Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys

Mazen Danaf, Angelo Guevara, Bilge Atasoy, Moshe E. Ben-Akiva

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract


Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or choice situation) depend on the previous choices of the same individual (as well as the alternative attributes in the previous menus). Nevertheless, the literature is indecisive on whether the parameter estimates in such cases are consistent or not. In this paper, the authors present a Monte Carlo experiment mimicking a recommender system for Mobility as a Service (MaaS) plans, showing cases where the estimates are consistent and those where they are not. In addition, they provide a theoretical explanation for this inconsistency and discuss the implications on the design of these systems and on model estimation. The authors conclude that endogeneity is not a concern when the likelihood function accounts for the data generation process and when all the data are used in the estimation. This can be achieved when the system is initialized exogenously and when this initialization is accounted for in the estimation.
Original languageEnglish
Title of host publicationThe Transportation Research Board (TRB) 98th Annual Meeting, 2019
Place of PublicationWashington DC, USA
PublisherTransportation Research Board (TRB)
Number of pages9
Publication statusPublished - 2019
EventTransportation Research Board 98th Annual Meeting - Walter E. Washington Convention Center, Washington D.C., United States
Duration: 13 Jan 201917 Jan 2019

Conference

ConferenceTransportation Research Board 98th Annual Meeting
Abbreviated titleTRB 2019
CountryUnited States
CityWashington D.C.
Period13/01/1917/01/19

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