So welcome, everyone, to today's strategy lecture.
In case you don't know, the Strachey she lectures are a distinguished lecture series named for Christopher Strachey,
a pioneer of computer science, known in particular for his contributions to programming languages and the notational semantics.
And the founder of The Discipline at Oxford, the first professor of computing science in Oxford Street.
He lectures a generously supported by Oxford Asset Management. The computer science department is very grateful for their support.
Under normal circumstances, pre pandemic.
This enabled us to hold lectures in the splendid venue and with very nice refreshments enjoyed by all the participants.
We don't have those that is today. But what we do have is is an excellent lecturer.
So it's my great pleasure to introduce today's straight lecturer, Professor Sonia Smedes.
Sonia is Professor of Logic and Epistemology at the Institute for Logic, Language and Computation LLC at the University of Amsterdam.
And since 2016, she has been the director of L.L.C., which is one of the major world centres for the study of logic and computation.
Amongst her many activities and recognitions, too many to mention them all.
But let me just mention that she was awarded the book of On the Human Prise for her work in Quantum Logic.
She has held a Rosalind Franklin fellowship and she was elected to membership of the Academia Europea, the European Academy in 2019.
Sonia's work has spanned a range of topics, notably dynamic quantum logic, dynamic epistemic logic, formal epistemology and social epistemology.
Much of her recent work focuses on the use of logic to characterise and study information flow,
opinion formation, social influence, informational cascades, homophily and polarisation in social networks.
And the title of her lecture today is, I think, very resonant for us all.
The Quest for Truth in the Information Age.
Just before I hand over to Sonia, let me mention that if you have a question, please use the Q&A facility to send your question.
You can find the Q&A panel by clicking on the icon with a question mark in it on the right hand side, top of your screen.
After the lecture, we will have a Q&A session where I will read questions to Sonia and she will respond.
OK, without further ado, I have a great pleasure in handing over to Sonia for the lecture.
Sonia, please. OK, well, thank you so much for this invitation and thank you some some for a very nice introduction.
It's a really great honour to give the stretchy lecture and computing science today.
So the title is The Quest for Truth in the Information Age. So let me start by reflecting on worldwide connectivity as we know it today.
So there are many advantages in worldwide connectivity, from finding information, from finding high school friends,
creating new friends, finding jobs, finding supports, etc. So plenty of advantages, but with disadvantages.
Also on the down side, there are many disadvantages as well.
So we see the spread of misinformation. We see online believe manipulation, online radicalisation, many cases of online bullying in schools,
the formation of informational cascade's and echo chambers to which we come back later on.
So that brings me to the question can truth survive the information age?
Now, answering this question requires a study of the mechanisms for mass information
aggregation and information exchange in relation to our ability to track the truth.
So I will focus on the social and epistemic benefits and costs of such mechanisms,
and I will reflect upon the successes and failures of individual versus collective rationality.
So the approach that I adopt brings together many insights from a series of different fields.
So one is I as a magician, I use logic to model the dynamics of information and we look specifically at knowledge and belief.
We take input from belief, revision, theory and philosophy and another area from philosophy.
We use formal epistemology and a particular social epistemology.
Epistemology is a theory of knowledge.
We use input also from judgement, aggregation and social choice theory, and our latest work uses more input from network theory.
So let's focus on this aspect of tracking the truth and pose the first question.
So how the group successes and failures to track the truth?
How are they related to individual successes and failures to track the truth?
Not answering this means that we have to look at what is known as wisdom versus madness of crowds,
and maybe more specifically, what actually does a group know?
So what is the knowledge that a group can possess? So I'll call this the group's potential knowledge.
And what I mean by that is the knowledge that the members of a group may come to possess
by combining their individual knowledge and using their joint epistemic capability.
But in what sense can a group process beliefs or knowledge?
Of course, you can infer this by looking at the group's behaviour.
This is the joint actions that they can do, that the members of the group can perform after combining their individual knowledge.
So that still brings the question, so how smart is a group?
So does a group no more than each of the members that that is in that group.
So on the one hand, we can say yes, yes, because the new information that was initially unknown to any of the agents may be combined,
may be obtained by combining individual different pieces of private information possessed by the different agents.
So in potentially we know more as a group than each of us individually.
But the opposite may also happen when some, maybe all individual knowledge is made inaccessible to the group.
And now you can think, well, maybe at first sight the opposite never happens.
Maybe the group's potential knowledge includes everything known by any of the members.
So that exactly is what is called wisdom of the crowds.
And there's a lot of popularising literature on that also book with this title.
So the wisdom of the crowds is this idea that the potential knowledge of the group is
typically much higher than even the knowledge of the most expert member of the group.
Now, if you looking into this a little bit more detail, you see that there are different examples of wisdom of the crowds.
And I will make a distinction between two kinds of examples. And each of those is based on.
Different forms of information aggregation, and they have different explanations.
So if we focus on the first type, then you would say, OK, a different way of aggregating information would be by communication.
So a group can actualise a piece of group knowledge by communication on the one hand.
