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The Human Factor: Understanding Humans by Artificial Systems

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Part of the book series: Theory and Decision Library A: ((TDLA,volume 47))

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Notes

  1. 1.

    The fact that the SEN must be given such an explicit information about prototype theory is no serious objection: In several courses on qualitative social research we gave the example of Tom, i.e. showed the students the text of the interview with Tom, and asked for an interpretation. Although the theories of Bandura and Rosch belonged to the curriculum of the students and were known to them no one got the idea to interpret Tom with the help of these theories. Therefore, it is no unfair advantage for the SEN if it gets this theoretical framework.

  2. 2.

    There are numerous jokes and stories about patients of psychotherapists who react just that way, i.e. reducing each question to always the same subject.

  3. 3.

    “Repetition is the mother of studies. Always something (at least) will remain.”

  4. 4.

    Religiously motivated one-sidedness does certainly not lead always to fanaticism. We know a lot of religious people who everything refers to the benevolent intervention of God and who are mild and kind individuals. We use the case of religiously motivated fanaticism not only because it is still a very current problem but also because it is a very apt illustration of the general case of restricted message processing.

  5. 5.

    It is important to note that IN are usually not trained like, for example, feed forward nets or self-organized networks. As far as we know we are the first to implement a certain learning rule into IN.

  6. 6.

    More details with respect to these rules and additional examples can be found in the MA-thesis of one of our former students, Christian Kunz.

  7. 7.

    For the history of Adidas and its main competitor Nike cf. Casanova, M: http://www.wolfgang-bolsinger.de/recources/Interview_mit_Marco_Casanova.pdf

  8. 8.

    Adidas had become too self-assured because of its former success. Therefore it did not spend enough money for own campaigns.

  9. 9.

    The famous slogan “don’t trust anybody older than thirty” that originated in the late sixties is an apt illustration for the new high regard of youth.

  10. 10.

    This may be one explanation for the recent big selling success of the iPhone of Apple.

  11. 11.

    We do not know the fate of Tom after the interview that was performed several years ago. His orientation to violence and aggressiveness gives not much hope for a Happy End. Yet it is highly significant that the only person whom Tom respected was the interviewer who acted as an educator in the hostel at the time of the interview. The interviewer, a rather powerfully built student, regularly trained together with Tom in the fitness room of the hostel and won the respect of Tom by showing physical prowess. This can be taken as a hint how a re-socialization of Tom might perhaps work.

  12. 12.

    Such attempts were tried by several early pioneers of AI but without great success (cf. e.g. Schank and Riesbeck 1981).

  13. 13.

    We had used this story before in a course about Artificial Intelligence and the reasoning of literary detectives in order to demonstrate how different AI -systems could solve such detective puzzles. We published the according experiments that were not the same as that described in this subchapter in Klüver and Stoica (2006).

  14. 14.

    It is a frequently discussed problem that the probands in many socio-psychological experiments are students and often even students of the experimenters. That is why we successfully tried to get probands from other social contexts.

  15. 15.

    Gregor Braun implemented the whole program and also performed the experiments as part of his MA-thesis.

  16. 16.

    About one third of the probands were women whose success was in the average approximately equal to that of the male probands. Female readers may excuse us if for the sake of brevity we use only the pronoun “he”.

  17. 17.

    The detailed analysis of these discourses was subject of the MA-thesis of our former student Bianca Kierstein who also applied the computer program developed by us. Christian Oldenhausen and Christian Pfeiffer did the implementation of the program.

  18. 18.

    Sometimes the artificial communicators did not communicate about all concepts their networks contained but used only a part of it by permanently repeating the same thematic concept triples. In 2007 we described the same phenomenon with respect to a similar program. Although we were consoled at a conference that this fact showed that our program was very humanlike we wished of course that the networks should “talk” about all concepts, as did their human counter parts. That is why we repeated in these cases the simulations and usually obtained more satisfying artificial discourses.

  19. 19.

    The husband refers to several incidents at German schools where pupils had run amok and shot other pupils and teachers.

  20. 20.

    We are quite aware that the networks of the couple do not mirror that division of interest one would usually expect. The usual assumption with respect to such themes would be that the man preferred to talk about technical aspects of the computer and the woman about pedagogical ones. As we personally know this specific couple we can only say that this reversing of usual expectations in this dialogue was certainly a bit consciously exaggerated by the couple but not untypical for them.

  21. 21.

    Of course, this is not to say that the son is not important to the wife and that the husband is not interested in computers – on the contrary, as we know (see preceding footnote). It just means that in this communication the wife mainly wishes to speak about technical aspects of computers and the husband about pedagogical problems concerning the computer.

  22. 22.

    In contrast to the test persons of the experiment described in the preceding subchapter the students were because of their common work with us used to the construction of such networks.

  23. 23.

    The fact that, at least in Germany, the number of marriages is still rising after a severe decline in the eighties and nineties indicates that young people again or still believe in marriage and married life.

  24. 24.

    To avoid a misunderstanding that might here occur: The authors are married with each other and also still believe in – at least – the possibility of happiness in a marriage.

  25. 25.

    We repeat these formal details in such length because they are part of the methodical basis of the new experiments described below.

  26. 26.

    Jochen Burkart performed these and the experiments described below as part of his PhD-thesis.

  27. 27.

    It may be noted as a curious detail that frequently the program did better to predict single networks if the respective person was a woman than it did with predicting the networks of male probands. Yet because the significance of these differences is not very high we just note it as an amusing puzzle. We certainly do not infer from these results that women are easier to predict – and hence to understand – than men.

  28. 28.

    The permanent and expensive voyages of politicians to politicians of other countries are usually legitimated with the argument that it is important to personally understand the other states men and women. We leave it to the readers if this argument is convincing.

  29. 29.

    It must be added that we had only ten test persons at our disposal, divided into three groups. Burkart had for his experiments described below significantly more test persons.

  30. 30.

    To be sure, there are examples where the communicative partners know rather well what the other will say in the next communicative step, for example couples who have been married for a long time and who know each other very well. Yet usual communicative processes consist of participants who know each other not so well; in our experiment the students knew each other only slightly, if at all. Therefore, we believe that our results are representative and that our simulation program is “humanlike” in the sense that it cannot do what humans usually also cannot do.

  31. 31.

    More details and statistical analyses of the experiments can be read in the published version of the dissertation of Jochen Burkart (forthcoming).

References

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Klüver, J., Klüver, C. (2011). The Human Factor: Understanding Humans by Artificial Systems. In: Social Understanding. Theory and Decision Library A:, vol 47. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9911-2_5

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  • DOI: https://doi.org/10.1007/978-90-481-9911-2_5

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