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Predicting War - Minority Report Meets World Politics

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so it is in the and
the CAC
and thank you very much for the introduction and predictions are very very difficult especially
about the future of um when I was about 10 years I discovered 1 of my favorite books in my grandparents basement and uh there was a book an illustrated book about the future from the 19 sixties what is interesting about these pictures and what fascinated me how um back then already is that they're so wrong and so hilariously wrong the and later discovered that this fascination of Scott Ritter futurism how yesterday viewed
tomorrow this 1 is particularly interesting it's called leaving the upright in the year 2 thousand and some 1882
and what I like about it that it has 2 elements and has flying cars flying call something it's something we don't have yet and going to the opera as a mainstream activity which sadly no longer really have so in conclusion and sad to say that we are extremely bad at making predictions and this of course
also applies to political political science and international relations and this is in the image from the election Crimean according to the and the society for worm just over this just a politican Germany as the famous think tank in Germany almost no security expert could have foreseen that Crimea is not part of
rational if you like another recent example in June 2014 the Islamic State captured major parts of northern Iraq even though this region is covered by a lot of political experts hardly ever hardly anybody anticipated that this would take place the this phase of the phase of political experts as also documented by science that a famous book by by the
expert Philip Tetlock called expert political judgment in which over the course of 20 years he tested the predictions of 284 experts and his conclusion is at at a lot of
times chance makes better predictions than political experts even a basic algorithm would make better predictions than political experts this of course raises the questions 1 of we have more data and better algorithms will we also be able to make better predictions and so this is
a patient a pattern which Amazon files uh in the end in 2012 on which was
granted by the end of 2013 and that's a patent for anticipatory shopping so but this algorithm does it and anticipate what you're going to buy before you have bought the and the data they use for this prediction is previous orders products searches wish list the shopping carts in even how long your occurs stays on the item that so the method they use of course is you state of the past to make predictions about the future and so and this raises the
question of course will dictate and using the term now and and more data and better predictive analytics methods make predictions easier and better also in political science so this this by Fourier and an
example for the the interest the recent interest and predictions is a book called is a book by Patrick Tucker called the naked Future of world that predicts our every move meeting everybody in the room is familiar with the Big Data debate and also the criticism that occurred over the last years but what we tend to forget about what I would like to stress is that this is also a debate which changes that are understand our relationship to the future the before I would like to talk about odds
and then give you some examples of them using predictive analytics and political science I would like to take a step back and argue that this is actually not a new thing the so the the desire to predict the future is actually an ancient wish and this is the era the image you see is the Oracle of Delphi the that the oracle bestowed on behalf of the gods and was not only consulted for personal matters but also to predict the outcomes of war for example the and I think what this example shows is that even though the idea of the future as progress and the future
as well as inevitably improving is the child of the Enlightenment thinking about the future is a very human thing to do the very basic human activities making plans investing building requires some working image of vision of the future the the the 2nd thing which is new about the dream to use predictive
analytics to make predictions and is the fact that there's been a long tradition in social science to use quantitative methods and
statistics in order to make inferences about the future and this example here uh so 1 of the earliest example for the use of them also computers is the 1972 prediction by the club of Rome called the limit of growth the and what you see here this is a machine building 1949 by the economist Bill Phillips and this is an analog computer which is used to predict the economic outcomes and so if you change 1 parameter of the economy what effects but have some other
aspects of the economy and the 3rd part which
is not new but the fantasy to use big data to make predictions is the fact is that
actually stems from science fiction and there's a book um ever like to talk about diet and is that of them off its foundations theories as of science fiction series uh in which the author predicted or invented assigned to find a sci-fi science called psychohistory and that was as early as the 19 fifties so in the novel psychohistory in the foundation novels I off um pictures of portrays the scientists Hari seldom he develops the signs called psychohistory which uses mathematics history and sociology to collect all the data of the Galactic Empire and predict its future and as the dollar test this prediction
is of course ultimately to change the course In this favor the so if you go back to the original question and we have pretty bad at making predictions and the idea of using data and mathematics to make predictions is a very ancient desire and and of course this also has a tradition in political science and and a lot of important Institutes for example which calculate correlates of war
on try to predict which factors influence genocides but what I would like to introduce you now at 3 examples which worked with the narrative that I've tried explain at beginning of this talk so what you see here this is a school in Pakistan In the past 7 years over 500 terror attacks were and suicide attacks have been carried out and in the country and and and and a very favorite target of these attacks of schools so the company predicted from the uh 1 of the co-founders also from Pakistan the he joined in December 2014 with the United Nations
