is just like, Do you register to vote?
Great. That's driving to the election polls right now because they know from the modelling that most likely,
if you just get a thousand people, that's going to be a really, really key thing.
So that's a kind of get out the vote strategy that separates clearly from a persuasion strategy, a similar thing.
You may want to focus on the people who are unlikely to vote and forgo the people who are very likely to vote.
So if I know like, OK, I've already persuaded you to vote for my candidate and you are ninety three percent likely to vote,
I don't really want to bother knocking on your door come Election Day because you'll probably be there anyways,
because the one election you missed was because you were on a holiday or something on you.
And then you can use the further subdivision of the psychometrics individual's psychological differences like, for instance, personality measures.
So you can see how this creates this beautiful patchwork of further segmentation,
further further segmentation where you can figure out what does this person believe in?
What's the likelihood of credibility of that guy? And should I contact that guy for persuasion?
Should I contact that guy for behaviour, like for the get out to vote? And what can you do with this?
Let's say that you generate like 80 bins of people that you can then filter
them into four personality four beliefs for priorities of political leanings,
but you can start AB testing that so you can start running experiments on what is the most effective way of
contacting that subdivision that steps up steps up segment of the population isn't using a particular type of word.
Is it to use like a particular message type?
So one example is like if you have three guys in America and they all kinds of Democratic Republican leaning,
but maybe slightly on the fence and they all care deeply about guns.
If you have someone who is strongly conscientious, you might contact them and say, like, Americans are law abiding people.
We keep our country safe. We we know how to follow, like and adhere to rules.
So guns are protected by the Second Amendment. [INAUDIBLE] obviously stand.
I think that's a very simplistic so like civil kind of an argument.
Then you may have a pro-gun message for extroverted people, which is more like guns, a part of American way of life.
We hunt together, we barbecue together with or like you or Becks or hunter like hunters of frontier kind of people.
And that might work really well for them in the AB testing that you continuously trying to refine to make sure that the way that you are contacting
that subset of that voter will be as as sort of spot on for their proclivities psychologically and belief wise as you as you possibly can.
Finally, like if you have a deeply neurotic person, you can go like, oh, like the Democrats coming for your guns, which is obviously flare up.
And so imagine that you want to sue in state here, Columbus, Ohio,
and you go on a street level so you can start drawing maps of this because obviously this personal data is linked to your personal profile on a live.
Because those registries of that. So what I would do, that's an analytic microtargeting and targeting campaign person.
I would then start to figure out like, OK, there's a bunch of houses who are relevant and irrelevant because they hate my candidate or
they or they they really hate my issue and they hate my candidate and they're unlikely to vote.
And like, forget about these guys. Then there's a bunch of people where you may want to think about like, Oh, these guys on the fence.
So for instance, you've got the Smith household where my model says, like all probability, you're voting like pretty high.
Ninety three percent, my data is kind of distributed and as illustrated by this graph,
like you have like 200 data points in that person and they they're not like really
clear on whether or not this person is a Democrat or a Republican from Twitter.
I know that you talk a lot about economy, environment and women's rights and some psychometrics,
in which case I'm going to try and persuade you with something that is specifically designed
for that sub segment of the population as driven by the data analysis that I've been doing.
Similarly, when it comes to get out the vote, you get these guys.
So like the Taliad House, where the probability of voting is pretty low, but all for all intents purposes,
the data analysis that you've been doing suggests that the person is strongly in your camp.
So you know that this is someone who probably liked your candidate but is unlikely to vote,
maybe because of despite discouragement of disinterest in the political system or something of that in which case that person gets it,
gets a get out the vote message so you can start building up this.
So a fascinating picture of a whole of a nation. You can zoom in on street level and figure out exactly how you're going to approach that person
who you're not going to approach and who you're going to approach for the get out the vote part.
So this is kind of reportedly what Cambridge Analytica did, they sort of mined deeper and deeper and deeper, which is great.
It's a very strong perspective on basically marketing purposes like it's not anything more fancy than sort of swanky.
Marketing is basically just like, what is this person like? And then throwing a dash of psychological profiling.
Again, I will say this doesn't detract from detract from the idea that you can get increasingly sophisticated models as we go along.
