Improving Swarm Performance by Applying Machine Learning to a New Dynamic Survey

<p>A company, Unanimous AI, has created a software platform that allows individuals to come together as a group or a human swarm to make decisions. These human swarms amplify the decision-making capabilities of both the individuals and the group. One way Unanimous AI increases the swarm’s collective decision-making capabilities is by limiting the swarm to more informed individuals on the given topic. The previous way Unanimous AI selected users to enter the swarm was improved upon by a new methodology that is detailed in this study. This new methodology implements a new type of survey that collects data that is more indicative of a user’s knowledge on the subject than the previous survey. This study also identifies better metrics for predicting each user’s performance when predicting Major League Baseball game outcomes throughout a given week. This study demonstrates that the new machine learning models and data extraction schemes are approximately 12% more accurate than the currently implemented methods at predicting user performance. Finally, this study shows how predicting a user’s performance based purely on their inputs can increase the average performance of a group by limiting the group to the top predicted performers. This study shows that by limiting the group to the top predicted performers across five different weeks of MLB predictions, the average group performance was increased up to 5.5%, making this a superior method.</p>

Name thesis:degree_name

MS in Electrical Engineering

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2018

Author dc:creator

• Jackson, John Taylor

Contributors dc:contributor

• Franz Kurfess

• Swarm Intelligence
• Machine Learning
• Dynamic Survey
• Applied Behavior Analysis
• Graphics and Human Computer Interfaces
• Other Computer Sciences

Identifier

10.15368/theses.2018.34

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3163