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  1. #51
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    Quote Originally Posted by Goga View Post
    I don't understand what you are talking about. My background is political science. Like I wrote in my previous post, political science is part of the social science. Scientists with the social science background (sociology, politicology, psychology and EVEN history) have to use descriptive & inferential statistics all the time. Otherwise their studies and theories would never be considered 'scientific'/academic. Social science tries to explain the behavior of humans, political actors, interaction of societies on different levels. To explain behavionarism and explain different (historical) paradigms you have to use statistics.

    History doesn't mean only field research or just read a historical book or something like that. It is to UNDERSTAND the history and make future 'predictions'. I know people who studied just history and they are using statistics all the time, since history repeats itself and since there are many patterns in human behavior truth history. That's how you can 'predict' the downfall of dictators (political entities/actors), since there is a correlation (patterns) in their behavior. That's why (and how) you can predict the downfall of let say Turkey.


    I don't understand what you are trying to prove. You are 'tilting at windmills'...
    You must be kidding, right? There is no serious maths in social sciences. Using statistics, making statistics, making predictions, calculating probabilities and stuff like that aren't real maths. That's the kind of things everybody does. I consider that if something is not harder than university entrance level, then it's not worth mentioning it. I have compiled the statistics table for the haplogroups on this sites, which underlie all my research and my articles and were used to make my haplogroup frequency maps. But I would never say that this is mathematics. If I go to the supermarket and add up mentally the price for all the items in the basket, I am not turning into a mathematician. That's something everybody does. When I translate my articles into French or Italian, I don't consider myself a translator, because that's not my job or my field of research or interest. That's just something I have to do. I made all the web design of this site but you never see me call myself a web designer. I can replace a light fixture or fix a leaky pipe under a sink in my house, but that doesn't make of me an electrician or a plumber. The maths used in social sciences is so basic compared to those used in physics or artificial intelligence that it falls into the same 'everyday use' category. And by the way, I also studied economics and I know that theoretical models in social sciences are far from accurate and sometimes dangerously wrong.

    My disagreement with Mark and Debbie above comes mainly from the fact that they seem to believe that it is possible to make useful predictions about historical population migrations solely based on theoretical models, completely disregarding all the other fields I have cited in this discussion. You can predict the trajectory of a comet using theoretical models and maths, but you simply can't do it for haplogroup migrations. I have never seen it work, and I can't imagine how it could possibly work. But I remain open to a demonstration. We seem to hold two completely opposite positions:

    - I favour a transdisciplinary approach using logic, statistics and analysis of all evidence from every discipline to maximise our understanding of human population history. I favour hard facts over abstract theoretical models. From my point of view, each large migratory event in history (e.g. Neolithic expansion from the near East) is unique and should be used very carefully to extrapolate on other migrations in different ages, cultures and environments (because humans are not just numbers and their behaviour and success is by nature unpredictable).

    - They insist that the only valid and recognised methods in population genetics is based on theoretical models that can be mathematically tested. They believe that once we have found a model that works, it can be applied in any population, like the immutable laws of physics. From their standpoint, my methodology is useless because it is case by case and cannot be tested and re-used (which would be understandable if we were working on exact sciences, but we aren't).


    I also believe that there are too many unknown factors even for simple mathematical models to be accurate in that field. For example, the age or TMRCA of haplogroups can be calculated from the accumulated mutations, each tempered by the statistical chance of a mutation occurring at that position in the DNA sequence (since mutations are known to happen more frequently in some places than others). But that doesn't take into account the historical population size at each point in the phylogenetic tree, nor the local radioactivity, which increase the chances of mutation happening. A major difference in population size between two branches of a same haplogroup, for example between Siberian R1a (low population) and Indian R1a (high population) can lead to a number of accumulated mutations hundreds or thousands of times superior in the Indian group over the same period of time. Without knowing exactly where a subclade evolved and what kind of population size it had during this development, calculations are doomed to be mistaken, especially in region with unusually high or low population densities or growth.