But as we all know, human beings are not stuck in a well. Rather, we are interconnected, so we constantly talk with each other.
And what's the main danger of increasingly just digging down into a well, metaphorically speaking,
is imagine like for the completely soul of imagine the example that you would have a presidential candidate who had, let's say, a racist or a segment.
I know it's a completely made up example, obviously, but like,
go with me for for for the for the purpose of illustration and then you want to figure out how am I really
going to motivate this guy like these guys to come out and vote like I really need to motivate my base?
Well, that may be to send an incredibly like you can sort of refine and refine and refine and optimise and optimise and optimise and figure out like,
Oh, I just need to send them a supremely bigoted and mixed message about Mexicans or something that that's going to get them out of the door.
But what happens then in this imagine example, if one of these guys goes on Twitter and goes like,
Oh, the president is doing something really great here? Hashtag get on my country kind of a thing.
And it goes viral. Then everyone in the middle of the political spectrum who might find that to be abhorrent are now pushed away from the candidate,
and it works counterproductive. And so the deeper you go,
the larger you increase your probability of hitting something that might not be palatable for someone who's not as strong you within your base.
And the problem in this modern day of age is that we can talk to each other via Twitter,
via Facebook, via Messenger and stuff that so we have agency as citizens.
So what? I want to end the loss of like five 10 minutes of the talk about is how is it perspective?
I see this as a dynamic thing rather than as analytic thing.
So information flow on social networks, and I've been doing some work on this with Typekit Hilditch and Richard Bailey and me or may not be here,
but I've been doing work on this on echo chamber formation, on micro-targeted and campaigns.
And the idea here is that you can simulate individual citizens and their connectivity
and see how things play out once you release your campaign into that environment.
So they take this takes into account the agency of citizens to share information with each other as well as receive information,
which means that there's less like there's more of a give and take. It also takes into account people's social network position.
So who is really important here? So one excruciatingly important person in the recent midterm election was today.
Hey, hey, hey. And Taylor Swift, for people who don't know her, is an American singer songwriter.
And she tweeted out, Remember to register to vote? And within twenty four hours, forty nine thousand people had registered to vote.
That's a pretty good return, but she lives in California.
Any kind of analytical marketing and micro-targeting segmentation model worth their salt would have disregarded her any day of the week.
But there's a huge power to be had from understanding the perspective of what is a social network and how information flows,
how it has influence so levels on information networks.
So one way of modelling this is what is known as agent based modelling.
I'm not going to bore you with the details,
but suffice to say that you simulate a bunch of individual agents and you can make them as heterogeneous as you want.
And so you can populate them with whatever kind of models that your analytic, micro-targeted campaign says that they have.
Then you contact them with each other and you set them into social networks.
So you have interactions and you let it loose and you see, how are they going to share information?
How are they going to act? And once the election campaign is over?
So an example of this is we want to see if we can simulate echo chamber formation and we could.
I'm going to skip slightly over this. But the main points taken away from this paper is that even rational agents can become stuck in echo chambers.
And when we increase connectivity in the social network, the problem becomes worse, not better.
So suck on that sucker back, and education broadcasts get less effective in dispelling HQ chambers with time.
And so this is just an example where you can show that you can use these dynamic
models to test some really fundamental problems of democracy and information.
So one way of conceptualising an election instead of conceptualising it from
this tree branch that goes down is to conceptualise it as a dynamic model.
We have some voters who talk to each other. You've got some news pundits trying to influence them.
You've got some other campaigns trying to influence them as well.
It's all groups and they are also responsive to what goes on within that social network.
So let's say that there's a shift in in the perspectives of the citizens that may cause the other campaigns to shift their strategies,
which may mean that you have to switch your trustees to a conference sort give and
take and a constant interaction between like those peoples and campaign strategies.
So and this also points towards a completely different perspective where instead of constantly pouring down into even deeper wells deeper and deeper,
like stronger and stronger isolation between the bins of citizens,
you conceptualise the heterogeneity and you populated and stimulated within a model.
You give them some possibilities for interacting with each other. And you throw them like some campaign strategies and you see, how do they react?
How do they respond to this? Like, if I go out and send that like bigoted message, how does the electorate so form around that?