And on the other hand, it would use a different method for aggregation, which would be pulling the opinions together, some form of judgement,
aggregation, and how good a group will be in realising its epistemic potential or bringing about the potential group.
Knowledge will depend on each of these methods, but also on different factors.
So the different factors that play a role are well, first of all,
it's important what the topology of the communication network will be like, who will talk to whom, but also trust plays a role.
So the mutual trust graph is important.
So how trustworthy do you think that the information is that you get from another agent or a subgroup of agents?
And self trust will play a role, so this includes the threshold of what is needed for an agent to change her beliefs.
So from which point onwards, can you be influenced or are too influential to be subjected to information that comes from others?
And the last point here on that slide is about the the agents epistemic interest plays a role.
This means her own agenda, her own questions, what she is interested in and how she filters the information that she gets her learning goals.
So if we look a little bit more closer, then you see that these two different ways of obtaining or pulling information together,
one via communication and the other via some aggregation method, they lead to two different notions of group knowledge.
So the first one, the first notion of group knowledge that we see would be the one that is based on communication.
So so here you see that the agents will have a piece of information or a piece
of evidence that typically is truthful and the agents share their knowledge.
And examples of this type of group knowledge would be joint authorship on a paper.
So agents that collaborate on the project, such as maybe a big science project like the Human Genome Project,
maybe proof of a mathematical theorem for us last year, but also a deliberation that happens in the hiring committee.
So these are all explanations were individual experts.
They collaborate together, they share their knowledge and they communicate doing so.
And that brings evolved form of group knowledge. And the form that it brings about is typically described as.
So the explanation in epistemic logic and epistemology that is given for this is distributed knowledge.
So distributed knowledge is a specific form that is based on information sharing via communication between agents were as an example.
I know p you know, that implies. Q So we pull the information together and we use our reasoning powers.
So we do multiple opponents in this case. This means we both know P and by modest performance, we both know Q.
So that was the first type of knowledge. Now the second type is an entirely different type.
So here, in contrast to the first type, communication is not allowed.
So here we see agents that aggregate independent opinions so they aggregate their beliefs.
And these beliefs are based on private observations of pieces of evidence that they have.
But in this case, the evidence is soft. So it's possibly that the evidence is fallible and it is important here.
This is a different type of group knowledge because it requires agents to pull together their information based on independent observations.
So the evidence that they have, they make an independent observation of this and they base their opinions on that.
So examples that you see in the literature are they include the verification of experimental results.
And in the popular science literature, you will see examples that refer to there is an important example,
the case described by Francis Galton in nineteen of six, he went to a livestock fair or that was an ox on display.
And what Francis did was he collected all the so there was a contest where people could win a prise
if they guessed the weight of the ox correctly and they each had to ride their guests on the ballot.
So Francis collected all these ballots. And what he saw was that in the end, if you would take the mean of all the guesses,
take the average, then that actually gets to the almost exact weight of the ox.
So he saw that collecting that independent information from all the agents and then saying this gets you to almost a perfect answer.
Other examples in the literature are, for instance, counting jellybeans in a jar.
This is also an example that was done by Jack Trainer already in the 1920s.
In his classroom, he took a jelly bean jar to his classroom, asked the students to to give an estimate of the number of beans in the jar.
And he saw that if you take the mean of all the gases, you get almost the correct answer.
So here indeed, this works very well. This method for aggregating the beliefs works very well.
But it is based on this idea that no communication is allowed and the guesses of the individual participants are independent.
Now, we can illustrate that with a more concrete example.
So this will be what I call the first or an example. So let's take a sequence of individual agents.
And I enumerate these agents a want to. And and the idea is that these agents entered into a room.
So they stand in a line. So it's a sequence. They enter into that room.
And it is common knowledge that in that room there is one of two possible urns they don't know and they can't see which one from the outside.
So it is either urn w an urn w contains two white walls, one black wool or A or B and urn B contains two black males and one white ball.
So that is common knowledge that one of these answers in the room and in person goes into the room there,
a ball from the urn looks at it and makes a guess about which urn is placed in the room.
So the guess is either W or B. And in this case, the guesses are secret.
So no communication is allowed. So imagine the launch sequence of agents, each entering and having a guest that may be writing that guess on a ballot.
Now, at the end, the poll is taken of all the people's guesses.
And what we see is that the majority guests actually will then be what we call the potential group knowledge.
Given the size of the group gets to infinity, the group gets virtual knowledge of the real estate,
with a probability approaching to one of the size of the group is large enough then the truth.
Tracking power of this group via this method is excellent.
And the standard explanation for this can be given by some variation of council says here, which is essentially based on the law of large numbers.
And what it says here is that when you perform many independent observations, the individual errors are the pieces of private evidence.
Supporting the false hypothesis will be outnumbered by the truth supporting evidence.
So have statement of the theorem a little bit more specifically correct state.
So the term Sesto, the probability that the majority selects the correct alternative approaches to one when the size
of the group and goes to infinity whenever the following three constraints of conditions are met,
namely, one, that each individual makes an independent decision based only on private information,
the likelihood of the private signal to be correct has to be bigger than than a half.
