PsyPost reports:

A new study suggests that artificial intelligence systems approach strategic decision-making with a higher degree of mathematical optimization than human players, often outperforming humans in games requiring iterative reasoning. While these large language models demonstrate an ability to adapt to complex rules and specific competitive scenarios, they differ fundamentally from human cognition by failing to identify certain logical shortcuts known as dominant strategies. The findings appear in the Journal of Economic Behavior and Organization.

Large language models are advanced artificial intelligence systems designed to process and generate text based on vast datasets. These models are increasingly integrated into economic workflows, ranging from market analysis to automated negotiation agents. As these tools become more prevalent in settings that involve social interaction and competition, it becomes necessary to understand how their decision-making processes compare to human behavior.

Previous psychological and economic research indicates that humans often rely on bounded rationality, meaning their strategic thinking is limited by cognitive capacity and time. Iuliia Alekseenko, Dmitry Dagaev, Sofiia Paklina, and Petr Parshakov conducted this study to determine if artificial intelligence mirrors these human limitations or operates with a distinct form of logic. The authors are affiliated with HSE University, the University of Lausanne, and the New Economic School.

“This study was motivated by a growing debate about whether large language models can meaningfully serve as substitutes for human decision-makers in economic and behavioral research. While recent work has shown that LLMs can replicate outcomes in some classic experiments, it remains unclear how they reason strategically and whether their behavior truly resembles human bounded rationality,” the researchers told PsyPost. [Continue reading…]