Affective engineering in application to bi-level human migration models

Kalashnikov, Vyacheslav V. y Kalashnykova, Kalashnykova Ibanovna y Acosta Sánchez, Yazmín Guadalupe y Kalashnikov, Vitaly (2014) Affective engineering in application to bi-level human migration models. In: Industrial applications of affective engineering. Springer, New York, pp. 27-38. ISBN 9783319047973

[img]
Vista previa
Texto
Affective Engineering in Applications to Bilevel Human Migration Models.pdf - Versión Publicada
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (632kB) | Vista previa

Resumen

In this paper, we develop a bi-level human migration model using the concepts of affective engineering (Kansei Engineering) and conjectural variations equilibrium (CVE). In contrast to previous existing works, we develop a bi-level programming model in a natural form. The upper level agents are municipalities of competing locations, whose strategies are investments into the infrastructures of the locations (cities, towns, etc.). These investments aim at making the locations more attractive for both residents and potential migrants from other locations, which clearly demands affective engineering tools. At the lower level of the model, the present residents (grouped into professional communities) are also potential migrants to other locations. They make their decision where to migrate (if at all) by comparing the expected values of the utility functions of the outbound

Tipo de elemento: Sección de libro.
Divisiones: Economía
Usuario depositante: Lic. Jesús E. Alvarado
Creadores:
CreadorEmailORCID
Kalashnikov, Vyacheslav V.NO ESPECIFICADONO ESPECIFICADO
Kalashnykova, Kalashnykova IbanovnaNO ESPECIFICADONO ESPECIFICADO
Acosta Sánchez, Yazmín GuadalupeNO ESPECIFICADONO ESPECIFICADO
Kalashnikov, VitalyNO ESPECIFICADONO ESPECIFICADO
Fecha del depósito: 06 Oct 2015 22:12
Última modificación: 05 Oct 2018 17:08
URI: http://eprints.uanl.mx/id/eprint/7692

Actions (login required)

Ver elemento Ver elemento

Downloads

Downloads per month over past year