And in this case, the choice has to be binary. So we have two mutually exclusive alternatives that people vote on or that they choose from.
And that's an important term.
It has also been used in a lot of different contexts, so the term of Canossa has been used to argue in favour of epistemic democracy.
That is the idea that in normal circumstances, the democratic forms of belief aggregation are indeed conducive to the truth.
So I refer you here to the work of Christian listing Goulden for this and what they did in their work.
I've quoted here, I've put the reference for the paper is that they actually extended the term in their argument to go beyond the binary choice.
That was the last condition in the in the statement of the term.
And so they looked at voting scenarios that involved many correct options and they still saw that that mattered.
So this extended version of the concept here is very good.
And as a as a belief aggregation method that is conducive to the truth.
Now, what if the jury tarim, if we look at it?
Of course, it assumes that the agent's opinions are based only on independent observations, like when the people entered that room in the urn example,
they look at the bull and they have to make their guess independently and they have their own private independent evidence for doing so.
Now, what happens if we allow some communication of observations?
Right in the example to now, communication was not allowed.
If we do that, if we allow communication, then the opinions will no longer be independent, which means that the Condorcet jury theorem will not apply.
And intuitively, we might think that after communication,
the agents entering the room in their own example later on now they would have maybe more information than just their private observations.
So you would intuitively expect that the group's knowledge would increase.
However, as we'll soon see when I extend the example, this is actually not what happens.
So that brings me to the fragility of group knowledge.
So we've talked about two forms of wisdom of the crowds and two specific forms that we're linked to, two types of group knowledge.
So both types are actually very fragile because any change in the conditions that are necessary
to bring about or actualise that form of group knowledge can lead to under optimal results.
So let's look at the first type of group knowledge, which is the one that is based on communication.
Now, in this case, if the information exchange is limited by the network or by the degree of trust or by the agents agendas,
then the group will only actualise something much less than full distributed knowledge.
So this type of group knowledge can fail. And the reason for the failure is that we would say that it this some kind of selective hearing.
So in this case, the agents listen only to certain sources, mainly only their friends.
They listen only if it fits the information, fits their own interests or fits their own agenda.
And maybe they only accept new information if it is consistent with their own
information that they already had from before or their pre-existing beliefs.
So in my work with my co-authors,
we looked at the specific example which we called the curse of the committee because of many deliberating committees.
After long sessions of deliberations, information sharing, a lot of argumentation,
it often happens that the only thing at the end that the committee agrees on
is actually what they already agreed on before the start of the deliberation.
Of course, in these cases, the potential knowledge of the group seems to boil down to the groups prior common knowledge.
So the information that was common knowledge at the start of the deliberation,
but not at the end, and that is typically much poorer than distributed knowledge.
Distributed knowledge is the ideal of what they could reach. So in a sense, this is the worst possible outcome.
And now one reason why this happens is the selective hearing principal were an agent's actual or potential
knowledge is limited by her own epistemic interests and her own questions and the things that she's interested in.
So the liberating agents here are process only the information that is relevant to their own questions or issues.
So in joint work with example Baltar and Rachel body,
we explain this example of the curse of the committee by looking at the at each of the agents epistemic
interests that they have to actually give a full formalisation of this which are not doing this talk,
but I will give a reference to it. So we use a version of interrogative epistemic logic that is an epistemic logic with an interrogator component,
which means we are also modelling the questions on the issues that the agents have,
and we put a specific constraint on how we represent the knowledge of the agents,
including the fact that one can only know things that are actually relevant to the issues
that other agent has so relevant to the questions or the agenda of that specific agent.
And the same constraint also applies on the dynamics that we that we model.
So so the dynamics means that here we look at public announcements and that has to comply with the selective hearing principle.
So an agent only learns from an announcement if it's relevant and only learns from an
announcement what the relevant consequences are relevant to the agent's own agenda.
When you do that, then it's easy to see that this indeed can lead to radical disagreement.
Now in the paper rules to formalise what the actual group knowledge is in such an agenda driven context,
so what we call the the groups, the group Jews epistemic potential or group potential group knowledge,
we the this case superscripts that will amount to whatever will become common knowledge after each
agent in the group updates her knowledge with the group's information that answers her questions.
So this potential will typically lie in between initial common knowledge and what is known in the literature as distributed knowledge.
So what we saw in our paper is that distributed knowledge and common knowledge,
which are the main concepts from epistemic logic and the standard literature,
they can actually be seen as too extreme versions of this potential group knowledge,
giving us the upper and the lower limits of the group's epistemic potential.
So if every agent in the group is interested in all the information processed by all everybody else, so when the agendas are very cohesive,
then the potential norm, which is maximal and then the potential group knowledge will be equally distributed knowledge.
But on the other side, if the agents have very dissimilar agendas, namely the questions they are interested in are orthogonal to each other.
So no agent has opened questions that can actually be answered by the group than a potential group.
Knowledge is very minimal.
And in that case, as in the curse of the committee example, they will fall back on what was already common knowledge of START.
