Show simple item record

dc.contributor.authorHodrali Ramesh, Sujay Bhatt
dc.date.accessioned2019-10-15T16:47:30Z
dc.date.available2020-02-29T07:01:12Z
dc.date.issued2019-08-30
dc.identifier.otherHodraliRamesh_cornellgrad_0058F_11593
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:11593
dc.identifier.otherbibid: 11050493
dc.identifier.urihttps://hdl.handle.net/1813/67511
dc.description.abstractThe creation and widespread adoption of social media has positively contributed to the sensing ability of people in the society. People are influenced by the views and opinions shared by other members of the society (the social network), and in turn share their experiences, contributing to the societal wisdom. An approach to decision making with the information continuously generated online is learning the mechanism of the data generation process by people in the social network, and using these models to perform decision analysis. This approach not only provides insights about the collective behavior of the people in the social network, but also provides a means to make testable predictions when exogenous interventions or influences are present. We thus take a model based approach to people-centric decision analysis in this thesis and marry ideas from microeconomics & social psychology with traditional model estimation and decision analysis techniques from statistical signal processing & stochastic control. We consider two well studied and empirically validated models from social psychology and microeconomics, namely, communication flow model based on social influence and social learning model, for modeling the behavior of people when they are situated in a social network. With these models to capture the behavior of people, we consider three frameworks for controlled social sensing: i.) Framework I: People sequentially take one-shot decisions by learning from the social network. A controller learns the underlying parameters that are a driving force behind their decisions. Application studied: Quickest change detection of market shocks using the actions generated by risk-averse traders. ii.) Framework II: People sequentially take one-shot decisions by learning from the social network and a controller exogenously intervenes in their decision making process. The controller learns the underlying parameters that are a driving force behind their decisions by controlling the intervention. Applications studied: (1) Monopoly pricing with risk-averse customers to maximize sales and revenue, by learning about the product quality. (2) Honest information elicitation via incentivization to learn about the product quality. iii.) Framework III: People form opinions about an event or object of interest after repeated interactions with their social network. A controller learns the underlying parameters that are a driving force behind their opinions. Application studied: Adaptive polling in hierarchical social influence networks to learn the parameters driving public opinion. The key unifying theme of this thesis is to provide structural results for people-centric stochastic control problems, i.e, to derive mathematical insights about the interaction between the controller and the social network.
dc.language.isoen_US
dc.subjectHuman-Computer Interaction
dc.subjectSocial psychology
dc.subjectAdaptive Polling
dc.subjectMonopoly Pricing
dc.subjectPOMDP
dc.subjectSocial Sensing
dc.subjectStructural Results
dc.subjectElectrical engineering
dc.subjectEconomics
dc.titleControlled Social Sensing: A POMDP Approach
dc.typedissertation or thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh.D., Electrical and Computer Engineering
dc.contributor.chairKrishnamurthy, Vikram
dc.contributor.committeeMemberZhao, Qing
dc.contributor.committeeMemberGurvich, Itai
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/78dn-y624


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Statistics