Miquel Noguer I Alonso (Artificial Intelligence Finance Institute) has posted “AGENTS: A Historical Perspective 1948-2024” on SSRN. Here is the abstract:
This paper provides an in-depth analysis of three fundamental types of agents in computational systems: Agent-Based Models (ABM), Reinforcement Learning (RL) agents, and Large Language Model (LLM) agents. We explore their theoretical foundations, mathematical formulations, and practical applications while examining their historical development. Through detailed mathematical analysis and case studies, we demonstrate how these agent paradigms can be integrated to create hybrid systems capable of addressing complex real-world challenges. Special attention is given to recent developments in multi-agent systems, emergence phenomena, and the convergence of different agent architectures. This work contributes to the growing body of research on intelligent agents by providing a unified framework for understanding and comparing different agent types, highlighting their strengths and limitations.