So the lesson that we learn from this is that a group formed of agents with very dissimilar agendas will have a very poor epistemic power,
they say, actually with what they already knew. And when you have a committee or a group with a very cohesive agenda, with dissimilar expertise,
still need specific experts coming together, they will have a very strong epistemic potential.
They can actually learn from each other.
And here I gave also the reference to the paper in which we explore these results, where we make this formally precise.
So the curse of the comment,
the example that we investigate that can actually be related to what is known as the common knowledge of fact and social psychology.
So that common knowledge of fact refers to the case where groups tend to focus more
on information that is already common rather than on sharing their private evidence.
Now, the specific relation to this common knowledge of facts should still be explored because
it's my understanding that they actually talk about another notion of knowledge,
which is not really common, but more something like mutual knowledge of what everybody knows.
But still, it's interesting that this effect has been has been studied and points out that information that the committee had already before the
deliberations starts is something that the group tends to focus more on that rather than on sharing their own private information.
So if I go back now to our information society, then what we see happening is that.
People choose with whom to associate because information technology also enhances visibility for more choice based on our preferences or agendas,
and having more choice is a good thing. At least that's what we think.
But these examples also show that information exchange can be filtered through these choices.
So more connectivity without more epistemic sophistication can actually accelerate effects of polarisation.
The formation of Cascades echo chambers. So these examples bring me to the to the fragility of group knowledge again.
But now let's look at the second type. So we've we have already seen the fragility of group knowledge that is based on communication and
what can go wrong when people filter incoming information through their agendas and interests.
But let's look at the second type of group knowledge, which is not based on communication,
but which is based on some judgement aggregation method of voting out where people have to bring together independent opinions.
So this type is also very fragile and also prone to failure because in this case, any breach of the agent's independence.
So any communication can lead the group astray.
And typical examples here are indeed the formation of an informational cascade, which you can think of as some form of rational epistemic bandwagon's.
Forms of groupthink and notion of pluralistic ignorance is the scenario where we point to a lack of common knowledge leading to group.
The group suppresses generally shared results. And another example would be radical disagreement, which will lead to group polarisation.
So let's zoom in on the first example here on the formation of an informational cascade and on its social and epistemic features.
So so imagine a sequence of people.
So the line of of agents as an example and the informational cascade happens when the people in that line base their
decisions by observing the decisions of the predecessor and they follow their the decisions of their predecessors,
ignoring their own private evidence that they have. So these situations happen.
And it is not necessarily the case that this is just mindless imitation that we're imitating our predecessors.
Now, these people are not brainwashed. They're not part of mass hysteria.
So actually, this happens in cases where people are assumed to be rational and they have a rational choice.
So let's go back to our own example, which is very fit to study this,
so take the same or an example that I have had before where the standard explanation was given by the commerce security here,
except now I will allow a limited form of communication.
So when each agent makes her guess for W.O. or be the guest now is immediately publicly announced.
So they go into the room, the drawable they look at it and they make a guess.
But instead of writing it on a ballot, they go out and they make a public announcement.
And what we see is that an informational cascade arises with a probability of at least one of the nine.
If the first two votes that are drawn are of the minority type.
So suppose that the person in the room is or w and the first two agents who enter the room, they take a ball and it is a black ball.
Then the first two agents rationally will guess B. And they make their guesses publicly.
There is a public announcement of their guesses to everybody else, everybody else can hear it,
then all the agents will rationally follow and they will all select the wrong option B. So the wrong option will then be unanimously selected.
So now we can ask the question, is this rational? And according to an analysis, actually, the answer is yes,
because given the available information that the agents have been agents that follow probability
theory and based based update's rule when they are interested in individual truth tracking,
they will behave exactly in this way. So this means that individual Bayesian rationality can lead the whole group to the wrong answer.
So to group irrationality, some people throw doubt on the Bayesian proof because it doesn't make explicit the higher order reasoning of the agents.
So you can do the whole analysis of what happens in the unexampled right down a right of the proof.
And what you will see is that it has credence is there for a simple belief,
but it doesn't take into account the high order reasoning in higher order beliefs of the agents.
So the question we ask ourselves is that if agents who who have a theory of the
mind and who can reflect on the overall protocol that they can reflect on,
on the whole scenario that is happening and they can realise that they are part of a cascade and they reason about other agents minds.
Can they avoid the cascade so they can piece everything together?
They have a full theory of the mind. Can they avoid the cascade?
And that can happen for a number of cases. The answer may be yes, but it is not for an example.
So whatever reasoning powers that we give our agents,
it's not going to help them to make a rational decision to break this cascade of what we did in our paper.
So I've given the reference to the paper here. Below the slides is that we we have a proof for that.
So we use dynamic epistemic logic to prove three, prove the argument.
But now in an epistemic logic setting where you can make the reasoning powers of the agents explicit so the agents have unlimited reflective power,
these are super duper agents. And I mean the tools that we have for this, they work very well because indeed,
epistemic logic incorporates all the levels of knowledge and belief that agents have.
