The wisdom of the crowd with partial rankings: A Bayesian approach implementing the Thurstone model in JAGS.
Behav Res Methods
; 2024 Jul 30.
Article
en En
| MEDLINE
| ID: mdl-39080123
ABSTRACT
We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Behav Res Methods
Asunto de la revista:
CIENCIAS DO COMPORTAMENTO
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos