Economic Rationality under Specialization: Evidence of Decision Bias in AI Agents

With the rapid development of artificial intelligence technology, the potential demonstrated by large language models in various complex tasks has garnered significant attention. The research conducted by Chen et al. (2023) [01] validates this through a series of economic decision-making experiments: when faced with economic tasks such as budget allocation and risk preference, GPT can exhibit a level of economic rationality comparable to or even exceeding that of average participants. This finding has sparked widespread discussion in academia and has also attracted considerable attention in the industry, as it suggests that large language models may not only excel in natural language communication but can also make decisions approximating human rationality in classical economic scenarios such as utility maximization (Kosinski, 2023 [09]; Rahwan et al., 2019 [13]). It is important to note that GPT--a large language model--is not the only AI solution for addressing complex decision-making. In fact, many expert systems based on large models also play critical roles in economic decision-making scenarios such as financial market forecasting, medical resource allocation, and industrial production planning (Lin et al., 2020 [10]). These systems are typically trained in depth for specific industries or disciplines; for instance, biotechnology expert agents focus on experimental safety, ethical compliance, and research prioritization, while economist agents often employ game theory or cost-benefit analysis to guide their decisions (Obermeyer et al., 2019 [12]; Chen et al., 2006 [05]). Intuitively, these specialized models seem more likely to outperform general models in terms of economic rationality and decision effectiveness. However, this paper tests within the experimental framework established by Chen et al. (2023) [01] whether the economic rationality of agents significantly enhanced in specialization can indeed exceed the high standards set by GPT when faced with the same or similar economic tasks.