Decision-Making in Hybrid Human-AI Agent Societies

The analysis and modelling of decision-making processes are of interest across economics, game theory, biology, psychology, and computer science, among other fields. Mathematical and computational models have been developed to provide insights into the underlying mechanisms and emerging behavioural patterns. Recently, there has been a surge of interest in studying societies composed of artificial agents (and possibly humans) that learn strategies through interaction. At the same time, the AI landscape has undergone a revolution with the advent of Large Language Models (LLMs). These models exhibit capabilities that were unimaginable just a few years ago. An important open research area involves integrating LLMs into the design of autonomous agents and decision-support systems. There are several open research questions in this space, ranging from the ethical underpinnings of the decision-making process to the potential for adversarial exploitation of AI agents.

In this talk, I will present an overview of my lab’s work on modelling societies of artificial learning agents. I will discuss the design and evaluation of various decision-making architectures based on reinforcement learning and generative models. Finally, I will highlight open challenges and key research questions in this fascinating and rapidly emerging field.

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