Intuitionistic Fuzzy Logit Model of Discrete Choice

In the real-world multicriteria decision making, the evaluations of the various criteria are often vague (or not crisp). The existing choice models are difficult to apply in such situations. In this paper, we introduce an intuitionistic fuzzy variant of the multinomial logit model, which helps us to suggest a decision-maker's likely choices with vague evaluations. The applicability of the proposed model is shown through a real multicriteria decision-making application.

[1]  Robert D. Retherford,et al.  Multinomial Logit Regression , 2011 .

[2]  L. Zadeh.  Probability measures of Fuzzy events , 1968 .

[3]  Nadia Vozlyublennaia.  Does Idiosyncratic Risk Matter for Individual Securities , 2012 .

[4]  Sergei Ovchinnikov,et al.  Fuzzy sets and applications , 1987 .

[5]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[6]  Adam Astrid Kemperman,et al.  Vacation length choice : a dynamic mixed multinomial logit model , 2014 .

[7]  Dominique Lord,et al.  Comparing Three Commonly Used Crash Severity Models on Sample Size Requirements: Multinomial Logit, Ordered Probit, and Mixed Logit Models , 2014 .

[8]  A. Çelik,et al.  A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey. , 2014, Accident; analysis and prevention.

[9]  Peter Congdon.  Multinomial and Ordinal Regression Models , 2007 .

[10]  Zeshui Xu,et al.  Intuitionistic Fuzzy Aggregation Operators , 2007, IEEE Transactions on Fuzzy Systems.

[11]  Dennis K. J. Lin,et al.  Selection of multinomial logit models via association rules analysis , 2013 .

[12]  C. Engel,et al.  Predicting longitudinal trajectories of health probabilities with random‐effects multinomial logit regression , 2012, Statistics in medicine.

[13]  Ronald R. Yager,et al.  A note on probabilities of fuzzy events , 1979, Inf. Sci..

[14]  D. McFadden,et al.  URBAN TRAVEL DEMAND - A BEHAVIORAL ANALYSIS , 1977 .

[15]  T. Åstebro,et al.  More than a Dummy: The Probability of Failure, Survival and Acquisition of Firms in Financial Distress , 2001 .

[16]  Early warning systems and systemic banking crises in low income countries : A multinomial logit approach 1 , 2013 .

[17]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy interpretations of Barcan formulas , 2017, Inf. Sci..

[18]  Masaharu Mizumoto,et al.  Some Properties of Fuzzy Sets of Type 2 , 1976, Inf. Control..

[19]  Ruxian Wang.  Capacitated Assortment and Price Optimization under the Multinomial Logit Model , 2012 .

[20]  Jhareswar Maiti,et al.  Evaluation of Risk of Occupational Injuries Among Underground Coal Mine Workers Through Multinomial Logit Analysis , 1999 .

[21]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[22]  David A. Hensher,et al.  Forecasting Corporate Bankruptcy: Optimizing the Performance of the Mixed Logit Model , 2007 .

[23]  Dwight Merunka,et al.  Neural networks and the multinomial logit for brand choice modelling: a hybrid approach , 2000 .

[24]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[25]  I. Woittiez,et al.  Client demands and the allocation of home care in the Netherlands. A multinomial logit model of client types, care needs and referrals. , 2003, Health policy.

[26]  Baibing Li.  The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis , 2011 .

[27]  Manish Aggarwal,et al.  A New Family of Induced OWA Operators , 2015, Int. J. Intell. Syst..

[28]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[29]  Florian Heiss,et al.  Discrete Choice Methods with Simulation , 2016 .

[30]  L. Thurstone,et al.  A low of comparative judgement , 1927 .

[31]  Sharon-Lise T Normand,et al.  On the use of discrete choice models for causal inference , 2005, Statistics in medicine.

[32]  Stewart Jones,et al.  An Error Component Logit Analysis of Corporate Bankruptcy and Insolvency Risk in Australia , 2007 .

[33]  Joshua Auld,et al.  A behavioral housing search model: Two-stage hazard-based and multinomial logit approach to choice-set formation and location selection , 2012 .

[34]  Uneb Gazder,et al.  A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport , 2015 .

[35]  Madhu Errampalli,et al.  Use of Artificial Intelligence for Mode Choice Analysis and Comparison with Traditional Multinomial Logit Model , 2013 .

[36]  Sven Müller,et al.  A comparison of linear reformulations for multinomial logit choice probabilities in facility location models , 2014, Eur. J. Oper. Res..

[37]  Simon Jackman,et al.  Bayesian Analysis for the Social Sciences , 2009 .

[38]  Krassimir T. Atanassov,et al.  On Intuitionistic Fuzzy Sets Theory , 2012, Studies in Fuzziness and Soft Computing.

[39]  René M. Stulz,et al.  Hedge Fund Contagion and Liquidity Shocks , 2010 .

[40]  J. Kacprzyk,et al.  A concept of a probability of an intuitionistic fuzzy event , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[41]  Pasquale Schiraldi,et al.  The Flexible Coefficient Multinomial Logit (FC-MNL) Model of Demand for Differentiated Products , 2013 .

[42]  Minxia Luo,et al.  A distance measure between intuitionistic fuzzy sets and its application in medical diagnosis , 2018, Artif. Intell. Medicine.

[43]  Hongzhi Guan,et al.  A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. , 2015, Accident; analysis and prevention.

[44]  Jacob B. Feldman,et al.  Bounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits , 2015 .