Title:
Exploring the Robustness of the Surprisingly Popular Signal

dc.contributor.advisor Thomas, Rick P.
dc.contributor.author Sukernek, Justin
dc.contributor.committeeMember Rahnev, Dobromir
dc.contributor.committeeMember Varma, Sashank
dc.contributor.committeeMember Dougherty, Michael
dc.contributor.committeeMember Gorman, Jamie
dc.contributor.department Psychology
dc.date.accessioned 2023-05-18T17:50:25Z
dc.date.available 2023-05-18T17:50:25Z
dc.date.created 2023-05
dc.date.issued 2023-04-18
dc.date.submitted May 2023
dc.date.updated 2023-05-18T17:50:25Z
dc.description.abstract A large portion of the decision-making literature is concerned with forecasting the future, often using wisdom of the crowd as the basis for successful forecasts. However, crowd wisdom can be limited when the consensus is incorrect. Bayesian truth serum and the Surprisingly Popular algorithm, two novel methodologies in this space, offer solutions to this limitation by leveraging social sensing to calculate the 'surprisingly popular signal' at the respondent and question level, respectively. In this dissertation, I present three experiments that compare the three methodologies across forecasting, consumer decision-making, and general knowledge. In all three experiments, SP yielded the highest accuracy when utilizing a subsample of the most knowledgeable participants, a finding that is coherent with the existing literature. Experiment two incorporated social influence, uncovering a positive effect of disagreement on BTS scores and bidirectional effects of social influence on respondents' perceptions of how others would answer. Furthermore, two of the experiments demonstrate evidence of the BTS's ability to identify subsamples of participants that increase SP's accuracy, performing a similar function to domain knowledge. Finally, a process-based simulation of knowledge and social influence on SP and BTS is conducted, corroborating empirical findings. Overall, results provide promising evidence of SP's effectiveness across all task contexts, as well as some evidence for a potential new application for BTS.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri https://hdl.handle.net/1853/71994
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Bayesian truth serum
dc.subject Surprisingly popular
dc.subject Forecasting
dc.subject Decision-making
dc.subject Wisdom of the crowd
dc.title Exploring the Robustness of the Surprisingly Popular Signal
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Thomas, Rick P.
local.contributor.corporatename College of Sciences
local.contributor.corporatename School of Psychology
relation.isAdvisorOfPublication 44e4bb42-7dc6-4dd1-80a5-2532238057b1
relation.isOrgUnitOfPublication 85042be6-2d68-4e07-b384-e1f908fae48a
relation.isOrgUnitOfPublication 768a3cd1-8d73-4d47-b418-0fc859ce897d
thesis.degree.level Doctoral
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