Fakultät für Mathematik und Naturwissenschaften

Zuwanderung und Integration in sich verändernden Gesellschaften

  1. Y.L. Chuang, T. Chou, M.R. D’Orsogna, A Network Model of Immigration and Coexistence, (2020).
  2. Y.L. Chuang, T. Chou, M.R. D'Orsogna, A network model of immigration: Enclave formation vs. cultural integration, Net. Hetero. Media, 14(1) (2019), 53-77.
  3. L. Gauvin, J.-P. Nadal, Modeling and understanding social segregation, In: H. Kaper, C. Rousseau (Eds.), Mathematics of planet Earth: Mathematicians reflect on how to discover, organize, and protect our planet, SIAM Philadelphia, 2015, Seiten 155-156.
  4. E. Hatna, I. Benenson, The Schelling model of ethnic residential dynamics: Beyond the integrated-segregated dichotomy of patterns. J. Artific. Societ. Soc. Sim., 15(1) (2012), 6.
  5. T.C. Schelling, Dynamic models of segregation. J. Math. Sociol. 1(2) (1971), 143-186.
  6. J.W. Berry, Living successfully in two cultures, Int. J. Intercult. Relat. 29 (2005), 697-712.
  7. United Nations High Commissioner for Refugees, Global trends: Forced displacement in 2016, The United Nations, Geneva, Switzerland, 2017.
  8. N. Van Hear, O. Bakewell, K. Long, Push-pull plus: reconsidering the drivers of migration, J. Ethnic Migration Studies 44 (2018), 927-944.

Mathematische Modellierung von Radikalisierungsprozessen

  1. C. Castillo-Chavez, B. Song, Models for the Transmission Dynamics of Fanatic Behaviors, Bioterrorism: Mathematical Modeling Applications in Homeland Security, Frontiers in Applied Mathematics, Band 29, 2003.
  2. F. Brauer, C. Castillo-Chavez, Mathematical Models in Population Biology and Epidemiology, Springer-Verlag, 2001.
  3. C. Castillo-Chavez, R. H. Thieme, Asymptotically autonomous epidemic models, in Mathematical Population Dynamics: Analysis of Heterogeneity, O. Arimo, D. E. Axelrod und M. Kimmel, Wuerz Publishing, 1995, Seiten 33-50
  4. F. J. Santonja, A. C. Tarazona, R. J. Villanueva, A mathematical model of the pressure of an extreme ideology on a society, Elsevier, Computers & Mathematics with Applications 56(3) (2008), 836-846.
  5. J.-C. Cortés, F. J. Santonja, A. C. Tarazona, R. J. Villanueva, J. Villanueva-Oller, A probabilistic estimation and prediction technique for dynamic continuous social science models: The evolution of the attitude of the Basque Country population towards ETA as a case study, Applied Mathematics and Computation 264 (2015), 13-20.
  6. M. Ehrhardt, M. Peco, A.C. Tarazona, R.J. Villanueva, J. Villanueva–Oller, Popular support to terrorist organizations: A short-term prediction based on a dynamic model applied to a real case, in Mathematical Modeling in Engineering and Social Sciences edited by J.C. Cortes, L.J. Sanchez, R.J. Villanueva, Nova Science Publishers, Hauppauge, NY (2013).
  7. Y.-L. Chuang, M.R. D'Orsogna, Mathematical models of radicalization and terrorism, arXiv preprint arXiv:1903.08485 (2019).
  8. Y.-L. Chuang, T. Chou, M.R. D’Orsogna, Age-structured social interactions enhance radicalization, The Journal of Mathematical Sociology 42.3 (2018), 128-151.
  9. Y.L. Chuang, M.R. D'Orsogna, T. Chou, A bistable belief dynamics model for radicalization within sectarian conflict, arXiv preprint arXiv:1805.07480 (2018).
  10. M. Youngblood, Extremist ideology as a complex contagion: the spread of far-right radicalization in the United States between 2005 and 2017, Humanities and Social Sciences Communications, 7(1) (2020), 1-10.
  11. T. Deutsch, Mathematische Modellierung von Radikalisierungsprozessen am Beispiel von rechtsradikalen Gruppierungen in Deutschland, Bachelorarbeit, Bergische Universität Wuppertal, November 2014.
  12. S. Galam, M.A. Javarone, Modeling radicalization phenomena in heterogeneous populations, PloS One 11 (2016), e0155407.

Faire Schulplatzvergabe: von Boston in die ganze Welt

  1. Ágnes Cseh, Heiraten nach Plan, KlarText.
  2. Ágnes Cseh, Complexity and algorithms in matching problems under preferences, Dissertation, TU Berlin, 2016.

Ausfallwahrscheinlichkeiten für Verschwörungstheorien

  1. D.R. Grimes, On the Viability of Conspiratorial Beliefs, PLoS ONE 11(1) (2016), e0147905.
  2. D.R. Grimes, Correction: On the viability of conspiratorial beliefs, PLoS ONE 11(3) (2016), e0151003.
  3. D.R. Grimes, Medical disinformation and the unviable nature of COVID-19 conspiracy theories, PLoS ONE 16(3) (2021), e0245900.
  4. K.M.C. Tjørve, E. Tjørve, The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family, PLoS ONE 12(6) (2017) e0178691.
  5. C. McCauley, S. Jacques, The popularity of conspiracy theories of presidential assassination: A Bayesian analysis, Journal of Personality and Social Psychology 37(5) (1979), 637-644.
  6. D. Jolley, K.M. Douglas, The effects of anti-vaccine conspiracy theories on vaccination intentions, PloS ONE 9.2 (2014): e89177.
  7. S. Bartoschek, Bekanntheit von und Zustimmung zu Verschwörungstheorien: eine empirische Grundlagenarbeit, Dissertation, Westfälische Wilhelms-Universität in Münster, 2017.
  8. P. Knight, M. Butter, Conspiracy Theories and the People Who Believe Them, Oxford University Press, 2018, pp. 33-46.
  9. P. Knight, M. Butter, Routledge handbook of conspiracy theories, 1st ed. London: Routledge, 2020.
  10. S. Vosoughi, D. Roy, S. Aral, The spread of true and false news online, Science 359 (2018), 1146-1151.
  11. Webseite, die Verschwörungstheorien untersucht: https://www.mimikama.at/

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