Enhancing Collective Estimates by Aggregating Cardinal and Ordinal Inputs


  • Ryan Kemmer Arizona State University
  • Yeawon Yoo Arizona State University
  • Adolfo R. Escobedo Arizona State University
  • Ross Maciejewski Arizona State University




There are many factors that affect the quality of data received from crowdsourcing, including cognitive biases, varying levels of expertise, and varying subjective scales. This work investigates how the elicitation and integration of multiple modalities of input can enhance the quality of collective estimations. We create a crowdsourced experiment where participants are asked to estimate the number of dots within images in two ways: ordinal (ranking) and cardinal (numerical) estimates. We run our study with 300 participants and test how the efficiency of crowdsourced computation is affected when asking participants to provide ordinal and/or cardinal inputs and how the accuracy of the aggregated outcome is affected when using a variety of aggregation methods. First, we find that more accurate ordinal and cardinal estimations can be achieved by prompting participants to provide both cardinal and ordinal information. Second, we present how accurate collective numerical estimates can be achieved with significantly fewer people when aggregating individual preferences using optimization-based consensus aggregation models. Interestingly, we also find that aggregating cardinal information may yield more accurate ordinal estimates.




How to Cite

Kemmer, R., Yoo, Y., Escobedo, A., & Maciejewski, R. (2020). Enhancing Collective Estimates by Aggregating Cardinal and Ordinal Inputs. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1), 73-82. https://doi.org/10.1609/hcomp.v8i1.7465