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Determining the Degree of Dominance of Factors Deriving the Comparative Choice Hierarchy: An Operational Generalization of Latent Choice Models

In: Analytics Enabled Decision Making

Author

Listed:
  • Salman A. Cheema

    (National Textile University Faisalabad)

  • Tanveer Kifayat

    (Shaheed Zulfikar Ali Bhutto Institute of Science and Technology)

  • Irene L. Hudson

    (Royal Melbourne Institute of Technology (RMIT))

  • Asif Mehmood

    (Air University Islamabad)

  • Kalim Ullah

    (Foundation University Medical College, Foundation University Islamabad)

  • Abdur R. Rahman

    (The Aga Khan University)

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.

Suggested Citation

  • Salman A. Cheema & Tanveer Kifayat & Irene L. Hudson & Asif Mehmood & Kalim Ullah & Abdur R. Rahman, 2023. "Determining the Degree of Dominance of Factors Deriving the Comparative Choice Hierarchy: An Operational Generalization of Latent Choice Models," Springer Books, in: Vinod Sharma & Chandan Maheshkar & Jeanne Poulose (ed.), Analytics Enabled Decision Making, pages 59-88, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-9658-0_4
    DOI: 10.1007/978-981-19-9658-0_4
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