Dense Neural Network used to Amplify the Forecasting Accuracy of real-time Human Swarms

Abstract

Artificial Swarm Intelligence (ASI) is a hybrid AI technology that enables distributed human groups to "think together" in real-time systems modeled on natural swarms. Prior research has shown that by forming "human swarms," networked groups can substantially amplify their combined intelligence and produce significantly more accurate forecasts than traditional methods. The present study explores whether the rich behavioral data collected during "swarming" can be used to further increase the accuracy of swarm-based forecasts. To do this, a dense neural network was used to process the data collected during a set of swarm-based forecasts and generate a Conviction Index (CI) for each forecast that estimates its expected accuracy. This method was then tested in an authentic forecasting task – wagering on sporting events against the Vegas odds. Specifically, groups of sports fans, working as real-time swarms, were tasked with predicting the outcome of 238 NBA games over 25 consecutive weeks. As a baseline, the swarms achieved an impressive 25% net return on investment (ROI) against the Vegas Odds. This was compared to an enhanced method that used Conviction Index to (a) estimate the strength of each forecast and then (b) wager only on forecasts of sufficient strength. The CI-selected wagers yielded a 57% net ROI against Vegas Odds. This is a significant gain, equivalent to more than doubling the ROI of the naïve swarm betting strategy.