TIME DEPENDENT ATTRIBUTE-LEVEL BEST WORST DISCRETE CHOICE MODELLING

Amanda Working; Mohammed Alqawba; Norou Diawara; Ling Li; Amanda Working; Mohammed Alqawba; Norou Diawara; Ling Li

doi:10.3934/bdia.2018010

Big Data and Information Analytics

2018, Volume 3, Issue 1: 55-72. doi: 10.3934/bdia.2018010

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TIME DEPENDENT ATTRIBUTE-LEVEL BEST WORST DISCRETE CHOICE MODELLING

1.
Department of Mathematics and Statistics Old Dominion University Norfolk, VA 23529, USA
2.
Department of Information Technology and Decision Sciences Old Dominion University Norfolk, VA 23529, USA

Published: 28 February 2019

Discrete choice models (DCMs) are applied in statistical modelling of consumer behavior. Such models are used in many areas including social sciences, health economics, transportation research, and health systems research and they are time dependent. In this manuscript, we review references on the study of such models, develop DCMs with emphasis on time dependent best-worst choice and discrimination between choice attributes. Referenced measurements of the dynamic DCMs are simulated. Expected utilities over time are derived using Markov decision processes. We study attributes and attribute-levels associated with the quality of life of seniors, report the estimation results, and discuss our findings.
- Discrete choice models,
- Case 2 best-worst scaling,
- Markov decision processes,
- utility,
- attribute levels.
Citation: Amanda Working, Mohammed Alqawba, Norou Diawara, Ling Li. TIME DEPENDENT ATTRIBUTE-LEVEL BEST WORST DISCRETE CHOICE MODELLING[J]. Big Data and Information Analytics, 2018, 3(1): 55-72. doi: 10.3934/bdia.2018010

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Abstract

Discrete choice models (DCMs) are applied in statistical modelling of consumer behavior. Such models are used in many areas including social sciences, health economics, transportation research, and health systems research and they are time dependent. In this manuscript, we review references on the study of such models, develop DCMs with emphasis on time dependent best-worst choice and discrimination between choice attributes. Referenced measurements of the dynamic DCMs are simulated. Expected utilities over time are derived using Markov decision processes. We study attributes and attribute-levels associated with the quality of life of seniors, report the estimation results, and discuss our findings.

References

[1]	[ A. Anas, Discrete choice theory, information theory and the multinomial logit and gravity models, Transportation Research Part B: Methodological, 17 (1983), 13-23.
[2]	[ P. Arcidiacono and P. B. Ellickson, Practical methods for estimation of dynamic discrete choice models, Annu. Rev. Econ., 3 (2011), 363-394.
[3]	[ J. Blanchet, G. Gallego and V. Goyal, A markov chain approximation to choice modeling, Operations Research, 64 (2016), 886-905.
[4]	[ I. Chadès, G. Chapron, M.-J. Cros, F. Garcia and R. Sabbadin, Mdptoolbox: a multi-platform toolbox to solve stochastic dynamic programming problems, Ecography, 37 (2014), 916-920.
[5]	[ T. N. Flynn, J. J. Louviere, T. J. Peters and J. Coast, Best-worst scaling: What it can do for health care research and how to do it, Journal of Health Economics, 26 (2007), 171-189.
[6]	[ S. Frederick, G. Loewenstein and T. O'Donoghue, Time discounting and time preference: A critical review, Journal of Economic Literature, 40 (2002), 351-401, URL http://www.jstor.org/stable/2698382.
[7]	[ W. H. Greene and D. A. Hensher, A latent class model for discrete choice analysis: Contrasts with mixed logit, Transportation Research Part B: Methodological, 37 (2003), 681-698.
[8]	[ S. A. Knox, R. C. Viney, Y. Gu, A. R. Hole, D. G. Fiebig, D. J. Street, M. R. Haas, E. Weisberg and D. Bateson, The effect of adverse information and positive promotion on women's preferences for prescribed contraceptive products, Social Science & Medicine, 83 (2013), 70-80.
[9]	[ S. A. Knox, R. C. Viney, D. J. Street, M. R. Haas, D. G. Fiebig, E. Weisberg and D. Bateson, What's good and bad about contraceptive products?, Pharmacoeconomics, 30 (2012), 1187-1202.
[10]	[ E. Lancsar, D. G. Fiebig and A. R. Hole, Discrete choice experiments: A guide to model specification, estimation and software, PharmacoEconomics, 35 (2017), 697-716.
[11]	[ E. Lancsar and J. Louviere, Conducting discrete choice experiments to inform healthcare decision making, Pharmacoeconomics, 26 (2008), 661-677.
[12]	[ E. Lancsar, J. Louviere, C. Donaldson, G. Currie and L. Burgess, Best worst discrete choice experiments in health: Methods and an application, Social Science & Medicine, 76 (2013), 74-82.
[13]	[ S. Lipovetsky and M. Conklin, Best-worst scaling in analytical closed-form solution, Journal of choice modelling, 10 (2014), 60-68.
[14]	[ J. J. Louviere, T. N. Flynn and A. A. J. Marley, Best-worst scaling: Theory, methods and applications, Cambridge University Press, 2015.
[15]	[ A. Marley, T. N. Flynn and J. Louviere, Probabilistic models of set-dependent and attributelevel best-worst choice, Journal of Mathematical Psychology, 52 (2008), 281-296.
[16]	[ A. Marley, T. Islam and G. Hawkins, A formal and empirical comparison of two score measures for best-worst scaling, Journal of Choice Modelling, 21 (2016), 15-24.
[17]	[ D. McFadden, Conditional logit analysis of qualitative choice behavior, in Frontiers in Econometrics (ed. P. Zarembka), Academic Press, New York, 1974.
[18]	[ D. Potoglou, P. Burge, T. Flynn, A. Netten, J. Malley, J. Forder and J. E. Brazier, Best-worst scaling vs. discrete choice experiments: An empirical comparison using social care data, Social Science & Medicine, 72 (2011), 1717-1727.
[19]	[ M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley & Sons, Inc., New York, 1994.
[20]	[ J. Rust, Structural estimation of markov decision processes, Handbook of Econometrics, 2 (1994), 3081-3143.
[21]	[ J. Rust, Dynamic programming, the new palgrave dictionary of economics, edited by steven n, Durlauf and Lawrence E. Blume.
[22]	[ D. J. Street and L. Burgess, The Construction of Optimal Stated Choice Experiments: Theory and Methods, vol. 647, John Wiley & Sons, 2007.
[23]	[ D. J. Street and S. A. Knox, Designing for attribute-level best-worst choice experiments, Journal of Statistical Theory and Practice, 6 (2012), 363-375.
[24]	[ K. E. Train, Discrete Choice Methods with Simulation, Cambridge university press, 2009.

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