    In other words, I blame them for having a too simplistic mathematical approach that cannot possibly taken into account all these factors. There has actually been quite a few population geneticists claiming that haplogroup R1a originated in India because genetic diversity was higher there, not realising that this diversity was the result of extremely high historical population size since the Bronze Age. This is what Sharma et al. (2009) claimed, and their study was not only published in a peer reviewed journal, but the most prestigious of them all, Nature. So much for the peer review system filtering out the pseudoscience. That 'peer' word doesn't mean anything. Having graduated in a scientific subject is not a proof of intelligence, critical sense or discernment. In fact the average IQ of university graduates is quite low; 115 to 130 in the US according to this site. Most wouldn't even make it to Mensa.
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    Quote Originally Posted by Maciamo View Post
    - I favour a transdisciplinary approach using logic, statistics and analysis of all evidence from every discipline to maximise our understanding of human population history. I favour hard facts over abstract theoretical models. From my point of view, each large migratory event in history (e.g. Neolithic expansion from the near East) is unique and should be used very carefully to extrapolate on other migrations in different ages, cultures and environments (because humans are not just numbers and their behaviour and success is by nature unpredictable).

    - They insist that the only valid and recognised methods in population genetics is based on theoretical models that can be mathematically tested. They believe that once we have found a model that works, it can be applied in any population, like the immutable laws of physics. From their standpoint, my methodology is useless because it is case by case and cannot be tested and re-used (which would be understandable if we were working on exact sciences, but we aren't).
    It is a good description how we all model population history in your brain. We come up with hypothesis, run many models and come up with predictions. Generally not much different than running predictive models in a computer. The difference is that so far good human brain is much better in it, because it analyzes data sets in much broader environment, among others human behavior, religion or climate change.



    I also believe that there are too many unknown factors even for simple mathematical models to be accurate in that field. For example, the age or TMRCA of haplogroups can be calculated from the accumulated mutations, each tempered by the statistical chance of a mutation occurring at that position in the DNA sequence (since mutations are known to happen more frequently in some places than others). But that doesn't take into account the historical population size at each point in the phylogenetic tree, nor the local radioactivity, which increase the chances of mutation happening. A major difference in population size between two branches of a same haplogroup, for example between Siberian R1a (low population) and Indian R1a (high population) can lead to a number of accumulated mutations hundreds or thousands of times superior in the Indian group over the same period of time. Without knowing exactly where a subclade evolved and what kind of population size it had during this development, calculations are doomed to be mistaken, especially in region with unusually high or low population densities or growth.
    Exactly, and in addition to my previous point. As you mentioned, the complexity of environment is enormes, and it will take a very long time to develop adequate and sophisticated enough computer models to take this huge complexity of human environment under consideration.
    Be wary of people who tend to glorify the past, underestimate the present, and demonize the future.

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    Quote Originally Posted by Maciamo View Post
    You must be kidding, right? There is no serious maths in social sciences. Using statistics, making statistics, making predictions, calculating probabilities and stuff like that aren't real maths. That's the kind of things everybody does. I consider that if something is not harder than university entrance level, then it's not worth mentioning it. I have compiled the statistics table for the haplogroups on this sites, which underlie all my research and my articles and were used to make my haplogroup frequency maps. But I would never say that this is mathematics. If I go to the supermarket and add up mentally the price for all the items in the basket, I am not turning into a mathematician. That's something everybody does. When I translate my articles into French or Italian, I don't consider myself a translator, because that's not my job or my field of research or interest. That's just something I have to do. I made all the web design of this site but you never see me call myself a web designer. I can replace a light fixture or fix a leaky pipe under a sink in my house, but that doesn't make of me an electrician or a plumber. The maths used in social sciences is so basic compared to those used in physics or artificial intelligence that it falls into the same 'everyday use' category. And by the way, I also studied economics and I know that theoretical models in social sciences are far from accurate and sometimes dangerously wrong....

