Rational Counterfactuals and Decision Making: Application to Interstate Conflict

Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

This chapter introduces the concept of rational counterfactuals which is an idea of identifying a counterfactual from the factual (whether perceived or real), and knowledge of the laws that govern the relationships between the antecedent and the consequent, that maximizes the attainment of the desired consequent. In counterfactual thinking factual statements like: ‘Saddam Hussein invaded Kuwait and consequently George Bush declared war on Iraq’ and with its counterfactual being: ‘If Saddam Hussein did not invade Kuwait then George Bush would not have declared war on Iraq’. In this chapter in order to build rational counterfactuals neuro-fuzzy model and genetic algorithm are applied. The theory of rational counterfactuals is applied to identify the antecedent that gives the desired consequent necessary for rational decision making. The rational counterfactual theory is applied to identify the values of variables Allies, Contingency, Distance, Major Power, Capability, Democracy, as well as Economic Interdependency that give the desired consequent Peace.

Keywords

Membership Function Simulated Annealing Markov Chain Monte Carlo Fuzzy Rule Fuzzy Inference System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Akhmatskaya E, Bou-Rabee N, Reich S (2009) A comparison of generalized hybrid Monte Carlo methods with and without momentum flip. J Comput Phys 228:2256–2265MathSciNetCrossRefMATHGoogle Scholar
  2. Anonymous (2010) Correlates of War Project. http://www.correlatesofwar.org/. Accessed 20 Sept 2010
  3. Araujo E (2008) Improved Takagi-Sugeno fuzzy approach. Proceedings of the IEEE international conference on fuzzy systems, pp 1154–1158Google Scholar
  4. Ata R, Kocyigit Y (2010) An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Syst with Appl 37:5454–5460CrossRefGoogle Scholar
  5. Babuska R (1991) Fuzzy Modeling and Identification. PhD Thesis, Technical University of DelftGoogle Scholar
  6. Babuska R, Verbruggen H (2003) Neuro-fuzzy methods for nonlinear system identification. Annual Rev Contr 27:73–85CrossRefGoogle Scholar
  7. Biacino L, Gerla G (2002) Fuzzy logic, continuity and effectiveness. Archive Math Logic 41:643–667MathSciNetCrossRefMATHGoogle Scholar
  8. Bih J (2006) Paradigm shift—an introduction to fuzzy logic. IEEE Potentials 25(1):6–21CrossRefGoogle Scholar
  9. Birke D, Butter M, Koppe T (eds) (2011) Counterfactual thinking—counterfactual writing. de Gruyter, BerlinGoogle Scholar
  10. Bishop CM (1995) Neural networks for pattern recognition. Oxford University, OxfordGoogle Scholar
  11. Bryan K, Cunningham P, Bolshkova N (2006) Application of simulated annealing to the biclustering of gene expression data. IEEE Trans Inf Technol Biomed 10(3):519–525CrossRefGoogle Scholar
  12. Cano-Izquierdo J, Almonacid M, Ibarrola JJ (2010) Applying neuro-fuzzy model dfasart in control systems. Eng Appl Art Intelli 23:1053–1063CrossRefGoogle Scholar
  13. Cantor G (1874) Über eine Eigenschaft des Inbegriffes aller reellen algebraischen Zahlen. Crelles J Math 77:258–262Google Scholar
  14. Celuch K, Saxby C (2013) Counterfactual thinking and ethical decision making: a new approach to an old problem for marketing education. J Mark Educ 35(2):155–167CrossRefGoogle Scholar
  15. Cox E (1994) The fuzzy systems handbook: a practitioner’s guide to building, using, maintaining fuzzy systems. AP Professional, BostonGoogle Scholar
  16. Dafflon B, Irving J, Holliger K (2009) Simulated-annealing-based conditional simulation for the local-scale characterization of heterogeneous aquifers. J Appl Geophys 68:60–70CrossRefGoogle Scholar
  17. Daftary-Kapur T, Berry M (2010) The effects of outcome severity, damage amounts and counterfactual thinking on juror punitive damage award decision making. Am J Forensic Psychol 28(1):21–45Google Scholar
  18. Das A, Chakrabarti BK (2005) Quantum annealing and related optimization methods. Lect notes in Phys 679. Springer, HeidelbergCrossRefGoogle Scholar
  19. De Vicente J, Lanchares J, Hermida R (2003) Placement by thermodynamic simulated annealing. Phys Lett A 317:415–423CrossRefMATHGoogle Scholar
  20. Devlin K (1993) The joy of sets. Springer, BerlinCrossRefMATHGoogle Scholar
  21. Ferreirós J (1999) Labyrinth of thought: a history of set theory and its role in modern mathematics. Birkhäuser, BaselCrossRefMATHGoogle Scholar
  22. Fogel SO, Berry T (2010) The disposition effect and individual investor decisions: the roles of regret and counterfactual alternatives. Handbook of Behavioral Finance, pp 65–80Google Scholar
