Abstract:
We present a flexible, robust approach to predictive decision-making using
simple, modular agents (WoC-Bots) that interact with each other socially and
share information about the features they are trained on. Our agents form a
knowledge-diverse crowd, allowing us to use Wisdom of the Crowd (WoC)
theories to aggregate their opinions and come to a collective conclusion.
Compared to traditional multi-layer perceptron (MLP) networks, WoC-Bots can
be trained more quickly, more easily incorporate new features, and make it
easier to determine why the network gives the prediction that it does. We
compare our predictive accuracy with MLP networks to show that WoC-Bots can
attain similar results when predicting the box office success of Hollywood
movies, while requiring significantly less training time.