to make predictions about when and where a terrorist attacks on schools will occur and they do 2 things the 1st thing they try to do is make simulations to test the risk preparedness of schools and the 2nd thing is to predict and they with a 94 94 % accuracy in it with a three-day noticed when and where a terrorist attack is going to come I try to get in touch with them
I couldn't reach and the final which data exactly they're using what they claim that using geospatial data primarily the the 2nd example
elements from 2010 this is the global database of ideas and tone this is also a this an open source project and it monitors the
wells and news media in over 100 languages in real time it identifies people location
things events and tone of the reporting of these events and so this is not so it you can see this assimilation from 2013 over to the 2014 which shows where on earth events happen this case study or this dataset has been used in 2013 uh by the then PhD
student at genome-wide to forecast political violence in Afghanistan and again he did not use traditional forecasting methods like poverty rate or income of the country but instead he used the entirely open source intelligence of information available in the world wide web the and the 3rd
example of like to give uh this is a canonical recorded future which I think it's a great
title and it's uh funded by and the investment arm of the CIA and Google and if you read the Hacker News recently there was a claim that recorded future monitors private Facebook messages which had turned out to be wrong nonetheless would be recorded their future does this they also use open source information from the web that it's broadcast news but also social media news in order to make predictions the main customer of recorded future private companies so you could say what recorded future does in fact business intelligence they try to find out
where do fibers so-called cyber attacks happened on the company uh our data which is going to happen but another activity the companies engaged is also the forecasting off political protest and what you could see here this is the work that its last year on the upcoming presidential elections each of and if your customer of recorded future you can see this is sort of the 1 of the screen you receive and you can see a forecast of future uprising in Egypt and so this well 1 of the
most common question I happen after so uh these predictions actually true can we actually predict the future I think is very important to find out and work out what the precise assumptions of each of these predictive methods are and this is what my fellow speaker Covey is going to do now the thank you be
it so thank you very much for a welcoming me so warmly and thank you for the conference for inviting me and for the record for organizing everything uh so what I'm going to discuss about these uh about uh what are the fundamental issue that exist In this predictions Monday my position will be more I am the professor of computer science and supposed to be somebody that 2 objects the rest of and try to be as much as possible the the objective and that wasn't going to present will be the
kind of criticism you from the technical scientific perspective and I'm going to ask the fundamental question is it possible to predict and if it is possible to predict would be useful and if it's useful would be war so at least we element of question will be the main topic of my year uh through what however going to say in the coming year minutes so 1st of all we have to figure out what is the basic underlying assumption that exist
with this kind of predictive technology and approach the 1st underlying assumption that the disease is
that's all you know all we have that some kind of global rules that govern and all in all the human history on the sociology or the economy
or any of the men in the related stuff is governed by a kind of mechanical all that we push the human to behave in some way the it's quite interesting that this question is nothing new wish we had something like 2 100 years of discussion uh in around this idea that has been in the context of sociology according to the discussion between positivist and the or uh or postpositivists the people that some people claiming that yes there exists come kind of all over all of rules and laws that will uh validate the fact that some topic uh all our calling that science for example politics these political science or sociology these uh kind of science as the same way as physics or chemistry the sigh indeed a the point of or this topic science or something like a philosophy that is not seen as a science as a different thing is by itself the question of interest and uh discussion and
long-term discussion for example if you go and infer that you will see that a topic I history you have some people that our problem and of the position that history is a science and you can predict history let me give you a very classical example of this if you go to historical materialism and you will to the jury of Marx's said that all these stories predict will we are having a kind of process that will push you push us but what communism and he gives as an example of this predictability a set of evidence that has happened in the past sometimes you might get lost in this kind of predictivity for example some people would said that the french revolution is the same nature as the English Revolution of Cromwell because the poor evolution cut the head off the take which is indeed is a question of uh uh user along you're ongoing discussion among the 2nd assumption that exist behind this is the assumption of rational all the human Russian and the the you might rational what call can we define rationality and indeed when we would end up with defined directionality we we are just uncle range another problem which is then there is a different type of rationality and now if I construct a model that will help me predict the behavior of human based on rationality what is rationality becomes a question of politics and we are beginning to see ideology coming into the story meaning that these kind of model and this kind of approach cannot be separated from ideology and in fact what you are modeling what your simulating eased the result of bad ideology of 2 persons to build the model and this is a kind of longer term the discussion you cannot say that when