And dynamic epistemic logic allows us to add the knowledge and belief that agents have about what is going on,
about the protocol, about the informational events. And the conclusion that we have is exactly the same conclusion as you would get in the case.
But now that the full logical omniscience, higher order reasoning,
reflecting that you're part of an informational cascade is not enough for you to rationally make a different decision and get out of this cascade.
If you look at the informational cascade, note that the the strength of evidence that agents have doesn't get higher,
doesn't rise as the cascade progresses, because the third guess in this example itself is actually uninformative.
Only the first two guesses, if you think about example, are informative.
So the information of the fourth person or the person on the line is actually exactly the same.
Of course, a reality we see that people are reinforced when they see millions of other agents and from them all making the same decision,
even though maybe the only informative part are the decisions made by the by the first two agents and not everybody else around it.
But it might increase. In reality, we see that people tend to have a stronger level of belief,
even though that is done in that case for the unexampled, that would not be justified.
What we did in our work is, well, it's a different paper here on the dynamic epistemic logics of diffusion and prediction and social
networks is that we generalise the case of Cascade's in in this linear networks to arbitrary networks.
So arbitrary, I mean, any type of topology, not necessarily linear line.
So we looked at how fashion and behaviour propagates in a network of agents with its theory of the mind.
And in particular here, these agents can anticipate or predict the behaviour of this of their friends.
So they use their higher order,
reasoning power to get reason about what the other agents are going to do and they can predict the behaviour of the others.
So what we saw the conclusion of that paper is that knowledge does make a difference.
So if you look at the speed of how fast?
It emerges then the epistemic ingredient is very important because knowledge can speed up the spread of this cascade formation,
so the spread of behaviour while on the other hand, uncertainty can slow it down.
Even more, if it is common knowledge that everybody knows everybody's preferences or behaviour,
and of course, also they need to have some knowledge about the network.
So the friendship relation. So who's connected to who then, those gates?
Could the principle be reached immediately? So that will immediately speed up the formation of the of the whole cascade.
So, again, the analysis that we did for that paper is fully based on dynamic epistemic logic where you can make
these aspects also the prediction of behaviour of others fully explicit in the logical language.
Now, these cascades happened in reality, and there are many examples of newspapers are full of them.
But let me give you one example that comes from the Netherlands.
So this is an example that hit the newspapers and it was called Project X.
So it brings me to 2012. So we we are in the city of Hama and Hanna is close to Thorning in the north northeast of the Netherlands.
So in 2012, Miftah invited all her Facebook friends for her 16th birthday party and she made the
message public on Facebook so that friends could bring in principle other friends.
And what we saw is that one friend invited five hundred other people, so five hundred other friends.
And then a day later, sixteen thousand people signed up for the party.
So by now, Metho and her parents, they were getting afraid of this and said, like, OK, this is getting out of hand.
This was not the intention. We are going to cancel the party. The party was cancelled.
But what happened on Facebook is that other organisers took over.
They called it Project X and they they kept sending out the announcement for the party.
So very soon. Thirty thousand people signed up.
And exactly on her birthday, four hundred thousand messages were posted on Twitter.
And in the evening for the birthday party, still thousands came to harden.
And this and the very bad side ended with riots with the police metho and her friends and her family.
They had to leave the town. They couldn't. The whole street, I think, was evacuated, or at least her family had to evacuate.
Examples of this type and saw, especially via Facebook and Twitter, we can see these online cascade's emerging.
If you look back at the classic example, specifically in the iPhone example,
then we saw that the cascade happened because agents were rewarded for their individual through tracking capabilities.
So they were I mean, we didn't make the reward or the payoff explicit in their own example.
But the way to set up is they actually only looked at their individual evidence and they tried to get each of them individually to to make it correct.
Guess for the earth to be W or B now, you could change that.
You could make the playoffs explicit and change these payoffs, rewarding the agents,
not when they got to the individual rights of their individual guests would be correct,
but maybe rewarding the agents only if and only if the whole majority, the whole group tracked the truth.
And in that case, the cascade should not form because rational players will then disregard the information that
they received themselves and they'll take the information that they got themselves into account.
But disregard the guesses, the information that we received from the others, simply guessing the urn that matches to the colour that they saw.
And they should take full advantage of the recessed herem, which was perfect,
the tracking the truth when they disregard the communication of the others.
Now, that means that if we look closer at the role of communication in this, so.
In the cascading earn example, agents were allowed to communicate and then the Cascades could form,
but it happened because there they were allowed to communicate their opinions,
but not their justification, not their private evidence or their private reasons for making a certain guess.
So communicate communities who do communicate all their private evidence,
you could think while this solves maybe the problem in the informational and the unexampled,
where we see that informational cascade happening because of people in that case would announce their private evidence again, should not happen.
But that is not always the case.
So communities who do communicate the private evidence aren't necessarily always guaranteed to track the truth and to do better.
So there are many other cascades that can happen where to enrich too much communication
actually is counterproductive and where too much communication leads us to a problem.
So this is in formal epistemology and in social epistemology.
