Sentimental Agents: Exploring Deliberation, Cognitive Biases, and Decision-making in LLM-based Multiagent Systems
Keywords: Large Language Models, Multiagent systems, Sentiment Analysis, Cognitive Biases, Decision-Making, Opinion Dynamics, Non-Bayesian Updating
Abstract: How does sentiment affect deliberative opinion dynamics in multi-agent systems using Large Language Models (LLMs)? In this paper, we introduce \textit{Sentimental Agents}, a framework designed to study collaborative decision-making in a society of agents, each equipped with a distinct Mental Model of Self. We propose a method to integrate sentiment analysis and a non-Bayesian update mechanism, to analyze and interpret agents' beliefs and interactions systematically. This method allows us to observe the volatility of the sentiment associated with different agent statements, as well as the change in opinion throughout the agents’ conversation. We further use it to model and compare collaborative decision-making approaches.
We situate these agents in a simulated Human Resource recruiting environment as a case study to evaluate a candidate’s fit for a role. We present a set of metrics to assess the quality of the agents’ output. Finally, we explore cognitive biases in the agents’ individual and collective opinion formation, a fundamental step to enhance decision-making capabilities and mitigate distortions in the system and the agents' collective reasoning.
Submission Number: 18
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