Irrationality-Aware Human Machine Collaboration: Mitigating Alterfactual Irrationality in Copy Trading

Artificial intelligence (AI) algorithms are trained on human-generated data, but what if that data reflects irrational human decision making? To tackle this challenge, Shen et al. developed a new irrationality-aware human-machine collaboration (IA-HMC) framework, designed to help AI recognize and adapt to human irrationality. A key concept introduced in this framework is “alterfactual irrationality”—a term used to describe human decisions influenced by irrelevant alternatives. The researchers applied this idea to copy trading, a popular investment strategy where everyday investors (followers) mimic the trades of expert traders. They identified two major irrational behaviors affecting followers: herding behavior—blindly following others without independent analysis; and identity bias—making investment choices based on who made the trade rather than its actual merit. By developing irrationality-aware machine learning methods, the study showed that AI can help followers make better trading decisions. Their approach led to a 49% improvement in success rates compared to human decisions alone and a 10.2% improvement over previous AI-driven methods. This research presents an innovative approach in human-AI collaboration, showing that for AI to truly align with human needs, it must first learn to account for and correct human irrationality.