by    in Data Science, prediction, Rankings

Cognitive Models for the Intelligent Aggregation of Lists

Ranker is constantly working to improve our crowdsourced list algorithms, in order to surface the best possible answers to the questions on our site.  As part of this effort, we work with leading academics who research the “wisdom of crowds”, and below is a poster we recently presented at the annual meeting for the Association for Psychological Science (led by Ravi Selker at the University of Amsterdam and in collaboration with Michael Lee from the University of California-Irvine).

While the math behind the aggregation model may be complex (a paper describing it in detail will hopefully be published shortly), the principle being demonstrated is relatively simple.  Specifically, aggregating lists using models that take into account the inferred expertise of the list maker outperform simple averages, when compared to real-world ground truths (e.g. box office revenue).  While Ranker’s algorithms for determining our crowdsourced rankings may be similarly complex, they are similarly designed to produce the best answers possible.

 

cognitive_model_aggregating_lists

 

– Ravi Iyer

by    in Data Science

The Moral Psychology and Big Data Singularity – SXSW 2012

Below is a narrated powerpoint from a presentation I gave at South by Southwest Interactive on March 11, 2012.  The point of this presentation was to explore the intersection of technology and psychology, and hopefully to convince technologists to try to use our data to examine intangible things like values.  While the talk focuses more on psychology, many of the ideas were inspired by the semantic datasets we work with at Ranker.  Working with semantic datasets puts one in the mindset of considering synergy among different fields with different kinds of data.