Language-based game theory in the age of artificial intelligence
This work addresses the need for improved models of human behavior in economics, psychology, and AI, particularly as generative AI advances, but it is incremental as it builds on existing experimental insights.
The paper tackles the problem of understanding human decision-making in strategic interactions by shifting from outcome-based to language-based utility functions, proposing sentiment analysis as a tool and showing through a meta-analysis of 61 dictator game instructions that it can explain behavior beyond economic outcomes.
Understanding human behaviour in decision problems and strategic interactions has wide-ranging applications in economics, psychology, and artificial intelligence. Game theory offers a robust foundation for this understanding, based on the idea that individuals aim to maximize a utility function. However, the exact factors influencing strategy choices remain elusive. While traditional models try to explain human behaviour as a function of the outcomes of available actions, recent experimental research reveals that linguistic content significantly impacts decision-making, thus prompting a paradigm shift from outcome-based to language-based utility functions. This shift is more urgent than ever, given the advancement of generative AI, which has the potential to support humans in making critical decisions through language-based interactions. We propose sentiment analysis as a fundamental tool for this shift and take an initial step by analyzing 61 experimental instructions from the dictator game, an economic game capturing the balance between self-interest and the interest of others, which is at the core of many social interactions. Our meta-analysis shows that sentiment analysis can explain human behaviour beyond economic outcomes. We discuss future research directions. We hope this work sets the stage for a novel game theoretical approach that emphasizes the importance of language in human decisions.