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on Neuroeconomics |
| By: | Challoumis, Constantinos |
| Abstract: | While some may view artificial intelligence as a contemporary phenomenon, its roots sink deep into the annals of human ingenuity. Central to the understanding of this domain is the distinction between artificial intelligence (AI) and machine learning (ML). AI manifests as a complex branch of computer science that endeavors to emulate human cognitive functions, thereby enabling machines to perform tasks typically requiring human intelligence, such as understanding language, recognizing patterns, and making decisions. On the other hand, machine learning is a subset of AI, focusing primarily on the development of algorithms that allow computers to learn from and make predictions based on data. As data accumulates, these algorithms enhance their performance autonomously—without explicit programming, symbolizing a fundamental shift in our interaction with technology. |
| Keywords: | AI revolution, monetary landscape, job opportunities |
| JEL: | F00 H0 Z0 |
| Date: | 2024–11–15 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122514 |
| By: | Harris Borman; Anna Leontjeva; Luiz Pizzato; Max Kun Jiang; Dan Jermyn |
| Abstract: | Large Language Models (LLMs) have demonstrated the ability to adopt a personality and behave in a human-like manner. There is a large body of research that investigates the behavioural impacts of personality in less obvious areas such as investment attitudes or creative decision making. In this study, we investigated whether an LLM persona with a specific Big Five personality profile would perform an investment task similarly to a human with the same personality traits. We used a simulated investment task to determine if these results could be generalised into actual behaviours. In this simulated environment, our results show these personas produced meaningful behavioural differences in all assessed categories, with these behaviours generally being consistent with expectations derived from human research. We found that LLMs are able to generalise traits into expected behaviours in three areas: learning style, impulsivity and risk appetite while environmental attitudes could not be accurately represented. In addition, we showed that LLMs produce behaviour that is more reflective of human behaviour in a simulation environment compared to a survey environment. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05801 |