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Mathematical and computational modeling of political systems

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Abstract

This paper proposes a new modeling approach for describing the dynamics of political systems. For that purpose, real-world data series from several countries is considered. More specifically, democratic systems of six European countries involving elections that produce a parliament supporting the government are analyzed. First, the information is processed by means of multidimensional scaling and computational visualization techniques. Second, an analogy toward multi-particle systems is formulated, leading to an interpretation close to those adopted in physics.

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References

  1. Davis, O.A., Hinich, M.J., Ordeshook, P.C.: An expository development of a mathematical model of the electoral process. Am. Polit. Sci. Rev. 64(2), 426–448 (1970)

    Article  Google Scholar 

  2. Meur, G.D., Gassner, M., Hubaut, X.: An expository development of a mathematical model of the electoral process. Eur. J. Polit. Res. 13(4), 409–420 (1985)

    Article  Google Scholar 

  3. Johnson, P.E., Rodin, E. (eds.): Formal Theories of Politics. Mathematical Modelling in Political Science. Modern Applied Mathematics and Computer Science. Pergamon Press, Oxford (1989)

    Google Scholar 

  4. Johnson, P.E.: Formal theories of politics: the scope of mathematical modelling in political science. Math. Comput. Model. 12(415), 397–404 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  5. Campbell, D.K., Mayer-Kress, G.: Chaos and politics: applications of nonlinear dynamics to socio-political issues. SFI working paper: 1991-09-032, Santa Fe Institute (1991)

  6. Clarke, K.A., Primo, D.M.: Modernizing political science: a model-based approach. Perspect. Polit. 5(4), 741–753 (2007)

    Article  Google Scholar 

  7. Ambrose, C., Jones, J., Larson, K., Orozco, L., Uminsky, D., Wirkus, S.A.: A mathematical model of political affiliation. Technical report, Applied Mathematical Sciences Summer Institute, Department of Mathematics and Statistics, California State Polytechnic University, Pomona (2007)

  8. Laver, M., Schilperoord, M.: Spatial models of political competition with endogenous political parties. Philos. Trans. R. Soc. B Biol. Sci. 362(1485), 1711–1721 (2007)

    Article  Google Scholar 

  9. Brams, S.J.: Mathematics and democracy: designing better voting and fair-division procedures. Math. Comput. Model. 48(9–10), 1666–1670 (2008)

    Article  MATH  Google Scholar 

  10. Tangian, A.: A mathematical model of Athenian democracy. Soc. Choice Welf. 31(4), 537–572 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  11. Tangian, A.: A mathematical model of Athenian democracy. Technical report, Hans Boeckler Foundation, Duesseldorf, Germany (2005)

  12. Boudin, L., Salvarani, F.: Modelling opinion formation by means of kinetic equations. In: Naldi, G., Pareschi, L., Toscani, G. (eds.) Mathematical Modeling of Collective Behavior in Socio-Economic and Life Sciences. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, Heidelberg (2010)

  13. Nagel, M.: A mathematical model of democratic elections. Curr. Res. J. Soc. Sci. 2(4), 255–261 (2010)

    Google Scholar 

  14. Zuckerman, M., Faliszewski, P., Bachrach, Y., Elkind, E.: Manipulating the quota in weighted voting games. Artif. Intell. 180–181, 1–19 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Misra, A.K.: A simple mathematical model for the spread of two political parties. Nonlinear Anal. Model. Control 17(3), 343–354 (2012)

    MathSciNet  MATH  Google Scholar 

  16. la Poza, D., Jódar, L., Pricop, A.: Mathematical modeling of the propagation of democratic support of extreme ideologies in Spain: causes, effects, and recommendations for its stop. Abstr. Appl. Anal. 2013(729814), 1–8 (2013)

    Google Scholar 

  17. Moore, W.H., Siegel, D.A.: A Mathematics Course for Political and Social Research. Princeton University Press, Oxford (2013)

    Book  MATH  Google Scholar 

  18. Marcinkowska, M.: Political business cycle: mathematical models. Math. Comput. Model. 42(2), 207–239 (2014)

    MathSciNet  MATH  Google Scholar 

  19. Bartels, L.M., Jackman, S.: A generational model of political learning. Elect. Stud. 33, 7–18 (2014)

    Article  Google Scholar 

  20. Bagarello, F.: An operator view on alliances in politics. SIAM J. Appl. Math. 75(2), 564–584 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  21. Boudin, L., Salvarani, F.: Opinion dynamics: kinetic modelling with mass media, application to the Scottish independence referendum. Physica A 444, 448–457 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  22. Nyabadza, F., Alassey, T.Y., Muchatibaya, G.: Modelling the dynamics of two political parties in the presence of switching. SpringerPlus 2016(5), 1–12 (2016)