How what's this off vibe on Twitter?
There's no way you can do this as an athletic model, but there's every which way that you can do this mathematically through simulated dynamic models.
Further, if you really want to use sure about it, you can wrap this kind of a model in what is known as an optimisation,
so you can find the optimal way of managing that complex human human environment system.
This is something that we've been doing in fisheries, where we are trying to figure out what is the best way of managing these dynamic systems.
So if we have some closing reflections on what do we need to think about when we think about microtargeting?
And first of all, we need to understand that the main modify as to whether or not these models are
effective is access to data skewed resources and access to experts and modelling.
Because if you have really bad models with really bad data and and almost no money to run it,
like good luck in your micro-targeted campaign, it's got to be shite.
But if you have really good data like extraordinarily good data, we can get modellers and all the time in the world to segment that electorate,
including their social positions and the dynamics going beyond pretty good standing
as data becomes more abundant and psychological models become more precise. We may expect candidates to increasing use of this.
And so what can we do? One thing maybe to modify campaigns access to data.
One proposal I have is make any data available to any campaign publicly available for any other campaign.
Because then if you collaborate with, say, external lobbying groups who give you data,
then everyone gets that data and they can all do the same modelling of that.
At least that would limit the degree to which you can buy sensible, like sensitive data and get an advantage in a strategic edge from doing that.
Also, obviously, we can enforce limits on campaign spending. In the UK, we have limits.
Thank God. But they don't in America. But this should also include any collaborators because otherwise I would just go like, Oh,
my buddy over here has a billion dollars, but it's not me, like, it's my superPAC back.
So this would also limit obviously access to development expertise and forced people to think about their money.
And also when considering all these negative aspects that are often been chucked out when we're discussing these microtargeting campaigns,
while also think about what we can do with these methods proactively.
So, for instance, optimise communication of scientific findings to people who don't believe in climate change,
and we can generate better campaigns for public health and environmental issues
like recycling because that underpins or relies on a fundamental understanding,
both on the psychology of the persons who are the targets of those campaigns,
but also the social fabric that binds them and what causes that behaviour.
And finally, we can use these models to measure the vulnerability of information systems and misinformation.
So to conclude based him can enable modellers to model subjective beliefs.
We've been doing this with prior beliefs, with perception of causal relationships,
with perceived credibility and how they see the dependency of various sources.
And we're getting increasingly good at predicting what people will do once they are exposed to particular parts of particular types of information.
Second, we can use personal data to populate so like Bayesian models.
This includes tomography, digital traces, psychometrics influence, an election and anything else you can imagine.
This kind of a list will grow as the models become more sophisticated.
Campaigns obviously need to understand beliefs as well as behaviour changes because
you can't just assume behaviour following deterministic form of her beliefs.
So these are two very separate things and also social things.
I should say analytic models fundamentally can't account for pushback from citizens agency.
And so precision can backfire if you increasingly borrow your way down,
which is what Cambridge Analytica reportedly did and believe information and behaviour on social networks are complex and non-linear.
Is this really crucial and the heterogeneous, dynamic adaptive?
But we can use these agent based models that we've been using to capture this.
And this is why pre-emptively said before that point, which is basically that we can optimise over the management of these kind of models.
And finally, it's worth remembering is always the amount of data that can save a bad candidate,
Jeb, because he had the access to the most data in 2016's primary election.
And look where that got him. So thanks very much for listening, and I will take some Q&A.
And so if you raise your hand, if you have a question or just if no one has questions, why oh yes.
I should say as well, I was asked to say, wait until you have a microphone before you ask the question, because otherwise they won't be recorded.
You talk about dynamic and analytic ways of targeting campaigns.
How much do you think the analytic campaigns are being used and if they're not being used very much at the minute?
How much of a threat does this put on, like the um, like how moral campaigns are in the future?
So how much are analytical models being used right now? Yeah. Oh, I would.
I would hope that every kind of every political campaign uses analytic modelling,
because if you think about analytic modelling is kind of just the degree to which you can segment and understand the electorate.
And that could just be like James Carville have the most simple analytic model.
Is the economy stupid? Like, that's that's an analytic model of just like a one issue thing.
