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Phoneotypic modeling of human behaviors and propensities
Descriptive
TitleInfo
Title
Phoneotypic modeling of human behaviors and propensities
Name
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Bati
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Ghassan F.
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1985-
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Ghassan F. Bati
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author
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Singh
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Vivek K
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Vivek K Singh
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Advisory Committee
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chair
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Gajic
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Zoran
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Zoran Gajic
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Advisory Committee
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internal member
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Marsic
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Ivan
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Ivan Marsic
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Advisory Committee
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internal member
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(type = personal)
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(type = family)
Atrey
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Pradeep
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Pradeep Atrey
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Advisory Committee
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outside member
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Rutgers University
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
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2019
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2019-01
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2019
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xx
Language
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eng
Abstract
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With the growth in mobile social networks, social internet of things, and cyber-physical-social systems, there is an ever growing need to model and understand human beings as they interact with other humans and socio-technical ecosystems. In this dissertation, we focus on modeling three core human concepts – trust propensity, altruism propensity, and interpersonal trust using mobile phone metadata. Traditional methods for understanding an individual’s propensities and behaviors have been surveys and lab experiments. However, the growth of “personal big data”, which includes the use of various personal ubiquitous devices, is allowing for human behaviors and propensities to be modeled via lower-cost, quick, automated methods. This dissertation proposes a new methodology to model human behaviors and propensities based on phoneotypes (phone-based observations of a combination of people’s traits) that aims to complement traditional methods like surveys with a ubiquitous data-driven automated method. The analysis and modeling employ multiple deep and shallow machine learning algorithms and are based on two datasets - Rutgers Well-being Study and MIT friends and family dataset. Overall, the findings suggest that: (1) many phone-based features are associated with participant’s altruism, trust, and interpersonal trust scores;
(2) phone-based prediction models for altruism, trust propensity, and interpersonal trust performed statistically significantly better than comparable demography-based models. This dissertation paves way to study the associations between human behavioral propensities and long-term “in the wild” socio-mobile behavior, and to utilize “personal big data” with shallow and deep machine learning approaches to model altruism, trust, and interpersonal trust. A better modeling approach for human beings will have multiple applications in fields like healthcare, well-being, and urban planning.
(2) phone-based prediction models for altruism, trust propensity, and interpersonal trust performed statistically significantly better than comparable demography-based models. This dissertation paves way to study the associations between human behavioral propensities and long-term “in the wild” socio-mobile behavior, and to utilize “personal big data” with shallow and deep machine learning approaches to model altruism, trust, and interpersonal trust. A better modeling approach for human beings will have multiple applications in fields like healthcare, well-being, and urban planning.
Subject
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Topic
Electrical and Computer Engineering
Subject
(authority = ETD-LCSH)
Topic
Trust
Subject
(authority = ETD-LCSH)
Topic
Altruism
Subject
(authority = ETD-LCSH)
Topic
Behavioral assessment -- Methodology
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Rutgers University Electronic Theses and Dissertations
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ETD_9370
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electronic resource
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Extent
1 online resource (97 pages : illustrations)
Note
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Ph.D.
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Includes bibliographical references
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by Ghassan F. Bati
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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doi:10.7282/t3-mndr-a738
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ETD doctoral
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Rights
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Bati
GivenName
Ghassan
MiddleName
F.
Role
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Permission or license
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2018-11-26 11:32:30
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Name
Ghassan Bati
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Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2020-01-23
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2020-06-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after June 30, 2020.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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