This is what is called now the Solman effect. So it refers to Kevin Zillman, who works at the University of Pittsburgh,
and he studied the effect of connectivity on the ability of the scientific community to track the truth,
because we as scientists, we would think that more common connectivity in science is better.
Right. So lots of conferences, lots of communication. That should be good.
Now, what Kevin found and that shocked the community is that while the answer to this is actually no,
there are contexts in which a community of scientists as a whole will be more reliable
when the members are actually less aware of the experimental results of their colleagues.
And the reason for that is that too early elimination of correct alternative theories or options or experiments that can actually block the
group from tracking the truth so they influence each other and they might just decide not to explore these alternative options anymore,
which might be the key for the whole community to get it right.
So let me give a quote of Solomon from his 2007 paper.
So he says that in circumstances where speed is very important,
but where we think that our initial estimates are like likely very close to the truth, then connected groups of scientists will be more reliable.
So if we already happened to be close to the truth, that will do well. On the other hand, where we want accuracy above all else,
then we should prefer communities made up of more isolated individuals so that the number of conferences that we see exploding in our communities,
maybe not necessarily always good for the whole community to get to the truth.
An example of a cascade in which more communication is harmful,
all the while there are many examples, but it would lead, for instance, to believe polarisation.
So believe polarisation is an informational cascade that is then produced by the widespread mutual communication of a preliminary test results.
So not the final results. And that leads to two early elimination of alternative options or theories that are out there.
And together it leads to a strengthening of a biased majority opinion, and that biased majority opinion will not track the truth.
So in these scenarios, more communication means that it is much more likely that polarisation can actually occur.
So then the question is, how can we actually avoid this problem?
So how can we get out of that? Well, Spirit Sparks Networks would be would be an answer one would need to preserve the opinions
independence for long enough so that enough alternative theories and approaches are checked out.
As a result, the probability of a cascade will then be lower.
So on these past networks, they are more likely to be through tracking in the long run.
Now, of course, there is a Trade-Off between efficiency and speed and on the one hand and the reliability on the other hand,
by reliability I mean here the avoiding the avoidance of a cascading, merging or polarisation happening.
So because these past networks there may be very good for science,
but there are also very slow at converging to any common opinion whether that opinion is true or not.
So a very connected network converges very fast.
And so when it hits the truth, it does so in a very efficient way and much more efficient than a sports network.
But we are, of course, not guaranteed that it will hit actually truth if it is very connected.
That's exactly what Kevin shows in his work.
So. So indeed, we have this trade off between speed and reliability and what the community would meet and also what the community of scientists would
meet would be a communication network that lies somewhere in the middle ground between extreme sparsity and extreme connectivity.
Now, of course, there are agencies out there that know these theories and they can use it against the scientists.
So you can if you know that alternative theories are necessary to progress science and that
more alternative theories need to be investigated before actually full opinions are formed,
then that can be used to create doubt and to undermine scientific truths, maybe when they have already been established.
So when a community is too slow in tracking and maybe even just to convey the truth,
even when they know it already, it will leave room for unwanted effects.
So so I recently read an article in The New York Times and the headline there of that
article was Influencers say that they were urged to criticise the fire's SURFAXIN.
So disinformation efforts to reduce public confidence and covid-19 vaccinates try to role social media commentators in France and Germany.
So you see that this idea that diversity of attempts and theories is good for the progress of science.
Less activity is good. We don't need to converge to the truth too fast can also be used against us.
So, for instance, when people build an on their mind, a truth that has been already established and tried to communicate that very fast.
So if you take stock now of the different epistemic and social dilemmas that I've touched upon,
then a main factor that you'll see is that indeed rational individuals can form
irrational groups and rational groups might well be composed of irrational individuals.
This, of course, is nothing new in formal epistemology. This is known as the independence thesis.
And this refers to a paper that was written and published in Philosophy of Science in 2011 by Michael Wilson Commensal and they could Dang's.
So they also on their paper, give lots of examples that come from philosophy, science game theory, social choice theory.
Now, the question is, what kind of what can logic do?
So can logic help us understand these social epistemic dilemmas?
And I would argue that, well, yes, logic can provide us with good models to reason and predict about this epistemic distortion.
So for for a diagnosis that we want to to put on the problem, then, yes, logic is a very good tool.
And examples are given by the examples that I gave in the presentation here.
But so we use dynamic epistemic logic to investigate the formation of these informational cascades.
We also study a and social properties in interaction when we analyse the the behaviour of both online behaviour of influencers.
What triggers the formation of a polarised network of these filtering mechanisms that you might see in an echo chambers,
the effects of homophily, etc.? So there is, of course, network theory out there and a lot of simulations and models are provided by network theory.
But most of the models on the market don't take the epistemic ingredients into account.
And I think for studying groups of rational agents, the study criteria are crucial.
So that leaves another question. So can computer science help us verify and debunk our social software, diagnose the weaknesses,
provide tools that could be used to prevent the worst forms of groupthink?
And maybe that's a question for computer scientists and the audience.
So that brings me to my last slide. So here's my conclusion.
So let's look back at the question of the lecture. So will the facts.
