Artificial prediction markets present a novel opportunity for human-AI collaboration

TL;DR

This paper explores artificial prediction markets as a new approach to enhance human-AI collaboration, analyzing how market parameters and human-like agents influence performance on classification tasks.

Contribution

It introduces a comprehensive experimental analysis of prediction market models and demonstrates the impact of human-like agents, laying groundwork for hybrid human-AI algorithms.

Findings

Market parameters significantly affect model performance

Exogenous human-like agents influence market outcomes

Prediction markets can facilitate human-AI collaboration

Abstract

Despite high-profile successes in the field of Artificial Intelligence, machine-driven technologies still suffer important limitations, particularly for complex tasks where creativity, planning, common sense, intuition, or learning from limited data is required. These limitations motivate effective methods for human-machine collaboration. Our work makes two primary contributions. We thoroughly experiment with an artificial prediction market model to understand the effects of market parameters on model performance for benchmark classification tasks. We then demonstrate, through simulation, the impact of exogenous agents in the market, where these exogenous agents represent primitive human behaviors. This work lays the foundation for a novel set of hybrid human-AI machine learning algorithms.