    ...
    In other words, I blame them for having a too simplistic mathematical approach that cannot possibly taken into account all these factors. ...
    Social science is to broad. Let's take history. I am making it more difficult for me since history is less mathematical than sociology or psychology that makes use of statistics for their theories. They make use of probilities and empirical studies mostly for predicitions. (like very simple statistical null hypothesis testing)


    But now let's talk about history and more heavy models.


    If you want to measure some population growth within an area truth history you need some kind of environmental population models.

    If you want to know how much money French Revolution costed the French society, you need to use some economical models. If you want to know how much money Dutch goverment earned from it's colonies in SouthEast Asia during the Dutch Golden Age you need some heavy models.

    How much money did WW1 cost the Germans? For how many years did the development in Europe stopped due to the The Black Death? For all this questions you need different mathematical models.


    Scientists are making mistakes all the time(, it is part of a process). But that's because they are using wrong or too simplistic social research methods. But that doesn't mean that using a method is wrong. In contratry in sciense you have to use the models to support your theories. The ART of science is to find the right methology. And that is very difficult. For every problem there has to be a model. Great scienctists can find this model and use it properly, while weak scientists have hard times to find a propriate model or don't know how to use it efficiently..

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    Quote Originally Posted by LeBrok View Post
    It is a good description how we all model population history in your brain. We come up with hypothesis, run many models and come up with predictions. Generally not much different than running predictive models in a computer. The difference is that so far good human brain is much better in it, because it analyzes data sets in much broader environment, among others human behavior, religion or climate change.
    Not to mention that it would take forever to encode all those variables into a computer.

    Exactly, and in addition to my previous point. As you mentioned, the complexity of environment is enormes, and it will take a very long time to develop adequate and sophisticated enough computer models to take this huge complexity of human environment under consideration.
    It will be much easier and faster once computers/robots will learn to adapt models, measure, gather and encode the data on their own. But at that point humans won't have anything left to do. That should be some time during the 2040's.

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    Quote Originally Posted by Goga View Post
    Social science is to broad. Let's take history. I ammacking it more difficult for me since history is less mathematical thansociology or psychology that makes use of statistics for their theories. Theymake use of probilities and empirical studies mostly for predicitions. (like very simple statistical null hypothesis testing)

    But now let talk about history and heavy models.


    If you want to measure some population growth withinan area truth history you need some kind of environmental population models.


    If you want to know how much money French Revolutioncosted the French society, you need to use some economical models. If you want to know how much money Dutch govermentearned from it's colonies in SouthEast Asia during the Dutch Golden Age youneed some heavy models.


    How much money did WW1 cost the Germans? For how manyyears did the development in Europe stopped due to the The Black Death? For allthis questions you need different mathematical models.


    Scientists are making mistakes all the time. But that's because they are using wrong or too simplistic social research methods. But that doesn't mean that using a method is wrong. In contratry in sciense you have to use the models to support your theories. The ART of science is to find the right methology. And that is very difficult. For every problem there has to be a model. Great scienctists can find this model and use it properly, while weak scientists have hard times to find a propriate models or don't know how to use it efficently..
    In none of these examples can any mathematical model make any useful estimate. There are so many unknown factors and variables that all you'll get is a very, very rough estimate, and often one that is very far from the truth. It is dangerous because people who don't really understand how these calculations are made (most ordinary people) tend to believe the numbers they are fed because it 'looks scientific', even though it's rubbish.

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    Quote Originally Posted by Maciamo View Post
    In none of these examples can any mathematical model make any useful estimate. There are so many unknown factors and variables that all you'll get is a very, very rough estimate, and often one that is very far from the truth. It is dangerous because people who don't really understand how these calculations are made (most ordinary people) tend to believe the numbers they are fed because it 'looks scientific', even though it's rubbish.
    It was just a quick example. Maybe those questions are to broad and need to be rephrased, but I hope that you do understand where I was heading toward. Sometimes you also have to measure correlation (effect or dependence) between let say two historical events, or between events and actors (subject/object) during a given period. Some statistical coefficients are very simple to determine, but sometimes much more work needs to be done to find correlation coefficients.