And then, I mean, the actual facts, things that are true in the world eventually emerge from the noisy network of irrelevant data,
self confirming rumours, idiosyncratic likes, or, to put it more bluntly, will truth survive the information age?
Now, I am an optimist. So according to me, knowing the mechanisms for mass information aggregation, for information exchange,
we have to learn and know the epistemic benefits and costs and all that is just a start, but it is a very good start.
It will hopefully allow us to perform some of the most urgent interventions very soon.
I know that some interventions already are being taken.
So there is also when you look at how the platform, the main social platforms are being organised, there is input from the legal side,
their actions on decisions made by politicians, the platforms themselves, also how fact checking algorithms, tool to fight fake news, etc.
So that is very good. But I also think that this epistemic factors need to be studied more and they play a crucial role in the formation of these
online cascade's and all these negative side effects and distortions that we saw for the different types of wisdom of the crowds.
So with this, I think this concludes my lecture.
So thanks for listening and for attending, and I don't give the floor back to some.
Thank you very much. But that was a wonderful lecture. And you showed us how logic and fútbol methods can reach deep into a lot of the prominent
features of our information age in a way that I think many of us will be really illuminating.
So, OK, so we we couldn't go to Q&A.
We if you do have questions for Sonia, please send the questions on the on the Q&A panel, which you can find, as I said, with the icon.
But the question mark. So let me just start with a couple that have been sent, one coming, I think, from a philosophical side,
simply asking, since you speak of truth, what is the notion of truth that you're working with?
Is it as simple as a simple bubble theory account, enough to fully encapsulate truth?
So that's the question. Oh, yes. The notion of truth.
I mean, it's the one that we use and logics of correspondence to the facts, I would say.
So it's a very simple one. It's not so that many different notions of truth out there.
So, for instance, coherence, theories of truth, where information needs to be coherent or things like that.
So that's not the one I'm referring to, but just something that corresponds to basic facts and reality.
So something that can always be checked. So, yes, of course, also on the logic side there,
there are logics to deal with different theories of truth and to formalise that explicitly, but that we don't do so.
It's always mixture. An external factor to the models that we have is somewhere a little point that says this is the true option.
Yes, this is the model who has to build in that option and he needs to have that information available.
So we assume that that is possible to get that information available, even if the agents don't know it.
So agencies don't necessarily have to know that something is true.
Knowledge is truthful, but not necessarily all the agents in the model will have access to that point,
that is the real world, but the modellers should have that have access to that point.
No. OK, thanks. So another question, what would your what would your suggestions be for us to come?
I mean, this is the sort of the big practical question that, you know, they so the question is,
what would your suggestions be for us to combat fake news in today's super connected society with all the powerful social media and,
for example, Facebook, whose whole kind of mission is to make people more connected?
So that's what they're all about. Oh, yeah.
So there there are many things that you can do to combat fake news.
So I think an important one is to educate people, self critical thinking, self reflection, sending our students to school,
giving them logic classes from the beginning, that that is crucial for critical thinking because and I do think that will help.
So that is one thing.
So educate people so that they can they can think first when they see something, without retweeting it, without sending it further,
if it is not coming from a reliable source or not from something that they're absolutely certain about.
So that would be one thing then.
Currently, I think some universities designed this fact checking games that people can play so people can then see how well they how good they are.
In fact, checking some of the fake news themselves and can they discern the fake from the real news articles.
So these games are excellent because means that people will then think about these things and they can they can learn.
And are we also see that the younger generation actually is already much better at doing
so than the ones that are growing up with all the social media and are fully connected.
They will not necessarily believe every message that they see.
So they already have built in better mechanisms of finding or ways of of eliminating the falsehoods from the truth.
Now, of course, the platforms themselves can build fact checking algorithms, which they do.
So there is a lot of work on the computer science side and that should continue.
I think that is highly urgent that we do that, that should improve.
So I don't have details because I didn't look at the algorithms and how they are designed and how they work.
But that is, of course, very good. And then regulation is the other thing that the politicians need to regulate the information that is being spread,
because the newspapers that are sending out information and the news channels, they have very, very clear and strict code of conduct.
They they know what they are allowed to do. And they are not just broadcasting anything.
So we know when a news agency is a reliable news agent, but on these new information platforms and social media, we don't have these these mechanisms.
So they are totally not yet regulated up to the level that it should be in order to
prevent people from being influenced by fake news and taking it as a truthful fact.
So, yeah, so there are many things that we should do.
And it starts with education. I think these games that are on the market.
So there's a lot that people themselves can do and making them aware of this is is also very good.
OK, great, thanks. We have a there was a there was another question by Mike and Mike, Mike Wooldridge, which is similar, I think,
to what you've just been answering, sir, about this sort of work on automated fact checking and fake news, fake news detection and so on.
I think he's mentioning in particular using deep learning to detect fake news.
So he's asking if you think logic can play a practical role in this as well?
Well, yes, I think that I mean,
logic is the science for truth plays an essential role where I would say that the machine learning algorithms they don't necessarily have.