you're using an underwritten were completely objective because any algorithm is based on some measure mission and assumptions are based on the ideology of the person and when we want to work uh do work still uh assimilation this analysis we have to go back and ideology and figure out what is value legitimate was behind uh this
is a very important point that we learn and also supposed to participate you have an order session just after where we will be discussing dividend deeper about this and think about the written stuff deterred assumption
that is on the laying there and it is important to take into account is the assumption that the only in on a so we had as the 1st step the day issue of uh being the science over and rationality and deterred assumption is
but the fact that there we are able to chew the predicate that this uh uh this behavior and we are able to have some kind of models that will enable us to uh do this prediction and what is more important is that the model that we have uh we are using we are able to seperate the fact of observing most relating from the event's sense meaning that we are not making something that would be a self fulfilling prophesy and this assumption is a
very very strong assumption that is very very difficult to uh to analyze and based on
these we get into as the most book and as you move define
that the uh obstacle history here the book in the forties when he was writing the book and we see that what everyone has said is complex the relevant to our discussion he says that a Harris so that has developed a new mathematical method that is able to predict the future of the empire that is the uh
this is the story and said that these 2
core very has 3 principal in fact defined twomain principle and in the last sentence of the last walk up the 2nd so we he's divided to our
principle the 1st principle that is defined is that it is only epic able to logical for people in the order of billion of human and this is a very important thing all the data mining technique that go using all the prediction technique that we are using or not application built with single person when you say that I'm going to use another attempt to detect the terrorists is simply and subjective assumption can never be object the 2nd the point is that in order to to story to be valid you should make sure that people do not know about the cost meaning that does not able to react and to adapt their behavior to the fact that their behaviorist predicted and the charge rules that as you move defined in the last sentence of his last move is that tically stories on the even we have on the human in the loop meaning that we can predict the rationality and if you have some order elements like in the book Roberts it's not anymore it's possible to apply to gracefully and you
see that Asimov in the forties he just cause in defining to to read in criticizing in defining the is that they give you at the burst is it possible to find global law yes if we are looking at the origin and long enough spec 2nd we need to assume that the death a a but if you go on the 2nd rule of rising after that we are not interacting with the process that go looking at and at the top of the assumption that as you move was doing was that we were able to capture the rationality meaning that we are only dealing with 1 space or 1 type of rationality of human and indeed we can ask or self has you men of today the wall of from the amount of past time or are we not involving all the time meaning that can we assume that all rationality today is the city of directionality of apparent or when further meaning that taking capturing this rationality is base of difficult of they do not say this to a teenager of 14 15 years because he's aspect would be that my rationality is completely different from the rational give my power so the known at school and the risk
and benefit of this assignments as a scientist as an engineer we have been have been educated to always look at benefit and with and evaluate the benefit and risk of whatever I do the most important thing kept that engineer dual is now there are some cases where I can evaluate the benefit I can evaluate the risk and I can trade off between some case where I come along with the benefit but they cannot evaluate the In this case frequently the fallacies to say I cannot evaluate the reason so that which doesn't exist that's about that because
I cannot evaluated so let's get it out of the story but indeed in a lot of cases the Riesz off using a technique or technology is Saul be that we cannot evaluate its maximum value and because of this we should not use this technology because we're not able to evaluate that's me give you a very a concrete example of why we are against that and of the fact that law against that kind the East can be yeah based on some kind of objective and like that he's not reducing the number of crime but the main reason that for against that penalties that the epic of a risk of depend on the which is used to kill 1 innocent is much larger than any benefit that we can find by putting a criminal out of society meaning that we accept that that penalties not accept table just because the research and ethical risk that think the ball is large twice for something that we have to take into account is that all are we capturing or a measuring the release of using this kind of predictive tool indeed when you use the predictive toward for selling 1 device on Amazon to somebody the risk is that the person will buy for much but when I
am making a research and analysis and predicting the wall the reason is that I am beginning to do was set for fair and prophesy because the fact that they know that there's going to be a walk every action that I'm going doing will be an action that would drive me to the wall or make me um getting further from the war and if I am able to avoid a war right predict indeed and able also to make war the term predicting it meaning