You're going to vote on that. But then you can splice that into two.
So any kind of any kind of data driven segmentation or ideologically driven segmentation is kind of like model.
Now how mobile it is, I think, depends entirely on obviously the campaign in the sense of like if the analytic campaign that's being done is to say,
decrease criminality or increase recycling, I think that's a pretty good thing.
But if it's to like, sell more coal, I might not personally think of that as a small thing.
But I'm I'm kind of keen that this should be disassociated in principle from those
in the sense that like this is a tool through which you can sort of like segment
and optimise persuasion campaigns both analytically and also simplistic way or
dynamically in a more interactive way and in whichever purpose that you use it.
But it can be good or bad. It's a bit like studying medicine in a way like who better to poison you than a doctor?
Like, we know, like they really know. But who better to say to you as well?
Yeah. To down. And then some APC things genes for great and scary talk about the macro data management.
My question is, what experience do you have with that dynamic micro targeting models like the AVMs that you
have used with data because the one you mentioned was about more empirical and stylised facts,
I think. Yeah. Have you used any of the dynamical models?
With actually with training them on actual data. Not yet.
And but. And spoiler, I am a in the process of doing so.
And I have just submitted a grant application, which would exactly do that.
So take stuff like digital traces and calibrate and validate dynamic models to figure out how, for instance,
the networked self prunes as in like, who do I follow and who do I unfollow as a product of like whatever they say on Twitter or something?
And one more time then then I think maybe some up here. Thank you for the talk.
I appreciate that you weren't trying to sort of get to right or wrong on it.
But if I could draw you slightly on one of your suggestions, which was that we might open up whatever data one campaign has to every campaign.
Do you not think that that's perhaps overreach in terms of what an electoral commission or whatever sort of body is actually doing?
For example, one party or campaign might have newspapers on their side, and we wouldn't enforce that.
A newspaper gives equal coverage to all campaigns by enforcing sort of the data that each campaign can have access to.
Are you sort of artificially levelling the playing field? So like, I mean,
those are two separate things as in like the degree to which a newspaper is on the
side of the candidate or party is more about like the general support from say,
like the Telegraph, the Tories or the Guardian for Labour.
But what I'm talking about is more about if I get as a campaign,
if I get socioeconomic data because I've got like a bucket load of money and I can disproportionately unfairly sharpen my models to the extent that,
like in this old marketplace of ideas, I get all silly things else being equal, an advantage over my slightly less rich candidate.
And the proposal would be to force that campaign, to share that data,
to limit the incentive to buy incredibly precise data that costs umpteen millions.
And so it is an artificial leveller of the playing field.
But that's kind of the point is to ensure that there's more of a level playing field for getting your ideas out there.
And so I think we should take some questions up here. The two of you sorry, I understood what you were saying about I'm sorry.
I understood what you were saying about the economics of campaigning.
And you you had these various people with various ideas. But how did you get them in there?
How did you know? How did you know how to address me? Or how did you know how to address the kind of audience that is here?
You were you were very, very specific about what you do with the information when you've got it.
But less clear about how this information arrives in forms which could be manipulated.
Yeah. What did I say to my friends last night that would change your thinking and so on?
So. And so I, for instance, and the things I mentioned about your demographic data.
So if I can get access to your your shopping habits or your Googling habits, and if I have how the dosh money like a lot of this is for sale.
So in particular, in America, a lot of these personal personal data is for sale.
So I don't know if everyone does it, but like some companies where they have loyalty cards, when you shop, that's pretty valuable data.
So yeah, that's one way. Then a second method of doing it is scraping.
So if I go into someone's Twitter profile, if it's public and I just scrape all the tweets that they've ever done and I run it through a
machine optimisation algorithm that picks out whether or not they are more environmental words,
also a Democratic virtue Republican birds, I can then again get a version of that person.
So let's say that I got your Twitter data, I've got your shopping data, I've got your Google data,
I've got a whole host of different types of data sets, and I merged those two.
So I'll try and figure out what are you roughly like where you roughly in terms of your political position?
That's how they that's how they can be used for that. Thanks for.
And thank you for your talk. So you mentioned at one point that Google searches could essentially be bought.