So they might be able to deal with a large set of data.
But the way that they deal with that data is not necessarily that they need to have an exact item that is labelled as the true one, right.
So logic inherently is about valid inference.
The notion of validity and truth is built into the whole practise of how logic works.
That is not necessarily the case in the machine learning algorithms, of course, for fact checking.
They will have something that allows them to to find the source of the information that goes to to the the one that is labelled correct.
But it will then be automatically from the outside.
Still needs to be labelled as this is the correct one. I think that logic can play a role and it should play a role because for but
that is more an answer for not only for fact checking in the fake news case,
but for all of A.I. where you do want to make you want to give an explanation of why a certain decision is made.
Right. And so the machine learning algorithms by themselves are not necessarily very good at exactly that point.
So explaining how you get from input to output.
So that whole track of the inferences is something where logic is very good.
And so combining these two methods would be ideal, where you have one tool that can handle large datasets much better than what the logic side can do.
But for the explanation part, according to me, logic is very good.
So so ideally, you would build a new system that combines the two.
I know that people are working on this, but yeah, that's actually a big challenge.
So combining the old and new AI together. Yes.
OK, thanks very much then. Well, actually, let me ask another question.
This is, do you think there could be an application for research and market simulation?
Um, I think so. I mean, so the one of the things that we are doing now is, for instance, using model checking to verify.
So you use logic to specify certain properties of behaviour of the network, of the agents,
of the beliefs that they have now, then you can apply that to a whole bunch of situations.
And then the question is, OK, this gives us insight in the behaviour.
And so you could use it, for instance, when when agents anticipate that you are going to buy the same type of shoes that as your friends, et cetera.
So so if we can anticipate that a cascade is going to happen, then we can predict what the next fashion is going to be.
Of course, it is very difficult to do that.
Many factors play a role and we, the logicians typically abstract away and reason at a very high level of abstraction.
But could the principle be used for that? Now, if you actually want an actual application, then I think so.
Going back to the to the A.I. story, what you could do there is that in cases where you don't have a lot of data available and
you want to reason very exactly about the behaviour and the specifications of the agents,
you could do run a model tracking algorithm for that behaviour to check if it is true in a network.
Right. So it will be much more accurate than some of the other algorithms that are out there.
So then you would use some of the tools from computer science with the logical specification of these properties,
and you would do the role model checking on these networks that can lead directly to some application.
And you could. Yeah, it's not necessarily for predicting what the market's going to be or what will happen to cryptocurrency or with the next version.
But so this is more on spotting the influencers or the bots in the network, for instance.
Right. Yes, thanks. If I could add a question of I mean, I find this discussion fascinating on one of the other information,
Kaskade example that you mentioned, but the models with communication and the way that this kind of defeated event sort of dynamic,
dynamic, epistemic well, analysis with where you could model higher order beliefs in some sense,
I'm following a comprehensive analysis that you still wouldn't kind of refute this or detect in some sense this information.
Cascais, on the other hand, you know, we standing outside can see that there's something wrong here.
So, I mean,
does this suggest a further kind of level of analysis we could put back into a logic that would do a better job of detecting information flow?
Cascades, Cascade's? Yeah, indeed.
Of course. So if we would model the modellers that are modelling the phenomena so that some kind of.
Yes, some kind of reflection, you need an extra reflection, which is not just what the agents that are making the decisions,
but that is just the the ones from the outside who are looking at this and strategically may be reasoning about it.
And we see this happening. Right. So agencies are looking at the formation of these cascades,
because if I can predict the market and maybe just start market cascade so that everybody will buy the same type of shoes tomorrow,
namely the grant that I might be producing,
then I can make sure that I can manipulate the first agents in the group and hope that the North will follow.
And I can set the trends right. So so these mechanisms mean that you don't understand how this works and there are models for it.
But these are at a higher level because at the level of the agents themselves, they're higher order reasoning.
Power is not going to break the code, but the ones from the outside,
they can say they can see exactly where to intervene, where they could manipulate the formation of the cascade.
They know exactly which information to drop out, at what point in order for the cascade to derail or to stop.
So, yeah, so that we didn't do that in our logical setting. That would be very interesting.
Yes. Shows how subtle the and rich the sort of modelling capacity that one is led to and analysing these situations.
I think also having to make a comment, taking out what's already been said.
We now see there's a whole genre of books that because there's been a lot of statistics around in the public domain with the pandemic's,
we see a whole chapter of books covid trying to interpret this for the public at large.
It occurs to me that maybe something else also brings in this epistemological
ideological aspect would be a very valuable resource for the for the general public.
So maybe in some of the spare time for all the other things that you're doing, you should think of doing that.
OK, so do you have any any other questions for Sonia?
OK, if not that I don't see any right now, then yes.
Well, thank you very much again for a fascinating lecture. And it's really been shown that logic is going into these new and actually very central
and important areas and in current life and doing so with a lot of incisive ideas.
So thank you very much, Tanya. Thank you for the invitation.
Was almost a pleasure to give this lecture. OK, so that will conclude there.
OK, thanks, everyone.