Abstract
This chapter fundamentally aims at the development of generalized framework encapsulating a wide range of dynamic utility functional and resultant latent choice models. The objectives are served by the application of well cherished exponential family of distributions capable of entertaining numerous probabilistic articulations through a single comprehensive and elegant expression. Moreover, the utility of the proposed scheme is further substantiated by delineating the working pedagogy in accordance with the rapidly embraced Bayesian paradigm. The legitimacy of the devised mechanism in the pursuit of optimal decision-making is advocated with respect to diverse experimental states. We entertained varying extent of worth parameters describing the preference ordering, different sample sizes and distinguished stochastic formations to inject the prior information or historic data in the demonstration of choice behaviors.
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References
Annis, D. H., & Craig, B. A. (2005). Hybrid paired comparison analysis, with applications to the ranking of college football teams. Journal of Quantitative Analysis in Sports, 1(1), 1–31.
Beaudoin, D., & Swartz, T. (2018). A computationally intensive ranking system for paired comparison data. Operations Research Perspectives, 5, 105–112.
Cattelan, M., Varin, C., & Firth, D. (2013). Dynamic Bradley-Terry modelling of sports tournaments. Journal of the Royal Statistical Society: Series C (Applied Statistics), 62(1), 135–150.
Ceschi, A., Demerouti, E., Sartori, R., & Weller, J. (2017). Decision making process in workplace: How exhaustion, lack of resources and job demands impaired them and affect performance. Frontiers in Psychology, 8, 313.
Cheema, S. A., Hudson, I. L., Kifayat, T., Shafqat, M., Kalim-ullah, & Hussain, A. (2019). A new Maxwell paired comparison model: Application to a study of the effect of nicotine levels on cigarette brand choices. In 23rd International Congress on Modelling and Simulation.
Dhami, M. K., Mandel, D. R., Mellers, B. A., & Tetlock, P. E. (2015). Improving intelligence analysis with decision science. Perspectives on Psychological Science, 10(6), 753–757.
Elbanna, S., Thanos, I. C., & Jansen, R. J. G. (2019). A literature review of the strategic decision-making context: A synthesis of previous mixed findings and an agenda for the way forward. AIMS, 2(23), 42–60.
Fischhoff, B., & Broomell, S. B. (2020). Judgment and decision making. Annual Review of Psychology, 71, 331–355.
Huber, J., Payne, J. W., & Puto, C. P. (2014). Let’s be honest about the attraction effect. Journal of Marketing Research, 51(8), 520–525.
Johnson, M. R., Middleton, M., Brown, M., Burke, T., & Barnett, T. (2019). Utilization of a paired comparison analysis framework to inform decision-making and the prioritization of projects and initiatives in a highly matrixed clinical research program. The Journal of Research Administration, 1(50), 46–65.
Kifayat, T., & Aslam, M. (2016). The Rayleigh paired comparison model with Bayesian analysis. Hacettepe Journal of Mathematics and Statistics, 45(5), 1541–1551.
Kingsley, D. C., & Brown, T. C. (2010). Preference uncertainty, preference learning, and paired comparison experiments. Land Economics, 86(3), 530–544.
Lee, S. Y. (2022). Gibbs sampler and coordinate ascent variational inference: A set-theoretical review. Communications in Statistics—Theory and Methods, 51(6), 1549–1568. https://doi.org/10.1080/03610926.2021.1921214
Pacheco-Colón, I., Hawes, S. W., Duperrouzel, J. C., Lopez-Quintero, C., & Gonzalez, R. (2019). Decision-Making as a latent construct and its measurement invariance in a large sample of adolescent cannabis users. Journal of the International Neuropsychological Society, 25(7), 661–667.
Rayner, J. C. W., & Best, D. J. (2001). A contingency table approach to nonparametric testing. CRC Press.
Schauberger, G., & Tutz, G. (2017). Subject-specific modelling of paired comparison data: A lasso-type penalty approach. Statistical Modelling, 17(3), 223–243.
Stern, S. E. (2011). Moderated paired comparisons: A generalized Bradley Terry model for continuous data using a discontinuous penalized likelihood function. Journal of the Royal Statistical Society: Series C (Applied Statistics), 60(3), 397–415.
Sung, Y. T., & Wu, J. S. (2018). The Visual Analogue Scale for Rating, Ranking and Paired-Comparison (VAS-RRP): A new technique for psychological measurement. Behavior Research Methods, 50(4), 1694–1715.
Walters, D. J., Ferbach, P. M., Fox, C. R., & Sloman, S. A. (2017). Known unknowns: A critical determinant of confidence and calibration. Management Science, 63(12), 4298–4307.
Wang, J., Shi, N., Zhang, X., & Xu, G. (2022). Sequential Gibbs sampling algorithm for cognitive diagnosis models with many attributes. Multivariate Behavioral Research, 57(5), 840–858. https://doi.org/10.1080/00273171.2021.1896352
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Cheema, S.A., Kifayat, T., Hudson, I.L., Mehmood, A., Ullah, K., Rahman, A.R. (2023). Determining the Degree of Dominance of Factors Deriving the Comparative Choice Hierarchy: An Operational Generalization of Latent Choice Models. In: Sharma, V., Maheshkar, C., Poulose, J. (eds) Analytics Enabled Decision Making. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-9658-0_4
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DOI: https://doi.org/10.1007/978-981-19-9658-0_4
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