    No, history is not exactly rocket science and according to some folks history is not even science at all because many times it is subsidiary on personal interpretation. But I do believe that history is part of the social science since there are many patterns in it and since I believe that every 'right' question within this discipline can be approached by a statistical model. Historians research & analyze their sources.

    more analitical/statistical approach means more scientific process

    If my explanation is wrong and you still don't understand me, maybe this is more helpful:


    "4. Historians, local historians and statistical analysis" : http://humanities.uwe.ac.uk/bhr/Main...0_analysis.htm
    "why historians started counting" : http://historymatters.gmu.edu/mse/numbers/why.html
    "Quantitative Methods for Historians" : https://books.google.nl/books/about/...QC&redir_esc=y
    "QUANTITATIVE SKILLS FOR HISTORIANS" : https://www.heacademy.ac.uk/system/f...0100131_01.pdf

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    Interesting discussion, if I may.

    In the humanities or social sciences, research methodology is often viewed as 'soft' (as opposed to hard) science due to a lack of quantifiable data. The majority of papers are qualitative and therefore based on phenomenological and textual analysis.

    An important determinant in attempting to acquire 'truth' in a historical context is to determine whether one subscribes to a universal or relativistic truth.

    Quantitative analyses as in the field of Mathematics most often provide objective measures for universal 'truth' objectifying and deconstructing constituent variables in meaningful ways. This is the basis of statistical analysis.

    However, in qualitative research we are left with subjective meaning units, textual analysis and phenomenological understanding unique to the individual providing the experience/data. In this case one would follow a relativistic 'truth' approach and attempt to extract universal meaning units only if corroborated by quantitative data. This however is contentious as memory has been shown to be neither consistent nor factual.

    When we use terms such as 'likelihood' or 'probability' we are using inference to determine a plausible outcome. This is predictive and based on a static contextual environment. Most research questions are based on inference to begin with, the aim being to confirm or deny the hypothesis under review.

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    Quote Originally Posted by Dorianfinder View Post
    Interesting discussion, if I may.

    In the humanities or social sciences, research methodology is often viewed as 'soft' (as opposed to hard) science due to a lack of quantifiable data. The majority of papers are qualitative and therefore based on phenomenological and textual analysis.

    An important determinant in attempting to acquire 'truth' in a historical context is to determine whether one subscribes to a universal or relativistic truth.

    Quantitative analyses as in the field of Mathematics most often provide objective measures for universal 'truth' objectifying and deconstructing constituent variables in meaningful ways. This is the basis of statistical analysis.

    However, in qualitative research we are left with subjective meaning units, textual analysis and phenomenological understanding unique to the individual providing the experience/data. In this case one would follow a relativistic 'truth' approach and attempt to extract universal meaning units only if corroborated by quantitative data. This however is contentious as memory has been shown to be neither consistent nor factual.

    When we use terms such as 'likelihood' or 'probability' we are using inference to determine a plausible outcome. This is predictive and based on a static contextual environment. Most research questions are based on inference to begin with, the aim being to confirm or deny the hypothesis under review.
    It is obvious how many times archeologists rewrote prehistory in the last 100 years.

    I'm very sceptical about many 'social' studies. I think the science of human psychology will be rewritten many times the next few decades.

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    Quote Originally Posted by Goga View Post
    It was just a quick example. Maybe those questions are to broad and need to be rephrased, but I hope that you do understand where I was heading toward. Sometimes you also have to measure correlation (effect or dependence) between let say two historical events, or between events and actors (subject/object) during a given period. Some statistical coefficients are very simple to determine, but sometimes much more work needs to be done to find correlation coefficients.

