arXiv:2411.10109v3 Announce Type: replace-cross Abstract: Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes. Such models are typically outcome-specific, however, requiring training data for each target outcome, limiting their applicability to new domains. We test whether large language models (LLMs) can relax these requirements by using self-report data to build attitudinal and behavioral simulations, or "generative agents," that can predict responses across outcomes without outcome-specific training data. Using data from
Advances in large language models are enabling more sophisticated grounding techniques, making general-purpose individual simulation a tangible research goal, as evidenced by this arXiv paper.
This development suggests a potential pathway to more robust and less outcome-specific models for human behavior, which could revolutionize fields from social science to marketing and policy design.
The ability to simulate individual attitudes and behaviors without extensive outcome-specific training data means a significant leap towards truly generalizable AI agents, changing how human decision-making is modeled and predicted.
- · AI researchers
- · Psychology & social science
- · Marketing & advertising industry
- · Policy analysis
- · Traditional survey research
- · Behavioral economics (relying on single-outcome models)
- · Black-box behavioral prediction models
Advanced AI agents gain the ability to predict human responses more accurately and broadly.
This could lead to more persuasive AI systems or more effective and dynamic social engineering tactics.
These 'generative agents' could form the basis of highly realistic digital populations for economic and social planning, potentially questioning the nature of individual agency.
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Read at arXiv cs.LG