that there are some medical risk in some kind of more fundamental research that exists in this kind of stuff meaning that even if I am able to do it what I'm not thinking that it's possible just because of the tree uh previous topic that they said being not clear it's not something that they would like to do and knowledge of they would like to have a very classical example of this is that all of these methods are based on the assumption that the hidden assumption that past will tell us something than the picture we and good In the recent theory there is 1 term that is used for this fallacy which is the battle black swans are animals that we never see them before going to Australia so up to the 19th century the definition of a swan was it white and among that is this 1 that you using to see and now you're getting tossed right and you see a blacks there is another
much more fundamental thing which is the risk of uniformity the fact that
I in dueled a prediction define what is the p called and what
is a typical what is key because these what follow my prediction what is at the
because is and what not for only prediction meaning that by the fact of prediction I find a kind of way of separating what is normal from what is abnormal and what is abnormal what I'm going to do I would put it aside that we see the alkali yeah I was see the somebody that should be removed from the past might vary if we do it is what would be the society that problem to deal because giving the pull their of deciding what is normal and what is abnormal to a statistical model or any type of model we'll also no change in the nature of whom or humanity and will be kind of enabled as the last sentence I'm saying as a as a uh a computer scientist the little mathematician I am not against predictive techniques and not against this kind of metal and just saying that the risk involved and the risk involved or not only the we speculate can access using my purity cult world mathematical statistics meaning that if I am going to do this I have to understand that I have a responsibility analytical responsibility on the unknown effect the what I'm doing and maybe if we have time discussable little more about the on uh non effect and in the next session that they will have to do would be more concrete the and
few
so are we moving from Precrime to free water this is 1 of the questions that come that can be can be as they rocket of title of our talk and anything to its answer this question to some questions the relevant affairs is it possible to predict warm and what we said throughout this talk is the a very good example by analogy to explain the current state of predicting politics as the weather were versus the climate so in the past we were not able to predict the weather or forecast the weather for the next days and even now we're still not able to predict the weather for the next 15 years 50
days and also not 15 years and when you talk to scientists and 1 of the explanation is it's not because we don't have enough data about the whether they're censors everywhere around the world but the problem is in the predictability of the whether the system is simply too complex so counter-argument to this would be that's what I'm saying right now is a prediction in itself and that in the future we might have a technology that enables us to predict the weather and but I think it's still a good point that more data is not always does not always improve the ability to predict at the same time however we are able to model and predict the climate but it's a
very different level of the effect of abstraction the weather is a different thing compared to the climate and the 2nd question is of course even if were able to predict or forecast long-term trends in society if you can say in this neighborhood there's a certain probability that a crime will occur and we can also say in
this region of the world with a certain probability of civil unrest revolution will occur and the question is should we forecast that predicts the future if we can and
I and I think and again this is so in the next section session and you in a couple of other people away from our institute will talk more about this but what has emerged in the criticism are critical debate of do that big data and the last years is that 1st of all predictions are never objective but especially the use of open-source intelligence data from the web to the data as biased in a structural way for example if you go back to the global database of events and tone a lot of there's certain regions in the world which are simply not as well covered as other regions last every public uh there was a talk I Predict a Riot which was fascinating by a mathematician who predicts complex as system and also working on the London Riots and what I found really interesting and also asked the question after the talk was and was used police arrest records in order to predict riots and police arrest records are 1 of the prime example of this and systematically biased data certain people get simply arrested more than others in certain areas of police more than others if you use these days how to model and make a prediction you protect trade imbalances which existed before and then finally yes of course the question is should be predict
um and I think it it's important it sounds very scary but at the same time predicting conflict also means predicting genocides predicting and warrants predicting at terrorist attacks and and even the company recorded future they also predicts a when them they're also predicts the likelihood of data breaches this falls under the category of cybercrime and under this category also lot of legitimate political dissent occurs but at the same time as a think of even the room are against data breaches and would like the data to be protected in a way I'm
and I will conclude by saying and the predictions can be bad at at the same time extremely problematic maybe the most concerning the danger of predictions of
the blind belief in that truth and their reliability and this of course the famous grow by John Lennon Morozova if you wanted so warranties is not an informational problem there enough worst happening right now in the world which we simply don't care enough to prevent so I think it if you do risk and benefit analysis of prediction 1 might come to the conclusion that in certain cases it's probably better not predict thank without
it in only OK so again thanks very much for this panel and I would now say we're open for