    No, history is not exactly rocket science and according to some folks history is not even science at all because many times it is subsidiary on personal interpretation. But I do believe that history is part of the social science since there are many patterns in it and since I believe that every 'right' question within this discipline can be approached by a statistical model. Historians research & analyze their sources.

    more analitical/statistical approach means more scientific process

    If my explanation is wrong and you still don't understand me, maybe this is more helpful:


    "4. Historians, local historians and statistical analysis" : http://humanities.uwe.ac.uk/bhr/Main...0_analysis.htm
    "why historians started counting" : http://historymatters.gmu.edu/mse/numbers/why.html
    "Quantitative Methods for Historians" : https://books.google.nl/books/about/...QC&redir_esc=y
    "QUANTITATIVE SKILLS FOR HISTORIANS" : https://www.heacademy.ac.uk/system/f...0100131_01.pdf
    If I understand well what you are trying to say is that an analytical/statistical approach makes social sciences more scientific? We were talking about the utility of mathematics in social sciences, so I am a bit perplexed by this change of direction since I don't consider data analysis or statistics, nor even statistical analysis to be the prerogative or mathematicians, not any more than the use of language is the prerogative or linguists or philologists. Analytical skills are a prerequisite for all intellectual subjects, and I would go as far as to say that it is indispensable for most aspects of everyday life as well. But then I am surely a far more analytical person than most, and indeed it has happened that people I just met ask me if I am a psychologist or a data analyst because of my tendency to analyse everything around me, including people.

    Statistics is just the organisation of data. Analysis is the way the brain process data. Rational thinking wouldn't exist without it. Neither of them is a branch of mathematics in itself, although both are used extensively in mathematics (as in many of subjects). We also use reason, critical thinking, reading and writing in maths, but they aren't branches of mathematics either. That's why I am not really grasping what you are trying to prove. That social sciences are rational endeavours making use of the prefrontal cortex of one's dominant hemisphere? Way to state the obvious.

    Nowadays even primary school kids make graphs and pie charts on computers. You can call it 'quantitative skills' if it you prefer the sound of it and it makes it look more professional or important. It's very popular nowadays to try to enhance the image of one's activity using nicer sounding terms, so that a secretary becomes an 'executive assistant' and a cleaner becomes a 'domestic engineer'. That doesn't change their qualifications or job function. It's purely aesthetic (and hypocritical). It's the same with the quantitative skills to describe the use of graphs and statistics in social sciences. That doesn't make it advanced mathematics (i.e. beyond calculus, trigonometry and linear algebra that all secondary schoolers have to learn in order to graduate, at least here in Belgium where there are no elective A-level subjects and everyone must study maths to that minimum level).

    Personally I don't care much for linguistic embellissements that make people look and feel more important or intelligent. Many academics who don't have much to say try to hide their incompetence behind flowery jargon. What matters to me is the content, perspicacity and validity of one's arguments, not how many big words one can fill into a sentence. As C.W. Ceran said “Genius is the ability to reduce the complicated to the simple.” This is especially true on a public forum like this one where where one writes for all kinds of people from different backgrounds and with different mother tongues. A good academic or teacher/professor, in my opinion, is someone who can recognise the complexity of one subject, see through that complexity, and re-explain it concisely and clearly for everyone else to understand. As Albert Einstein said (as I am in a quoty mood) “If you can't explain it to a six year old, you don't understand it yourself.” That's why I write my articles about historical population genetics "as simple as possible, but not simpler" (once again getting inspiration from my adolescence idol, Einstein). I think that that approach should be a prerequisite for teaching any subject.
    Last edited by Maciamo; 17-12-16 at 09:05. Reason: rephrasing

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    2 out of 2 members found this post helpful.
    Quote Originally Posted by DebbieK View Post
    Maciamo

    I don’t have time right now to get involved in a lengthy discussion so I’m only responding to a few comments very briefly.

    As regards cousin matching, everyone has different reasons for testing. I am more interested in using DNA testing to verify my genealogical research.