    Google Scholar 

  23. Rowden, J.: Application of two mathematical modelling approaches for real world systems. Ph.D. Thesis, Department of Mathematics, University of Surrey, United Kingdom (2014)

  24. Temur, C.: Mathematical model of transformation of two-party elections to three-party elections. GESJ Comput. Sci. Telecommun. 52(2), 21–29 (2017)

    Google Scholar 

  25. Krueger, T., Szwabiński, J., Weron, T.: Conformity, anticonformity and polarization of opinions: insights from a mathematical model of opinion dynamics. Entropy 19(371), 1–21 (2017)

    Google Scholar 

  26. Lei, R., Gelman, A., Ghitza, Y.: The 2008 election: a preregistered replication analysis. Stat. Public Policy 4(1), 1–8 (2017)

    Article  Google Scholar 

  27. Makela, S., Si, Y., Gelman, A.: Graphical visualization of polling results. In: Atkeson, L.R., Alvarez, R.M. (eds.) Oxford Handbook on Polling and Polling Methods. Oxford University Press, Heidelberg (2018)

  28. Shirani-Mehr, H., Rothschild, D., Goel, S., Gelman, A.: Disentangling bias and variance in election polls. J. Am. Stat. Assoc. 113(522), 607–614 (2018)

    Article  MathSciNet  Google Scholar 

  29. Ghitza, Y., Gelman, A.: Voter registration databases and MRP: toward the use of large scale databases in public opinion research. Working Paper (2018). To appear in Polit. Anal

  30. Kruskal, J.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1), 1–27 (1964)

    Article  MathSciNet  MATH  Google Scholar 

  31. Kruskal, J.B., Wish, M.: Multidimensional Scaling. Sage Publications, Newbury Park (1978)

    Book  Google Scholar 

  32. Borg, I., Groenen, P.J.: Modern Multidimensional Scaling: Theory and Applications. Springer, New York (2005)

    MATH  Google Scholar 

  33. Machado, J.A.T., Duarte, G.M., Duarte, F.B.: Analysis of financial data series using fractional Fourier transform and multidimensional scaling. Nonlinear Dyn. 65(3), 235–245 (2011)

    Article  Google Scholar 

  34. Machado, J.A.T., Duarte, G.M., Duarte, F.B.: Identifying economic periods and crisis with the multidimensional scaling. Nonlinear Dyn. 63(4), 611–622 (2011)

    Article  Google Scholar 

  35. Machado, J.A.T.: Complex dynamics of financial indices. Nonlinear Dyn. 74(1–2), 287–296 (2013)

    Article  Google Scholar 

  36. Machado, J.A.T.: Relativistic time effects in financial dynamics. Nonlinear Dyn. 75(4), 735–744 (2014)

    Article  MathSciNet  Google Scholar 

  37. Machado, J.A.T., Lopes, A.M.: The persistence of memory. Nonlinear Dyn. 79(1), 63–82 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

The author thanks the help of the following colleagues in collecting and understanding the raw data from their countries: Robin De Keyser, Anastasios Lazopoulos, Dariusz Brzeziński, Juan Luis Garcia Girao, António Vega, and Mohamed Torbati.

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Correspondence to J. Tenreiro Machado.

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Appendix

Appendix

See Tables 2, 3, 4, 5, 6 and 7.

Table 2 Number of seats \(n_i(t_k)\), \(i=1,\ldots ,7\), \(k=1,\ldots ,12\), of the parties versus election time (in years) for Belgium during period 1977–2014
Table 3 Number of seats \(n_i(t_k)\), \(i=1,\ldots ,5\), \(k=1,\ldots ,17\), of the parties versus election time (in years) for Greece during period 1974–2015
Table 4 Number of seats \(n_i(t_k)\), \(i=1,\ldots ,7\), \(k=1,\ldots ,8\), of the parties versus election time (in years) for Poland during period 1991–2015
Table 5 Number of seats \(n_i(t_k)\), \(i=1,\ldots ,6\), \(k=1,\ldots ,11\), of the parties versus election time (in years) for Portugal during period 1983–2015
Table 6 Number of seats \(n_i(t_k)\), \(i=1,\ldots ,9\), \(k=1,\ldots ,13\), of the parties versus election time (in years) for Spain during period 1977–2016
Table 7 Number of seats \(n_i(t_k)\), \(i=1,\ldots ,4\), \(k=1,\ldots ,27\), of the parties versus election time (in years) for the UK during period 1918–2017

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Machado, J.T. Mathematical and computational modeling of political systems. Nonlinear Dyn 96, 1471–1490 (2019). https://doi.org/10.1007/s11071-019-04865-2

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