  23. Hájek P (1995) Fuzzy logic and arithmetical hierarchy. Fuzzy Sets and Syst 3:359–363CrossRefGoogle Scholar
  24. Hájek P (1998) Metamathematics of fuzzy logic. Kluwer, DordrechtCrossRefMATHGoogle Scholar
  25. Halpern JY (2003) Reasoning about uncertainty. MIT, CambridgeMATHGoogle Scholar
  26. He R, Hwang S (2006) Damage detection by an adaptive real-parameter simulated annealing genetic algorithm. Comput Struct 84:2231–2243CrossRefGoogle Scholar
  27. Hegel G, W F (1874) The logic encyclopaedia of the philosophical sciences, 2nd edn. Oxford University Press, LondonGoogle Scholar
  28. Howlett JR, Paulus MP (2013) Decision-making dysfunctions of counterfactuals in depression: who might I have been? Front Psychiatry 4:143CrossRefGoogle Scholar
  29. Hsu Y-C, Lin S-F (2009) Reinforcement group cooperation-based symbiotic evolution for recurrent wavelet-based neuro-fuzzy systems. J Neurocomput 72:2418–2432CrossRefGoogle Scholar
  30. Hume D (1748) An enquiry concerning human understanding. Harvard Classics, vol. 37, part 3, Copyright 1910 P.F, Collier & SonGoogle Scholar
  31. Iplikci S (2010) Support vector machines based neuro-fuzzy control of nonlinear systems. J Neurocomput 73:2097–2107Google Scholar
  32. Jang J, S R (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans on Syst, Man and Cybern 23:665–685Google Scholar
  33. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, TorontoGoogle Scholar
  34. Jing L, Vadakkepat P (2009) Interacting MCMC particle filter for tracking maneuvering target. Digit signal process. doi: 10.1016/j.dsp.2009.08.011Google Scholar
  35. Johansson M, Broström L (2011) Counterfactual reasoning in surrogate decision making—another look. Bioethics 25(5):244–249CrossRefGoogle Scholar
  36. Johnson P (1972) A history of set theory. Prindle, Weber & Schmidt, BostonMATHGoogle Scholar
  37. Kahneman D, Miller D (1986) Norm theory: comparing reality to its alternatives. Psychol Rev 93(2):136–153CrossRefGoogle Scholar
  38. Khajeh A, Modarress H (2010) Prediction of solubility of gases in polystyrene by adaptive neuro-fuzzy inference system and radial basis function neural network. Expert Syst with Appl 37:3070–3074CrossRefGoogle Scholar
  39. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Sci New Ser 220:671–680MathSciNetMATHGoogle Scholar
  40. Klir GJ, Folger TA (1988) Fuzzy sets, uncertainty, and information. Prentice Hall, New JerseyMATHGoogle Scholar
  41. Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall, New JerseyMATHGoogle Scholar
  42. Klir GJ, St Clair UH, Yuan B (1997) Fuzzy set theory: foundations and applications. Prentice Hall, New JerseyMATHGoogle Scholar
  43. Kosko B (1993) Fuzzy thinking: the new science of fuzzy logic. Hyperion, New YorkGoogle Scholar
  44. Kosko B, Isaka S (1993) Fuzzy logic. Sci Amer 269:76–81CrossRefGoogle Scholar
  45. Leach JK, Patall EA (2013) Maximizing and counterfactual thinking in academic major decision making. J Career Assess 21(3):414–429CrossRefGoogle Scholar
  46. Lewis D (1973) Counterfactuals. Blackwell, OxfordGoogle Scholar
  47. Lewis D (1979) Counterfactual dependence and time’s arrow. Noûs 13:455-476 (Reprinted in his (1986a)Google Scholar
  48. Liesenfeld R, Richard J (2008) Improving MCMC, using efficient importance sampling. Comput Statistics and Data Anal 53:272–288MathSciNetCrossRefMATHGoogle Scholar
  49. Lombardi MJ (2007) Bayesian inference for [Alpha]-stable distributions: a random walk MCMC approach. Comput Statistics and Data Anal 51:2688–2700MathSciNetCrossRefMATHGoogle Scholar
  50. Mamdani EH (1974) Application of fuzzy algorithms for the control of a dynamic plant. Proc IEE 121:1585–1588Google Scholar
  51. Marwala T (2009) Computational intelligence for missing data imputation, estimation and management: knowledge optimization techniques. IGI Global Publications, New YorkCrossRefGoogle Scholar
  52. Marwala T (2010) Finite element model updating using computational intelligence techniques. Springer, HeidelbergCrossRefMATHGoogle Scholar
  53. Marwala T, Lagazio M (2004) Modelling and controlling interstate conflict. Proceedings of the IEEE international joint conference on neural networks, pp 1233–1238Google Scholar
  54. Marwala T, Lagazio M (2011) Militarized conflict modeling using computational intelligence. Springer-Verlag, LondonCrossRefGoogle Scholar
  55. Marx K (1873) Afterword to the Second German Edition. Capital Volume 1. In Collected works, vol. 35, pp. 12–20Google Scholar
  56. Mashrei MA, Abdulrazzaq N, Abdalla TY, Rahman MS (2010) Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members. Eng Struct 32:1723–1734Google Scholar
