Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning
arXiv:2606.19883v1 Announce Type: new Abstract: We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model. To capture human preferences, we use cumulative prospect theory (CPT) that weighs the actions of the agent in a nonlinear fashion using a ($\alpha$-H\"older continuous) weight function. CPT has been widely used in behavioral economics and risk sensitive machine learning to emulate human preferences. We analyze the state-of-the-art learning algorithm with CPT weight distorted rewards and obtain a p
The ongoing advancement in AI research is increasingly focusing on integrating human behavioral models to create more robust and effective AI systems, reflecting a maturing understanding of human-AI interaction.
This research is crucial for developing AI agents that can operate effectively and ethically in complex, competitive environments involving human decision-makers, directly impacting the 'AI agents' narrative.
The explicit incorporation of human psychological models like Cumulative Prospect Theory into multi-agent AI systems shifts the paradigm towards more human-centric and potentially adversarial-resilient AI design.
- · AI ethicists
- · Behavioral economics researchers
- · AI agent developers
- · Risk management solution providers
- · Overly simplistic AI models
- · Systems not accounting for human irrationality
- · Traditional game theory applications
AI agents become more adept at anticipating and influencing human decisions.
Increased trust and adoption of AI in sensitive domains due to better alignment with human behavior.
The development of new regulatory frameworks specifically designed to manage AI systems that mimic human cognitive biases.
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