    I’m somewhat confused about your reference to “historical population geneticists”. Historians specialise in history and population geneticists specialise in population genetics. Ideally we should leave the historians to study history and let the population geneticists analyse the getetic data.
    And then what? Wait for a reporter to interpret the two and come up with a silly "conclusion" for the sake of good headlines?
    This comment screams indifference.
    So I just have to give you the benefit of the doubt that had you had more time on your hands, you would have re-read what you have written and not published that part.

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    Quote Originally Posted by Mike Dammann View Post
    And then what? Wait for a reporter to interpret the two and come up with a silly "conclusion" for the sake of good headlines?
    This comment screams indifference.
    So I just have to give you the benefit of the doubt that had you had more time on your hands, you would have re-read what you have written and not published that part.
    I'm afraid I don’t understand your comment. Journalists aren't historians or population geneticists. A reporter’s job is to report on research and not to come up with his or own interpretation of the data. A good science journalist would seek out experts in the field and ask for their comments though sadly this rarely happens.

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    Quote Originally Posted by DebbieK View Post
    I'm afraid I don’t understand your comment. Journalists aren't historians or population geneticists. A reporter’s job is to report on research and not to come up with his or own interpretation of the data. A good science journalist would seek out experts in the field and ask for their comments though sadly this rarely happens.
    If we speak the same English that's exactly what Mike was saying. Journalists have no particular competence to write about history or genetics. If we follow your suggestion that only geneticists should write about genetics and historians about history, who should write about the genetic history of populations? Journalists have a large audience but little expertise in complex fields like this. That's why if no academic has the expertise to combine both history and genetics journalists will interpret the data themselves to write their stories and the result isn't going to be pretty. That causes misinformation on a grand scale for the public. I quite like Maciamo's initiative to combine the two fields into a new field of historical genetics. Perhaps it's time that universities start teaching it. In the mean time I think that Maciamo is doing a fine job in explaining the history of populations using genetic data and you should recognise that.

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    Quote Originally Posted by Coriolan View Post
    If we speak the same English that's exactly what Mike was saying. Journalists have no particular competence to write about history or genetics. If we follow your suggestion that only geneticists should write about genetics and historians about history, who should write about the genetic history of populations? Journalists have a large audience but little expertise in complex fields like this. That's why if no academic has the expertise to combine both history and genetics journalists will interpret the data themselves to write their stories and the result isn't going to be pretty. That causes misinformation on a grand scale for the public.
    We need historians, archaeologists, linguistics, geneticists and academics from other related disciplines to work together to write about these complex topics and this is in fact already happening. Projects are increasingly multidisciplinary. No single individual can possibly be an expert on every subject. Journalists are not academic researchers. The misinformation arises when journalists don't filter out the pseudoscience which was the subject of the Buzzfeed story that started this thread.

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    1 out of 1 members found this post helpful.
    The Eurogenes blog has alerted us to this excellent new article by Patrick J. Geary and Krishna Veeramah which is very relevant to this discussion. The article summarises the problems of using modern DNA (and especially Y-DNA and mtDNA) to investigate past population histories. The article also stresses the importance of including historians and archaeologists as "integral participants in the planning, collection, and evaluation of data": http://eurogenes.blogspot.co.uk/2016...it-at-all.html
    Last edited by DebbieK; 26-12-16 at 19:24. Reason: Clickable URL

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    Quote Originally Posted by DebbieK View Post
    The Eurogenes blog has alerted us to this excellent new article by Patrick J. Geary and Krishna Veeramah which is very relevant to this discussion. The article summarises the problems of using modern DNA (and especially Y-DNA and mtDNA) to investigate past population histories. The article also stresses the importance of including historians and archaeologists as "integral participants in the planning, collection, and evaluation of data": http://eurogenes.blogspot.co.uk/2016/12/do-it-right-or-dont-do-it-at-all.html
    Thanks Debbie for sharing.

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