Abstract:
Wisdom-of-Crowds-Bots (WoC-Bots) are simple, modular agents working together
in a multi-agent environment to collectively make binary predictions. The
agents represent a knowledge-diverse crowd, with each agent trained on a
subset of available information. A honey-bee-derived swarm aggregation
mechanism is used to elicit a collective prediction with an associated
confidence value from the agents. Due to their multi-agent design, WoC-Bots
can be distributed across multiple hardware nodes, include new features
without re-training existing agents, and the aggregation mechanism can be
used to incorporate predictions from other sources, thus improving overall
predictive accuracy of the system. In addition to these advantages, we
demonstrate that WoC-Bots are competitive with other top classification
methods on three datasets and apply our system to a real-world sports
betting problem, producing a consistent return on investment from 1 January
2021 through 15 November 2022 on most major sports.