My impression is that I don't know if they can. But in principle, and that can be themed.
I don't know if they actually said it. I can say my impression is that they sell very broad categories.
Yeah, probably. And so if. Someone told me recently that it was as few as 12 categories,
but if you could help us understand a little bit more about that, I think it would be helpful.
I don't know how much data Google sells, I'm afraid. But again, for me, that's a case specific thing because like I now say,
the sucker may like for Facebook may hold off from selling particular data points.
That is in no way a conceptual guarantee that the next big company because Facebook is dying of easy.
But the next big company is social media. There's no guarantee that they would have the same principles.
So like Facebook, post that API once they realised it was there.
But that in in in principle doesn't safeguard us in any kind of way, which is my point exactly,
is that we have to understand what is the data that is out there about each person, how is it regulated and how is it potentially sold?
And if we don't understand what that fundamental marketplace of data acquisition is, we are just like struggling.
We're blind in that way in terms of regulating this kind of stuff.
And I should say I promised at six o'clock to say that there was a wine reception.
And so I'm going to take two more questions. Sorry, it's three past six.
I would love to take more questions, but yes, you and then the lady in the front.
Just quick, quick, quick question at the you have mentioned that you can use this model for positive reasons like we go coverage.
Do you know any cases that are actually use except for election campaign and the question that is U.K.,
you mention that in the US, the more data is available. Yeah.
Where does the UK stand in terms of election? How susceptible?
And we until tomorrow we're standing pretty well because there's EU regulations on GDPR, which limits the transaction of personal data.
And yeah, so ask me again on Monday, it would be my mind depressing answer.
So right now, like in Europe, there's a lot more protection. So you can probably do what they do in America for two reasons.
One, because the access to data is more restricted and more protected,
but also be because literally the campaign finances are restricted much more severely.
And I can't remember exactly how much each campaign is allowed to spend, but it's in the tens of millions,
like maybe 40 million or something for a political campaign and that's across the board.
You cannot spend anymore. That's it. Comparatively, oh, is it $5.6 billion dollars spent on the last midterm election?
Like Pfizer as a magnitude, it's not just like the size of the country, and that's just an open flood.
It's not the case for the size of this. Oh, sorry that any other cases that are used this kind of money.
Oh, and I mean, again, in the same way that I responded to that woman down there, I think every model,
every campaign must have some kind of an idea of the kind of audience they're talking to.
But I would doubt that many campaigns that are publicly funded would have this amount of of sophistication.
I doubt it. But again, I haven't run in, so I can't say.
I've been wondering whether devices like Alexa play a significant role in information gathering,
and they do so like Alexa, for instance, and trained like.
Like, I mean, the specific again, I hasten to not make too big a point of specific things, like specific things.
But like an Alexa, you have to go into a subset of your settings to allow it to not use your vocal data to train it on itself.
As I was constantly listening, but it's for the purpose of machine learning, optimisation of picking out exactly your voice.
And so again, I don't think Amazon is selling that data,
but there's nothing in principle that would stop them unless we have strong
regulation and strong top down management from a from a societal perspective,
which is exactly why I think this is a key thing. Like I hasten to say, I don't want to be doom and gloom because like I am,
micro-targeting campaigns and dynamic microtargeting campaigns are great in getting your message out.
And if you have a good message, that's fine. But the uneven playing field,
the impact of money on elections and the skewed access to data gives a disproportionately unfair advantage to people who are moneyed,
which is a democratic problem.
So as a society, we shouldn't think like, Oh, these data hungry machines can do everything like they can't predict what shoes I'm going to wear today.
Maybe, maybe they can, but probably not. And so they can't predict everything, but they also can't.
They're not like nothing, so they can predict something for sure.
And the degree to which those models are sophisticated is just increasing what they can predict.
And this is why I think this is a key area of research.
But yeah, I think, um. Hello. And that was a great conclusion and a really informative but unsettling talk.
And my prior my prior belief about Danish football teams has changed so much.
So thank you all very much for coming. There is the wine reception. I'd also like to draw everyone's attention to a talk coming up next Monday,
28th on the geographies of the platform economy with Professor Mark Harrison, which proves to be a very interesting one.
So once again, thank heavens. And that's my.