  57. Mathe P, Novak E (2007) Simple Monte Carlo and the metropolis algorithm. J Complex 23:673–696MathSciNetCrossRefMATHGoogle Scholar
  58. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equations of state calculations by fast computing machines. J Chem Phys 21:1087CrossRefGoogle Scholar
  59. Mill JS (1843) A system of logic, The collected works of John Stuart Mill, vol. 7. A system of logic ratiocinative and inductive: being a connected view of the principles of evidence and the methods of scientific investigation, part 1, books 1–3, University of Toronto PressGoogle Scholar
  60. Moita JMS, Correia VMF, Martins PG, Soares CMM, Soares CAM (2006) Optimal design in vibration control of adaptive structures using a simulated annealing algorithm. Compos Struct 75:79–87CrossRefGoogle Scholar
  61. Montazer GA, Saremi HQ, Khatibi V (2010) A neuro-fuzzy inference engine for farsi numeral characters recognition. Expert Syst with Appl 37:6327–6337CrossRefGoogle Scholar
  62. Novák V (1989) Fuzzy sets and their applications. Adam Hilger, BristolMATHGoogle Scholar
  63. Novák V (2005) On fuzzy type theory. Fuzzy Sets and Syst 149:235–273CrossRefMATHGoogle Scholar
  64. Novák V, Perfilieva I, Močkoř J (1999) Mathematical principles of fuzzy logic. Kluwer Academic, DordrechtCrossRefMATHGoogle Scholar
  65. Paya-Zaforteza I, Yepes V, Hospitaler A, Gonzalez-Vidosa F (2009) CO2-Optimization of reinforced concrete frames by simulated annealing. Eng Struct 31:1501–1508CrossRefGoogle Scholar
  66. Ratick S, Schwarz G (2009) Monte Carlo simulation. In: Kitchin R, Thrift N (eds) International encyclopedia of human geography. Elsevier, OxfordGoogle Scholar
  67. Roese N (1997) Counterfactual thinking. Psychol Bull 121(1):133–148CrossRefGoogle Scholar
  68. Salazar R, Toral R (2006) Simulated annealing using hybrid monte carlo. arXiv:cond-mat/9706051Google Scholar
  69. Sentes M, Babuska R, Kaymak U, van Nauta Lemke H (1998) Similarity measures in fuzzy rule base simplification. IEEE Trans on Syst, Man and Cybern-Part B: Cybern 28:376–386CrossRefGoogle Scholar
  70. Shaffer MJ (2009) Decision theory, intelligent planning and counterfactuals. Mind Mach 19(1):61–92CrossRefGoogle Scholar
  71. Simioni S, Schluep M, Bault N, Coricelli G, Kleeberg J, du Pasquier RA, Gschwind M, Vuilleumier P, Annoni J-M (2012) Multiple sclerosis decreases explicit counterfactual processing and risk taking in decision making. PLoS ONE 7(12):e50718CrossRefGoogle Scholar
  72. Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science Publication Company, AmsterdamGoogle Scholar
  73. Sugeno M, Kang G (1988) Structure identification of fuzzy model. Fuzzy Sets and Syst 28:15–33MathSciNetCrossRefMATHGoogle Scholar
  74. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans on Syst, Man, and Cybern 15:116–132CrossRefMATHGoogle Scholar
  75. Talei A, Hock L, Chua C, Quek C (2010) A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling. Expert Syst with Appl 37:7456–7468CrossRefGoogle Scholar
  76. Tettey T, Marwala T (2007) Conflict Modelling and Knowledge Extraction Using Computational Intelligence Methods. In: Proceedings of the 11th IEEE international conference on intelligent engineering systems, pp 161–166Google Scholar
  77. von Altrock C (1995) Fuzzy logic and neurofuzzy applications explained. Prentice Hall, New JerseyGoogle Scholar
  78. Weinberger E (1990) Correlated and uncorrelated fitness landscapes and how to tell the difference. Biol Cybern 63:325–336CrossRefMATHGoogle Scholar
  79. Weizhong AN, Fengjuan YU, Dong F, Yangdong HU (2008) Simulated annealing approach to the optimal synthesis of distillation column with intermediate heat exchangers. Chin J Chem Eng 16:30–35CrossRefGoogle Scholar
  80. Woodward J (2003) Making things happen: a theory of causal explanation. Oxford University, OxfordGoogle Scholar
  81. Woodward J, Hitchcock C (2003) Explanatory generalizations. Part I: a counterfactual account. Noûs 37:1–24CrossRefGoogle Scholar
  82. Wright S, Marwala T (2006) Artificial intelligence techniques for steam generator modelling. arXiv:0811.1711Google Scholar
  83. Zadeh LA (1965) Fuzzy sets. Info and Control 8:338–353MathSciNetCrossRefMATHGoogle Scholar
  84. Zemankova-Leech M (1983) Fuzzy relational data bases. PhD Dissertation, Florida State UniversityGoogle Scholar
  85. Zimmermann H (2001) Fuzzy set theory and its applications. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Engineering and the Built EnviromentUniversity of JohannesburgAuckland ParkSouth Africa

Personalised recommendations