questions from the audience so 1st one's 1st and before we start the QDA I would politely ask you to shortly introduce yourself say your name and where come from so was 1st yeah so I'm an insidious I'm a journalist based in Britain uh my question to what the example that you were um induced at the end of from last year's talk about predicting rights you said some bad these predictions were based on a very biased set of data that the arrest records and if you were to act on them some you would perpetuate that bias if you were not to act on them would that be a possibility to toward establish the
the correctness of the prediction you know you don't have to act on that prediction in the sense that you know you would increase for example police surveillance and and monitoring and that could also of course be some 8 1 reason for their it still would start to but if you were not going to act on them uh would that be a mess of methods to at least establish the correctness of the predictions in that case in that example I would say it when it comes to predictive policing um and more in the case of my it so so she said she worked with the she works that is the island of Great work and which is also working with the London police in order to help prevent riots in the future so these predictions of these models are actually used to make police work more efficient and I think it's not about uh whether or not these models work I think they do work to otherwise they would not be called science and not not receive that much publicity but at the same time and you can see this from Wikipedia everywhere the data on the
Web for participation is not equal to their certain people who
are much more likely to write an article on Wikipedia for example there's certainly were much more likely to tweet any prediction you make on the basis of Twitter data even if it's a good prediction in the sense that it was not uh is based on biased data would say and let's say the the question
is very uh very good long and it's not only related to the case of the right
prediction the kind of more fundamental issues in statistics something that fell we have to figure out what that this that's a lot of us have to report in statistics and uh generally statistics is seen as a kind of tool that can answer questions but in fact status can never answer question he can just to reject the opposite of the question this is a kind of mental twisting that we should take into account meaning that when statistics predict you
something it is saying that it is not able to replicate the inverse meaning that a statistician would not be able to predict the existence of a writer you will all only be able to say that the fact that they would not be right up the probability is less than some the these twisting in fact shoulder the uh uh give us kind of caution to use in some of results for example you were saying correctly that the fact that you predicting that there will be a right to have the probability that the of this happening that is high mean that you are willing to put more police the fact that you put more police will increase eventually like like
you but I I do believe a lot of a lot of the predictions are not being published so when the US government publishers now climate or terrorists risk is that science the this could be as a set a strategic move but at the same time that will be predictions which just have effect on have different effects on the people who have access to them and this creates new dynamics of power and analysis of information OK Q and A. is close than for question request last question
thank think you know crystals such a common in front and my Christian assuming
asked Moss along the whole point of the creation of precursors so this is very dangerous because it can be easily abused Our undue afraid that the tools that could be created to predict wall could be menu committed to create 1 of the 1st things yeah said if
you are able to predict war 2 of which are able to predict what avoid them or even to predict more to make them happen and now the basic question is
the while worries of free will of the political question here is that it is possible to build several things but are we going to let them have or we thinking you know the pessimistic view of humanity would be to say that the or like sheets . told to
do something and they do it a more optimistic view would be to say that these totalitarian viewpoint is not new the about and a higher and higher in book the totalitarianism said the 2 most dangerous to competitors and use the totalitarianism that we call by not
saying that I am a totalitarian government because of god why but the totalitarian government that can come out and said that tend to tell Italian because scientists use and in the americas predicted it means for us as citizens that we should be aware of the species and we should be behaving correctly for me as a computer scientist and just have to say that nowadays I think very important to transfer to my students this kind of competition that are raising this kind of discussion by the way In computer science we have no place for having to course know 1 of the existing curricula contain course on ethics if you will put Department of Civil Engineering you will have a course on social responsibility and ethics if you go to order domain of the during the same computer science has not yet been we are just getting awake I do research that exist in that we should and I would say that my situation as a computer scientist is willing to listen lot to the nuclear physicist in the fifties where they were saying that are summaries and by the way 1 of my inspiration way when they talk about the ethics of this kind of stuff is to go and see what they have done and the position of the 2 in the fifties you know what you think your religious
belief is that 1 of them was coming so that such
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Formale Metadaten

Titel Predicting War - Minority Report Meets World Politics
Serientitel re:publica 2015
Teil 115
Anzahl der Teile 177
Autor Kaltheuner, Frederike
Salamatian, Kave
Lizenz CC-Namensnennung - Weitergabe unter gleichen Bedingungen 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben.
DOI 10.5446/31942
Herausgeber re:publica
Erscheinungsjahr 2015
Sprache Englisch
Produktionsort Berlin

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract "Making predictions is very difficult, especially about the future". An introduction to ethical dilemmas and philosophical assumptions of algorithmic political forecasting.

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