Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Abstract

Cumulative Prospect Theory (CPT) is a modeling tool widely used in behavioral economics and cognitive psychology that captures subjective decision making of individuals under risk or uncertainty. In this paper, we propose a dynamic pricing strategy for Shared Mobility on Demand Services (SMoDSs) using a passenger behavioral model based on CPT. This dynamic pricing strategy together with dynamic routing via a constrained optimization algorithm that we have developed earlier, provide a complete solution customized for SMoDS of multi-passenger transportation. The basic principles of CPT and the derivation of the passenger behavioral model in the SMoDS context are described in detail. The implications of CPT on dynamic pricing of the SMoDS are delineated using computational experiments involving passenger preferences. These implications include interpretation of the classic fourfold pattern of risk attitudes, strong risk aversion over mixed prospects, and behavioral preferences of self reference. Overall, it is argued that the use of the CPT framework corresponds to a crucial building block in designing socio-technical systems by allowing quantification of subjective decision making under risk or uncertainty that is perceived to be otherwise qualitative.

Comment: 17 pages, 6 figures, and has been accepted for publication at the 58th Annual Conference on Decision and Control, 2019

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1904.04824

• http://arxiv.org/pdf/1904.04824

• http://xplorestaging.ieee.org/ielx7/8977134/9028853/09029508.pdf?arnumber=9029508,

http://dx.doi.org/10.1109/cdc40024.2019.9029508

• https://dblp.uni-trier.de/db/journals/corr/corr1904.html#abs-1904-04824,

https://arxiv.org/abs/1904.04824,

https://arxiv.org/pdf/1904.04824,

https://ui.adsabs.harvard.edu/abs/2019arXiv190404824G/abstract,

https://doi.org/10.1109/CDC40024.2019.9029508,

http://www.arxiv-vanity.com/papers/1904.04824,

https://academic.microsoft.com/#/detail/3012298492